GPS and GNSS Technology in Geosciences 0128186178, 9780128186176

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GPS AND GNSS TECHNOLOGY IN GEOSCIENCES

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GEORGE P. PETROPOULOS Assistant Professor of Geoinformatics, Department of Geography, Harokopio University of Athens, Greece

PRASHANT K. SRIVASTAVA Assistant Professor Institute of Environment and Sustainable Development Banaras Hindu University, India

Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, Netherlands The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States Copyright © 2021 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-12-818617-6 For information on all Elsevier publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Candice Janco Acquisitions Editor: Amy Shapiro Editorial Project Manager: Lena Sparks Production Project Manager: Paul Prasad Chandramohan Cover Designer: Mark Rogers Typeset by TNQ Technologies

Contributors Nikolaos Katsenios Department of Soil Science, Institute of Soil and Water Resources, Hellenic Agricultural Organization e Demeter, Lycovrisi, Attiki, Greece

Christos Chalkias Department of Geography, Harokopio University of Athens, Athens, Greece Prem Chandra Pandey Center for Environmental Sciences & Engineering, Shiv Nadar University, Uttar Pradesh, India

Eleni Kokinou Department of Agriculture, Hellenic Mediterranean University, Heraklion, Greece; Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Greece

Alison de Oliveira Moraes Instituto de Aeronáutica e Espaço e IAE, São José dos Campos, SP, Brazil

Amit Kumar Department of Geoinformatics, Central University of Jharkhand, Ranchi, Jharkhand, India

Aspasia Efthimiadou Department of Soil Science, Institute of Soil and Water Resources, Hellenic Agricultural Organization e Demeter, Lycovrisi, Attiki, Greece

Pavan Kumar College of Forestry and Horticulture, Rani Lakshmi Bai Central Agricultural University, Jhansi, India

Antigoni Faka School of Environment, Geography and Applied Economics, Department of Geography, Harokopio University of Athens, Athens, Greece

Sanjay Kumar Atmospheric Research Laboratory Department of Physics, Banaras Hindu University, Varanasi, Uttar Pradesh, India

Victor Hugo Fernandes Breder Instituto Tecnológico de Aeronáutica e ITA, São José dos Campos, SP, Brazil

Shubham Kumar Department of Geoinformatics, Central University of Jharkhand, Ranchi, Jharkhand, India

V.G. Ferreira School of Earth Sciences and Engineering, Hohai University, Nanjing, Jiangsu, China

Preet Lal Department of Geoinformatics, Central University of Jharkhand, Ranchi, Jharkhand, India

João Francisco Galera Monico Sao Paulo State University e UNESP, Presidente Prudente, SP, Brazil

Lawrence Lau, PhD Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China; Department of Civil Engineering, The University of Nottingham Ningbo China, Ningbo, Zhejiang, China

Grigoris Grigorakakis Department of Geography, Harokopio University of Athens, Athens, Greece Moisés José dos Santos Freitas Instituto Tecnológico de Aeronáutica e ITA, São José dos Campos, SP, Brazil

qukasz Lrmieszewski Jakub Paradyz_ University, Faculty of Technology, Gorzów Wielkopolski, Poland

Kleomenis Kalogeropoulos Department of Geography, Harokopio University of Athens, Athens, Greece

Kamil Maciuk AGH University of Science and Technology, Krakow, Poland

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CONTRIBUTORS

R.K. Mall DST - Mahamana Centre of Excellence in Climate Change Research (MCECCR), Banaras Hindu University, Varanasi, Uttar Pradesh, India Jorge Martínez-Guanter Aerospace Engineering and Fluids Mechanics Department, University of Sevilla, Sevilla, Spain Yenca O. Migoya-Orué The Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy H.D. Montecino Departamento de Ciencias Geodésicas y Geomática, Universidad de Concepción, Los Angeles, Biobío, Chile Adam Narbudowicz Trinity College Dublin, the University of Dublin, CONNECT Centre, Dublin, Ireland; Wroclaw University of Science and Technology, Telecommunications and Teleinformatics Department, Wroclaw, Poland C.E. Ndehedehe Australian Rivers Institute and Griffith School of Environment & Science, Griffith University, Nathan, QLD, Australia Evgeny Ochin Jakub Parady_z University, Faculty of Technology, Gorzów Wielkopolcki, Poland Manish Kumar Pandey Remote Sensing Laboratory, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, Uttar Pradesh, India Zoi Papadopoulou Department of Natural Resources and Agricultural Engineering, Agricultural University of Athens, Athens, Greece Alastair Pearson University of Portsmouth, School of the Environment, Geography and Geosciences, Buckingham Building, Lion Terrace, Portsmouth, UK Manuel Perez-Ruiz Aerospace Engineering and Fluids Mechanics Department, University of Sevilla, Sevilla, Spain George P. Petropoulos Department of Geography, Harokopio University of Athens, Athens, Greece; School of Mineral Resources Engineering, Technical University of Crete, Kounoupidiana Campus, Greece Sandro M. Radicella The Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy

S.S. Rao Department of Physics, Institute of Science, Banaras Hindu University, Varanasi, Uttar Pradesh, India Eurico Rodrigues de Paula National Institute for Space Research e INPE, São José dos Campos, SP, Brazil Purabi Saikia Department of Environmental Sciences, Central University of Jharkhand, Ranchi, Jharkhand, India Lucas Alves Salles Instituto Tecnológico de Aeronáutica e ITA, São José dos Campos, SP, Brazil Martin Schaefer University of Portsmouth, School of the Environment, Geography and Geosciences, Buckingham Building, Lion Terrace, Portsmouth, UK Hao Sha Gyrfalcon Technology Inc., Milpitas, CA, United States Jyoti Kumar Sharma Center for Environmental Sciences & Engineering, Shiv Nadar University, Uttar Pradesh, India A.K. Singh Atmospheric Research Laboratory Department of Physics, Institute of Science, Banaras Hindu University, Varanasi, Uttar Pradesh, India R.P. Singh Atmospheric Research Laboratory Department of Physics, Banaras Hindu University, Varanasi, Uttar Pradesh, India Arpine Soghoyan Gyrfalcon Technology Inc., Milpitas, CA, United States Panagiotis Sparangis Department of Soil Science, Institute of Soil and Water Resources, Hellenic Agricultural Organization e Demeter, Lycovrisi, Attiki, Greece Prashant K. Srivastava Remote Sensing Laboratory, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, Uttar Pradesh, India; DST - Mahamana Centre of Excellence in Climate Change Research (MCECCR), Banaras Hindu University, Varanasi, Uttar Pradesh, India Nikolaos Stathopoulos Institute for Space Applications and Remote Sensing, National Observatory of Athens, BEYOND Centre of EO Research & Satellite Remote Sensing, Athens, Greece

CONTRIBUTORS

Baohua Sun Gyrfalcon Technology Inc., Milpitas, CA, United States Prasoon Tiwari DST - Mahamana Centre of Excellence in Climate Change Research (MCECCR), Banaras Hindu University, Varanasi, Uttar Pradesh, India Dimitris Triantakonstantis Department of Soil Science, Institute of Soil and Water Resources, Hellenic Agricultural Organization e Demeter, Lycovrisi, Attiki, Greece Amit Kumar Tripathi Center for Environmental Sciences & Engineering, Shiv Nadar University, Uttar Pradesh, India Andreas Tsatsaris Department of Surveying and Geoinformatics Engineering, University of West Attica, Athens, Greece Konstantinos Tserpes Harokopio University, School of Digital Technology, Department of Informatics and Telematics, Athens, Greece

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Shrini K. Upadhyaya Biological and Agricultural Engineering Department, University of California, Davis, CA, United States Bruno César Vani Federal Institute of Education, Science and Technology of Sao Paulo e IFSP, Presidente Epitácio, SP, Brazil Michalis Vidalis-Kelagiannis Department of Geography, Harokopio University of Athens, Athens, Greece T. Xu Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China Lin Yang Gyrfalcon Technology Inc., Milpitas, CA, United States P. Yuan Geodetic Institute, Karlsruhe Institute of Technology, Karlsruhe, Baden-Württemberg, Germany

Foreword Although the Global Positioning System (GPS) technology, developed by the US Air Force to track their nuclear submarines, was ingeniously used by geoscientists in the 1990s to detect nano-strain deformation of the earth’s surface, its potential applications in data-guided geo-science services to society began to sprout only after the US Government, in 2000, ended the selective availability of its error-free signals. This landmark decision, by dramatically reducing real-time location errors by an order of magnitude, fueled the design and development of a wide variety of progressively miniaturized receiver systems and algorithms for guiding management strategies, environmental monitoring, resource conservation, as well as individuals in planning their lives and works which, in turn, drove the evolution of new supportive public infrastructure. Concomitantly, the depoliticization of GPS signals catalyzed evolution of the transformative Global Navigation Satellite System (GNSS) which allows a civilian user to exploit the technical interoperability of the various national and regional satellite networks, notably the modernized GPS, the European Galileo, and the restructured Russian Glonass, to meet user demands for ever more precise estimations of earth coordinates and time. A commitment by GNSS to promote the development of and support to complementary

systems that would continue to enhance location and time accuracy and also diversify the use of spatiotemporal data toward sustainable development offers a highly promising approach toward building a hazard resilient society. This volume edited by scientists of proven credentials who have personally contributed to advancing the wavefront of GNSS applications from its initial tracking and time stamping uses to the Internet of Things has rightly identified the critical elements of scientific knowledge and the computational and technological challenges needed to translate these into knowledge products, to fashion its contents. These, contained in 27 chapters, systematically address the important links in the long chain of system structure and processes that reduce the end product of a highly sophisticated technological system into one of equally high social value. This book is thus admirably designed to inform, educate, and given the requisite motivation, empower both curious and dedicated individuals to professionally engage in aspects of the system that fire their interest.

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Vinod Gaur Bangalore, February 10, 2021

C H A P T E R

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Introduction to GPS/GNSS technology Amit Kumar1, Shubham Kumar1, Preet Lal1, Purabi Saikia2, Prashant K. Srivastava3, 4, George P. Petropoulos5, 6 1

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Department of Geoinformatics, Central University of Jharkhand, Ranchi, Jharkhand, India; Department of Environmental Sciences, Central University of Jharkhand, Ranchi, Jharkhand, India; 3Remote Sensing Laboratory, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, Uttar Pradesh, India; 4DST - Mahamana Centre of Excellence in Climate Change Research (MCECCR), Banaras Hindu University, Varanasi, Uttar Pradesh, India; 5Department of Geography, Harokopio University of Athens, Athens, Greece; 6School of Mineral Resources Engineering, Technical University of Crete, Kounoupidiana Campus, Greece

1. Background The Global Navigation Satellite System (GNSS) has become a crucial player in terms of the country’s capability to monitor real-time activities across the world. The rapid growth in GNSS was first observed through the development of commercial applications through building navigation satellites and associated equipment. The next-level progression was made in the positioning techniques using GNSS such as Global Positioning System (GPS), the infrastructure of the mobile network, and their integration for applications such as automatic vehicle location, tracking systems, navigation have drawn the attention of various countries such as the United States, India, and China. Satellite navigation system (SNS) is the system of offering real-time location service using navigation satellites to the users in air, sea, ground, or space [59]. It is most popular among other navigation technologies as it offers a real-time location in terms of position, velocity, and time (PVT) with very high precision. GNSS is a combined collection of satellite systems that directs to all the prevailing worldwide SNSs as well as regional and advanced navigational systems. These SNSs constitute several augmented systems to enhance system performance to achieve specific requirements. These are Japan’s Multi-functional Satellite Augmentation System, United States of

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America’s Wide Area Augmentation System, India’s GPS-aided GEO augmented navigation (GAGAN), and Europe’s European Geostationary Navigation Overlay Service (EGNOS). Navigation is the science of providing directions from one place to another, based on landmarks or reference points, and the human sense of direction [5,26,58]. Using the Sun and the stars as reference for navigation on land as well as on ocean surfaces, (Hofmann-Wellenhof et al., 2003; [53]) have various limitations such as nonvisibility during cloudy conditions, the relative change in position of these references during various seasons, and position on the Earth [3]. With the advent of geographical coordinates (latitudes and longitudes) and altitude, the challenge with respect to two-dimensional and three-dimensional reference for terrestrial navigation has been resolved [4,14]. In the recent past, the radio signals have helped in the navigation to ensure safety during maritime and inland journeys [47]. Celestial navigation is based on the triangulation method, in which celestial bodies are used as reference points, and the GPS is based on the concept of trilateration, which uses GPS satellites’ locations as reference [47]. GPS can measure the time, altitude, longitude, and latitude based on the available satellite signals above the horizon [50] and contributes in determining the precise positioning of an object on Earth that revolutionized the navigation and tracking applications [13,63]. It is one of the most popular satellite-based navigation radio systems due to the global availability of signal as well as performance. The fundamental operations of the GPS are one-way ranging that depends on satellite atomic clock predictability. GPS works in an integrated manner with various supporting parameters such as satellite geometry, communication link, the antenna of satellite and receiver, the position of the antenna, and decoding parameters [43]. It is independent of any weather conditions, and day or night limitations, and provides autonomous spatial positioning with global coverage. Real-time kinematic (RTK) GPS has high producibility, is comparatively more flexible, and is cost- and time-effective, which reduces the cost by w50% and time by w75% compared to traditional techniques. It allows measuring positions of an object in real time with an accuracy of a few centimeters [54]. The first GPS receivers were very simple, providing very basic information of latitude and longitude with monochrome screens and higher prices. Over the years, the next-generation SNS receivers brought more user-friendly map-based location devices with color screens with in-built multiple advanced features, at comparatively lower prices. GPS also operates independently, which makes it accessible by anyone and provides the ability to work freely with other GPS receivers. Nowadays, it is being used by civil, military, and commercial users vastly around the world with crucial information including speed, elevation, and geolocation with the added base map. The system has revolutionized today’s technology by becoming more interactive, effective, and useful in multiple industries. This chapter will explore the basic principles of GPS, its various hardware that make it work in-depth, and the operation of the system, including the theoretical calculations for positioning, speed, bearing, and distance to destination. The history of navigation goes back as early as the invention of the magnetic compass as mentioned by Ceruzzi [7]. The navigation in the later period was carried by a chronometer as given by Ceruzzi [8], which resolved the problem of longitude. This was replaced by Quartz oscillators in the 1920s. The next concurrent advancement was radio or the wireless. The next advancement was Omega and Loran, which were the radio-based inertial navigation systems. This was further taken over by satellite-based navigation systems in the 1960s. The evolution of GNSS as given in NASA (2020) is listed in Table 1.1. I. General introduction to GPS/GNSS technology

2. Major segments of GPS

TABLE 1.1

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The evolution of GNSS.

1960

US Air Force and Navy commence research

1973

US Department of Defense unveils GPS project

1978

First US GPS satellite launched

1982

First Russian GLONASS satellite launched

1983

KAL 007 flight disaster

1994

Worldwide coverage achieved by GPS

2000

Full civilian accuracy permitted in the United States

2000

First Chinese BeiDou satellite launched

2005

First European Union Galileo satellite launched

2011

Worldwide coverage achieved by the Russian GLONASS system

2018

GPS III satellite launched

2020

Worldwide coverage is projected for China’s BeiDou constellation and the European Union’s Galileo constellation

The commercial market of GPS emerged during 1983e95 [8], and the market converged during 1995e2015 [8]. From 1995 to 2005, GPS found its use in several areas ranging from research, surveying, military, and in hiking and hunting. In the second decade, from 2005 to 15, it drew public attention, and several new applications were created, which were never thought of earlier, for example, in cell phones, in drones, in a smartphone, tracking and privacy, etc., to name a few. The future market growth of GNSS could be estimated only after the full deployment of the Galileo and BeiDou satellite constellations is over. The European GNSS Agency projects the current value of six billion GNSS deployed devices to grow to over nine billion by 2023 (Jacobson, 2017). According to Research and Markets NASA (2020), the GNSS market is estimated to grow at a compound annual growth rate of around 9.0% during 2018e22. As per GNSS Market Outlook 2022 NASA (2020), market dynamics would be led by location-based services, transportation, surveying activities, and agriculture.

2. Major segments of GPS GPS primarily consists of three different segments viz. (a) satellite constellation, (b) ground control stations, and (c) receivers [10]. The space segment consists of constellations of satellites that transmit pseudorandom noise (PRN)ecoded signals, which are used for the true line-of-sight (LoS) range (speed*time) along with various error sources including satellite clock error, atmospheric delays, receiver clock error, tracking errors, and receiver channel delays [40]. The coded signals comprise the information about the position of the satellite, which

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can be used by an unlimited number of users at a time [26]. The GPS satellite constellations in the space segment are being monitored and controlled by the GPS control segment (CS) by resolving satellite anomalies and collecting pseudorange and carrier-phase measurements at the control stations to ascertain and refurbish satellite clock rectification, almanac, and ephemeris at least once per day [49]. Additionally, the CS monitors the state of the satellite’s health, controls its orbital position, and regulates the satellite bus and payloads [45]. The CS has three different physical components such as the master control station (MCS), monitor stations, and ground antennas. The receiver/user segment includes all military and civilian users using the GPS signal for various purposes [13]. Each GPS receiver processes the transmitted signals received from the satellites to determine the PVT of the receiver anywhere in the world.

3. Functioning of GPS GPS works on the ranging and trilateration by combining various groups of satellites [34], functional in space as reference points. These satellites transmit a navigation message consisting of information related to almanac, i.e., the orbital information about the entire satellite constellation, general system status messages, as well as ephemeris, and the detail of the individual satellite’s position to regulate the orbital position of satellites. A minimum of four common satellites are required in a group to determine the precise receiver’s position at any time [21]. Only three distances to three simultaneously tracked satellites are needed to obtain the latitude, longitude, and altitude information. However, the fourth satellite accounts for the receiver clock offset and contributes in time rectification [27]. The GPS positioning is further improved at subcentimeter to a few meters with the deployment of two receivers simultaneously tracking the same GPS satellites [31]. GPS employs three basic binary codes viz, (PRN code including precision (P) code, Coarse Acquisition (C/A) code, and the navigation code. The PRN code is a sequence of very precise time marks that allow the receivers to estimate the transmission delay between the satellite and the control station [33,56]. The GPS satellites broadcast two carrier waves viz. L1 (390 MHz) and L2 (1500 MHz), which are modulated by the coded information signal that is transmitted by the satellites to communicate with the receivers. They are derived from the frequency of 10.23 MHz through a very precise atomic clock. The high-frequency signals transmitted from the satellites travel in a straight line and have very low power (50 W). It is very essential that the antenna of the GPS receiver should have a direct view of the satellite. L1 and L2 carrier waves are broadcasted at 1575.42 MHz and 1227.60 MHz, respectively. L1 carrier waves are modulated with the C/A code at 1.023 MHz and the P-code at 10.23 MHz, while the L2 carrier wave is modulated with only one code, i.e., P-code at 10.23 MHz. These coded signals are used to calculate the transmission time of radio signals from the satellite to the receivers on the Earth, i.e., the time of arrival, which is multiplied by the velocity of the signal to estimate the satellite range, which is the distance from the satellite to the receiver. The GPS signal contains a navigation message of a low frequency (50 Hz), which is modulated on the L1 and L2 carriers [16].

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3.1 Pseudorange Pseudorange is the measure of apparent signal propagation time from the satellite to the GPS receiver on the Earth. It is calculated by dividing the distance with the speed of light, which is denoted with c, i.e., a universal physical constant. The apparent signal propagation time is the deviation of signal reception by the receiver and the time of signal transmission by the satellite. In other words, it is the time delay between the clocks of GPS receivers and satellites on the Earth, determined from the P-code and C/A code. Generally, the signal from the satellite to the GPS receiver reaches in 0.06 s, if in case the satellite is in the overhead position of an observer. It is called pseudorange because the clocks in the GPS receiver and the satellite are not synchronized, and it is influenced by satellite orbital errors, user clock error, and ionospheric delay.

3.2 Carrier-phase measurement The range between the carrier signal generated from the satellite and the carrier signal generated by a GPS receiver’s internal oscillator can be obtained through the carrier-phase measurement. The ranges calculated with the carriers are much more accurate than those calculated with the pseudorange codes due to the better resolution of the carrier phase (19 cm) in the case of L1 frequency than that of the pseudorange codes [33].

3.3 GPS broadcast message, ephemeris, and almanac The navigation message included three types of components (a) the current date, time, and the health of the satellite; (b) orbital information (ephemeris); and (c) the status of all the satellites in the GPS program (almanac). Each GPS satellite broadcasts microwave signals regarding clock corrections, system and satellite status, and its position or ephemeris data. The navigation message transmitted by the satellite contains the predicted satellite positions in real time referred to as broadcast ephemeris. Each GPS receiver is capable of acquiring either C/A code or P-code and can acquire the broadcast ephemeris in real time. This broadcast ephemeris is estimated using the past continuous tracking of the GPS satellites in space by ground station and analyzed by the MCSs. New parameters for the satellites are transmitted back to the GPS satellites on the hourly basis through a navigation message to predict new orbital elements. In contrast, the more accurate satellite positions are obtained by postprocessing of actual tracking of GPS satellite data, referred to as precise ephemeris, and are available at a later date [21,51]. Almanac data are transmitted from the satellite to the receivers and used to be stored in the GPS receiver’s memory. The almanac consists of the data about the position of satellites in space at any given time including coarse orbit, status information of satellites’ constellation, an ionospheric model, and information to relate GPS-derived time to Coordinated Universal Time. The entire almanac from a single satellite used to be received in ca. 12.5 min. GPS receivers in functional condition receive the latest corrected data within the last 4e6 h and are referred to as warm condition, whereas almanac data are not updated in case GPS receivers are not turned on for a long time and are referred to as cold receivers.

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4. GPS errors Both the GPS pseudorange and carrier-phase measurements are affected by different types of random and systematic errors (biases) [42]. Based on the source of its origin, it can be classified broadly into three categories, i.e., the ephemeris or orbital errors, satellite clock errors, and the errors originating at the satellites’ end. The receiver clock errors, multipath error, receiver noise, and antenna phase center variations are the errors originating at the receiver end. The delays occurred during the GPS signal pass through the ionosphere and troposphere are the signal propagation errors, also called atmospheric refraction [28,29].

4.1 Satellite and receiver clock errors The GPS satellite clocks are highly accurate but not perfect as their stability is about 1e2 parts in 1013 over a period of 1 day, which leads to the satellite clock error of ca., 8.64e17.28 ns/day. Cesium clocks have better stability compared to rubidium clocks and tend to perform better over a longer period [35]. Satellite clock errors can cause several GPS navigations errors that can be corrected through differencing between receivers. It may leave an error of the order of several nanoseconds, which translates to a range error of a few meters, as 1 nanosecond error is equivalent to a range error of about 30 cm [12]. In contrast, the inexpensive crystal clocks used in GPS receivers are much less accurate than the satellite clocks [29], and their errors can be rectified through differencing between the satellites.

4.2 Multipath error The interaction of GPS signals with various surfaces including large buildings or other elevations surrounding the receiver antenna before being captured by the receiver causes multipath error in GPS signals. It distorts the original signal through interference with the reflected signals at the GPS antenna, which affects both carrier-phase and pseudorange measurements [56]. The reflected signal takes more time to reach the receiver than the direct signal resulting in errors in the range of a few meters that can be verified using a day-today correlation of the estimated residuals [21]. The pseudorange multipath error is reduced to several meters, even in a highly reflective environment with the help of new technology viz. Strobe correlator (Ashtech Inc.), and MEDLL (NovAtel Inc.), and multipath mitigation methods [55].

4.3 Ionospheric delay Ionospheric propagation at the GPS L-band frequencies (1.2 and 1.6 GHz) is of great interest for GPS. The ionosphere is a dispersive medium with maximum electron density in layer F2 (210e1000 km). The altitude and thickness of those layers vary with time due to the changes in the solar radiation and the magnetic field of the Earth. The F1 layer disappears during the night and is more prominent in the summer than the winter [30]. This atmospheric layer bends the GPS signal path and causes a range error, particularly if the satellite elevation

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9

angle is greater than 5 degrees. It also causes a significant range error by speeding up the propagation of the carrier phase beyond the speed of light, in contrast by slowing down the PRN code and the navigation message at the same rate [21]. Ionosphere range delay on GPS signals is a major error source in GPS positioning and navigation [60]. Total electron content (TEC) of the ionosphere produces most of the effects on radio signals, and the GPS signal delays are caused by the ionosphere to be proportional to TEC along the path from the satellite to a terrestrial GPS receiver [60]. The highest TEC in the world occurs in the equatorial region, and it is maximum usually in the early afternoon and minimum usually just before sunrise. Variations in TEC along the slant path connecting GPS satellites and receivers represent irregularities and turbulence in ionospheric plasma density [46]. Conversely, the steep gradients of ionospheric plasma cause the navigation satellite signals scintillation in phase as well as amplitude. GPS is an effective tool to study the ionospheric disturbances and irregularities caused by space weather due to these scintillations [9,41,48]; Cherniak et al. (2018); [52].

4.4 Tropospheric delay The electrically neutral troposphere (w50 km from the surface of the Earth) acts as a nondispersive medium for radio frequencies below 15 GHz [19], which result in a longer satellite-to-receiver range than the actual geometric range. Temperature, pressure, and humidity in the signal path through the troposphere are the factors responsible for the tropospheric delay. The signals from satellites at low elevation angles travel a longer path through the troposphere than those at higher elevation angles. Therefore, the tropospheric delay is minimum in the user’s zenith and maximum near the horizon [6]. The tropospheric delay is frequency independent and can be removed by the addition of a second C/A code on L2 as part of the modernization program [51].

4.5 GPS ephemeris errors The position of each satellite in the constellation is a function of time because they keep on moving with respect to time. It is included in broadcast satellite navigation messages and predicted from previous GPS observations at the ground control stations. Typically, overlapping 4-h GPS data spans are used by the operational control system to predict fresh satellite orbital elements for each 1-h duration. The predicted satellite orbital information cannot consider the forces influencing the GPS satellites, which may lead to some errors in the estimated satellite positions (2e5 m) referred to as ephemeris errors. The ephemeris error for a particular satellite is identical to all GPS users worldwide [12].

4.6 Other limitations GPS was originally designed in such a way that the real-time autonomous positioning and navigation with the civilian C/A code receivers would be less precise than military P-code receivers. The GPS signals were intentionally introduced by the United States to disrupt position, navigation, and time through either spoofing (making a GPS receiver calculate a false

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1. Introduction to GPS/GNSS technology

position) or jamming (overpowering GPS satellite signals locally so that a receiver can no longer operate). Antispoofing (A/S) is an encryption of the P-code induced to prevent “the enemy” from imitating a GPS signal. A/S does not pose a significant problem as precise GPS techniques rely on measuring the phase of the carrier signal itself, rather than the pseudoranges derived from the P-code. Modern geodetic receivers can, in any case, form two precise pseudorange observables on the L1 and L2 channels, even if A/S is switched on. However, the United States stopped the intentional degradation of GPS satellite signals in May 2000, thereby eliminating a source of uncertainty in GPS performance to civil GPS users worldwide [1]. The United States implemented the selective availability (SA) on Block II GPS satellites to deny accurate real-time autonomous positioning to unauthorized users to ensure national security. SA was officially activated on March 25, 1990 [21], to either the satellite clock or delta error or an additional slow varying orbital error or epsilon error. With SA turned on, nominal horizontal and vertical errors can be up to 100 and 156 m, respectively, at the 95% probability level [15]. The effect of signal spoofing in degrading the navigation solution can have serious impacts on both military and civilian applications, especially those related to safety-of-life services. Various techniques have been developed to detect and mitigate spoofing [25]. DGPS (to overcome the effect of the epsilon error) [12], signal quality monitor [38]; Ledvina et al., 2010), and vestigial signal defense [57] are being used for better accuracy than the standalone P-code receiver due to the elimination or the reduction of the common errors, including SA.

5. GPS technologies There is a variety of methods employing GPS to improve the accuracy and increase the applicability of the system. RTK survey and differential GPS are few of them. A differential GPS is an advanced form of GPS, providing very accurate and precise location-based services. In general, two receivers that are relatively closer (within 10e15 km) receive the signal from approximately the same GPS satellites and experience similar atmospheric errors. In DGPS, the difference between the concurrent coordinates with respect to known coordinates (base receiver) is estimated and applied to fix the concurrent coordinates of unknown locations (rover receiver). The corrected information can be applied to the roving receiver in real time in the field using radio signals or through postprocessing after data capture using special processing software. RTK surveying is a carrier phaseebased relative positioning technique that employs two (or more) receivers simultaneously tracking the same satellites. RTK increases the accuracy while surveying a large number of unknown points located in the vicinity with reference to a known point, provided the area of investigation falls within 10e15 km to the known point, the connection between rover and static is established, and the LoS and the propagation path are relatively unobstructed [32]. In this method, the base receiver remains stationary over the known point and is attached to a radio transmitter. The rover receiver is normally carried in a backpack and is attached to a radio receiver. The base receiver measurements and coordinates are transmitted to the rover receiver through the communication (radio) link [33].

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11

6. Global Navigation Satellite System

6. Global Navigation Satellite System The GNSS is defined as the group of all SNSs and their augmentations. There are 195 countries across the globe, but very few countries host their own navigation system through a specific satellite (Jiang et al., 2013). Globally four countries host navigation systems: GPS (US), GLObal NAvigation Satellite System (GLONASS of Russia), Galileo (EU), and BeiDou (China). Additionally, two countries have regional navigation systems: Quasi-Zenith Satellite System (QZSS of Japan) and Indian Regional Navigation Satellite System (IRNSS) or Navigation Indian Constellation (NavIC of India). The GNSS constellation system is depicted in Fig. 1.1.

6.1 NAVSTAR GPS is a commonly used acronym of NAVSTAR (NAVigation System Time and Ranging) and is the first SNS developed by the US Department of Defense in 1978. It is the first fully operational GNSS consisting nominally of a constellation of 24 operational satellites completed its initial operational capacity (IOC) on December 8, 1993 [21]. Its orbits are

NS:35 NOP:3 OIA:55 OC:12 h 55 min SO:MEO 21500 GEO 36000 IGSO 36000

NS:24 NOP:6 OIA:55 OC:11 h 58 min SO:MEO 20220

GPS

Beidou

GNSS GLONASS

Galileo

NS:24 NOP:3 OIA:64.8 OC:11 h 15min SO:MEO 19130

NS:30 NOP:3 OIA:56 OC:13 h SO:MI:O 23222

NS: Number of Satellites; NOP: Number of Orbital Planes; OIA: Orbital Inclination Angle; OC: Operation Cycle; SO: Satellite Orbit in Km.

FIGURE 1.1

GNSS constellation systems. Adapted from Wu, J., Ta, N., Song, Y., Lin, J., Chai, Y., 2018. Urban form breeds neighborhood vibrancy: a case study using a GPS-based activity survey in suburban Beijing. Cities 74, 100e108. https:// doi.org/10.1016/j.cities.2017.11.008; pp.1e29).

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approximately circular with an inclination of about 55 degrees at the satellite altitude of about 20,200 km above the Earth’s surface [36]. NAVSTAR GPS provides users with location-based services very precisely in very little time. The satellites in NAVSTAR constellation orbit the Earth in every 12 h transmitting continuous navigation signals in L1 and L2 frequencies. NAVSTAR has four generations of satellite constellation viz. Block I (1978e85), Block II (1989e90), Block II A (1990e97), Block II-R (1997e2004, Block IIR-M (2005e09), Block II-F (2010e16), Block III-A (2018-present). Each newer Blocks replaced older Blocks after completing their active service period (end of life) and are of the improved version. The satellites are orbiting at an altitude of ca. 20,200 km and arranged in a way that at least six satellites are always above the horizon everywhere on the globe (Fig. 1.2).

6.2 GLONASS GLONASS is a satellite-based navigation system operated during the last decades of the twentieth century by the Russian Aerospace Defence as an alternative to the US-based NAVSTAR. At present, it is complimentary as well as an alternative option for an operational navigation system with related precision and full coverage [20]. The launching of satellites started in 1982 until the constellation was completed in 1995. The life cycle of GLONASS navigation satellites was 5e7 years, and the new satellites are to be launched after a specific time interval to fill the gap due to aging satellites [2,37,39]. In 2011, the full global coverage was established with upgraded satellite constellations under GLONASS-K. GLONASS consists of 24 satellites that are uniformly deployed in three approximately circular orbital planes at an inclination of 64.8 degrees to the equator at the satellite altitude of about 19,100 km above the Earth’s surface. Each GLONASS satellite transmits standard and high accurate signals in L1 (1598.06e1604.40 MHz) and L2 (1242.94e1248.63 MHz) frequencies. The modern age GPS receivers are compatible with both NAVSTAR and GLONASS, thus providing more flexibility of positioning and better accuracy.

FIGURE 1.2 Satellite constellations and orbital altitude of major navigation systems.

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13

6.3 Galileo Galileo was developed by the collaboration of the European Union and European Space Agency in 2011, and the satellite constellation was completed in 2020 (https://ec.europa. eu/growth/sectors/space/galileo/launches_en) with 30 satellites in orbit (24 operational and 6 active spares) [11,18]. Additional satellites will be launched after in-orbit validation phase to achieve IOC. Galileo will give position measurements, i.e., horizontal and vertical, having the range of 1-meter precision. This positioning service even at high latitudes proves more efficient than other relatively positioning systems. The Galileo constellation is evenly distributed among three orbital planes inclined at 56 degrees relative to the equator with a nominal semimajor axis of about 30,000 km. Galileo will transmit radio navigation signals in E1 (1559e1594 MHz), E6 (1260e1300 MHz), E5a (1164e1188 MHz), and E5b (1195e1219 MHz) frequencies. The EGNOS provides an augmentation signal to the GPS standard positioning service (SPS). Global Search and Rescue function is a unique feature of Galileo. Apart from Russian GLONASS and US GPS, high precision has been achieved in the Galileo navigation and positioning system.

6.4 Compass/ BeiDou China developed its own navigation satellite system “Compass/BeiDou” with five geostationary satellites and 30 nongeostationary satellites to date. BeiDou-1 consists of three satellites and offers limited coverage (to users of China and their neighboring countries) and applications. The second generation of this navigation system, referred to as Compass, is a global SNS comprising 35 satellites. It has been operational with 10 satellites in orbit in China since December 2011. By 2020, it is expected to be available to all global customers [23,61]. It uses two different orbits with 55 degrees inclination for navigation satellites: (i) medium Earth orbit (21,500 km) and (ii) inclined geosynchronous orbit (36,000 km). It works on three channels: (i) B1: 1559.052e1591.788 MHz, (ii) B2: 1166.22e1217.37 MHz, and (iii) B3: 1250.618e1286.423 MHz frequencies. The system is providing two types of service at the global level: open service (with a positioning accuracy of 10 m, a timing accuracy of 20 nanosecond, and a velocity accuracy of 0.2 m/s) and authorized service (with a provision of more reliable PVT information and communications services as well as integrity information) [44].

6.5 Quasi-Zenith Satellite System QZSS (also known as Michibiki) is a regional navigation satellite system developed by Japan. It is a combination of four satellites (now expanded to four satellites) that are inclined on orbital planes at 39 degreese47 degrees on two altitudes, 39,581 km and 31,911 km, which provide navigation for East Asia, including Japan, and Oceania. The three satellites of this constellation were fully operational in 2013 and the fourth satellite of QZSS services was operational since November 1, 2018, and three more are satellites planned till 2023. The design and concept of QZSS are purely different from GPS and GLONASS systems due to the policy of national development [24]. QZSS is targeted to achieve communicationrelated services, i.e., audio, video, and data with location information, and is useful in mobile applications. QZSS is also termed as GNSS augmented service. It works on four frequency of

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signal: (i) L1 (L1 C/A and the L1-SAIF: center frequency 1575.42 MHz), (ii) L2 (center frequency 1227.6 MHz), (iii) L5 (center frequency 1176.45 MHz), and (iv) LEX (center frequency 1278.75 MHz) frequencies [62].

6.6 IRNSS/NavIC IRNSS/NavIC is a regional SNS, developed by ISRO (Indian Space and Research Organisation). It would comprise of two services, i.e., SPS for civilian users and restricted service for authorized military users. Both services work on L5 (1176.5 MHz) and S-band (2492.08 MHz) frequencies. The proposed navigation system would have a constellation of seven satellites and a supported ground segment, and three satellites from the constellation will be kept as geostationary satellites. GPS with aided augmented navigation system is initiated in India with the collaborations of ISRO and Airport Authority of India (AAI), which is termed as GEO augmented system (GAGAN). This system is used to enhance the accuracy of a GNSS receiver based on reference signals. When GAGAN will be fully operational, it will fulfill the requirements of the three geostationary satellites (GAGAN will help to get more accuracy for IRNSS when it is fully completed and it will fulfill requirements of three geostationary satellites). The Indian subcontinent (India and neighboring countries) will be covered with help of the footprint of its signal. The operational Satellite Based Augmentation System implemented by AAI’s efforts tends to be a step in the field of modern communication, air traffic control, and management and navigation (Table 1.2).

7. Applications of GPS/GNSS 7.1 Navigation Navigation is of the most common uses of GPS, which aids in aviation, maritime, shipping, and rail and road transportation. It also supports the public in their day-to-day activities by providing the precise location with respect to the surroundings including geotagging, carpools, helping blind people navigate, safety and emergency assistance, security applications including tracking of vehicles, vehicle guidance, hiking, skiing, paragliding, skydiving, etc. (Jacobson, 2017).

7.2 Military services The GPS of military services are far more precise than GPS used by civilians around the world. It uses dual-frequency equipment to avoid signal distortions that could jeopardize its mission or research. Although now dual frequencies are also used by government organizations and commercial services, commonly for civilians, it is single-frequency GPS receiver that makes a difference in precision too. It supports military operations, reconnaissance, and surveillance and to navigate the unfamiliar areas and enhance the awareness of GPS-guided missiles attack. Advanced GPS receivers are used for various military operations to achieve goals and diffuse enemy installations, including navigating to the target locations, tracking the movement of enemies, and supply delivery on the battlefield with precise computation.

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7. Applications of GPS/GNSS

TABLE 1.2

Details of the major global and regional navigational satellite system.

System

GPS

GLONASS

BeiDou

Galileo

NavIC

QZSS

Owner

United States

Russia

China

European union

India

Japan

Orbital altitude (km)

20,188

19,130

21,150

23,222

36,000

36,000

Total number of orbits

01

01

01

01

02

01

Total number of orbital planes

06

03

03

03

02

04

Total number of planes is constellation

6*MEO

3*MEO

3*GEO, 3*IGSO, 3*MEO in BeiDou-III phase

3*MEO

3*GEO, 4*IGSO

3*GSO, 1*GEO

Total number of operational satellites

31

24

35

22

08

04

Period

11.97 h

11.26 h

12.63 h

14.00 h

23.93 h

23.93 h

Number of satellites

72

24 satellites in three orbital planes whose ascending nodes are 120 degrees apart

5 GEO satellites, 30 MEO satellites

30

14 (8 active and 1 failed and 5 planned)

7 (4 satellites in constellation and 3 satellites are planned by 2023)

Frequency

L1, L2

L1*, L2*

B1, B2, B3

L1

L5, S-band

L1, L2, L5, L6

Status as in 2020

Operational

Operational

Operational

Operational

Operational

Operational

Year of first launch of the satellite

1978

1982

2000

2011

2013

2010

Period of fully operational satellite series

Fully operational but more satellites planned

Fully operational but more satellite planned till 2030

Fully operational

Fully operational

NA

2023

Horizontal accuracy

500e30 cm

5e10 m (with vertical accuracy: w15 m)

3.6 m (public) 2.6 m (Asia pacific, public) 10 cm (encrypted)

1 m (public), 1 cm (encrypted)

1 m (public), 10 cm (encrypted)

PNT > < qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 r2 ¼ ðx2  xv Þ þ ðz2  zv Þ  Dr > > qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi > > : r3 ¼ ðx3  xv Þ2 þ ðz3  zv Þ2  Dr

(2.4)

FIGURE 2.8 Iterative algorithm to compensate for the systematic error Dr. The pseudorange to the GNSS satellite is equal to the difference between the time of the receiver at the time of signal reception and the time of the satellite at the time of signal transmission, multiplied by the speed of light. The measured range ðri þDrÞ differs from the geometric distance ðri Þ by the correction value ðDrÞ due to the nonsynchronism of the onboard timescale of the GNSS navigation spacecraft and the time line of the GNSS consumer navigation equipment.

4

Atomic clocks use vibrations that occur in atoms. Atomic clocks are used in spaceships, ballistic missiles, airplanes, submarines, in GNSS, in base stations of mobile communications, time services, etc.

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1. GNSS structural organization

The system of three Eq. (2.4) contains three unknowns fxv ; zv ; Drg. The calculation of Dr gives the clock error Dt ¼ Dr=c for synchronization. The synchronization requirements should be high enough. So, for example, the clock out of sync for 106 s leads to an error Dr ¼ 106 $s ¼ 106 $3$108 ¼ 300 n.

1.9 3D GNSS using N satellites We write Eq. (2.4) for 3D GNSS and three satellites as qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 8 2 2 2 > r ¼ ðx1  xv Þ þ ðy1  yv Þ þ ðz1  zv Þ  Dr > 1 > > < qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 2 r2 ¼ ðx2  xv Þ þ ðy2  yv Þ þ ðz2  zv Þ  Dr > > qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi > > : r3 ¼ ðx3  xv Þ2 þ ðy3  yv Þ2 þ ðz3  zv Þ2  Dr

(2.5)

Positioning accuracy increases with an increase in the number of “visible” satellites; therefore, in the general case for N satellites, one can write: qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 8 > > r ¼ ðx1  xv Þ2 þ ðy1  yv Þ2 þ ðz1  zv Þ2  Dr 1 > > > qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi > < 2 2 2 r2 ¼ ðx2  xv Þ þ ðy2  yv Þ þ ðz2  zv Þ  Dr > > . > > ffi qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi > > : 2 rN ¼ ðxN  xv Þ þ ðyN  yv Þ2 þ ðzN  zv Þ2  Dr

(2.6)

To solve the system of Eq. (2.6), relatively complex iterative algorithms are used to calculatefxv ; yv ; Drg, consideration of which is beyond the scope of this book. See, for example, Ref. [7].

1.10 Summary and conclusions on the topic structural organization of GNSS The GNSS architecture is an understanding of GNSS from the perspective of a GNSS user. There is little interest in coordinate systems and time in satellite technology, the theory of flight of artificial Earth satellites, the influence of the ionosphere and troposphere on SRNS signals, how the CS (or Ground Segment) is organized, and many other details that are of interest only to GNSS developers. The user must understand how the GNSS structure is organized in terms of the satellite segment and what are the main parameters of positioning accuracy and time. And, of course, the user need to clearly understand how the main elements of the GNSS structure interact, what will be discussed in the next part “GNSS functional organization.”

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2. Fundamentals of structural and functional organization of GNSS

2. GNSS functional organization 2.1 GNSS functional principle GNSS is based on the use of the principle of no-request range measurements between navigation satellites and the user. This means that satellite coordinate information is transmitted to the user as part of the navigation signal. At the same time (synchronously), measurements of ranges to navigation satellites are carried out. The range measurement method is based on calculating time delays of the received signal from the satellite compared to the signal generated by the user equipment. Figs. 2.9 and 2.10 shows a diagram of user position determinations with coordinates x, y, and z based on measurements of range to navigation satellites. The true range differs from the pseudorange by an amount equal to the product of the speed of light per the amount of offset of the user's clock with respect to the system time. Ideally, when measurements are made accurately and the satellite clock and user readings coincide to determine the position of the user in space, it is sufficient to measure up to three navigation satellites. In fact, the clock readings that are part of the user's navigation equipment differ from the clock readings on board navigation satellites. So to solve the navigation task, add another one to the previously unknown parameters (three user coordinates)dthe offset between the user's clock and the system time. It follows that in general, in order to solve the navigation problem, the consumer must “see” at least four navigation satellites.

FIGURE 2.9

The GNSS positioning.

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2. GNSS functional organization

35

FIGURE 2.10 LADGNSSdLocal Area Differential GNSS; RADGNSSdRegional Area Differential GNSS; WADGNSSdWide Area Differential GNSS.

2.1.1 Systems of coordinates Navigation satellite systems require data on the Earth's rotation parameters, fundamental ephemeris of the Moon and planets, data on the Earth's gravitational field, models of the atmosphere, and highly accurate data on coordinate and time systems used. The origin of the geocentric coordinate systems coincides with the center of the Earth's mass. Such systems are called Earth-wide or global (Table 2.2). 2.1.2 Time systems GPS system time is associated with Coordinated Universal Time or UTC as observed by the US Naval Observatory. Nominally, the GPS timescale has a constant of 19 s, a discrepancy with the time atomic international TAI. The time count is in GPS weeks and seconds within the current week, the start of the count is 00 h 00 min January 06, 1980. In the GPS system, the week number is recorded with a 10-bit binary number, the maximum value of the week number is 1023. The zero number of the week was repeated at midnight from August 21 to August 22, 1999 [8].

TABLE 2.2

In modern navigation satellite systems, various, usually national, coordinate systems are used.

GNSS

Coordinate system

GLONASS

PZ-90 (1990 Earth Parameters)

NAVSTAR GPS

WGS-84 (World Geodetic System)

GALILEO

GTRF (Galileo Terrestrial Reference Frame)

BeiDou

CGCS 2000 (China Geodetic Coordinate System 2000)

QZSS

JGS (Japanese geodetic system)

NavIC

WGS-84 (World Geodetic System)

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2. Fundamentals of structural and functional organization of GNSS

2.1.3 Factors affecting accuracy There are many factors that influence the accuracy of coordinates, speed, and time. Some of them are as follows: • Errors related to onboard satellite and ground GNSS control systems are mainly due to imperfections in time frequency and ephemeris support. • Errors are due to the difference between the speed of propagation of radio signals in the Earth's atmosphere and the speed of their propagation in a vacuum, as well as the dependence of speed on the physical properties of different layers of the atmosphere. • Hardware errors are divided into systematic errors of radio signal hardware delay and fluctuation errors caused by noise and consumer dynamics. • Mutual positioning of navigation satellites and the consumer: The quantitative characteristic of the error of position determination and correction of clock readings related to the features of the spatial position of the satellite and the user is Geometric Definition of Precision [9]. 2.1.4 GNSS accuracy improvement There are a number of tasks that require high navigation accuracy. These tasks include navigation in coastal waters, navigation of helicopters and vehicles, and others. The classical method of improving the accuracy of navigation definitions is the use of differential (relative) mode of calculation. The differential mode involves the use of one or more base receivers located at points with known coordinates, which simultaneously with the user receiver receive signals of the same satellites. Improvement of navigation accuracy is achieved due to the fact that errors of measurement of navigation parameters of user and basic receivers are correlated. When generating differences of measured parameters, most of such errors are compensated. Differential method is based on knowledge of coordinates of reference point control and correction station or system of RSs, relative to which corrections to definition of pseudoranges before navigation satellites can be calculated. If these corrections are taken into account in the user equipment, the accuracy of calculation, in particular, of coordinates can be increased by tens of times. To ensure differential treatment for a large regiondfor example, for Europe, Russia, and the United Statesdthe transmission of corrective differential corrections is carried out using geostationary satellites. Systems implementing this approach have been called broad-zone differential systems. Since the travel time of the signal from the satellite to the receiver is known, the distance between the transmitter and the receiver can be measured. Position estimation based on distance measurements is called trilateration when three measurements are used or multilateration when more than three measurements are used. The basis of GNSS positioning is distance measurement by ToA. For this purpose, each satellite transmits signals with a time stamp associated with it. On the user side, the GNSS receiver measures the reception time of the transmitted mark and then estimates the propagation delay for each satellite as the difference between the measured reception time and the transmission mark time. With this difference, the distance between the satellite and the user can be calculated as d ¼ c$ToA, with c being the speed of light. I. General Introduction to GPS/GNSS technology

37

2. GNSS functional organization

The calculated distance provides the radius of a spherical surface centered on the satellite and containing the user's location. However, the clocks in the satellite and receiver are not synchronized, hence all distance measurements shift. The corresponding measurements are too short or too long by the total amount of error.

2.2 GNSS signal structure, encoding, and frequency GNSS navigation messages can highlight structural commonality. Navigation messages are transmitted continuously and periodically repeated. In the initial stage of GNSS creation, satellite transmitting equipment radiated signals at two carrier frequencies L1 and L2. Ranges of these frequencies for GPS and GLONASS systems are shown in Tables 2.3e2.5. C/A code ¼ a pseudorandom bit string that is mainly used by commercial GNSS receivers to determine the coverage of the transmitted GNSS satellite. The 1023 GPS C/A code repeats every 1 ms, which gives the chip code length of 300 m, which is very easy to block. Signals from satellites are modulated by pseudorandom digital sequences by phase-shift keying. The L1 frequency is modulated by two types of codes, the C/A code (free access code) and the P-code (authorized access code). The L2 frequency is only the P-code. In addition, both carrier frequencies are further encoded by a navigation message containing orbital data of artificial Earth satellites, atmospheric parameter information, and system time corrections. This principle of system signal generation makes it possible to determine vehicle speed by measurement of the Doppler shift in carrier frequency and the range to satellite from delay of elements of the range code. The service code carries auxiliary information (satellite ephemeris, system almanac, etc.) necessary to ensure operation of the navigation receiver. TABLE 2.3

L1 and L2 frequency ranges for GPS and GLONASS. GNSS

The bearing frequency [3]

GPS frequency range, MHz

GLONASS frequency range, MHz

L1

1597.4525

1620.6100

1565.1900

1585.6500

L2

1241.3275

1261.6100

1217.3700

1237.8300

L5

960.0000

1215.0000

960.0000

1215.0000

GNSS L1 frequency ¼ GNSS carrier frequency 1575.42 MHz, which includes the course acquisition code (C/A), as well as the encoded P-code and navigation messages used by commercial GPS receivers. GNSS L2 frequency ¼ GNSS secondary carrier frequency 1227.60 MHz, containing only coded P-code. Currently, GNSS satellites transmit civil C/A code on L1 frequency and military P(Y) code on L1 and L2 frequencies. Block GNSS IIR-M satellites transmit the same signals as previous GNSS satellites but will also have a new signal, called L2C, on the L2 frequency. GNSS L5 frequency ¼ third civilian GNSS frequency at 1176.45 MHz and is transmitted starting from GNSS Block IIF satellites. This frequency is in the 960e1215 MHz frequency band. The L5 signal is evenly split between the phase (I) data channel and the quadrature (Q) data channel, which improves interference immunity, especially from systems emitting pulses in the same band as L5.

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2. Fundamentals of structural and functional organization of GNSS

TABLE 2.4

The corrections of pseudoranges for all vehicles in the j-one limited scopes and twodimensional interpolation on an irregular grid of the pseudoranges in the area of the all-region (U is the symbol of interpolation, Table 2.4). RSj ; j [ 1; M 2

. M


rs rs rs  DDrs 1;2 x2 ; y2 ; z2


rs rs rs  . DDrs 1;M xM ; yM ; zM


rs rs rs  2 DDrs 2;1 x1 ; y1 ; z1


rs rs rs  DDrs 2;2 x2 ; y2 ; z2


rs rs rs  . DDrs 2;M xM ; yM ; zM

. .

.

. .

1
rs rs rs  Sati ; i ¼ 1; N;N 1 DDrs 1;1 x1 ; y1 ; z1 4

N

TABLE 2.5

Interpolation for the region fxmin £x £xmax ; ymin £y £ymax ; zmin £z£zmax g

DDrs N;1




rs rs xrs 1 ; y1 ; z1



DDrs N;2




rs rs xrs 2 ; y2 ; z2



.

DDrs N;M




rs rs xrs M ; yM ; zM



.

The corrections of pseudoranges for all vehicles in the j-one limited scopes and two dimensional interpolation on an irregular grid of the pseudoranges in the area of the all-region (U is the symbol of interpolation, Table 2.5). RSj ; j [ 1; M

Sati ; i ¼ 1; N; N

1

2

.

M

1


rs rs  DDrs 1;1 x1 ; y1


rs rs  DDrs 1;2 x2 ; y2

.


rs rs  DDrs 1;M xM ; yM

2


rs rs  DDrs 2;1 x1 ; y1


rs rs  DDrs 2;2 x2 ; y2

.


rs rs  DDrs 2;M xM ; yM

.

.

3

N

. .

 rs rs rs rs DDrs . DDrs N;1 x1 ; y1 N;2 x2 ; y2

.


rs rs  DDrs N;M xM ; yM

Interpolation for the region fxmin £x £xmax ; ymin £y £ymax g

.

The encoding of a satellite-radiated radio signal has several purposes: • Enabling synchronization of navigation satellite and receiver signals; • Creation of the best conditions for signal discrimination in receiver equipment against noise background (pseudorandom codes have such properties); • Implementation of limited access mode to GNSS signals, when high-precision measurements are possible only with authorized use of the system.

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2. GNSS functional organization

39

The periodically repeated navigation message in GPS and GLONASS systems is called a superframe. The superframe represents the first level of structuring navigation messages. The superframe is separated by 25 frames representing the second layer of structuring the navigation message. In the GPS system, frames are divided into subframes in the third structuring layer. In the fourth structuring layer, the subframes are divided into 10 words, each word is divided into 30 information characters. In the GLONASS system at the third level of structuring, frames are divided into lines. In GNSS GLONASS, each line contains 100 information characters. The start/end of the third-level structure unit in both systems is used to transmit time stamps. The time stamp transmission period in GPS is 6 s and in GLONASS 2 s. Duration of data symbol transmission in both systems is 20 ms (frequency 50 Hz). In both systems, a portion of the third-level structural units in each frame (page) are diverted to transmit ephemeris information. The content of these units in all frames of the superframe is the same. The remainder of the Layer 3 structural units in each frame is allocated for transmission of almanac data and other auxiliary data. The content of these structural units in all frames of the superframe is different. However, the full amount of data transmitted per superframe contains the almanac information of all satellites included in the system. Selection of types and parameters of signals used in GNSS is made so as to provide high accuracy of measurement of signal arrival (delay) and its Doppler frequency, as well as high probability of correct decoding of navigation messages. In order for the signals of individual GPS satellites to differ reliably, they have a low level of cross-correlation. In addition, GNSS signals are highly resistant to deliberate and unintentional interference of various kinds.

2.3 Pseudoranges The pseudorange to the GNSS navigation spacecraft is equal to the difference between the time of the receiver at the time of signal reception and the time of the satellite at the time of signal transmission, multiplied by the speed of light in vacuum. The measured range to the GNSS navigation spacecraft differs from the geometric distance to the GNSS navigation spacecraft by the correction value due to the nonsynchronism of the onboard timescale of the GNSS navigation spacecraft and the time line of the GNSS consumer navigation equipment. The clock on the satellite and in the receiver, as a rule, diverge, which leads to errors in the magnitude of the measured distance. Additional errors are caused by delays on the path of radio signals passing through the atmosphere (ionosphere and troposphere). Simultaneous calculation of distances to several satellites allows you to calculate the coordinates, speed, and direction of movement of the vehicle. Depending on the purpose of the tasks to be solved, absolute and relative (differential) methods of coordinate definitions are distinguished. When performing phase measurements of the carrier oscillations by satellite receivers, the determined value is the phase of the carrier oscillations received from the satellite, which is compared with the phase of the corresponding oscillations generated in the receiver. Due to

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2. Fundamentals of structural and functional organization of GNSS

the high frequency of the carrier oscillations and the associated high sensitivity of the used phase measuring devices, the potential capabilities of these methods turn out to be extremely high and correspond to the millimeter level of accuracy. In this regard, phase methods are fundamental in solving a variety of geodetic problems, which usually provide for high measurement accuracy. At the same time, when performing phase measurements, specific difficulties arise for them (in particular, the problem of resolving ambiguity), to overcome which it is necessary to develop appropriate methods.

2.4 GNSS positioning The distance from a vehicle to satellites Sati can be written as qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Dvi ¼ ðxi  xv Þ2 þ ðyi  yv Þ2 þ ðzi  zv Þ2 ¼ cTiv ; i ¼ 1; N; N  4

(2.7)

Since the measurement of distance from the vehicle to the satellites is carried out by b v ¼ Tv þ DTv of GNSS signals from Sati to the vehicle, measuring the propagation time T i i then Eq. (2.7) can be represented as (excluding time synchronization errors): qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi b iv þ DT v Þ; i ¼ 1; N; N  4 ðxi  xv Þ2 þ ðyi  yv Þ2 þ ðzi  zv Þ2 ¼ cð T (2.8) As Dr ¼ sDTv , then Eq. (2.8) can be written in the form qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 2 b iv ; i ¼ 1; N; N  4 ðxi  xv Þ þ ðyi  yv Þ þ ðzi  zv Þ þ Dr ¼ c T

(2.9)

The navigation processor in the vehicle solves the system of Eq. (2.9) and calculates the position of the vehicle ðxv ; yv ; zv Þ and timing errors on board Dt, which are then used to correct the GNSS navigation clock. 8 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 9 2 2 2 > > > ðx1  xv Þ þ ðy1  yv Þ þ ðz1  zv Þ þ Dr > > > Iteration algorithm > > > > ffi > > < qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi = for Sati ; i ¼ 1; N 2 2 2 ðx2  xv Þ þ ðy2  yv Þ þ ðz2  zv Þ þ Dr ¼)ðxv ; yv ; zv Þ (2.10) > > > > . > > > > qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi > > > > : ; 2 2 2 ðxN  xv Þ þ ðyN  yv Þ þ ðzN  zv Þ þ Dr Because Dr is not an unknown value,
instead  of the exact value ðxv ; yv ; zv Þ, we will get xv; b approximate results of measurements b y v ; bz v : ffi 9 8 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 2 > ðx 1  xv Þ þ ðy1  yv Þ þ ðz1  zv Þ > > > Iteration algorithm > > > > > > < qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi = for Sati ; i ¼ 1; N
 ðx2  xv Þ2 þ ðy2  yv Þ2 þ ðz2  zv Þ2 xv; b ¼) b y v ; bz v (2.11) > > > > . > > > ffi> > > : qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ; ðxN  xv Þ2 þ ðyN  yv Þ2 þ ðzN  zv Þ2

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2. GNSS functional organization

41

Analytical methods of solving the systems of Eq. (2.11) do not work, and the description of numerical methods of solving such a system of equations goes beyond the scope of this chapter. See, for example, Ref. [7].

2.5 Differential GNSS architecture In order to increase the accuracy of GNSS to a level that provides ships that are underway in rivers and canals with sufficiently accurate positioning coding, DGNSSs are divided into three categories (DGNSS base station antenna is set to within a few millimeters), consisting of ground differential base stations that receive signals from satellites, counting errors for signals about its (actual) position in the systems WGS-84, PZ-90 or others, and transmitting errors by a special radio network or by satellite. Correcting ReedeSolomon codes are used for error-correcting coding. DGNSSs are divided into three categories. Local Area Differential GNSS (LADGNSS) is the DS transmitting correction information up to 100e200 km of coastline. Regional Area Differential GNSS (RADGNSS) is formed by combining data of a few LADGNSSs located in the same region. Wide Area Differential GNSS (WADGNSS) is formed by combining data of a few RADGNSSs located in a same region, a same state, or a group of bordering states. The transmitting of correction information in the unlimited field of the Earth can be implemented through a communication satellite or a group of satellites, for example, using the Network Transport of RTCM via satellite link or through the Internet, for example, using the Network Transport of RTCM via Internet protocol. 2.5.1 Local Area Differential GNSS positioning The LADGNSS method uses two or more pseudorange receivers. One of the receivers is permanently installed at a point with a known position in the common Earth coordinate system. It is called a RS or control and correction station. The second receiver is at the point whose coordinates must be determined. It is known that the influence of various sources of errors on the measurement results is the same for both the reference and the mobile receiver. In those cases when the required accuracy of determining the coordinates is estimated at about 1 m (as, for example, on certain types of work in marine geodesy), resort to the use of differential pseudoranging methods [10]. The essence of the practical use of such differential methods is that along with the mobile satellite receiver installed on the vehicle, another (reference) receiver is installed at the point with known coordinates, working simultaneously with the first one. Between the two receivers, a radio communication channel is organized to transmit information (correction terms) from the reference receiver to the mobile one. The noted information contains corrections that are received at the RS by using both the measured values of the pseudorange and the distances to the satellites, calculated on the basis of the application of the known coordinates of this point (Fig. 2.11).

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2. Fundamentals of structural and functional organization of GNSS

FIGURE 2.11 Local Area Differential GNSS Positioning. A receiver is at a known position (the reference station) and a second receiver is on the vehicle at an unknown position. Because the GNSS position errors for the reference station and for the vehicle are approximately the same, the difference between the known and unknown locations of the reference station can be used to improve the accuracy of the positioning.

Since RS is at known location ðxrs ; yrs ; zrs Þ, we can compute the real distance from to satellites Sati as qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 2 ¼ ðxi  xrs Þ þ ðyi  yrs Þ þ ðzi  zrs Þ ; i ¼ 1; N; N  4 (2.12) Drs i We calculate the assessment of the distance from RS to satellites Sati (pseudorange) by determining the signal propagation time from RS to the satellites Sati as b rs ¼ c T b rs ; i ¼ 1; N; N  4 D i i

(2.13)

and now we can compute the correction of a pseudorange for all vehicles in limited scope:
rs  rs b DDrs i ¼ D i  Di ; as

i ¼ 1; N; N  4

(2.14)

The radio beacon transmits the correction DDrs i to all vehicles, adjusting their pseudorange 
v 
v b  DDrs ; ev ¼ D b  DDrs ¼ c T D i i i i i

i ¼ 1; N; N  4

In this case, the system of Eq. (2.12) assumes the form qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

v 2  e v ¼ cT b  DDrs ; ðxi  e xv Þ2 þ y i  e yv þ ðzi  e zv Þ 2 ¼ D i i i

I. General Introduction to GPS/GNSS technology

i ¼ 1; N; N  4

(2.15)

(2.16)

2. GNSS functional organization

43

The navigation processor of vehicle solves the system of Eq. (2.15) and calculatse the
position of the vehicle e xv ; e yv ; ezv . The positioning error may be defined as qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi 2
2 2 (2.17) DD ¼ ðx  e xv Þ þ y  e yv þ ðz  e zv Þ on condition that the real coordinates of the vehicle (x,y,z) are known since the geodesic accuracy. The support for DGNSS positioning technology will solve the positioning problem in high accuracy (10e20 cm). 2.5.2 Regional Area Differential GNSS positioning Let us pretend that RADGNSS comprises from M RS (Fig. 2.12). RSj ; j ¼ 1; M (2.18) n o rs rs Since RSj are at known locations xrs , we can compute the real distance from RSj j ; yj ; z j to satellites Sati as rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi     ffi  Drs i;j ¼

xi  xrs j

2

þ yi  yrs j

2

þ zi  zrs j

2

; i ¼ 1; N; N  4; j ¼ 1; M

(2.19)

FIGURE 2.12 Regional Area Differential GNSS: this figure shows receivers at a known position (the reference stations) and receivers on board of the vehicles at an unknown position (i.e., the rover or user) for relative positioning. The radio beacons transmit the local corrections across a radio or wired communication channel to Master Station and Master Station after approximation of local data transmits the regional correction across radio beacons or may be through a communication satellite (a selection of communication channel depends on the size and configuration of the region) to all vehicles in this region.

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2. Fundamentals of structural and functional organization of GNSS

We calculate the assessment of the distance from RSj to satellites Sati (pseudoranges) by determining the signal propagation time from RS to the satellites Sati as b rs ¼ c T b rs ; i ¼ 1; N; N  4; j ¼ 1; M D i;j i;j

(2.20)

and now we can compute the correction of a pseudoranges for all vehicles in the j-one limited scopes:  rs  b  Drs ; i ¼ 1; N; N  4; j ¼ 1; M ¼ D (2.21) DDrs i;j i;j i;j The radio beacons transmit the local correction DDrs i;j to Master Station, which solves the problem of interpolation of a plurality of samples DDrs i;j into distribution functions of positioning. For each navigation satellite, we know the value of the function (14) at the interpolation nodes, and we can determine the value of at any point ðx; yÞ of a region, in which there are RS. The solution to this problem is to construct a polynomial interpolation of the receiving nodes in the prescribed values and the calculation of the value of this polynomial in a point of interest to us ðx; yÞ. After approximation of local data, Master Station transmits the regional correction across radio beacons or may be through a communication satellite (a selection of communication channel depends on the size and configuration of the region) to all vehicles in this region. Each vehicle adjusting their pseudoranges as (2.22) In this case, the system of equationsEq. (3.15) assumes the form (2.23) The navigation processor of  vehicle solves the system of Eq. (2.23) and calculates the po
yv ; ezv . sition of the vehicle e xv ; e It is assumed that tabulated (sampling and quantization), i.e., interpolation results are presented in the form of a four-dimensional array: (2.24) xmin where k ¼ 0; xmaxDx  1; l ¼ 0;

pling of function

ymax ymin Dy

zmin  1; n ¼ 0; zmaxDz  1; ðDx; Dy; DzÞ are steps for sam-

.

2.5.3 Wide Area Differential GNSS positioning A network of RSs is the base unit that provides high-precision coordinate time information about the location of vessels in coastal waters.5 This network is organized in areas where the density of traffic and the existing navigation and hydrographic support of navigation safety 5

This section deals with the problems of maritime transport, but the results of this section can be extended to all types of vehicles.

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2. GNSS functional organization

45

are demanding higher levels of courts in order to protect the environment and reduce downtime of vessels and to achieve smooth operation of the fleet, providing quality rescue at sea. The radio beacons transmit the local correction DDrs i;j to Master Station, which solves the problem of interpolation of a plurality of samples DDrs i;j into distribution functions of positioning. For each navigation satellite, we know the value of the function (2.24) at the interat any point ðx; yÞ of a region, polation nodes, and we can determine the value of in which there are RSs. The solution to this problem is to construct a polynomial interpolation of the receiving nodes in the prescribed values and the calculation of the value of this polynomial in a point of interest to us ðx; yÞ (Fig. 2.13). The expression (2.24) takes the form (2.24a) (2.24a) xmin where k ¼ 0; xmaxDx  1; l ¼ 0;

ymax ymin Dy

 1, Dx; Dy are steps for sampling of function

. In practice, the task of 2D interpolation on a nonuniform grid can be solved using standard software procedures, such as MATLABda 2D interpolation on the irregular grid: Syntax: ZI ¼ griddata(x, y, z, XI, YI) Description: Function ZI ¼ griddata(x, y, z, XI, YI) returns an array of ZI, which is defined on the new grid {XI, YI} as a result of interpolation of the initial function z, defined on a non-uniform grid {x, y}, i.e., z ¼ f ðx; yÞ (Fig. 2.14).

FIGURE 2.13 Wide Area Differential GNSS: this figure shows receivers at a known position (the reference stations) and receivers on board of the vehicles at an unknown position (i.e., 5) for relative positioning. The radio beacons transmit the local corrections across a radio or wired communication channel to Master Station and Master Station after approximation of local data transmits the wide correction across a communication satellite or group of satellites to all vehicles.

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2. Fundamentals of structural and functional organization of GNSS

FIGURE 2.14 A two nonuniform grids: (A) 13 reference stations (RSs) on the ground (~60  110 km); (B) 8 RSs by the sea (~50  100 km).

Two examples of the task solutions of 2D interpolation on a nonuniform grid for 13 RSs6 (a) and for 8 RSs (b), located in {14  E  15; 54  N  55} (Fig. 2.14). As Z used the sum of two fields (random and deterministic)  ZðE; NÞ ¼

 ðE14;5Þ2 ðN54;5Þ2  RENDðÞ 1 2 2s2 N  1:2 þ e 2sE 10 2psE sN

(2.25)

where 0  RENDðÞ  1 is a random number, sE ¼ sN ¼ 0:5 (Fig. 2.15).

FIGURE 2.15

Two-dimensional interpolation on a nonuniform grid for 13 reference stations as in Fig. 2.14A.

E ¼ [14.00 14.05 14.05 14.25 14.50 14.50 14.50 14.50 14.50 14.75 14.95 14.95 15.00]; N ¼ [54.50 54.05 54.95 54.50 54. 00 54.25 54.50 54.80 55.00 54.50 54.05 54.95 54.50]; Z ¼ [0.40 0.17 0.17 0.75 0.39 0.73 0.85 0.65 0.37 0.70 0.15 0.22 0. 39]; e ¼ 14:0.1:15; n ¼ 54:0.1:55; [XI,YI] ¼ meshgrid(e,n); ZI ¼ griddata(E, N, Z, XI, YI); mesh(XI, YI, ZI), hold on, plot3 (E, N, Z, 'ok').

6

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47

In this chapter, the application griddata is admissible only for 2D vehicle (ship, vessel, boat, 7 car, etc.), i.e., for the case where zrs j ¼ const. For each navigation satellite, i ¼ 1; N; N  3, we     rs rs rs rs rs rs ; y ; z ; y replace 3D nonuniform grid DDrs x on 2D nonuniform grid DD x i;j i;j j j j j j , j ¼ 1; M. Determine uniform grid {XI, YI} in the region fxmin  x  xmax ; ymin  y  zmax g with sampling steps fDx; Dyg and we use procedure griddata as in Fig. 2.16.

For i ¼ 1; N %N  3 because it considered only 2D vehicle (ship, vessel, boat, car, etc.)  rs rs ZI¼ griddata xrs ; y ; DD ; XI; YI % symbol (*) means all elements of the array  1 i; Di;; ¼ ZI End

To conclude this section, we briefly consider two Wide Area Differential GNSS Positioning: WADGNSS NASA8 and WADGNSS StarFire9 from NavCom. Global GPS Network WADGNSS NASA consists of about 100 stations. Real-Time Net Transfer software was developed for data transfer. The main focus is on obtaining accurate estimates of the orbits of GPS satellites and the parameters of their clocks from a relatively rare network of strongpoints distributed throughout the globe. The calculated corrections

FIGURE 2.16

Two-dimensional interpolation on a nonuniform grid for 8 reference stations as in Fig. 2.14B.

7

N  3N  3 because it considered only 2D vehicle (ship, vessel, boat, car, etc.).

8

https://www.gdgps.net.

9

R. Hatch, T. Sharpe, P. Galyean. A Global, High-Accuracy, Differential GPS System// https://www.ion.org/ publications/abstract.cfm?articleID¼3799.

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2. Fundamentals of structural and functional organization of GNSS

are transmitted over the Internet. WADGNSS NASA delivers a decimeter level of accuracy anywhere in the world. Full services are available for GPS, GLONASS, BeiDou, and Galileo. WADGNSS StarFire provides a decimeter level of accuracy anywhere in the world and even solves the problem of local kinematics in real timedreal-time kinematic. As a rule, StarFire WADGNSS does not require the use of local base stations, while the results are absolute (not relative). Measurement accuracy within 24 h does not exceed 1 dm.

2.6 Summary and conclusions on the topic functional organization of GNSS The energy that electromagnetic waves carry over time is distributed over a larger and larger surface (Fig. 2.17). Therefore, the energy transmitted through the surface of a single site (shown in blue in Fig. 2.17) per unit time, i.e., the radiation flux density, decreases with distance from the source as I¼

DW DW 1 ¼ SDt 4pDt R2

(2.26)

that is, the study flux density decreases inversely with the square of the distance to the satellite's antenna. If we take into account that the GNSS orbits are at altitudes of about 20,000 km, then Eq. (2.26) can be written as I¼

DW DW 1 ¼ SDt 4pDt 400; 000; 000; 000; 000

(2.27)

FIGURE 2.17 The radiation flux density decreases with distance from the satellite in inverse proportion to the square of the distance to the satellite's antenna.

I. General Introduction to GPS/GNSS technology

References additional

49

This means that the radiation flux density upon reaching the navigator's antenna decreased 400 trillion times. From here, we make an exceptional first important conclusion. With the help of a primitive “jammer” at the cost of tens of dollars, any uneducated terrorist can suppress the GNSS operation within a radius of several tens of meters. If you need to increase the range of the “jammer” to several tens of kilometers, you will have to pay several thousand dollars, unless, of course, military jammers are sold to you. We will discuss more on how jammers and spoofers work in the Security of GNSS chapter. From here, we make an exceptional second important conclusion. To ensure the safety of piloting autonomous vehicles, it is necessary to consider GNSS as an auxiliary tool for positioning and navigation, equipping vehicles with INS, or the like. Several types of INS have been developed for maritime, air, and land applications. A number of ground moving objects have odometric coordinate systems based on counting the number of revolutions of a standard wheel. Information on the distance traveled is obtained by the odometer, and information on the direction is obtained by the gyroscope. If the tool starts from a known position, then information about the distance and direction can be used to determine the position at any time. Being an environment-independent system, INS provides the same high accuracy as GNSS, but for a short time after initialization. Moreover, INS provides high-speed data updates comparable to GNSS. The main disadvantage of INS is that they accumulate an invalid positioning error over long time intervals.

References [1] Official U.S. Government Information about the GLOBAL Positioning System (GPS) and Related Topics. www. GPS.gov. [2] Information and Analysis Center for Positioning, Navigation and Timing (IAC PNT). www.GLONASS-IAC.ru. [3] The ESA Earth Observation Portal. CNSS Compass/BeiDou Navigation Satellite System. https://directory. eoportal.org/web/eoportal/satellite-missions/content/-/article/cnss. [4] European Space Agency's Galileo website. https://www.esa.int/Applications/Navigation/Galileo. [5] Indian Regional Navigation Satellite System/NavIC. https://www.isro.gov.in. [6] Quasi-Zenith Satellite System. https://qzss.go.jp. [7] M. Trott, The Mathematica Guidebook for Numerics, Springer Science, 2006. [8] The United States Naval Meteorology and Oceanography Command (NMOC). Precise Time. https://www. usno.navy.mil/USNO/time. [9] GIS Geography. GPS Accuracy: HDOP, PDOP, GDOP, Multipath & the Atmosphere. https://gisgeography. com/gps-accuracy-hdop-pdop-gdop-multipath/. [10] DGNSS Fundamentals. https://gssc.esa.int/navipedia/index.php/DGNSS_Fundamentals.

References additional [1] Multi-Constellation GNSS Receivers Becoming a Standard. https://www.geospatialworld.net/blogs/multiconstellation-gnss-receivers-norm/. [2] Determining Local GPS Satellite Geometry Effects on Position Accuracy. http://freegeographytools.com/2007/ determining-local-gps-satellite-geometry-6effects-on-position-accuracy. [3] Radio Regulations (ITU-R International Telecommunication Union, Radiocommunication Sector, Geneva, 2012. http://www.itu.int/pub/R-REG-RR. [4] B.W. Parkison, et al., Global Positioning System: Theory and Applications, vols. I, II, AIAA Press, 1996. [5] E. Kaplan, C.J. Hegarty, Understanding GPS/GNSS: Principles and Applications, third ed. [6] M.S. Grewal, A.P. Andrews, C.G. Bartone, Global Navigation Satellite Systems, Inertial Navigation, and Integration, fourth ed.

I. General Introduction to GPS/GNSS technology

C H A P T E R

3

Security of GNSS Evgeny Ochin, Łukasz Lrmieszewski Jakub Parady_z University, Faculty of Technology, Gorzów Wielkopolski, Poland

1. Introduction Imagine that you drive your car in the city center, take a look at the screen of the navigator and understand that the navigator “sees” you outside the city, near the airport. This is a real example of the use of Global Navigation Satellite System (GNSS) spoofing technology, that is, the substitution of GNSS coordinates by broadcasting from the ground a stronger fake GNSS signal that drowns out the satellite signal There are many practical applications for this focusdfrom car theft (there are known cases of theft of Transport International Routiers (TIRs) to interfering with the navigation of yachts and tankers. Therefore, protective systems against this type of attack began to appear. Computers have been hacked for decades. Now you can hack not only a computer but also GNSS, which manages everything from car navigation to a high-voltage unified network. Such hacking is called “false GNSS-like signals” or “GNSS spoofing” (Fig. 3.1). The development of satellite navigation technology and electronic information technology has promoted the wide application of navigation, positioning, and timing technology and also made it face a series of threats and challenges, including intentional spoofing. Spoofing directly leads to “wrong” location information of the positioning terminal and “wrong” time information of the timing terminal, thus affecting the normal production and life order.1 There is no doubt that the frequency of jamming or/and spoofing will increase, as methods are constantly being improved. The story of the American unmanned aerial vehicle (UAV) in Iran [1] has raised doubts that drones are well protected. There were suspicions that an attacker could intercept the control of the device. The representatives of Iran themselves said that this was the case: they intercepted the UAV's control using Global Positioning System (GPS) spoofing, that is, replacing the satellite signal. 1

Todd Harrison and others. Space Threat Assessment, 2020, A Report of the CSIS Aerospace Security Project// https://csis-prod.s3.amazonaws.com/s3fs-public/publication/200330_SpaceThreatAssessment20_WEB_FINAL1. pdf?6sNra8FsZ1LbdVj3xY867tUVu0RNHw9V.

GPS and GNSS Technology in Geosciences https://doi.org/10.1016/B978-0-12-818617-6.00015-9

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© 2021 Elsevier Inc. All rights reserved.

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3. Security of GNSS

FIGURE 3.1

GNSS spoofing. Spoofing interferes with the structure of GNSS transmission and deliberate modification of the receiver's route. This is a terrorist information attack involving the use of original data from a reliable source of information to fraudulent customer security by sending distorted or false data in its place.

Few people believed the statements of the Iranian side. First, the Iranians did not provide any evidence. Second, no one has ever been able to carry out a GPS spoofing attack on a military drone beforedit was hard to imagine that Iran had qualified radio engineers to perform such a hack. Later, it was reported that high-class specialists from Russia or China helped Iran, but the information was not confirmed. In the end, it was decided that Iran probably used regular jamming. On June 22, 1917, the US Department of Merchant Marine Affairs received a message: a ship's captain near Novorossiysk discovered that the GPS had incorrectly set its location; it was allegedly on land, at the airport of Gelendzhik [2]. After making sure that the equipment itself was working properly, the captain contacted neighboring vessels and found that the signals from the air navigation information service indicated that they were all at the same airport. “This affected at least 20 courts,” the newspaper writes. The author notes: “Although the incident has not yet been confirmed, experts believe that this is the first recorded case of manipulation with GPS spoofing attacks, which have long been warned about, but in “nature” it has not been observed until now.” According to a report [3] by the ICAO (International Civil Aviation Organization), GNSS interference has been reported throughout the Middle East, with 65 incidents in the region in the last 2 years. Euro control has received more than 800 reports of GPS disruption in Europe and surrounding areas during the first half of 2018 alone. When trucks use GPS signals to track and report their positions, criminals can also use GPS spoofing to hide a truck's location. “In Brazil, they lose a billion dollars a year from trucks being hijacked, and many of those hijacks are using GPS spoofers.”

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In this case, spoofing is a fake signal from a ground station that misleads the satellite receiver. “Reports of problems with satellite navigation on the Black Sea suggest that Russia may be testing a new GPS “spoofing” system,” “says New Scientist” [4]. “This may be the first hint of a new form of electronic weapons that will be available to everyone from rogue countries to petty criminals,” writes journalist David Hambling. Ships are not the only objects that are “incorrectly” located in Russia. People in the center of Moscow often see on GPS navigators that they are at Moscow's Domodedovo airport, about 25 miles away. A Norwegian reporter posted a video of himself in front of the Kremlin, and a GPS navigator tells him that he is at Vnukovo International Airport, 20 miles to the southwest [5]. Discussion of this and other similar issues is outside the scope of this book, but it is appropriate to mention that most amateur and semiprofessional drones have a built-in geofencing mechanism that automatically lands them if they approach airports or other restricted areas. The GNSS is, in general, a reliable system, and in order to keep it that way, professional GNSS receivers must be protected from all possible vulnerabilities, both accidental and man-made. GNSS spoofing may become quite dangerous for military and also for civil use of GNSS. Unlike military GNSS signals, civilian GNSS satellite signals are not encrypted or verified. This allows even relatively uninspired adversaries to easily jam, alter, or fake them. Let us brief comment about self-spoofing (or limpet spoofing). Here the receiver is owned by the attacker and it can falsify its geographical or temporary location to avoid house arrest or identification. “For example, fishermen who are required to wear GPS monitoring devices have already tried to make clumsy attempts at spoofing,” says Paul Kintner [6] of Cornell Universityd“There are a lot of reasons why people don't want to be tracked, and would love to pay for a spoofer.” The scope of this chapter is to introduce GNSS interference, GNSS jamming, GNSS meaconing, and GNSS spoofing and their detection.

2. GNSS interference Potential sources of GNSS interference operate within the same frequency bands as GNSS. Interference can be intentional (“jamming”) or unintentional. GNSS Interference is a phenomenon where other radio signals disrupt the GNSS signals causing reduced positioning accuracy or even the complete lack of position availability [7]. The GNSS signal is a low-power signal. It is comparable to the power emitted by a 60W light bulb located more than 20,000 km away from the surface of the Earth. This means that the signal could easily be disturbed by any ground source located near an aircraft and emitting in the GNSS L1 frequency band (1575.42 MHz
10 MHz), leading to the loss of GNSS data. An extraneous signal of sufficient power within these bands reduces the signal-to-noise ratio, thereby reducing the accuracy of measurements, which can also lead to failure of tracking the code and carrier frequency of the GNSS signal.

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Interference occurs when a vehicle emits signals in the GNSS band with or without permission. For example, the Swiss air navigation service reported interference with GNSS receivers when landing at Lugano Airport. In addition, a high level of interference was detected in the GNSS signal band when flying over the entire Southern Europe [8]. Noise can also be generated from electrical devices. The receiver can pick up interference through the antenna or through power circuits and wiring. GNSS noise can affect receivers with weak signal filtering. For example, on one marine vessel, the GNSS receiver became inoperable in Stavanger Harbor, Norway, due to exposure to a 1 km away radio line transmitter operating at 1533.005 MHz. Interference is a third-party disturbance that prevents the correct reception of GNSS signals. Interference sources can be located either outside the receiver or inside the GNSS receiver. Artificial industrial interference is the radiation from industrial installations, household electrical appliances, etc. Natural interference is the interference created by natural sources that emit electromagnetic, acoustic, and other energy. Atmospheric interference arises from electromagnetic radiation of a wide range of frequencies resulting from the action of atmospheric electricity. Atmospheric interference makes it difficult for GNSS receivers to work and reduces the range and quality of GNSS signal reception. Radio interference arises from electromagnetic radiation that makes it difficult or impossible to receive GNSS signals and extract useful information from them. Technical methods for eliminating interference are shielding; grounding; balancing; filtering; isolation; separation and orientation; adjusting the impedance of the circuit; cable selection; and suppression (in the frequency or time domain).

3. GNSS jamming GNSS jammers [9] are devices that intentionally generate harmful interference to GNSS signals to impair or deny their reception. Jammers can be built by people with basic technical competence from readily available commercial components and publicly available information (Fig. 3.2). Intentional interference or jamming of GPS is the emission of radio frequency energy of sufficient power and with the proper characteristics to prevent receivers in the target area from tracking the GPS signals. The jamming signals can be generated with relatively lowcost equipment. GNSS jammers exist in a variety of sizes and output power levels. Small, lightweight, short-lived jammers with power from 1 to 10 W can cost less than $100. Significant efforts were made to overcome the jamming challenge, leading to the development of several technologies over the past few years. The first approach is to implement filtering banks (in the time or frequency domains) at receiver RF front end to excise the spurious signal. The efficiency of these techniques depends of the nature of the interferers and of the computation resources (and cost) dedicated to the filtering. Continuous wave interferers can easily be removed by low-cost filtering such as Notch filters. Chirp signal jammers (technology widely found in in-car GNSS jammers), however, are more difficult to combat as these kinds of devices sweep a large frequency band.

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3. GNSS jamming

FIGURE 3.2

55

GNSS jammer, meaconer, and spoofer.

Another approach is to avoid receiving the jammed signal at antenna level. Considering that the main threats are jamming devices emitting from the ground, the idea is to use antennas with patterns designed to receive only signals coming from the sky or able to control patterns to nullify signals coming from the direction where the jamming signal is detected. These technologies are, however, costly and used only in applications where GNSS is critical. Finally, although canceling jamming is challenging, detecting it remains easier (using automatic gain control monitoring, for instance). The last solution is to design a system implementing contingency measures to be able to switch to a complementary solution (for example, an inertial navigation system [INS]2) in case GNSS jamming is detected. GNSS jamming is defined as “the emission of radio frequency energy of sufficient power and with the proper characteristics to prevent receivers in the target area from tracking the GNSS signals” [10]. Jamming signals can be characterized by their center frequency and by their power described as jamming-to-signal ratio (J/S) in dB. The J/S decreases with the distance from the jammer to the receiver. Moreover, of course, you need to keep in mind the differences in legislation in different countries [11]. In some cases, jamming is a preliminary step to spoofing, forcing the target receiver to lose the tracking of the real signal in order to reacquire a stronger spoofing signal. Jammers are used by intelligence agencies in storming buildings [12], as well as to prevent the activation of remote explosive devices. 2

INSdan inertial navigation system is composed of motion sensors (accelerometer, gyrometer, and magnetometer) allowing determination of the absolute movement of a platform. Using this information and knowledge of the last position, it is possible using dead reckoning to provide an estimation of position, velocity, and time of the platform after spoofing or jamming detection.

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4. GNSS self-jamming Self-jamming is an ordinary jamming, provided that the jammer is on the board of a vehicle (Fig. 3.3).

5. GNSS meaconing One of the simplest spoofing techniques is meaconing, i.e., the interception and rebroadcasting of the navigation signals so that the victim computes the ranging information based on the spoofer location and not on the satellites. More sophisticated versions of this attack selectively forge delayed versions of the ranging signals so that the spoofer can freely decide the false position detected by the victim. GNSS signals are rebroadcast on the received frequency, typically with power higher than the original signal. The meaconing stations cause aircraft or ground stations to receive inaccurate PVT (user Position, Velocity and precise Time) [14]. This attack has the advantage of being easy to be mounted, but, however, it does not allow to induce a desired PVT solution, and it is easy to be detected because of the induced PVT jump at the receiver side. The meaconing can be performed either by analog repeater or by digital regeneration. In order to induce a different PVT from the one of the spoofer antennas, the attacker shall have the capability of separating the signal components from different satellites, and to introduce a different delay for each of them.

FIGURE 3.3 Example of a car GNSS self-jamming. GP5000 intended for GPS tracking. GP5000 can jam: Verizon Fleet Administrator, OnStar Family Link, Track What Matters, DriveCam GPS, and others [13].

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6. GNSS spoofing Spoofing is an attack on a GNSS, in an attempt to deceive the GNSS receiver by transmitting powerful false signals that mimic the signals from the true GNSS, exceeding the power of these true signals. Spoofer is a complex computer and radio equipment for the implementation of GNSS spoofing. The GNSS signal generation is based on public parameters, and to the present date, none of the systems provides any means to verify its authenticity or cryptographic integrity. To answer the arising concerns on GNSS security, the European Commission has recently announced that Galileo will offer Navigation Message Authentication [15] (NMA) as a protection against falsified signals, i.e., the spoofing attacks. NMA is but one tool in the arsenal that GNSS receivers can deploy against spoofing. Spoofing in a broad sense is the act of disguising a communication from an unknown source as being from a known, trusted source. Spoofing is often the way a bad actor gains access in order to execute a larger cyberattack such as an advanced persistent threat or a man-in-the-middle attack. Spoofing can be applied to a number of communication methods and employ various levels of technical knowledge. GNSS spoofing is an attack in which a radio transmitter located near the target is used to transmit inaccurate coordinates. GNSS spoofing can affect a phone's location data, as well as cyberattacks against networked systems that rely on GNSS data. GNSS is also used for accurate timing, and attackers can interfere with that function. In this chapter, we will consider the problems of GNSS positioning of all modes of transport in normal mode (GNSS before Spoofing) and in spoofing mode. It is important to emphasize here that real GNSS signals continue to reach the vehicle's receiving antenna; however, more powerful fake GNSS-like signals from the spoofer displace real navigation and make the vehicle move in a false direction. In the most common example, an attacker places a broadcast antenna and directs it to a receiving antenna to interfere with GNSS signals for nearby vehicles. Spoofer can be launched with the help of a drone and follow the victim. Known smartphone applications can override the actual positioning of the smartphone, for example, the fake GPS application for Android. The methods of spoofing attacks can be divided into simplified, intermediate, and advanced (sophisticated). In a simplified attack, there is no need to know the original position, speed, and time of the victim (PVT) because this technique is based on the use of a GNSS simulator used to broadcast satellite navigation signals in order to fake the geographical coordinates of the attacked GNSS receiver. The GNSS RF simulator receiver tracks the current GNSS signals and uses this information to generate the appropriate spoofing signal. This form of attack can be easily detected, for example, by monitoring pseudorange (pseudomeasurement distance on the road from the satellite to the navigation satellite receiver) S/N, i.e., the signal carrier wave power in relation to the spectral power density of noise and interference, and frequency peaks with the Doppler effect that will appear. In an intermediate attack, the attacker reads the victim's original PVT information and aims to replicate and transmit a false GNSS signal when approaching the victim. The victim's receiver will stately take over a false signal with a higher power, without realizing that it takes the attacker's PVT arbitrary value. One way to detect this type of attack is to monitor the Doppler effect and pseudorange while the victim's receiving antenna is moving.

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The same process as the previous one takes place in an advanced attack. It uses several coordinated spoofers that are used to try and emulate the spatial signal domain. This type of device requires multiple transmit antennas to successfully implement it. This is very difficult to detect for a conventional single-antenna receiver. Most commercial spoofers do not attack the GNSS signal itself but instead place fake information directly onto the victim's receiver input, which requires physical or software access to it. Multiantenna RF signal simulators are much less common, and commercial GNSS signal generators cost around 100,000 euros. Although they are not capable of performing indirect spoofing attacks, they can effectively disrupt the satellite signal and impersonate a standard GNSS receiver. Spoofing uses GNSS-like signals to trick GNSS receivers into computing false positions, velocities, and/or times. The spoofing organization is as follows: the GNSS signal generator transmits a signal simulation of several satellites at the GNSS frequency. If the level of the simulated signal exceeds the signal strength of real satellites, the GNSS receiver will “capture” the fake signal and calculate the position based on it. All receivers that fall into the spoofing zone will calculate the same coordinates, whereas the receivers located in different places will have a slight mismatch in time. Even though GNSS signal specifications are open, spoofing has long been considered as difficult to implement and only possible for governmental organizations because considerable resources are needed to generate credible false signals. The relatively recent availability of low-cost Universal Software Radio Peripheral allows GNSS-like signals to be generated in software and then transmitted in GNSS bands. While in 1998, the cost of GNSS simulator STR2740 as the basis of building a spoofer was V150,000, after 10 years, the price fell 25 times to V6000. After another 10 years, the price fell to V5, which is 1200 times. A simple V5 USB adapter can spoof L1 GPS signals using open source software available on the Internet. In response to this threat, the GNSS community developed several technologies to defeat GNSS spoofing both at receiver and system levels. These techniques encompass spoofing detection by monitoring signal metrics in order to detect flaws in the forged signal (signal power, time inconsistency, etc.). These techniques, however, mostly allow detection of spoofing only, not avoidance. The ultimate solution to fight against spoofing is to provide a way to avoid forging of a false signal. This is achievable by ciphering the whole GNSS signal such as in the Galileo Signal Authentication Service. Satellite-based navigation remains the predominant navigation solution for both commercial and military applications. However, proven threats to GNSS from jamming, spoofing, and environmental blockages have convinced the military, as well as many commercial technology firms, that now is the time to find new navigation solutions that can enhance GNSS. The problem of continuous position availability is one of the most important issues connected with human activity at sea. Because the availability of satellite navigational systems can be limited in some cases, for example, during military operations, one has to consider additional methods of acquiring information about the ship's position. In the chapter [16], one of these methods, which is based on exploiting landmarks located on a coastline, is presented. Increasing the accuracy of positioning [17] has acquired a particular urgency in the process of designing ship autopilots for autonomous navigation of marine transport systems [18].

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Cloud technologies are data processing technologies in which computer resources are provided to the Internet user as an online service, for example, Xbox Live, Windows Live, OnLive, Google Docs, Office 365, Skype, SkyDrive, Dropbox, Google Drive, and many others. The idea of cloud technologies was first expressed by J.C.R. Licklider [19] in 1970, when he was responsible for the development of ARPANET. The idea is that each person connected to the ARPANET can receive not only data but also programs. Later, this idea was called cloud computing (CC). The wide distribution of CC began in 2006, when Amazon introduced the web services infrastructure, which provides not only hosting but also remote software and corresponding processing power to the client. Soon Google, Sun, IBM, and Microsoft introduced similar services with its cloud-based operating system Windows Azure. Modern satellite navigation is based on the use of no-request range measurements between navigational satellite and the user. It means that the information about the satellite's coordinates given to the user is included into the navigation signal. The way of range measurements is based on the calculation of the receiving signal time delay compared with the signals generated by the user's equipment. Satellite-based positioning provides the world's most precise location information. It is possible to acquire positioning anywhere in the world that GNSS satellite signals are available, any time of day, at data rates up to 100 Hz. Measurements can be generated in real time or processed postmission to achieve the highest level of accuracy. Currently four GNSS, including the US system Navstar GPS, the Russian GLONASS, the European Galileo, and the Chinese BeiDou, in total will provide more than 100 satellites including about 40 visible satellites at a time, anywhere on the Earth. The problem will be, though, the huge amount of data to be processed by the user receiver in the face of increasing influence of interferences and abnormal propagation effects. Analysis of computing resources shows that the iterative GNSS process places significant demands on the performance of the user's workstation, and the widespread use of mobile computing resources (smartphones, gadgets, etc.) makes the solution of GNSS difficult to implement. One way to radically solve this problem is to transfer the GNSS software to the “cloud.”

6.1 The cloud-based GNSS positioning Cloud technology is the ability to access data without installing special applications on the device (Fig. 3.4). With GNSS CC, users can significantly reduce the cost of building hardware and software solutions to ensure the continuity and availability of GNSSdas these costs are absorbed by the cloud service provider. The number of GNSS satellites is constantly increasing, new information channels appear, and processing algorithms are constantly being improved. This limits the ability of consumers to respond flexibly to market requirements, while cloud technologies provide the ability to timely upgrade the software and increase the performance of virtual computing resources. Among the four basic cloud infrastructure (private, public, community, and hybrid), the private is the most preferred infrastructure for GNSS positioning (Table 3.1).

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FIGURE 3.4 The cloud-based GNSS positioning.

GNSS technologies are constantly improving: the number of satellites and the number of available signals are increasing, new systems are being put into operation, and existing ones are being expanded and modernized. GNSS signals are detected by consistent filtering under conditions of unknown Doppler frequency shift. The signal-to-noise ratio at the output of the matched filter determines the potential accuracy of estimates of the parameters of the received signal. Receiving and processing of GNSS cigars consists of several stages. Fig. 3.5 shows the separation of data processing functions between the consumer and the “cloud”. TABLE 3.1

Four basic infrastructures of cloud technology.

Cloud

Definition

Private

An infrastructure designed for use by one organization, including several consumers (for example, units of the same organization), is also possible for customers and contractors of this organization.

Public

Infrastructure intended free use by the public.

Community Infrastructure designed to be used by a specific community of consumers from organizations with common tasks (for example, missions, security requirements, policies, and compliance with different requirements). Hybrid

This is a combination of two or more different cloud infrastructures (private, public, or community) that remain unique objects but are interconnected by standardized or proprietary data and application technologies (for example, short-term use of public cloud resources for balancing the cloud load).

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FIGURE 3.5 The separation of data processing functions between the consumer and the “cloud”.

7. The cloud-based GNSS spoofing detection All spoofers can be divided into two classes: one-antenna spoofers and multiantenna spoofers. Here we confine ourselves to considering only one-antenna spoofers, since the solution of the problem “spoofing detection” is in the stage of scientific research. A spoofer transmits simulated signals of several satellites. If the level of the simulated signals exceeds the level of signals from real satellites, the GNSS receiver captures the false signal and calculates the false coordinates. Differences between four different cloud-based GNSS spoofing detection modes are as follows: I. A spoofer is motionless and broadcasts signals of the visible part of GNSS satellite constellation; thus, a repeater of GNSS signals [20] is used as the meaconing (Fig. 3.1). II. A spoofer is motionless and broadcasts signals of the visible part of a GNSS satellite constellation with the introduction of signal delays from each of the GNSS satellites; thus, a repeater of the GNSS signals with a programmer of signal delays from each of GNSS satellites is used as a spoofer (Fig. 3.1). III. A spoofer is motionless and broadcasts a signal's record of the visible part of a GNSS satellite constellation; thus, a GNSS recorder is used as the spoofer (Fig. 3.1).

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IV. A spoofer is motionless and broadcasts simulated GNSS signals; thus, a simulator of GNSS signals is used as a spoofer (Fig. 3.1). We will be limited to consideration of only the modes II and I.

8. Some notation and definitions for detection of spoofing • • • • •

ðx; y; zÞdthe real coordinates of a victim.  the precise coordinates of the reception antenna of the spoofer.
ðxs ; ys ; zs Þd b xs; b ys; b z s dthe calculated coordinates of the reception antenna of the spoofer. Dvs dthe distance from the spoofer to the victim. Dt vs dthe signal transit time from the spoofer to the victim.

In mode I, a spoofer is motionless and broadcasts signals of the visible part of GNSS satellite constellation; thus, a repeater of GNSS signals is used as the meaconing (Fig. 3.1). A victim receives the same signal as the spoofer, but with the possibility of programming delays of signals for each satellite Dtvs (Fig. 3.1). It means that all receivers in the spoofing zone calculate the same false coordinates regardless of distance from the spoofer to the victim:

8 > > > > > >
> > > > =

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðx1  xv Þ2 þ ðy1  yv Þ2 þ ðz1  zv Þ2 þ Dvs qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 2 ðx2  xv Þ þ ðy2  yv Þ þ ðz2  zv Þ þ Dvs

> > . > > qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi > > 2 2 2 : ðx N  xv Þ þ ðy N  yv Þ þ ðz N  zv Þ

> > > > > v; þD


 xs; b 0 b y s ; bz s

(3.1)

s

where Dvs ¼ cDtvs . For the detection of GNSS spoofing, various methods are suggested. We list some of them: 1. Detection based on the determination of the direction to the radiation source of the spoofer, comparing the phases of the signal to several antennas. 2. Detection based on the definition of Doppler frequency shift. 3. You can use a military GNSS signal as a reference (without the need to know the secret key). 4. You can compare the indications of the INS and the data from the GNSS receiver.

8.1 Dual-antenna spoofing detector On the spoofing detector (SD), we install two antennas (Fig. 3.6). Denote the distance between the antennas as D12 .

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FIGURE 3.6 Spoofer and dual-antenna spoofing detector (SD). Y, antennas of DS; D1 and D2, the distances from the antenna of the spoofer to the antennas of DS; D12 , the distance between antennas Y of SD.

8.2 Measuring the distance between antennas in normal navigation mode The SD measures the coordinates of the antennas Y1 and Y2 :

8 > > > > > >
ðx1  xv1 Þ þ ðy1  yv1 Þ þ ðz1  zv1 Þ > > > vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi > > u =
u ðx2  xv1 Þ2 þ ðy2  yv1 Þ2 þ ðz2  zv1 Þ2  t x v1 ; b 0 b y v1 ; bz v1 > > . > > > > > > ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi q > > > : ðx N  xv1 Þ2 þ ðy N  yv1 Þ2 þ ðz N  zv1 Þ2 > ;

(3.2)


x v1 ; b y v1 ; bz v1 where ðxv1 ; yv1 ; zv1 Þ is the unknown precise coordinates of the antenna Y1 and b is the calculated coordinates of the antenna Y1 .

8 > > > > > >
2 2 2 > ðx1  xv2 Þ þ ðy1  yv2 Þ þ ðz1  zv2 Þ > > vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi > > u 2 2 2 = u ðx2  xv2 Þ þ ðy2  yv2 Þ þ ðz2  zv2 Þ
 t x v2 ; b 0 b y v2 ; bz v2 > > . > > > > > > ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi q > > > 2 2 2 : ðx N  xv2 Þ þ ðy N  yv2 Þ þ ðz N  zv2 Þ > ;

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(3.3)

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 x v2 ; b where ðxv2 ; yv2 ; zv2 Þ is the unknown precise coordinates of the antenna Y2 and b y v2 ; bz v2 is the calculated coordinates of the antenna Y2 . The measurement results differ by some unknown values and, accordingly, the distance b 12 between the antennas will be comparable with the magnitude D12 : estimate D ffi qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
2 b 12 ¼ ðb y v1  b x v1  b x v2 Þ2 þ b y v2 þ ðbz v1  bz v2 Þ2 yD12 (3.4) D

8.3 Measurement the distance between antennas in spoofing mode A victim receives the same signal as the spoofer, but with some delay Dtvs . It means that all receivers in the spoofing zone calculate the same false coordinates, regardless of the distance from the spoofer to the victim:

8 > > > > > >
> > > > =

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 2 ðx1  xv1 Þ þ ðy1  yv1 Þ þ ðz1  zv1 Þ þ Dv1 s qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 2 ðx2  xv1 Þ þ ðy2  yv1 Þ þ ðz2  zv1 Þ þ Dv1 s

> > . > > qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi > > 2 2 2 : ðx N  xv1 Þ þ ðy N  yv1 Þ þ ðz N  zv1 Þ

> > > > > v1 ; þD


 x s0 ; b 0 b y s0 ; bz s0

(3.5)

s


0 0 0 v1 x s; b y s ; bz s is where Dv1 s ¼ sDts is the distance the from the spoofer to the antenna Y1 and b the calculated coordinates of the spoofer using an antenna Y1 .

8 > > > > > >
> > > > =

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 2 ðx1  xv2 Þ þ ðy1  yv2 Þ þ ðz1  zv2 Þ þ Dv2 s qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 2 ðx2  xv2 Þ þ ðy2  yv2 Þ þ ðz2  zv2 Þ þ Dv2 s

> > . > > qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi > > 2 2 2 : ðx N  xv2 Þ þ ðy N  yv2 Þ þ ðz N  zv2 Þ

> > > > > v2 ; þD


0 b x s00 ; b y s00 ; bz s00

(3.6)

s


v2 where Dv2 x s00 ; b y s00 ; bz s00 is s ¼ sDts is the distance from the spoofer to the antenna Y2 and b the calculated coordinates of the spoofer using an antenna Y2 . In this case, the distance between the antennas Y1 and Y2 is defined as qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2
2 2 b 12 ¼ ðb (3.7) x s0  b x s00 Þ þ b y s0  b y s00 þ ðbz s0  bz s00 Þ y0: D

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8. Some notation and definitions for detection of spoofing

8.3.1 The decisive rule 1 Comparing Eqs (3.4) and (3.7), we can write down the decisive rule for detecting spoofing:  else GNSS b 12  D then Spoofing if D

(3.8)

 is discriminant, determined on the basis of statistical studies at the stage of where D designing a real detection system.

8.4 Spoofing detection by the dispersion of the pseudorange difference of two antennas In the normal navigation mode, the pseudoranges of the antennas Y1 and Y2 differ from each other in some unknown, but significantly different, values: d Dri ¼ ðb r i0  b r i00 Þ;

(3.9)

Therefore, the root-mean-square deviation (RMSD) of the differences in the pseudoranges of the antennas Y1 and Y2 will be significantly different from zero: vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 uP
2 1  PN 0 u N b r i00  r i0  b r i00 Þ t i¼1 r i  b i¼1 ðb N sgnss ¼ [0 (3.10) N1 In the spoofing mode, the pseudoranges of the antennas Y1 and Y2 differ from each other by a certain constant value equal to D1  D2 . In this case, RMSD differences of pseudoranges of antennas Y1 and Y2 is practically zero, that is, ss y0

(3.11)

8.4.1 The decisive rule 2 Comparing Eqs (3.10) and (3.11), we can write the decisive rule of spoofing detection as vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 uP
2 1 PN 0 u N b r i00  r i0  b r i00 Þ t i¼1 r i  b i¼1 ðb sgnss  ss N if then Spoofing else GNSS (3.12) < N1 2 If we take sgnss [ss , then the decisive spoofing detection rule can be written as vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 uP
2 1 PN 0 u N b b r i00  ðb r  r Þ 0 00 t i¼1 r i  b i i i¼1 sgnss N if then Spoofing else GNSS < N1 2

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(3.13)

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3. Security of GNSS

8.4.2 Discussion of the decisive rules The SD can be designed based on one of the decisive rules or based on any combination of decision rules. In any case, it is necessary to calculate the probabilities of the “false alarm (false positives)”and “missing target (false negatives)” events (Table 3.2). The questions of optimal design and selection of boundary conditions with the aim of minimizing the probabilities of “false alarm” and “missing target” are beyond the scope of this chapter. Here we only note that one of the widely used techniques is the application of Bayes' theorem (or Bayesian formula).

8.5 Single-antenna spoofing detector Suppose that the vehicle is moving in an arbitrary direction. On the SD, we install a single antenna Y (Fig. 3.7). Denote the position of the antenna at the time t’ as Y0 , the position of the antenna at the time t00 ¼ t0 þ Dt as Y00 , and the distance between the two antenna positions as D12 .

8.6 Measuring the distance between two positions of single antenna in normal mode The SD measures the coordinates of the antenna Y in two positions:

8 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 9 > > > ðx1  xv0 Þ2 þ ðy1  yv0 Þ2 þ ðz1  zv0 Þ2 > > > > > > vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi > > >

> . > > > > > > ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi q > > > : ðxN  xv0 Þ2 þ ðyN  yv0 Þ2 þ ðzN  zv0 Þ2 > ;

TABLE 3.2

(3.14)

Mistakes of a decision of the first kind (false alarm) and the second kind (missing target).

The decisive rule or combination of decision rules Solving of spoofing detector

Valid mode GNSS

Spoofing

GNSS

The solution is right

Missing target

Spoofing

False alarm

The solution is right

I. General introduction to GPS/GNSS technology

8. Some notation and definitions for detection of spoofing

67

FIGURE 3.7 Spoofer and single-antenna spoofing detector (SD). Y0 and Y}, two positions of a single antenna Y; D1 and D2 , the distances from the antenna of the spoofer to the antenna Y of SD; D12 , the distance between two positions of a single antenna Y.

where ðxv0 ;yv0 ; zv0 Þ is the unknown precise coordinates of the antenna Y at the time t’ and
b x v0 ; b y v0 ; bz v0 is the calculated coordinates of the antenna Y at the time t’.

8 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 9 > 2 2 2 > > ðx1  xv} Þ þ ðy1  yv} Þ þ ðz1  zv} Þ > > > > > > vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi > > >

> . > > > > > > ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi q > > > : ðxN  xv} Þ2 þ ðyN  yv} Þ2 þ ðzN  zv} Þ2 > ;

(3.15)

where ðx t00 ¼ t0 þ
v00 ; yv00 ; zv00 Þ is  the unknown precise coordinates of the antenna Y at the time 00 x v00 ; b Dt and b y v00 ; bz v00 is the calculated coordinates of the antenna Y at the time t ¼ t0 þ Dt. The distance between the antenna Y at the time t0 and the antenna Y at the time t00 ¼ t0 þ Dt will be comparable with the magnitude D12 : qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi 2
2 2 b b b b b b b (3.16) D 12 ¼ ð x v0  x v00 Þ þ y v0  y v00 þ ð z v0  z v00 Þ yD12

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3. Security of GNSS

8.7 Measurement of spacing between two positions of single antenna in spoofing mode A victim receives the same signal as the spoofer, but with some delay Dtvs . It means that all receivers in the spoofing zone calculate the same false coordinates, regardless of distance from the spoofer to the victim:

8 > > > > > >
> . > > qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi > > 2 2 2 : ðxN  xv0 Þ þ ðyN  yv0 Þ þ ðzN  zv0 Þ

0

9 > > > > > =

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 0 ðx1  xv0 Þ2 þ ðy1  yv0 Þ2 þ ðz1  zv0 Þ2 þ Dvs qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 0 ðx2  xv0 Þ2 þ ðy2  yv0 Þ2 þ ðz2  zv0 Þ2 þ Dvs

> > > > > v ; þD


0 b x s0 ; b y s0 ; bz s0

(3.17)

0

s

0

v where Dvs ¼  sDts is the distance from the spoofer to the antenna Y at the time t’ and
b x s0 ; b y s0 ; bz s0 is the calculated coordinates of the spoofer using the antenna Y at the time t’.

8 > > > > > >
> . > > qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi > > 2 2 2 :

ðxN  xv00 Þ þ ðyN  yv00 Þ þ ðzN  zv00 Þ þ Dvs

00

9 > > > > > =

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 00 2 2 2 ðx1  xv00 Þ þ ðy1  yv00 Þ þ ðz1  zv00 Þ þ Dvs qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 00 2 2 2 ðx2  xv00 Þ þ ðy2  yv00 Þ þ ðz2  zv00 Þ þ Dvs 00

> > > > > ;


0 b x s} ; b y s} ; bz s}

(3.18)

00

v v 00 0 where
Ds ¼ sDts is the distance from the spoofer to the antenna Y at the time t ¼ t þ Dt and b x s00 ; b y s00 ; bz s00 is the calculated coordinates of the spoofer using the antenna Y at the time t} ¼ t0 þ Dt. In this case, the distance between the antenna Y at the time t0 and the antenna Y at the time t} ¼ t0 þ Dt is defined as qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2
2 2 b 12 ¼ ðb (3.19) x s0  b x s00 Þ þ b y s0  b y s00 þ ðbz s0  bz s00 Þ y0: D

8.8 The decisive rule Comparing Eqs (3.16) and (3.19), we can write down the decisive rule for detecting spoofing: if

b 12  D  then Spoofing else GNSS D

(3.20)

 is discriminant, determined on the basis of statistical studies at the stage of where D designing a real detection system.

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11. Briefly about antispoofing

69

9. GNSS spoofer DIY (Do It Yourself) GNSS satellite simulators generate the same RF signals that are broadcasted by navigation satellites to test any device or system with a GNSS receiver [21]. Usually all constellations (GPS, GLONASS, Galileo, BeiDou, QZSS, IRNSS) are available. You can define trajectories via a Google Maps interface, in real time from a data stream, from recorded data, and more. GNSS receivers can process the simulated signals in exactly the same way as those from actual GNSS satellites. With GNSS simulators/spoofers, terrorists can easily generate and run many different scenarios of spoofing. Simulators/spoofers generate GNSS constellation signals for any locations around the world or in space, with different times in the past, present, or future. Simulators/ spoofers model the motion of the vehicles containing GNSS receivers, such as aircrafts, ships, or automobiles for different routes and trajectories anywhere in the world.

10. GNSS self-spoofing Self-spoofing [22] (Fig. 3.3), often considered one of the more challenging spoofing scenarios, refers to the scenario in which the adversary has access to the GNSS antenna and has an incentive to deceive the system in order to gain advantage. If GNSS controllers provide communication with GSM and Iridium or other telecommunication satellites, then global coverage of monitoring zones is achieved, and dispatchers can receive traffic data even in the absence of a cellular signal. The driver of the vehicle with the help of a spoofer can create the illusion of driving along any nonexistent route in order to deceive the dispatcher. The driver can smoothly transition the receiver from the condition where it is illuminated by genuine signals only to that where it is illuminated by inauthentic signals only. The transition may either be smooth or abrupt, and it may coincide with a natural outage of GNSS (passing in tunnel) or may be applied from the receiver power on. The objective of the spoofer is to ensure that the receiver cannot reliably assert that these signals are not genuine. We will put an end to this so as not to upgrade the skills of potential attackers. All vehicles must comply the European Commission regulations (e.g., to ensure fair competition, protection of fishing stocks, road safety, minimum working conditions for professional drivers, etc.). European Union regulations prescribe mechanisms to prevent position falsification.

11. Briefly about antispoofing The beginnings of antispoofing can be seen in the patent [23] 1942, despite the fact that the main purpose of this patent was the fight of the American radio-controlled sea-based torpedoes with a radio jamming of German boats and submarines. The GNSS signal generation is based on public parameters, and to the present date, none of the systems provides any means to verify its authenticity or cryptographic integrity. To

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3. Security of GNSS

answer the arising concerns on GNSS security, the European Commission has recently announced that Galileo will offer NMA as a protection against falsified signals, i.e., the socalled spoofing attacks. For spoofing attacks, antennas should be positioned so that they are less likely to pick up ground-based [24] signals and to place them where they cannot be seen by the public. Install GNSS antennas in areas where they are not visible to the public or set up barriers such as plastic fencing that would hide their location without interfering with signals. The obvious approach is based on the use of four-antenna (2  2) GNSS receivers and beamforming technology [25]. This technology not only filters out noise and interference but also allows you to determine the direction from which the signal came. It is most likely that the spoofer is located on the side or bottom, but it is unlikely coming from the top. Another approach is a GNSS firewall that is installed between the GNSS receiver and its antenna. The GNSS firewall analyzes the GNSS signal according to a known set of rules and cuts off false signals. Various countries are investing in ensuring that GNSS is resistant to counterfeiting by creating a security system directly on their satellites. With OS-NMA [26] (Open service NMA), Galileo became the first satellite system to introduce a spoofing protection service directly on a civil GNSS signal. OS-NMA is a free service on the Galileo E1 frequency. It allows you to authenticate navigation data on Galileo satellites and even on GPS satellites. Such navigation data carry information about the satellite's location and, if changed, will lead to incorrect calculation of the receiver's location. OS-NMA is currently in development, but it is planned to be made publicly available in the near future. GPS is experimenting with the new Chimera authentication system. The Galileo system will offer a commercial E6 authentication service (CAS) with the highest level of security for security-critical applications such as autonomous vehicles. Signal strength encryption will be based on the same methods as military GPS signals. Only receivers that have a secret key can track such encrypted signals. The secret key is also required for generating the signal, which makes it impossible to fake it. Septentrio is currently prototyping CAS authentication methods in conjunction with the European Space Agency. To address issue of harmful interference to GNSS, International Air Transport Association (IATA) invites [27] 1. States and national frequency authorities to establish and enforce appropriate frequency regulations to protect allocated GNSS frequencies from harmful interference in line with ITU Radio Regulations; 2. States, when using GNSS jammers during military exercises and operations, to fully recognize the unintended impacts of the harmful interference to civil flight operations and to exercise extreme cautions to the maximum extent possible to protect the safety of civil aircraft; 3. National aviation authorities and Air Navigation Service Providers (ANSPs) to establish a process to detect harmful interference to GNSS and promptly notify airlines and airspace users; 4. States and ANSPs to analyze the risk level of harmful interference to GNSS and establish contingency procedures and infrastructure as appropriate;

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12. Summary and conclusions

71

5. States and ANSPs to take into account operational risks associated to harmful interference to GNSS during their planning for rationalization of conventional navigation and surveillance infrastructures; 6. Airspace users and ANSPs to inform flight crews and air traffic controllers of the impact of GNSS interference and establish effective contingency procedures and capabilities as appropriate; 7. Airlines intending to transit areas with reported GNSS interference to assess operational risks and limitations that may occur during loss of onboard GNSS capability. Alternative navigation capability based on INS/IRS (Inertial Navigation System/Inertial Reference System) or other conventional navigation aids can be helpful; 8. Airlines to report occurrences of harmful interference to GNSS to relevant national aviation and frequency authorities and IATA; and 9. ITU in cooperation with ICAO to analyze the reported cases of harmful interference to GNSS and establish appropriate measures to address the safety impact on aviation; and 10. ICAO, in coordination with airspace user communities, to develop a global strategy on alternative position, navigation, and timing, aiming to ensure continuity of flight and ATM operations during potential interruptions of GNSS availability. Jamming and spoofing can be a threat for every business. However, there are several tools in the market that help detect and locate the source of the interference, and you can find receivers with built-in interference mitigation technology. Drotek F9P RTK GNSS integrates advanced jamming and spoofing detection to achieve highest levels of security.

12. Summary and conclusions In this section, we discuss the threats caused by GNSS vulnerabilities and the keys to solving the challenge of providing reliable, unjammable positioning, navigation, and timing. The risk of losing GNSS signal is growing every day. The accessories necessary for the manufacture of systems for GNSS “jamming” and/or “spoofing” are now widely available, and this type of attack can be taken advantage of by not only the military but also terrorists. The distortion of the signal includes a signal capture and playback at the same frequency with a slight shift in time and with greater intensity, in order to deceive the electronic equipment of a victim and, therefore, the operator if there is one onboard the vehicle. The price of one chipset for such equipment is in the range of 1e10 thousand euros, depending on the dimensions and weight parameters. It is important to emphasize that GNSS is not only navigation. In the framework of the current threat model, GNSS interference is needed in order to drown out the reference signal of synchronous time that is used in a distributed network of radio electronic devices, that is, GNSS allows you to synchronize with high accuracy time on stand-alone passive devices. Synchronous time is also necessary for data transfer in low-visibility communication mode: receivers and repeaters must have a total time to correctly adjust the correlation

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3. Security of GNSS

parameters, allowing to isolate the masked signal, which is indistinguishable from noise for an outside observer. Moreover, this, of course, is not a complete list of data transfer operations. Spoofing of GNSS signals is a set of techniques that a malicious user intentionally uses in order to induce false ranging measurements to a legitimate user. As GNSS further penetrates into the civil infrastructure, it becomes a tempting target that could be exploited by individuals, groups, or hostile countries.

13. Postscript In 2011, a dramatic event took place. UAV RQ-170 Sentinel operated by the United States Air Force for the Central Intelligence Agency with a wingspan of 20 m, manned via a video channel from Afghanistan, landed on Iranian territory. Iran has officially stated that spoofing technology has been applied. The United States said the cause was a malfunction in the airborne equipment. After 10 years, but today, we can only assume approximately the following scenarios: 1. With the help of a powerful jammer, for example, 1L222 Carpool,3 which was previously purchased from Russia, the remote video piloting is interrupted. 2. The RQ-170 enters autonomous piloting mode using GPS. 3. Iranians use spoofing to intercept control, that is, carry out remote piloting, which ends with landing. 4. Iranians copy RQ-170 software and begin designing and manufacturing Iranian version of UAV RQ-170 Sentinel. This scenario raises several questions: 1. Is it possible to assume that the RQ-170, worth several million dollars, was not programmed to return to the base in case of loss of radio signals from its base? 2. Is it possible to assume that the RQ-170 onboard computers were not programmed to destroy important information if it ceased to respond to radio signals from its base? In 2020, we again returned to Iranian spoofing in connection with the publication of “GPS circle spoofing discovered in Iran.”4 A message that in the Tehran region, stationary GPS navigators sometimes go into the circular quasimovement mode of a user at a speed of 35 km/h was received. This post shows that Iran's level of technical development in GNSS spoofing is relatively high. However, “the devil is not so black as he is painted”. The combination of GNSS þ INS (see Chapter 3) allows you to effectively fight not only with jamming but also with spoofing.

3

http://militaryrussia.ru/blog/topic-598.html.

4

Dana Goward. GPS circle spoofing discovered in Iran. GPS WORLD April 21, 2020.

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References

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[24] L. Dobryakova, Ł. Lemieszewski, E. Ochin, Protecting Vehicles Vulnerable to Terrorist Attacks, Such as GNSS Jamming, by Electromagnetic Interference Shielding of Antenna, Scientific Journals Maritime University of Szczecin no, 2017, 50/, http://repository.am.szczecin.pl/bitstream/handle/123456789/2399/09-zn-am-50122-dobryakova-lemieszewski-ochin.pdf?sequence¼1. [25] Van Veen, B.D., K.M. Buckley, Beamforming: A Versatile Approach to Spatial Filtering, 1988. https://web. archive.org/web/20081122135044/http://www.engr.wisc.edu/ece/faculty/vanveen_barry/ASSP_Mag_88.pdf. [26] Lal ia7jtjt: GOSS pt sVuvjo[a? http://vestnik-glonass.ru/news/tech/kak-zashchitit-gnss-ot-spufinga/. [27] Harmful Interference to GNSS and its impacts on flight and air traffic management operations. https://www. iata.org/contentassets/d0e499e4b2824d4d867a8e07800b14bd/tib-gnss-interference-final.pdf.

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GNSS multipath errors and mitigation techniques Lawrence Lau, PhD1, 2 1

Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China; 2Department of Civil Engineering, The University of Nottingham Ningbo China, Ningbo, Zhejiang, China

1. Introduction Global Navigation Satellite System (GNSS) biases and errors affect positioning accuracy in different magnitudes. Ionospheric effect can cause few tens meters bias in GNSS measurements; however, its mitigation is accounted in the system design (i.e., dual or more frequencies are used). Many other biases and errors are mitigated or resolved by sophisticated models. The multipath effect is one of the most important GNSS error sources, especially in high-precision positioning. This chapter focuses on carrier-phase multipath errors in highprecision positioning because carrier-phase measurements and relative positioning techniques are used in geosciences. Multipath errors and their characteristics are described in Section 2. The theoretical backgrounds and effectiveness of the state-of-the-art multipath mitigation techniques are given in Section 3. Moreover, recommendations are made on the use of different phase multipath mitigation techniques for a variety of scenarios, such as static and kinematic antennas and longor short-delay multipath.

2. Multipath errors and their characteristics Multipath errors are caused when direct signals from satellites are mixed with those reflected from objects in the vicinity of the antenna as shown in Fig. 4.1. Any reflective surfaces in the site environment may cause reflected signals, hence multipath errors. The geometry of multipath effect is shown in Fig. 4.2, e denotes the elevation angle to a satellite and d denotes the distance of the antenna from the reflector. Multipath effect occurs with a specific

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FIGURE 4.1 Multipath from any surface in the vicinity of the antenna. From Lau L., Cross P., Phase multipath mitigation techniques for high precision positioning in all conditions and environments, J. Navig. 60 (3) (2007) 457e482.

FIGURE 4.2 Multipath: geometry of a signal, a reflector, and an antenna. From Lau K.Y.L., Phase Multipath Modelling and Mitigation in Multiple Frequency GPS and Galileo Positioning (PhD thesis), Department of Geomatic Engineering, University College London, University of London, 2005; Lau L., Cross P., A new signal-to-noise-ratio based stochastic model for GNSS high-precision carrier phase data processing algorithms in the presence of multipath errors, Proceedings of ION GNSS 2006, September 26e29, Fort Worth Convention Center, Fort Worth, Texas, 2006.

satelliteereflectoreantenna geometry. The additional path length is a þ b with respect to the direct signal, and it is also known as the differential path delay. The differential path delay in signal-in-space is always longer than the direct signal. In practice, it is possible that an antenna receives the reflected signals only but no direct signals. However, receiving only reflected signals is not multipath effect, it may be called as a noneline-of-sight problem. The magnitude of the multipath error in any particular GNSS measurement depends primarily on four factors: the reflecting environment, the satellite/antenna geometry, the type of antenna used, and the receiver hardware and firmware. The reflecting environment is clearly the main driver: highly reflective surfaces lead to strong multipath signals (i.e., large amplitude), close objects cause multipath errors with long wavelengths, and distant objects short

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wavelengths. The key geometrical factor is usually the satellite elevation angle with most reflected signals coming from nearby structures or the ground arriving at the receiving antenna at low-elevation angle. Multipath is also a function of the receiving antenna. For example, such antennas are usually designed to have lower gain for low-elevation incoming signals than for high-elevation incoming signals. Of course, such a design can lead to problems when reflections are caused by objects significantly higher than the antenna and when a particular application requires direct signals from low-elevation satellites such as in atmospheric studies. Receiver hardware design impacts multipath largely through the signal correlation process, although to date relatively little progress has been made with phase multipath mitigation in this way. For instance, the narrow correlator can effectively prevent code multipath error that is larger than the half-correlator spacing passing the delay lock loop (DLL) filter.

2.1 Code multipath error As stated in the above section, in the signal-in-space consideration, the reflected signal path must be longer than the direct signal path; however, the multipath errors in code/pseudorange measurements are not always positive (i.e., longer than the direct signal path). Fig. 4.3 shows the multipath effect when the resultant signal comes from constructive direct and reflected signals. The blue peak is the prompt correlator position of the direct signal, which is at the middle of the early and late correlators. The red positive peak (i.e., in phase with the direct signal) is the weaker reflected signal. The dotted line is the distorted correlation peak formed from the direct and reflected signals. In this case, the prompt correlator position obtained from the middle of the early and late correlators shifts toward the late correlator. The shift is the DLL tracking error. If the shift toward the late correlator is defined as a delay, the code multipath error is positive. Although, Fig. 4.4 shows the multipath effect when the resultant signal comes from destructive direct and reflected signals. The red

FIGURE 4.3 Code multipath error from constructive effect of direct and reflected signals.

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FIGURE 4.4

Code multipath error from destructive effect of direct and reflected signals.

negative peak (i.e., out of phase with the direct signal) is the weaker reflected signal. The dotted line is the distorted correlation peak formed from the direct and reflected signals. In this case, the prompt correlator position obtained from the middle of the early and late correlators shifts toward the early correlator. If the shift toward the early correlator is defined as an advancement, the code multipath error is negative. Therefore, code/pseudorange multipath errors in measurements can be positive and negative although the signal-in-space reflected signal path is always longer than the direct signal path. This code/pseudorange multipath effect in real data is shown in Fig. 4.10.

2.2 Phase multipath error, SNR/CNR, and their relationship Since the design of the phase lock loop (PLL) must be able to distinguish clock dynamics from user motion in order to prevent cycle slips, carrier-phase multipath at a certain level may always remain undetectable, leading to errors in carrier-phase measurement. Braasch [3] derives a formula for the carrier-phase multipath error in measurement according to the code and phase tracking loops of GNSS receivers; the formula can be simplified for short-delay multipath ( 0.75) with one another and that they depicted the overall water deficit in Southeast Brazil (Fig. 14.3). However, comparing IWV and precipitation, at each station, with ENSO showing a mean CC value of approximately 0.38 (ENSO event is characterized here using Niño1þ2, corresponding with regions with the smallest extension area and the largest variance of sea surface temperature). This might indicate a partial role of ENSO on IWV over Southeast Brazil, and monitoring the latter seems reasonable to anticipate the impact of the former in the region.

5. Conclusions & future outlook A case study in Southeast Brazil, which the widespread environmental impacts of the 2014e15 drought have resulted in several diagnostics studies, was performed using GNSS. It has been shown an example of the GNSS as a hydrometeorological sensor in which a single GNSS site provides predicted land water storage and IWV (integrated). Overall, the six GNSS stations used are of great importance to assess the water resources. Firstly, GNSS-predicted land water storage presented a reasonable accuracy (approximately 17%e22% NRMSD) regarding those from the GRACE-JPL mascon solution, which was taken as the main benchmark in the validation over the period 2009e15. Results seem better for GNSS-predicted land water storage than those estimated by GLDAS-Noah, which seems to underestimate the amplitudes probably due to the missing water compartments (i.e., groundwater and inland waters). Secondly, the use of GNSS radial displacements as drought indicator agrees with drought characterization based on 6-month SPI for most of the sites. Thirdly, IWV and precipitation are strongly correlated, and months with daily IWV below approximately 20 mm are simultaneity with the absence of rainfall. Finally, a moderated correlation between IWV and ENSO (characterized by Niño1þ2 region) indicates that monitoring the former might support prognostics of the impact of the later. Since the GNSS is able to sense almost all components of the hydrological cycle, it is recommended that future station could be placed taking into account also the needs from hydrology and climatology.

References [1] S.J. Marshall, Hydrology, in: Reference Module in Earth Systems and Environmental Sciences, Elsevier, 2013, pp. 1e4, https://doi.org/10.1016/B978-0-12-409548-9.05356-2. [2] M.F. McCabe, M. Rodell, D.E. Alsdorf, D.G. Miralles, R. Uijlenhoet, W. Wagner, A. Lucieer, R. Houborg, N.E.C. Verhoest, T.E. Franz, J. Shi, H. Gao, E.F. Wood, The future of Earth observation in hydrology, Hydrol. Earth Syst. Sci. 21 (2017) 3879e3914, https://doi.org/10.5194/hess-21-3879-2017. III. Applications of GPS/GNSS

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[3] Y. Bock, D. Melgar, Physical applications of GPS geodesy: a review, Rep. Prog. Phys. 79 (106801) (2016), https:// doi.org/10.1088/0034-4885/79/10/106801. [4] K.M. Larson, Unanticipated uses of the global positioning system, Annu. Rev. Earth Planet Sci. 47 (2019) 19e40, https://doi.org/10.1146/annurev-earth-053018-060203. [5] T. Van Dam, J. Wahr, P.C.D. Milly, A.B. Shmakin, G. Blewitt, D. Lavallée, K.M. Larson, Crustal displacements due to continental water loading, Geophys. Res. Lett. 28 (2001) 651e654, https://doi.org/10.1029/ 2000GL012120. [6] D.F. Argus, Y. Fu, F.W. Landerer, Seasonal variation in total water storage in California inferred from GPS observations of vertical land motion, Geophys. Res. Lett. 41 (2014) 1971e1980, https://doi.org/10.1002/ 2014GL059570. [7] A.A. Borsa, D.C. Agnew, D.R. Cayan, Ongoing drought-induced uplift in the western United States, Science 345 (2014) 1587e1590, https://doi.org/10.1126/science.1260279. [8] V. Ferreira, C. Ndehedehe, H. Montecino, B. Yong, P. Yuan, A. Abdalla, A. Mohammed, Prospects for imaging terrestrial water storage in South America using daily GPS observations, Remote Sens. 11 (679) (2019), https:// doi.org/10.3390/rs11060679. [9] Y. Fu, D.F. Argus, F.W. Landerer, GPS as an independent measurement to estimate terrestrial water storage variations in Washington and Oregon, J. Geophys. Res. Solid Earth 120 (2015) 552e566, https://doi.org/10.1002/ 2014JB011415. [10] S. Jin, T. Zhang, Terrestrial water storage anomalies associated with drought in Southwestern USA from GPS observations, Surv. Geophys. 37 (2016) 1139e1156, https://doi.org/10.1007/s10712-016-9385-z. [11] B. Zhang, Y. Yao, H.S. Fok, Y. Hu, Q. Chen, Potential seasonal terrestrial water storage monitoring from GPS vertical displacements: a case study in the lower three-rivers headwater region, China, Sensors (Switzerland) 16 (2016), https://doi.org/10.3390/s16091526. [12] M. Bevis, D. Alsdorf, E. Kendrick, L.P. Fortes, B. Forsberg, R. Smalley Jr., J. Becker, Seasonal fluctuations in the mass of the Amazon River system and Earth’s elastic response, Geophys. Res. Lett. 32 (2005) L16308, https:// doi.org/10.1029/2005GL023491. [13] M. Bevis, E. Kendrick, A. Cser, R. Smalley, Geodetic measurement of the local elastic response to the changing mass of water in Lago Laja, Chile, Phys. Earth Planet. Int. 141 (2004) 71e78, https://doi.org/10.1016/ j.pepi.2003.05.001. [14] K.J. Ouellette, C. de Linage, J.S. Famiglietti, Estimating snow water equivalent from GPS vertical site-position observations in the western United States, Water Resour. Res. 49 (2013) 2508e2518, https://doi.org/10.1002/ wrcr.20173. [15] Y. Birhanu, R. Bendick, Monsoonal loading in Ethiopia and Eritrea from vertical GPS displacement time series, J. Geophys. Res. Solid Earth 120 (2015) 7231e7238, https://doi.org/10.1002/2015JB012072. [16] C.C. Chew, E.E. Small, Terrestrial water storage response to the 2012 drought estimated from GPS vertical position anomalies, Geophys. Res. Lett. 41 (2014) 6145e6151, https://doi.org/10.1002/2014GL061206. [17] V.G. Ferreira, H.C. Montecino, C.E. Ndehedehe, B. Heck, Z. Gong, S.R.C. de Freitas, M. Westerhaus, Spacebased observations of crustal deflections for drought characterization in Brazil, Sci. Total Environ. 644 (2018) 256e273, https://doi.org/10.1016/j.scitotenv.2018.06.277. [18] S. Bonafoni, R. Biondi, H. Brenot, R. Anthes, Radio occultation and ground-based GNSS products for observing, understanding and predicting extreme events: a review, Atmos. Res. 230 (104624) (2019), https://doi.org/ 10.1016/j.atmosres.2019.104624. [19] S. Jin, J.-U. Park, J.-H. Cho, P.-H. Park, Seasonal variability of GPS-derived zenith tropospheric delay (1994e2006) and climate implications, J. Geophys. Res. 112 (2007) D09110, https://doi.org/10.1029/ 2006JD007772. [20] X. Wang, K. Zhang, S. Wu, Z. Li, Y. Cheng, L. Li, H. Yuan, The correlation between GNSS-derived precipitable water vapor and sea surface temperature and its responses to El NiñoeSouthern Oscillation, Remote Sens. Environ. 216 (2018) 1e12, https://doi.org/10.1016/j.rse.2018.06.029. [21] P. Llamedo, R. Hierro, A. de la Torre, P. Alexander, ENSO-related moisture and temperature anomalies over South America derived from GPS radio occultation profiles, Int. J. Climatol. 37 (2017) 268e275, https:// doi.org/10.1002/joc.4702. [22] C.A. Nobre, J.A. Marengo, M.E. Seluchi, L.A. Cuartas, L.M. Alves, Some characteristics and impacts of the drought and water crisis in Southeastern Brazil during 2014 and 2015, J. Water Resour. Protect 08 (2016) 252e262, https://doi.org/10.4236/jwarp.2016.82022.

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[42] V.G. Ferreira, Z. Liu, H.C. Montecino, P. Yuan, C.I. Kelly, A.S. Mohammed, L.Y. Han, Reciprocal comparison of geodetically sensed and modeled vertical hydrological loading products, Acta Geod. Geophys. 55 (2020) 23e49, https://doi.org/10.1007/s40328-019-00279-z. [43] C.E. Ndehedehe, N.O. Agutu, O. Okwuashi, V.G. Ferreira, Spatio-temporal variability of droughts and terrestrial water storage over Lake Chad Basin using independent component analysis, J. Hydrol 540 (2016) 106e128, https://doi.org/10.1016/j.jhydrol.2016.05.068. [44] A. Getirana, S. Kumar, M. Girotto, M. Rodell, Rivers and floodplains as key components of global terrestrial water storage variability, Geophys. Res. Lett. 44 (2017) 10359e10368, https://doi.org/10.1002/2017GL074684. [45] B.D. Tapley, M.M. Watkins, F. Flechtner, C. Reigber, S. Bettadpur, M. Rodell, I. Sasgen, J.S. Famiglietti, F.W. Landerer, D.P. Chambers, J.T. Reager, A.S. Gardner, H. Save, E.R. Ivins, S.C. Swenson, C. Boening, C. Dahle, D.N. Wiese, H. Dobslaw, M.E. Tamisiea, I. Velicogna, Contributions of GRACE to understanding climate change, Nat. Clim. Change 9 (2019) 358e369, https://doi.org/10.1038/s41558-019-0456-2.

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High-precision GNSS for agricultural operations Manuel Perez-Ruiz1, Jorge Martínez-Guanter1, Shrini K. Upadhyaya2 1

Aerospace Engineering and Fluids Mechanics Department, University of Sevilla, Sevilla, Spain; 2 Biological and Agricultural Engineering Department, University of California, Davis, CA, United States

1. Introduction During the past 10 years, the agricultural sector has undergone a spectacular digital transformation. In this chapter, the authors have updated and added new information to a previously published chapter in 2012 [1]. In this new journey, they narrate a part of their recent research experience and that of colleagues in application of Global Navigation Satellite System (GNSS) to precision agriculture. At present, there are four major variants of GNSS that are fully operational and commercially available to provide all-weather guidance virtually 24 h a day anywhere on the surface of the Earth. These systems are Global Positioning System (GPS) (US), GLONASS (GLObal NAvigation Satellite System; Russia), Galileo (EU), and BeiDou (China). The basic principle of operation on which GNSS is based is often referred to as resection (also called triangulation), which is described in detail later. Detailed information on GNSS technology is plentiful, with many books providing a complete description of these navigation systems [2e4]. The focus of this chapter, however, is on the applications of high-precision GNSS in agricultural operations. These applications include positioning for crop protection, monitoring soil, plants and production, variable rate application (VRA), agricultural unmanned aerial vehicles (UAVs), and autonomous vehicles.

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1.1 GPS system The GPS system was developed by the US Department of Defense to achieve an accurate navigation system for military purposes. The North American GPS system is made up of a network, in principle, of 24 satellites (21 þ 3 spare satellites), although currently there are 27 operational satellites, since some of the satellites continue to function beyond their originally planned useful life. The number of satellites in the GPS network observable at any given time varies between 4 and 10, having an elevation mask of 1 degrees. With an inclination to the equatorial plane of 55 degrees, the almost circular orbits have an eccentricity of less than 1%. The satellites have an elevation of 20,200 km and an orbital period of 11 h and 58 min, which means that they orbit the Earth twice a day at a speed of 3.9 km/s. The system, a pioneer in the development of these technologies, was completed in 1993 and became fully operational in 1995. Even today, new equipment is being established to replace the older satellites, and the constellation is being renewed to provide more services in new bands and frequencies. Three main segments or components comprise the GPS system: the space segment, the control segment, and the user segment. The space segment includes the satellite equipment in orbit, the 24 operational satellites, and those that are placed as spare parts. Nowadays, work is being carried out in the so-called GPS III phase, on which new equipment called SV01, SV02, and SV03 are being launched to complete a full renovation keeping the total number of satellites at 31. In addition, functionalities are being added to receive emergency signals through the COSPAS-SARSAT system, with added possibilities for communication between constellations. The control segment consists of a global network of tracking stations and monitoring stations (ECC) to track the satellites in order to know their exact location, almanac, and ephemeris, obtain data related to satellite integrity, satellite clocks, atmospheric data, etc., and upload the information and corrections to the GPS satellites. Finally, the user segment is made up of the wide range of GPS receivers located in the troposphere, obtaining the coordinates and precise locations calculated by the satellite equipment. Fig. 15.1 shows a typical GPS satellite with L- and S-band antennas. These satellites transmit positioning signals using L-band and S-band, which is used for uploading almanac and Solar panels

L-Band S-Band

FIGURE 15.1

A typical IIF GPS satellite with L- and S-band antennas (copyright free image from NASA).

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1. Introduction

ephemeris data to the satellites from uplink stations. Table 15.1 lists the current GPS constellation. Note that the system consists of IIR, IIRM, and IIF satellites (these are different generation satellites with different specifications and capabilities). Each satellite is recognized by a pseudorandom number (PRN), a space vehicle number, or its orbital number; for example, the three-dimensional 3D satellite corresponds to satellite number three of the D orbital panorama. Note that PRN is not fully random and is generated by a complex mathematical algorithm to identify a given satellite. Since timing is the key for receiver position determination as will be described later, each satellite is equipped with three or four atomic clocks with precision of 1015 s. TABLE 15.1

GPS constellation as of January 2012.

Satellites

PRN

SVN

Launch date

Plane

IIR-2

13

43

23 Jul 1997

F3

IIR-3

11

46

07 Oct 1999

D5

IIR-4

20

51

11 May 2000

E1

IIR-5

28

44

16 Jul 2000

B3

IIR-6

14

41

10 Nov 2000

F1

IIR-7

18

54

30 Jan 2001

E4

IIR-8

16

56

29 Jan 2003

B1

IIR-9

21

45

31 Mar 2004

D3

IIR-10

22

47

21 Dec 2003

E2

IIR-11

19

59

20 Mar 2004

C3

IIR-12

23

60

23 Jun 2004

F4

IIR-13

2

61

06 Nov 2004

D1

IIR-14M

17

53

26 Sep 2005

C4

IIR-15M

31

52

25 Sep 2006

A2

IIR-16M

12

58

17 Nov 2006

B4

IIR-17M

15

55

17 Oct 2007

F2

IIR-18M

29

57

20 Dec 2007

C1

IIR-19M

7

48

15 Mar 2008

A4

49

24 Mar 2009

IIR-20M* IIR-21M

05

50

17 Aug 2009

E3

IIF-1

25

62

28 May 2010

B2

IIF-2

01

63

16 Jul 2011

D2

(Source: ftp://tycho.usno.navy.mil/pub/gps/gpsb2.txt).

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The control segment consists of 16 monitoring stations and one master control station located in Colorado Springs, CO, USA. In addition, there is an alternate master control station at Vandenberg Air Force Base in California and 11 command and control antennas. All stations are unmanned and transmit data to the master control station by satellite communication. The command and control antennas consist of four dedicated GPS ground-based antennas and seven Air Force Satellite Control Network tracking stations with capabilities to upload almanac, ephemeris, and other relevant information to satellites. As mentioned earlier, GPS receivers constitute the user segment. These can consist of simple and inexpensive receivers costing about $100e150 or very expensive receivers costing thousands of dollars that provide high positioning accuracy. The positioning accuracy of inexpensive receivers may be about 10 m without any correction and can be improved to about 3 m with satellite-based wide area correction. More expensive receivers can provide centimeter-level positioning accuracy. While most receivers use a resection technique to determine their location, more expensive receivers employ carrier phase measurement to provide centimeter-level accuracy.

1.2 GLONASS system GLONASS was developed by the former Soviet Union in the 1980s almost in parallel with the development of GPS in the United States and is now operated for the Russian government by the Russian Space Force. GLONASS became operational in 1993 with 12 satellites in 2 medium Earth orbits (MEOs) at 19,100 km altitude with an inclination of 64.8 degrees and a period of 11 h and 15 min. Currently, there are a total of 31 satellites (24 active, 3 spare satellites, 2 in maintenance, 1 in service, and 1 in testing) in the constellation, and it operates in three orbital planes, with eight evenly spaced satellites in each.

1.3 Galileo system The Galileo system is the most recent GNSS program that is being coordinated by the European Commission and the European Space Agency. Developed as a European civil system, it was designed as a common strategy in the early 1990s. It is part of GNSS-2, a continuation of the first phase called GNSS-1 (European Geostationary Navigation Overlay Service, EGNOS). This European alternative to GPS services started with the first launches in 2011 and the first operational services in December 2016, after 17 years of development. The Galileo constellation will comprise a total of 24 fully operational satellites and a number of inorbit spare units. There are currently 22 operational satellites in orbit (two more are in space but are currently being tested), and a 10th more are under construction. Its deployment in two stages (in-orbit validation and full operational capability) means that in the next few years until 2022, there is a scheduled deployment of more equipment to join the system. The location of the satellites in three circular planes of the MEO will make it possible to provide coverage of the polar regions. The space segment has an orbital configuration inclined at 56 degrees to the equatorial plane, orbiting at a distance of 23,222 km above the Earth, with an orbital period of 14 h and 5 min. Like GPS, the Galileo constellation relies on a complex ground infrastructure as a control segment to monitor its operation and make necessary adjustments. Most modern ground segment equipment already incorporates chips capable of simultaneously receiving the positioning signals of the different constellations, so this is expected to increase accuracy, reliability, and security in geopositioning for surface users. III. Applications of GPS/GNSS

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1.4 BeiDou-Compass system The BeiDou (Big Dipper) Satellite Navigation and Positioning System is also a newer system developed by China. This system was designed to provide positioning, fleet management, and precision time dissemination to Chinese military and civil users. The deployment has been divided into three phases: - BeiDou-1 (2001) - BeiDou-2 or Compass (2011) - BeiDou-3 or BDS (2020) As of this writing (June 23, 2020), China successfully launched the last satellite to complete the BeiDou-3 GNSS. This system has 30 operational satellites with some spares. Unlike other systems, the 30 operational satellites consist of 6 GSO (geosynchronous) and GEO geostationary satellites in addition to 24 MEO satellites. In addition to the above systems, which either have or are expected to have GNSS capability, two other regional systems also provide position measurement over a limited region. The Indian Regional Navigational Satellite System is planned to have seven geostationary (GEO) satellites and is expected to provide 20 m accuracy in India and the area within 2000 km of its boundary. The Japanese Quasi-Zenith Satellite System is primarily a communication system with navigational capability. It consists of three highly inclined, geosynchronous satellites. At least one satellite is over Japan at all times. In future, the combined use of GNSS will increase the overall performance and robustness of satellite navigation for the benefit of all potential users.

2. GPS signal and structure The signals received from the GPS satellite segment consist of two carrier waves (L1 ¼ 1575.42 MHz or 19 cm and L2 ¼ 1227.60 MHz or 24.4 cm) together with two or more digital codes (coarse acquisition code or C/A on L1 and P-code on both L1 and L2) and a navigation message. While civilians have limited access restricted only to the C/A-code on L1, the P-code is a military code encrypted with an unknown W-code resulting in a Y-code (i.e., P (Y)) and is not available to civilians. This is called “antispoofing.” P-code provides highly accurate positioning (Precise Positioning Service), as ionospheric distortion can be completely removed by using L1 and L2 wave signals. In contrast, the use of the coarse acquisition code does not provide accurate estimates of position (Standard Positioning Service). The more modern satellites (belonging to the class II-RM) transmit two additional codes (L2 CMdcivilian moderatedand L2 CLdcivilian long), which are intended to minimize errors due to atmospheric effects of the ionosphere. The navigation message includes useful data and information about the almanac, ephemeris, clock correction, satellite health, atmospheric correction, etc., which are added to both the C/A code and the P-code. These codes also identify the referring satellite with a unique number (PRN) and include timing information. The codes are then added on to L1 (both C/A- and P-codes) and L2 (P-code only). Fig. 15.2 is a schematic diagram of the GPS signal. More recent satellites such as IIF carry an additional carrier wave, L5 specifically is meant for improving aviational and safety services, and the most recent ones (III and IIF) carry yet another code, L1C, which is designed to enhance interoperability between various GNSS. III. Applications of GPS/GNSS

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FIGURE 15.2 Composition of the signals from GPS satellites. Source: Peter H. Dana, The GPS Overview, The Geographer's Craft Project.

3. GPS positioning principle To obtain the position of an object on the Earth's surface with a GNSS system, a principle called resection is used. Fig. 15.3 details that the two-dimensional (2D) location of an object on the surface can be easily determined by measuring the travel times of signals sent from two satellites to a receiver located at position A. The product of the travel time of the signal by the speed of the electromagnetic wave (speed of light ¼ 299.729.458 m/s) determines the distance between the satellites and the receiver. Using the resection principle, the receiver's position A

B 4 sec

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FIGURE 15.3 Determination of two-dimensional (2D) position of object A using the pseudoranging principle: (A) no error in time measurement, (B) effect of error in measuring time, and (C) region of uncertainty (or estimate of error) when timing signals from three satellites are utilized for determining position in 2D.

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4. Carrier-phase measurement

is then located. The point of intersection of the two drawn circles with the satellites in the center of each one and the respective distances to point A are calculated by radial triangulation. Since there are two intersections in the circles, it follows that the other (position B in Fig. 15.3A) is an unacceptable position solution. This process is called pseudoranging, since the distances involved in its calculation are not actually measured but estimated from the travel times of the signals from the satellite to the receiver. Therefore, and due to the high speed of the electromagnetic wave (speed of light), a small error in the measurement of this travel time will constitute a large error in the estimation of distance and position. As shown in Fig. 15.3B, any error in time measurements may place the receiver in position B instead of the actual location A, without any indication of the magnitude of this error. However, when a third satellite is involved in the time signal measurement, a curved triangular region (B-B-B) can be determined, within which the receiver should be located, as shown in Fig. 15.3C. Thus, it is essential to obtain signals from at least three satellites to estimate the position and determine the relative accuracy of that measurement. Since all GPS position measurements are made in 3D, signals from at least four satellites are required to obtain the fixed position (latitude, longitude, and altitude) and a measure of the relative accuracy of that measurement. Thus, if (x0, y0, z0) are the unknown Cartesian coordinates of the receiver and (xi, yi, zi) are the respective coordinates of the ith satellite, the distance of the receiver from the satellite, di, can be obtained by 2

2

2

2

d2i ¼ ½ðcðti  et Þ ¼ ðxi  x0 Þ þ ðyi  y0 Þ þ ðzi  z0 Þ

(15.1)

where c is the speed of light and et is the error in measuring time. If time measurements are available from four different satellites (i ¼ 1, 2, 3, 4), then we can write four nonlinear algebraic equations in four unknowns (x0, y0, z0, and et) that can be solved. Note that positions of satellites are known because each satellite transmits ephemeris as a part of the navigation message. If time measurements from more than four satellites are available, least square minimization is used to obtain the best estimation of the receiver location and associated measurement error.

4. Carrier-phase measurement An alternative methodology for determining the receiver location involves measuring the phase angle of the received signal. Fig. 15.4 shows this principle, where the signal from the satellite will complete an integer number of cycles (N) and a fraction of the waveform on its way to the receiver. The receiver only measures the shape of the partial wave, called the phase of the signal, without knowing the whole number of cycles that have passed between it and the satellite. This is known as integer ambiguity. If the receiver monitors the satellite at certain points in time, it is possible to follow the phase change. With this information, together with a technical optimization procedure, the integer N of cycles that constitutes the unknown can be resolved. Knowing the wavelength of the carrier wave L1 (19 cm), the fraction of the wave or the phase angle is measured, which makes this technique able to

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FIGURE 15.4 Principle of carrier phase measurement. Source: http://nptel.iitm.ac.in/courses.

achieve millimetric accuracy, much higher compared to the C/A code metric. To achieve this level of accuracy, a local or virtual base station is required to provide an accurate reference. This system is often referred to as real-time kinematic GPS or RTK-GPS.

5. Real-time differential GPS correction Very high GPS accuracy can be achieved using postprocessing. However, for real-time applications that require on-the-go corrections, a differential GPS (DGPS) is preferred. A straightforward manner of accomplishing this is to use two GPS receivers (a rover and a base) that track the same satellites, so that many of the errors can be minimized and higher accuracy can be obtained in real time. Fig. 15.5 provides a schematic diagram of the principle involved in differential correction. Since the position of the base station is known accurately, the error in estimating the location of the base station using satellite signals can be determined. This correction information can be communicated to the field GPS receiver (i.e., the rover) by a radio link, and this information can be used to increase its accuracy. However, the deployment of two GPS receivers for agricultural applications could be expensive in many instances. An alternative to reduce the cost without degrading the positional accuracy is to use one of the available differential correction services. If the GPS users access one of these available services, only one receiver can be used as a rover and no base receiver would be required.

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FIGURE 15.5

307

Schematic diagram of a differential correction technique. Courtesy of Agrosap.

Agricultural use of GPS has significantly expanded due to the increased availability of differential correction. At present, there are various types of differential correction services that are readily available to the user. These are as follows: • DGPS radio beacons (e.g., US Coastguard DGPS beacons along major waterways): These services can provide submeter DGPS accuracy. Reliable coverage is available on land, sea, and air. However, this service, which used to be free, is being discontinued because of increasing accuracy, integrity, and robustness of newer GPS satellite signals, and availability of Space-based Augmentation System (SBAS) is based on free correction services such as Wide Area Augmentation System (WAAS) discussed below. • SBAS: It is satellite-based system that provides regional correction signals (e.g., WAAS within North America, EGNOS within Europe, Multi-functional Satellite Augmentation System within Japan and Southeast Asia, and GPS and geoaugmented navigation within India) over a wide area (L-band DGPS) through the use of additional satellite-broadcast messages. All of these systems work similarly and are compatible with each other; however, the accuracies of these free satellite-based systems vary. They consist of reference stations distributed over a wide area, master stations to process and upload data, and geostationary satellites to transmit the correction signals to users. The WAAS service within North America (the United States, Canada, and Mexico) is fully operational for safety-critical operations such as aircraft navigation and is specified at 7 m accuracy. Agricultural users have found WAAS to be a reliable source of correction, with an accuracy of better than 3 m and a much better pass-to-pass accuracy [5]. The two major commercial L-band satellite-based correction providers are Fugro (OmniSTAR service) and Deere (Starfire service). OmniSTAR provides almost complete worldwide coverage. The Starfire service is based on the NASA Jet Propulsion Laboratory correction system. Both of these commercial service providers have a high-accuracy service that uses dualfrequency receivers and antennas for performance in the decimeter range (100e300 mm). OmniSTAR and Starfire are subscription services.

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• Dedicated-use RTK base station and RTK networks: RTK systems provide the most accurate solution for GNSS applications, producing typical errors of less than 2 cm. This level of precision is not needed for general site-specific farming, but it does permit treatment of specific small location, such as a plant-specific operation, and is essential for precision guidance [6,7], controlled traffic farming [8,9], mechanical intrarow weed control, or thinning of crop plants [10]. In this method, a base station is located at a known point close to where the vehicle operates and communicates with the rover through a radio transmitter. Two disadvantages of RTK-GPS solutions are as follows: (i) the requirement that a base station be located within 10 km at all times, which limits its use when farms are large or spread out; and (ii) high capital cost. An alternative to the local base station that is becoming increasingly popular is the virtual reference station (VRS), which essentially creates a virtual reference point near the rover using a network of RTK base stations. The VRS service is available for a fee from vendors such as OmniSTAR.

6. Applications of high-precision GNSS in agriculture Since its launch in 1978, GPS has become an integral part of the operation of all modern economies. Satellite-based solutions have evolved and GNSS receivers are now used in many agricultural applications. GNSS receivers are an essential part of precision farming technologies, as position information is a prerequisite for site-specific crop management. This has made them the most widely accepted and adopted technology onboard agricultural machinery, as well as one of the keys to crop intensification and optimization. However, not all tasks to be performed in precision farming need the same level of positioning accuracy [11,12]. Some precision farming operations, such as yield monitoring, soil sampling, or VRAs, can be carried out using differential submeter precision GPS (DGPS), as errors of less than 1 m are acceptable for these applications. Other tasks, such as mechanical weed control (MWC) within the rows, thinning of crop plants, precise planting, or autonomous navigation within narrow rows, require decimeter or even centimeter-level accuracy. A solution to this demanding requirement can be found in RTK-GPS, as already mentioned. Applications of GPS for agricultural purposes have exploded in recent years, and the literature is rich with numerous interesting examples. In the following discussion, we limit our attention to five specific applications with which the authors are very closely involved: (i) (ii) (iii) (iv) (v)

GNSS GNSS GNSS GNSS GNSS

in in in in in

crop protection VRA monitoring soil, plants, and production agricultural UAVs ground platforms and autonomous tractors

6.1 GNSS in crop protection GNSSs are one of the core enablers of “precision” in precision crop protection (PCP). Obtaining accurate positioning of crop plants, weeds, or pests is of paramount importance

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when aiming to manage them according to their spatial variability [13]. This paradigm of information-guided crop production implies the study of crops, pests, and diseases [14] at a much finer resolution with a goal of low-input, high-operational efficiency goal. In PCP operations, two types of applications come into play, which can be contradictory a priori: those that aim to respond in spatially aware manner to the variability inherent to biotic/abiotic factors affecting the crops and those that strive to achieve complete uniformity required by certain tasks. However, both share the goal of maximizing efficiency and minimizing input consumption with the least disturbance to the biosystem. The use of GNSSguided farm machinery to perform these tasks directly affects the quality and quantity of the crop and the efficiency of operations and their environmental impact. Simultaneously, PCP involves the processing of field-collected data (in situ, in local off-site platforms, or in cloud-based online systems) and its use by agricultural decision-making support platforms [15] to control actuators present on modern agricultural machinery. Intensive data collection and processing requires GNSS data to match applications with inherent infield heterogeneity. In mechanized agriculture, real-time position information allows the determination of the coordinate of the exact point at which to carry out the operation, the heading and angle of the vehicle, and elevation data. This minimizes overlapping in the case of all-field phytochemical operations or adjusts the required dose based on the speed of the machinery. Similarly, PCP techniques involve the generation of georeferenced maps for quantification and characterization of the target elements in the field. These maps are often based on remote or proximal sensing, real-time or historical data, and field scouting and allow quantification of the spatial variability, incidence, and severity of diseases and pests. Subsequently, maps are used as inputs combined with positioning and guidance systems in order to perform site-specific applications [16]. These processes involve (i) the detection and mapping of weeds and pests, (ii) the variable allocation of agrochemicals, and, to some extent, (iii) MWC. Since all of these actions are dependent on GNSS systems onboard agricultural vehicles, this chapter addresses studies that have used this technology. 6.1.1 GNSS use in weed/disease detection and mapping Efficiency in weed control relies on the use of precision GNSS systems to detect and map weeds. GNSS systems contribute to the objective of making a specific application on a differentiated target, helping to detect and locate individuals or patches [17] on which to act. Automation in weed detection and recognition has gained attention in recent years [18], with the use of computer vision and spectroscopy techniques [19,20]. The wide availability of affordable cameras and sensors has positioned the vision-based weed mapping system approach as one of the most widely used in research and commercial models [21e23]. Object-based image analysis techniques [24] have been used to accurately locate in-field weeds (Fig. 15.6). However, building weed maps using GNSS systems pioneered precision farming. Hand-held GPS for patch surveys [25e27] paved the way for the development of more complex systems on ground [28,29] and aerial vehicles [30,31] fitted with RTK-GPS. The rise of automatic learning techniques, such as convolutional neural networks and semantic classification, for disease pattern and weed species recognition in a broad range of crops [32e36] is directly linked to the ability to precisely geoposition such diseases or pests in the field. Through the combination of visible/NIR spectral cameras with RTK-GPS

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FIGURE 15.6

15. High-precision GNSS for agricultural operations

Automated weed detection with object-based image analysis algorithms and weed mapping.

modules [23], several efficient ground-based field-scouting weed detection systems have been developed [37]. In the architecture of these platforms, GPS modules commonly have the dual role of supporting navigation and geolocating the data obtained through sensor fusion [16]. Robotic platforms, such as BoniRob [38], Naïo Dino [39], or HortiBot [40], among others, have integrated RTK-GPS modules for target weed detection, identification, and actuation. For the positioning of weeds and crops, in tomato crops, a new approach to geopositioning the tomato plants in a 2D planting pattern by obtaining RTK-GPS data during the transplant task was developed and validated [7,10,41,42]. Thus, precise weed management in this spatial context can be achieved by obtaining the “photographic negative” of the precise planting map. An innovative approach for plant detection based on crop signaling was developed [43] to reduce the operating time and increase precision in further automating weed control tasks. As reviewed in Deery et al. [44], the performance of high-throughput field phenotyping for disease monitoring and its incidence in phenological traits of the crop has become popular with the use of field platforms that are equipped with RTK-GPS systems [45e49]. Once the spatial dynamics of pest or weed infestation are known, the generation of georeferenced vector maps with specific management points and areas in the appropriate format enables the precision applications with the use of GPS systems shown hereafter. 6.1.2 GNSS for precision chemical crop protection tasks Conventional chemical applications on weeds and diseases have been made uniformly, using blanket prescriptions. This results in significant losses in both economics and efficiency, generates an undesired resistance to certain active substances, and causes a significant environmental impact. The detection and discrimination of infestation areas are translated into infestation spatial infestation distribution maps for precise application with GNSS-guided equipment. Since weeds have patchy distribution patterns, selective chemical spraying is one of the most promising weed/disease control approaches [50]. The growing presence of GNSS receivers with onboard controllers, and the ability to use interoperable implements under the ISO 11783 standard, has made asynchronous spraying systems a common element in the field. For prescription map-based applications, the GNSS system onboard must establish

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the position of the vehicle/implement and send that information to the controller, which will subsequently transmit the control instructions to the sprayer. The instructions sent take into account forward speed and the prescribed chemical flow, as well as parameters about the application geometry and feedback to generate as-applied maps. Shorter response times and improved efficiency can be achieved through RTK-GNSS data fusion in agricultural platforms. Interpolated weed mapping and advanced spraying decision rules become the input for precision GPS-guided controllers. First approaches for advanced site-specific patching applications were conducted [51] using incremental encoders as relative positioning systems. However, the resolution of these devices is limited by their cumulative sources of error due to vibrations, sudden movements in the field, or slippage. As reviewed in Fennimore et al. [52], multiple field sprayers using RTK-GPS receivers for precise field application have been developed [28,53,54]. Precise spraying distribution [55] equipment using GNSS can range from field/region level to drop-on-demand technology with savings close to 95% [56]. Through automatic boom section control to reduce active length of conventional hydraulic sprayers [57e60], or with auto-guided platforms that combine detection with microapplication (Blue River LettuceBot; bluerivertechnology.com) [61,62], a wide range of systems have been developed and evaluated. Boom sections control allows for geographic information system (GIS) map-based opening/closing and site-specific dosing according to GPS positions. Overlapping reduction with boom section control and GPS technology has been further studied [63e66], thus demonstrating substantial savings in active ingredients savings [28,67]. An interesting economic analysis of the return on investment for equipment use for PCP tasks that use RTK-GPS has been conducted [68e70], in which the profitability of both patching and selective spot spraying with commercial equipment has been analyzed. In recent years, robotic platforms capable of performing microspraying tasks [71] have used RTK-GPS to perform individual treatments per plant. A significant review of robotic platforms for field operations can be found in Fountas et al. [72], Fennimore and Cutulle [73]. Simultaneously with autonomous navigation [74], these robots perform the tasks of discrimination between healthy/diseased crop or between crop/weed [75] and deposit the phytochemical by plant, in a programmed and geoprecise manner (Fig. 15.7). This trend will undoubtedly increase in the coming years with the reduction in the cost of receivers and the miniaturization of components and their modular versatility.

FIGURE 15.7

Prototype automated variable rate sprayer for real-time application.

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6.1.3 GNSS in mechanical weed control The overuse of synthetic chemicals for weed and disease control has motivated researchers to propose other approaches to mitigate the rapid emergence of resistance and the associated social, economic, and environmental problems. The effectiveness of MWC hinges on the precision of the system to locate and remove weeds without affecting the crop. In a narrow space of centimeters, mechatronic developments that include an MWC system must perform actions such as cutting with blades [29,76], burying [77], uprooting [78], burning [79], or applying solids [80,81] to weeds. Mechanical removal of intrarow weeds still remains a challenge for both producers and researchers. This requires centimeter accuracy provided by RTK-GPS equipment, the predominant technology for precise guidance of automatic control platforms [52]. As also stated in Fennimore et al. [52], depending on the degree of infestation, it is advisable to merge RTK-GPS positioning data with vision-based systems [82,83]. Other approaches for accurate tomato plant distance measurements by combining vision-LiDAR systems have also been tested [84], seeking to advance in MWC. Advanced intelligent cultivators for intrarow hoeing using rotating tines or blades [85,86] have become commercial or precommercial MWC platforms, as reviewed in Fennimore et al. [52,73], Steward et al. [87]. According to Griffin and Lowenberg-DeBoer [88], 75% of robotic weeders use mechanical control techniques, which drives the need for positioning systems. Specific solutions developed for special crops (mainly vegetables and horticultural crops), such as Robocrop InRow (Garford Farm Machinery Ltd., England), Robovator System (F. Poulsen Engineering ApS, Denmark), or the newest designs of Dino robot (Naïo Technologies) and EcoRobotix, among others, use RTK-GPS systems for positioning and development of mechanical weeding tasks (Fig. 15.8). The combination of these systems with band spray applications [89], together with the price reduction of more accurate GNSS systems, makes them an effective alternative for PCP.

FIGURE 15.8 Automatic intrarow mechanical weeding corobot. (A) Schematic drawing of the weeding corobot showing the miniature pair of intrarow hoes (red triangles) and the odometry sensor (ground wheel on the left side). (B) Photograph showing the supervisor's view of the weeding corobot with the intrarow hoes in the open position. The two pneumatic, knife positioning cylinders and the interrow sweep knives are also shown. During actual operation, the miniature hoes are positioned ~2 cm below the soil surface.

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6.2 GNSS in variable rate application Heterogeneity of plots has been known since ancient times, but the way in which variability is handled has evolved radically during the past decade. In order to maximize crop yield and quality, optimize sowing, or improve input application, variable distribution technologies make use of advanced GNSS systems for submeter or centimeter positioning of inputs. GPS-based machinery guidance along with automatic yield monitoring was the initial technologies adopted on variable rate technology farms. Spatial variability in soils, crop needs, and final yield (as well as in the distribution of pests and diseases previously addressed) assists site-specific tools in providing better management practices at field scale. The accuracy and repeatability provided by RTK-GNSS positioning systems unlock efficiency in VRA, thus reducing skips and overlaps, saving time, and reducing operator fatigue. Potential benefits of VRA and ROI in guidance and positioning are generally higher with larger amount of spatial variability clustered into coherent patches with fewer but more significant rate changes. The operational scale and resolution of the variable application will be defined by the GNSS equipment availability at the farm level, together with the machinery capabilities. Land units are based on vehicle/implement size with onboard equipment for navigation and application. Thanks to the integration of GNSS with controllers and equipment, desired trajectories and positions are computed, and operations areperformed at the exact point. Furthermore, the adoption of positioning systems is also affected by the degree of precision required by the different handling tasks. Submeter GNSS-GPS systems (DGPS) onboard agricultural vehicles can therefore be used for tasks such as fertilization or yield mapping, while tasks such as precision seeding require RTK-GPS systems with centimeter accuracy. Regardless of the task, implementation of VRA requires a cyclic GNSS-supported precise geopositioned data collection, management plan creation, tactical field implementation, and evaluation. For spatial variable application, two approaches exist: the map-based and the real-time sensor-based VRA. In the first, geo-referenced rate change prescription maps generated with specific GIS software serve as inputs to the vehicle controller. In the second type, sensors recognize the crop's characteristics to be monitored in real time and, through transmission and analysis of signals onboard, carry out the operation. While the former type fully depends on GNSS, in the latter it is not essential but is recommended for registering asapplied maps. The role of GNSS systems and products in some of the tasks enabling VRA, such as soil characterization, seeding, fertilization, or yield monitoring, are presented below. 6.2.1 Variable rate seeding and precision seeding with GNSS The availability of GNSS equipment onboard modern seeding machines allows the adjustment of plant density within an individual field or management area based on a guidance map and an agronomic prescription [90]. A growing number of companies offer variable seeding (VRS) equipment and services to maximize the yield of crops and hybrids, including digital analysis and recommendation tools, such as Encirca (Pioneer) [91] or those described in Jeschke et al. [92]. The implementation of maps used for the differential geopositioning of the seed requires, in many cases, the study of the characteristics of the soil, the potential or historical yield or the agronomic decisions, and characteristics of the variety to be sown [93]. The economic viability of this type of sowing is somewhat unclear [94,95] and the profitability of managing specific densities is uncertain [96,97], especially in extensive crops, resulting in relatively low adoption rates, as stated by Griffin et al. in [98]. However, modeling the relationship between VRS potential and soil characteristics is still of interest III. Applications of GPS/GNSS

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today [99]. Seed density response curves are influenced by biotic and abiotic factors [100]. Therefore, zone-oriented VRS with a combination of GNSS and GIS systems to carry out different seeding rate treatments by zones has been widely studied [101,102]. In some recent studies, GNSS systems embedded in mobile equipment or tablets [103] and low-cost equipment [104] have been used to perform VRS tasks. The use of capacitive sensors to measure the dielectric properties of the soil on-the-go (i.e., those offered by Precision Planting LLC, Tremont, IL, US) is becoming increasingly popular in conjunction with precision seeding systems [105]. Combined with RTK-GPS systems, these capacitive sensors determine moisture and texture conditions under moving conditions and allow for variable sowing tasks. In addition to the VRS, precision seeding also makes intensive use of GNSS equipment in order to optimize crop establishment [106], seeding depth [107], and seed distribution in the soil. Precision seed drills integrate RTK-GPS positioning systems to obtain the coordinates of the seeds dropped into the furrows. This precise mapping of seeds in seeding shoes [31] in crops such as sugar beet [108] or transplanted plants [42] allows subsequent guidance of vehicles and implements to perform precise crop protection tasks. The efficiency of autonomous sowing equipment has been evaluated with RTK-GNSS positioning systems in recent years [109]. Furthermore, robotic seeding platforms evaluated by Pedersen et al. in [110] that incorporate RTK-GPS equipment will allow a remarkable scalability of these systems in the coming years. A rising challenge for these technologies is the potential for planting multihybrid patterns in grain crops. The choice of the seed corresponding to the most suitable hybrid for certain soil conditions or water availability is being made automatically guided by RTK-GPS equipment and ad hoc developed algorithms. 6.2.2 GNSS in fertilization tasks More rational use of fertilizers and less impact on biosystems can be achieved by adjusting application to the spatial variability of the crop and soil [111]. The increasing availability of GNSS systems for automatic vehicle and implement guidance has led to this management strategy being studied and adopted more often [112,113], especially in grain crops and nitrogen (N) fertilization operations. Nitrogen is a limiting factor with direct influence on crop growth, yield, and quality [114]. A mismatch between nitrogen availability and crop demand can cause low efficiency in its use and economic losses [115,116]. Furthermore, N in agricultural systems is subject to a range of static (soil conditions, chosen variety) and dynamic factors (weather, water availability, etc.). Uniform applications in dynamic systems usually do not take into account heterogeneity in soil nutrient content, plant extraction capacity, and response to application. Therefore, the data collection and analysis stages are crucial to make an accurate diagnosis of the crop conditions and needs. Different approaches for the creation of nutrient prescription maps from crop and soil measurements have been discussed in the literature, with those using precise positioning and guidance systems being predominant. Multilayer variability information from yield, soil, or normalized measurements [117,118] based on crop spectral reflectance in the visible/NIR range [119,120] can be used for the creation of management zones. Normalized difference indices such as normalized difference vegetation index (NDVI) or GNDVI acquired from ground-based radiometers, aerial/ terrestrial vehicles, or large-scale remote-sensing techniques [121] have become one of the most common approaches to study the variability in the spatial distribution of input requirements [122e124]. Geostatistical correlation between these biophysical variables [125] and crop response allows the modeling of optimal rates, detection of deficiencies, and the study of nutrient balance for creating within-field variability maps in different crops [126,127]. III. Applications of GPS/GNSS

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Extensive agronomic expertise is required in these two phases of the process, as the translation of measurements into recommendations has been a bottleneck in the past [128]. Once the handling zones have been established, Ag-GIS software is used to divide the variable attributes into potential handling classes [129,130]. These can be binary (high/low) or much more complex, depending on the subdivisions in the range of variability [130]. Decision support systems are now being implemented to automate much of this orthomosaic data generation work [131,132]. However, technical oversight and characterization of seasonal conditions remain relevant to generate optimized prescription maps. These maps, generally in vectorial polygon format, are loaded into the vehicle's onboard controller, which electronically controls the implement. Coupled to a proper GNSS system (a submeter precision DGPS can be used for fertilizer application), the controller receives as input the position and adjusts the desired rate for that point. At this point and for correct quantification, prior calibration of the fertilizer spreader is important [133]. Minimizing the response time between vehicle geoposition and dose change results in a reduction of threshold zones, thus reducing over- or underapplication of fertilizer [134e136]. In addition, VRA technology allows not only the quantification of the amount and location of inputs but also the mapping of applications for a detailed record. In recent years, equipment manufacturers such as Amazon and Kverneland have developed sophisticated electronic regulation and control systems [137] based on geopositioning to provide both on-the-go and map-based solutions for faster and more reliable operation. Similarly, work has been undertaken to develop equipment with increasing precision or lower acquisition costs, which will undoubtedly boost the adoption of these technologies for small holders [138].

6.3 GNSS in monitoring soil, plant, and production 6.3.1 Soil characterization and monitoring Soil has been conveniently defined as a continuum of variability (PA Basics) and one of the major causes of heterogeneity in crop yields. Soil compaction, its varying composition, and texture or topography are some of the characteristics involved in such variability. To quantitatively characterize soil variability, numerous studies have been carried out using GNSS equipment. Topography has been identified as a factor affecting irrigation efficiency, causing waterlogged areas or differences in drainage. Topographical reconstruction of agricultural soils has been carried out using precise RTK-GNSS equipment capable of generating contour maps or digital elevation models with centimeter-level accuracy [89]. Soil type, texture, and conditions are key to defining differential management zones. Research to determine soil's cutting resistance on a continuous basis has been undertaken using GNSS systems to transfer the differential measurements to terrain maps [139,140]. Soil sampling to obtain information about the micro- and macronutrient content of the soil (N, P, K, Ca, Mg, lime, PH, etc.) [141] has been based on specific measurements or the determination of sampling grids generated with information from DGNSS or RTK-GNSS systems and the subsequent application of geostatistical techniques to the data obtained [141]. Although this sampling allows accurate results to be obtained, it is often time consuming and tedious. Therefore, on-the-go proximal soil sensing techniques are gaining popularity. Lightweight vehicles, such as ATVs or buggies with onboard DGNSS or RTK-GNSS, are used to georeference ground measurements with trailing sensors that determine apparent electrical conductivity [142,143], with popular commercial models such as the EM38 or Dualem [144e147]. These physical measurements are related to the soil and its properties, and an extensive list of work has been done in this III. Applications of GPS/GNSS

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FIGURE 15.9 Spatial variability in crop yield, infiltration rate, bulk density of soil, and soil compaction level for a section of a processing tomato field located in Winters, CA, USA.

area [148e150]. Georadar equipment and gamma ray spectrometry [151] or X-rays have also been used for soil characterization coupled with DGPS. As reviewed in Rossel et al. [141], other multisensor platforms for soil characterizations [101] have used GNSS systems, allowing the simultaneous measurement of multiple soil properties [152]. Delineation of the management zones according to the spatial variability of the soil properties is considered one of the most relevant outputs of these characterization techniques [153,154]. This delineation and clustering of homogeneous management zones [155,156] has been done using fuzzy algorithms [157e159] and data fusion techniques [160] with georeferenced measurements [152,161] in arable crops [161e163], horticulture [164,165], or precision viticulture [158,166], among others. These management zones will determine the prescription maps for variable rate sowing, adjustment of fertilizer application rate, or differential yield. Research conducted at the University of California, Davis, in a processing tomato field has indicated that variability in the water infiltration rate caused by the variability in soil compaction is a major factor affecting processing tomato yield (Fig. 15.9). Soil compaction is often measured using an ASABE (American Society of Agricultural and Biological Engineers) standard cone penetrometer (force per unit area of a penetrating standard cone, known as the cone index [CI]). However, the CI is a point measurement that exhibits high variability. Moreover, it is labor intensive and time consuming to collect substantial amount of data needed to map a large field. To overcome these limitations, the compaction profile sensor shown in Fig. 15.10 was developed. This device consists of five 5.1 cm-long active cutting elements that are directly connected to five octagonal load cells and can measure the cutting resistance of soil directly ahead of the cutting elements. These active cutting elements are isolated from each other by 2.5 cm-long dummy elements. Moreover, a dummy element of length 8 cm is attached above the topmost active element. This long dummy element was included since an earlier study had indicated that the soil cutting data from the top layer was unreliable due to depth fluctuations and potentially a different mechanism of soil failure (i.e., crescent vs. lateral failure). This device is capable of obtaining soil cutting resistance data over a depth

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(b)

(a)

FIGURE 15.10 The compaction profile sensor developed at UC Davis. (A) An overview of the system when it is mounted on a toolbar. (B) Internal construction details of the sensing elements.

profile of 7.5e45.7 cm below the surface. A submeter accuracy DGPS receiver using coastguard beacon differential correction was included with the system to provide position information. In addition, a radar was employed to measure ground speed data. The compaction profile sensor was calibrated and then tested in agricultural fields in California and in the Midwest. An ASABE standard cone penetrometer was also tested in the same fields. The force acting on the unit area of the cutting element, termed CIE (cone index equivalent), was related to CI values at the same depth and the depth of operation of the cutting element. Fig. 15.11 shows the plot of the measured CIE values versus the predicted CIE values based on a multiple linear relationship given by the following equation:

FIGURE 15.11 Comparison between predicted CIE (cone index equivalent) values and measured CIE values obtained during the field tests in the Midwestern United States (R2 ¼ 0.997).

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FIGURE 15.12 The online visible and near-infrared soil sensor attached to the three-point linkage of a tractor.

CIEi ¼ 0.15CIi þ 2.244di þ 0.69CIi  di

(15.2)

where di is the depth of operation of the ith cutting element (i ¼ 1, 2, .5) and CIE and CI are the corresponding CIE and CI values, respectively. The map of the soil compaction level estimated from the force on the cutting element located in the soil layer between 15 and 22.5 cm deep layer of soil is shown in the lower left-hand side image of Fig. 15.9. The soil compaction map for this layer correlated very well with the yield map (upper right-hand side). The complete description of the compaction profile sensor and its application for mapping soil compaction profile can be found in Andrade-Sanchez et al. [167]. Mouazen [168] developed an on-the-go soil sensor consisting of a subsoiler that penetrated the soil to any depth between 5 and 40 cm depth and which made a smooth-bottomed trench in the soil, whose bottom is smoothened due to the downward forces acting on the subsoiler. An optical unit is attached to the backside of the subsoiler chisel to acquire soil spectra from the smooth bottom of the trench in diffuse reflectance mode (Fig. 15.12). The subsoiler and the optical unit are attached to a metal frame, which is mounted onto the three-point linkage of a tractor. During field measurements, the online sensor is set at 15 cm depth and driven at a ground speed of approximately 3 km/h. The sensor was proven to measure key soil fertility and physical attributes, for example, organic carbon, total N, moisture content, K, P, Mg, Ca, cation exchange capacity, pH, Na, plasticity index, clay, and sand [169e176]. 6.3.2 Plant monitoring Plant monitoring is one of the main applications of remote sensing in agriculture, which includes detection of water content, nutrients status, pest/disease damage, weed detection, and crop biomass. The use of sensors and technological devices to monitor crop condition has been employed for decades. Due to the growing global scarcity of water resources worldwide, the development of tools and sensors to monitor crop and soil water status has far exceeded that for monitoring any other crop trait in the field. A relevant aspect emerging in recent years is

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FIGURE 15.13 Thermal image processing was performed in this field trial to derive the mean sugar beet temperature in each experimental plot. The segmentation algorithm is based on histogram analysis and the FWHM rule.

the new crop monitoring systems that cover a wide area very quickly. While point sensors have traditionally allowed monitoring of the water status of a single plant at a very high temporal resolution (e.g., sap flow probes, dendrometers, pressure turgor probes) [177], the development of the unmanned aircraft industry for agricultural use has allowed these vehicles to be equipped with cameras (e.g., thermal, multispectral, and hyperspectral cameras) that capture crop information at a very high spatial resolution [178,179]. With this new paradigm, in which all the plants in a field can be sampled, precise positioning systems are required onboard the aircrafts to allow for accurate determination of geolocation of the information obtained. Quebrajo et al. [178] captured images with a thermal imaging camera mounted on an UAV to evaluate the water status of sugar beet in a plot with large spatial variability in terms of soil properties. This study provided a basis for recommendation different irrigation strategies, especially in highly heterogeneous plots (Fig. 15.13). Thermal imaging and multispectral/hyperspectral imaging are used to identify crop water status. For example, Alchanatis et al. [180] investigated mapping of water status in a vineyard using thermal and VIS images and reported that stomatal conductance and stem water potential were highly correlated with the crop water stress index. 6.3.3 Yield monitoring Yield monitoring systems enable the creation and visualization of yield, grain moisture, and other variable maps during harvest, providing real-time or historical observation of how field conditions affect crop yield. Since its introduction, in conjunction with the earlier versions of GNSS on combines, yield monitoring was the technology that has made VRA

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possible [181] and is the one being most rapidly adopted by farmers [182]. GNSS technology is a key part of yield monitoring as position data are critical in determining crop variability within the field. Yield mapping capability enables decision-making among adopters with a higher degree of accuracy. Geolocated yield data can be obtained with submeter DGPS or a centimeter accuracy with RTK-GPS equipment. Yield monitors have had greater acceptance in grain cereals, oilseeds, and cotton, although they have been successfully developed for peanut [183], sugarcane [184], and sugar beet [185,186], and specialty crops such as tomato [181]. By attaching GNSS coordinates to modern yield monitors, variables such as protein content, grain moisture, task efficiency, and fuel consumption can be mapped. As stated in the previous version of this book [187] and in Fulton et al. [132], yield monitors combine physical sensors (i.e., impact sensors) to measure grain flow, with near-infrared sensors or microwave barriers to measure volume. Other capacitive or resistive sensors to measure grain moisture or mowing bar height, or NIR devices to obtain on-the-go crop quality parameters, can be implemented in the combine. The possibility of comparing the performance of fields year-by-year allows the generation of stability maps of homogeneous zones [188], thus enabling the creation of more adjusted differential management zones for VRA. Historical data combination and persistence over time are fundamental for a greater accuracy in crop needs. The ability to compare between hybrids, nutrient deficient areas, or differences in drainage make yield monitoring data valuable sources of information for identifying and assessing trends or problems and making decisions in the precision farming cycle. As stated in Fulton et al. [132], Pérez Ruiz and Upadhyaya [187], the use of GNSS systems with differential signal correction (DGNSS) provides the required accuracy in yield monitoring operations and allows the forward speed and heading of the vehicle to be estimated. Although the use of remote sensing combined with advanced algorithms appears to be a promising trend in yield estimation, allowing greater scalability, the refinement and standardization of yield monitors on combines will continue to make it possible to quantify and obtain reliable and robust data on which to base rational decision-making. The yield monitor shown in Fig. 15.14 was used in a study conducted at the University of Sevilla, Spain. The yield monitor (model RDS Ceres II) was mounted on a combine harvester (model 216, Class Mega) and calibrated. Moisture and yield data were gathered by the yield monitor, while the GNSS receiver used EGNOS to obtain location data to within 3 m. The instantaneous yield, moisture, and GNSS data were simultaneously logged at 2 s intervals onto a Secure Digital card installed on the yield monitor. The combine had an effective cutting width of 6 m and traveled at an average speed of 4.5 km/h. Therefore, approximately one sample was collected from a 15m2 area. The goal of this study was to determine the extent of spatial variability and covariation between the wheat yield, N content, and NDVI in two conventionally managed commercial fields used for wheat production. Developed an electronic weighing device with an impact plate and a conveyor speed sensing system to measure the mass flow of tomatoes. Tomatoes impacted the plate as they dropped off the harvester boom conveyor, where the impact force and conveyor speed data were recorded continuously by a data logger. This weighing system was integrated into a commercial tomato harvester and tested during the 2004 and 2005 harvesting seasons (Fig. 15.15). A weigh wagon was used to verify the measurements of the impact type weighing systems with a coefficient of determination value exceeding 0.96.

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(a)

(b)

(c)

FIGURE 15.14 (A) Cereal yield monitor installed on a Class Mega 216 combine harvester and used at University of Sevilla, Spain. (B) Trimble GFX 750 display for automated guidance system with ISOBUS compatibility. (C) A block diagram of the yield monitoring system components.

FIGURE 15.15

Electronic weigh bucket unloading.

6.4 GNSS in agricultural UAV The accelerated development and adoption ofUAV technology in agriculture is a promising pathway for the adoption of precision agriculture [189]. Small fixed- or rotary-wing III. Applications of GPS/GNSS

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UAVs are becoming more popular in agricultural fields. Currently, remote-sensing techniques are the most common use for these platforms in the agricultural sector and have proven to be an effective method for obtaining field information [190]. Consumer-grade UAVs are cost-effective solutions that provide high-resolution images and data to characterize spatiotemporal variability [191]. The valuable role played by these devices comes from their ability to accurately identify specific areas with relevant features about crop conditions, water status, or the presence of diseases and pests, among others [192]. Multispectral [193e195] or hyperspectral indices [196,197] derived from crop reflectance, temperature measurements with thermal cameras [198] to determine water stress, or 3D reconstructions with lidar sensors, among many others, are some of the utilities of UAVs in precision agriculture. Even specific applications made with UAVs for crop protection are gaining popularity because of the flexibility and versatility of these devices [199,200]. Regardless of the application, GNSS systems play a fundamental role since they are the primary source of navigation for UAVs [201,202]. Preprogrammed flight paths are executed in the form of grid or linear sampling to automatically obtain information and generate products such as orthophotos or 3D point clouds. A series of waypoints on which to position the equipment is provided to the controller embedded in the UAV. This element computes the destination coordinates and navigates accordingly using the GNSS system onboard. Integrating these navigation commands into devices such as tablets or mobile phones for control has made this task more user-friendly. As a result, most flights made with a UAV are in semiautonomous mode, with the aim of repeatability and reliability in the collection of information (Fig. 15.16).

FIGURE 15.16

Unmanned aerial vehicle aerial application using single-nozzle configuration. III. Applications of GPS/GNSS

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Despite being considered as versatile devices in the collection of crop data in 3D space, most commercially available equipment use built-in common GPS receivers without addressing scenarios that require high-precision positioning [203,204]. The use of low-cost GNSS combined with inertial navigation system devices has made it possible to obtain accurate georeferenced information. Integration between the two systems [174] allows the equipment to be positioned in the 6 degrees of freedom in space and to precisely know the orientation of the sensors onboard. At present, many UAV applications in agriculture demand more accurate positioning than the conventional embedded GNSS [203]. For example, photogrammetric techniques such as structure from motion [205] require high-precision surveys to achieve ground sampling distance accuracies of 0.5e1.0 cm. Prior to the incorporation of kinematic systems in UAVs, these accuracies were achieved by using ground sampling points using RTK-GPS from topographic applications [205] and postprocessing corrections [206]. Modern RTK-GPS receivers onboard the vehicles allow for receiving corrections from a ground base and achieve centimeter accuracy. This is of great importance in tasks such as high-throughput phenotyping, where crop trait measurements such as plant height, individual count, or leaf area index can be accurately determined [46,207,208]. Finally, the future use of UAVs is envisioned to be integrated with other existing/emerging technologies to provide a high spatial resolution data acquisition and transmission platform. A fleet of UAVs intended for scouting or precise application tasks will necessarily include coordination between RTK-GPS systems and bi- or multidirectional communications using 5G modules and advanced ground stations.

6.5 GNSS in ground platforms and autonomous tractor The need to create efficient and productive agricultural systems has motivated the development of automated or robotic agricultural ground platforms (Fig. 15.17). The trend in recent decades has been to implement increasingly larger machines to cover larger work areas. This is detrimental to the ability to address high-resolution spatial and temporal

FIGURE 15.17 An autonomous fruit picking machine for robotic apple harvesting (Abundant Robotics, Inc.).

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variability. Therefore, in recent years, a large number of robotics companies, such as Naïo [209], GreenBot [210], and Bosch [211], have focused their efforts on developing systems that can compete in performance and precision by employing autonomous platforms capable of working efficiently. As reviewed in details in Slaughter et al. [76], Aravind et al. [212], Zhang and Wei [213], agricultural robotic platforms, such as the Raven Autonomy platforms, lighten the heavy workload in tasks including fruit picking and transport [214], weed control [209,215], spraying in tree-based crops [216], and fertilizer application [217]. Although the adoption of such platforms has been hindered by their high costs and the difficulty of developing reliable equipment capable of working in unstructured environments, their presence is increasing in mechanized agriculture. The role of GNSS is fundamental in the absolute positioning and navigation of autonomous terrestrial equipment, as extensively studied in Rovira-Más et al. [218]. For robotic systems, the perception of actual and target positions requires a set of complex data combination from different sources: RTK-GNSS, odometry systems, RGB, infrared or depth vision cameras, inertial units, and lidar sensors. This integration is necessitated by the possibility of operation failure of the GNSS receiver when its signal is blocked by the tree canopy [219]. A widely studied challenge for field robotics is route optimization and error identification in navigation [220,221]. Soil compaction, energy use, and pollution can be reduced by appropriate route optimization. Development of path planning and obstacle avoidance techniques for these platforms has been mostly based on RTK-GNSS systems for simultaneous location and mapping of elements in the field [222]. According to Emmi et al. [223], several categories of autonomous navigation have been established, with level three relating to autonomous steering capability and throttle control. Robots working in complex systems, such as farms, must take into account parameters including possible obstacles present in their route, the physical structures of the crop, the plantation framework, and the overlap required by the task [224]. Vougioukas et al. [225] categorized navigation in agricultural tasks as deterministic (path following) or reactive (obstacle avoidance) subtasks. Cost reduction of RTKGNSS guidance systems, such as those offered by SwiftNav [226] or Emlid [227], is enabling rapid prototyping of omnidirectional navigation platforms [228]. In addition, the simulated environments in the ROS operating system [229] are allowing researchers to model the platforms before they are physically built. A trend in the field of agricultural robotics with great potential is the development of collaborative robots, both in terrestrial fleets, such as the commercial SwarmFarm [230] (Fig. 15.18), and in UAV-UGV collaboration [231e234]. In both cases, these fleets employ combinations of RTK-GNSS and vision-based systems for coordination and context-based decision-making [204]. The decrease in the price of components

FIGURE 15.18 Fleet of autonomous sprayers working in a coordinated manner developed by SwarmFarm Inc.

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FIGURE 15.19 Robotic and autonomous spraying equipment developed by GUSS.

and their miniaturization, in conjunction with the possibilities offered by artificial intelligence systems [235], will undoubtedly lead to the widespread adoption of agricultural robots in the coming years (Fig. 15.19).

7. Conclusions and outlook The future applications of GNSS in precision agriculture operations know no bounds. The geopositioning in agriculture along with additional data on the vehicle status, soil properties, crop health, and fertilizer requirements provide the knowledge base for decision-making and management to improve productivity, safety, and quality while reducing cost and environmental impact. The central concept of precision agriculture is to apply only the inputsdwhat the crop needs where and when it is neededdand this can only be done if large amount of georeferenced data are available to make informed management decisions. Agricultural applications such as yield monitoring, VRA, plant mapping, precise weed management, etc., require many sensors to acquire data from the field, but these data can only be linked together through a map by means of the location information provided by the GPS or any other GNSS receiver. With this type of precision agriculture data, the prescriptions maps can be created for planning future farming tasks. One benefit of GNSS receivers over GPS-only is the increased number of satellite available for location calculations by the receiver. This is possible because GNSS-compatible equipment can use navigation satellites from other networks in addition to the GPS system. Moreover, reliability is increased in areas where GPS receivers cannot operate or provide poor accuracy. The higher end GNSS receivers can currently observe up to around 72 satellites and are capable of accommodating additional satellites as more satellite-based systems become operational. More benefits of GNSS receiver include (1) a shorter warm-up time (known as “time to first fix”); (2) reduced delay in recomputing a position if satellite signals are temporarily blocked by obstructions (reacquisition time); and (3) the ability to compute a position where

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it is difficult for a GPS receiver operation, especially near tree rows, building, big obstacles, etc. In order to clarify and avoid confusion among agricultural users, it would be worthwhile to remember that by design the GNSS receivers are compatible with GPS; however, GPS receivers are not necessarily compatible with GNSS. Currently, the scientific community is devoting great efforts to avoid the GNSS signal interruption caused by shading of the GNSS antenna by terrain or obstacles (e.g., trees, buildings, implements, etc.) or by interference from an external source to improve the accuracy of agricultural applications. The need to provide continuous location data or navigation during periods when the GNSS signal is interrupted is the impetus for integrating GNSS with various additional sensor (e.g., inertial sensor, dopplerometers, altimeters, odometers, etc.). The integration of GNSS products and services with sensors, its miniaturization, and the reduction of costs will expand the possibilities of agricultural use of this technology in the future even further.

Acknowledgment This work was partially financed by the Project AGL2016-78964-R funded by the Spanish Ministry of Economic and Competence. The authors thank Professor David Slaughter, Biological and Agricultural Engineering Department, UC, Davis, for his valuable suggestions during the writing process of this chapter.

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An evaluation of GPS opportunity in market for precision agriculture Prem Chandra Pandey, Amit Kumar Tripathi, Jyoti Kumar Sharma Center for Environmental Sciences & Engineering, Shiv Nadar University, Uttar Pradesh, India

List of abbreviations AI Artificial intelligence DGPS Differential GPS DSS Decision support system GAGAN GPS-aided GEO augmented navigation GIS Geographical information system GLONASS GLObal NAvigation Satellite System GNSS Global Navigation Satellite System GPRS General Packet Radio Service GPS Global Positioning System IRNSS Indian Regional Navigational Satellite System ISRO Indian Space Research Organization ML Machine learning NAVSTAR GPS Navigation System with Time and Ranging Global Positioning System PA Precision agriculture QZSS Quasi-Zenith Satellite System RS Remote sensing UAVs Unmanned aerial vehicles

1. Introduction This section focuses on the Global Positioning System (GPS) introduction, its design in brief, and functioning and points out its applications in different research domains. GPS has multiple applications in precision agriculture (PA). It is used for the planning of the farm, mapping of the field, sampling of soil, tractor guidance, crop scouting, and yield applications. Besides, it allows farmers to work in fog, rain, dust, and even in darkness when visibility is low [1].

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© 2021 Elsevier Inc. All rights reserved.

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1.1 Background NAVSTAR GPS (Navigation System with Time and Ranging Global Positioning System) is the first navigational satellite system launched by the US Army and started a revolutionary breakthrough in providing the accurate position using radio signals at most of the regions on the Earth [2e4]. Looking at the success of the United Statesebased GPS, Russia also launched their satellite GLONASS (GLObal NAvigation Satellite System), operational since 1993 [5]. GLONASS is the second navigational system in operation with global coverage, competing the precision level of GPS [6,7]. China launched BeiDou-1 (also called COMPASS) with regional or limited services in 2000; after that, BeiDou-3 (third generation) with two separate constellation systems was launched in 2015 with global coverage to be achieved by 2020 [8,9]. European Commission and European Space Agency introduced Galileo in 2002, in order to provide precise spatial locations [10,11].Galileo is still in early operational capacity as of January 2020 (https://galileognss.eu/). The space initiatives of GAGAN (GPS-aided GEO augmented navigation) and IRNSS (Indian Regional Navigational Satellite System) have been undertaken by ISRO (Indian Space Research Organization), Government of India [12]. This is fully under the control of Airport Authority of India [13], which will be fully operational from 2020. With the introduction of space-based regional navigational satellite system, IRNSS is only to improve the accuracy of a Global Navigation Satellite System (GNSS) receiver by providing reference signals. Moreover, GAGAN with IRNSS simply replaces the role of GPS in India by providing integrity, availability, continuity, and selectivity requirements to the users [13]. IRNSS system is modern than GPS, and NavIC satellites use dual-frequency bands (L5-band and S-band), which is why the system is relatively more accurate than GPS that uses a single band and makes compensation for error due to signal deterioration by the atmosphere [14]. IRNSS downlinks signals dual frequency: S1-band and L5-band. Thus, looking at the accuracy level, authors [15] stressed IRNSS systems to be used as stand-alone systems. Indian civilian users are dependent on the signals with a positional accuracy of 5 m. IRNSS system is intended to provide an absolute position accuracy of (i) better than 10 m throughout Indian landmass, (ii) better than 20 m over the Indian Ocean, and (iii) approximately in a region of 1500 km around India [15]. Quasi-Zenith Satellite System (QZSS), also known as Michibiki, is a satellite-based augmentation system launched by the Japanese Government. QZSS is a four-satellite regional time transfer system to enhance NAVSTAR GPS in the AsiaeOceania regions [16]. Detailed information about the history of GPS, GLONASS, BeiDou, and GAGAN systems can be examined from the literature [2,3,7,8,10,12]. This chapter summarizes the multiple applications of GPS in PA, benefits, and challenges faced during agricultural activities.

1.2 GPS design and functioning GPS systems provide an accurate navigation inside the field that assists soil sample collection and monitoring of crop growth. However, when normal GPS was employed at a regular basis during different season (at a very high temporal resolution), the accuracy

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decreases. For precise monitoring and mapping, Differential GPS (DGPS) is used to increase the accuracy of data. In DGPS, a receiver is placed at a known location to calculate the bias between actual location and the location calculated by the GPS itself. This technology is very much implemented for the agricultural purposes for precise monitoring of crops. GPS signal is received by the rover using four GPS satellites, which is bias corrected using a location of geostationary satellite and finally received by the base station on the ground, which analyzes and stores the recorded data. GPS positions typically include significant error due to satellite induced, atmospheric, and receiver-based errors [5]. These are interpolated orbital ephemeris models, satellite clock drift, ionospheric and tropospheric delay, and multipath and device-related errors that may be strongly correlated over time [17] and can be removed using several appropriate methods [18]. Since the simultaneous use of dual GPS receiver increases the accuracy, DGPS is suitable for reliable positional accuracy. GPS-based instruments were employed for field measurement and sampling to record data to assist in geographical information system (GIS) for further analysis. Most widely used handheld GPS receivers that are commonly used for field sampling [19] are supplied by several companies such as Leica, Garmin, Trimble, Ashtech, JAVAD, and Magellan Meridian receivers [20]. These handheld GPS instruments may cost from low to very high ranges and provide positional accuracies up to few centimeters [20,21]. Further to surprise, even GPS instruments allow accurate measurements in centimeter in a minute and is always keeping pace with GNSS development. There are a number of GPS receivers being built and supplied by companies such as NovAtel's WAAS G-III reference receiver, Satlab SLX-1 NG Multi-application, GNSS ComNav Technology M300 Pro GNSS Receiver, and VectorNav Technologies' VN-300 GNSS/INS for accurate measurements and for providing accurate reference range measurements and signal quality measurements for ground reference networks [22].

1.3 GPS applications The spatial information provided by GNSS include latitude, longitude, and height, which were very useful in locating the features on remotely sensed images, performing cross-validations of the classified objects, and several applications in different research activities. Therefore, it has found its role in the several research applications, military, navigation (ship, car, air), forestry, urban, field sampling measurement, surveying, urban mapping, air traffic control, and wildlife tracking for nomadic patterns. Even, GPS is nowadays a standard for geodesy and employed in different research fields [23]. Applications of GPS in urban management employ efficient coordination of roadway and its maintenance, maintaining roadway databases and accident inventories [24]. Table 16.1 demonstrates important research domains that employed GPS for more reliable and accurate outcome and management.

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TABLE 16.1

Outline of GPS applications in different research themes (selected only).

Application area

References (but not limited to provided ones)

1

Military

[25]

2

Geodynamics and earthquake studies

[26]

3

Aviationdcommercial airline and landings

[27]

4

Forestry

[28,29]

5

Mineral mapping

[30]

6

Physical activities (sports, cycling)

[31]

7

Tracking wild animals and habitat analysis

[32]

8

Topographic mapping and studies

[33,34]

9

Hydrological studies

[35,36]

10

Surveying and construction sites

[21]

11

Mining applications

[33,37e39]

12

Archeological studies

[40]

13

Disaster management

[41e43]

14

Animal movement and behavior

[44]

15

Agriculture

[45e50]

2. GPS applications in precision agriculture GPS has been harnessed in a wide range of ways to develop and manage natural resources on sustainable development with accuracy, such as agriculture for increasing crop yields with minimal input resources [51]. This section briefly provides the information about the GPS application and its urgent requirement in agricultural practices and thus contributing toward PA. PA is the science employing advanced technology and tools for improving crop yields, assisting in the agricultural management, and playing important role in decision. This concept is newly introduced worldwide and adopted by farmer practitioners to reduce the labor time and provide effective management for crop yields using optimal fertilizers and irrigation water. PA incorporates information based on large sample numbers to improve the efficiency of agricultural resources such as irrigational water, fertilizers, and manure to enhance the quality of crop and its yield [52]. Most important application of GPS in PA is to determine and provide spatial location data [46] to assist in other related activities. Therefore, PA can be understood in a very simple manner of providing the right amount of input or treatment at the appropriate time and at the appropriate location within a field. Hence, PA is an advanced innovation and optimized field level management to agriculture that aims to improve the productivity of resources on agriculture fields with minimal and optimal use of inputs. Thus, PA is now considered as an advanced technology in which farmers provide optimized inputs such as water and fertilizer to enhance productivity, quality, and yield [53]

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along with optimized route and path for applications. PA helps to collect and gather huge amount of field information and conditions about the crop during their growth or health at very high spatial resolution with accurate spatial location. PA is turning to be crucial in terms of economy and developing the business for the large-scale farmers and managing them with low input, increased yields, and less time and efforts, along with huge data source collection about the field information. There are diverse ways for above support in the field, which end in the increased crop productivity and yields, which is typically resulting in the minimal resource consumption or input to farming. The benefits of PA can be observed right from plowing to harvesting the crops. PA helps to acquire precise soil sampling, data acquisition, analysis, and interpretation. Furthermore, PA enables localized variation of chemical applications and planting density to suit specific areas of the agricultural field. GPS provides accurate navigation in the large farms, and therefore GPS-assisted machinery provides maximum agricultural field coverage in the shortest possible time while minimizing the redundant applications, wastage of resources, and skipped areas around the field. GPS-assisted equipment allow farmers to work in the large farm even during low visibility such as rain, dust, evening, or fog or allow them to work as per their convenient timing. This benefits of PA help farmers to monitor the crop yields and their data, which will provide future site-specific field preparation to fight against the problems that existed in last crop season. PA also reduces the farmer's dependency upon the human flaggers, by increasing the optimal route and path that increases spray efficiency. Food security for future generation requires sufficient quantity and quality of agricultural food. In this case, PA is useful to monitor the food production chain and manage quantity as well as quality of agricultural food [53]. Global food demands are increasing due the increasing size of global population. The continuous growing population and linearly increasing food demand are forcing world farmers to adopt resource intensive and unsustainable practices that are increasing both economic and environmental budgets. In many countries around the globe, such as developing countries, farmers face issues like small acreage, site-specific yield variability, time, and different farm practices. The decline in total productivity due to land fertility degradation, global climatic variation, and limited employment are major concerns in farming. GPS-based precision farming (use of unmanned aerial vehicles [UAVs] installed with GPS) is needed to trace the soil properties, management practices, and crop types and to identify the stressed crops in the large fields [54]. GPS based drone can also be employed for optimal path and route estimation for precise management of the input resources [63]. Fig. 16.1 illustrates the several activities and practical use of GPS applications in PA. GPS-based applications have revolutionized traditional farming techniques by gathering information on spatial as well temporal variation observed within the field and using that information for the improvement of the farm. GPS- and GIS-based information can be merged to get the actual crop needs for big as well as small areas inside a field, enhancing in that way site-specific farming for more enhanced crop yield. GPS device was even employed to characterize the error measurement of agricultural field area and provides reliable and accurate area and thus is helpful in yield estimation with other parameters. The word cloud of most of the technical terms is employed in the precision farming using GPS, its assisted technology, and GIS (Fig. 16.1). The many more other technical terms that can be included in the word cloud are root management, artificial intelligence (AI), machine learning. The shape of word cloud is generated in the form of water drop in order to stress the

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FIGURE 16.1 Word cloud illustrating the overall technical terms used and assessed during precision farming using GPS [Author generated image].

significance of water requirement during agricultural activities such as drip irrigation or flooding-assisted irrigation. GPS-assisted technology can be employed in the field of mapping, optimum route, and path assessment and field coverage during spray of fertilizers, pesticides, weedicides, and even it can be used to detect the disease in the field. UAVs mounted with GPS can be employed in the above-discussed activities and even used during yield estimation and prediction before harvesting the crops. In PA, for getting more yield per unit area, farmers practice three steps such as data collection, interpretation, and its application. During data collection, one has to look after the condition of soil, crop condition maps. The next stage is the data interpretation that helps in understanding the precise condition of soil to model crops and soil for better and more productive crops. They include crop models/soil models as well as treatment maps of the agricultural fields. The last step is the application parts using GPS for sowing, fertilizers/pesticides/insecticides spraying, and protection of crops. For example, scientists [55] demonstrated the GPS application in seed distribution in the large fields. The GPS-assisted seed distribution is more uniform and precise than the conventional seeders. The advantages are uniformity and less wastage of the seeds, while conventional seeders are not uniform and also sometimes cause wastage of seeds.

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2.1 Examples of GPS-based applications in agriculture Through the use of GPS, GIS, and remote sensing (RS), various information can be collected, which can help in precision farming for near real-time decision-making. GPSequipped instruments can assist farmers increase their production and improve their income due to increased yields through optimum inputs to agricultural activities. The several benefits to PA using GPS-assisted technologies are helping farmers one or other ways, such as follows: • The accuracy of GPS in various agroinstruments allows agroanalysts/scientists to create area maps with precise farm boundaries and road networks and enhance crop monitoring, soil sampling, etc. • GPS is playing a key role in precision farming as it gives a site-specific data that make monitoring and management of crops easier and on a real-time basis. PA is not something that is going to be similar for every field, so an extensive approach is needed for this technique, including data gathering, data interpretation, and management of the information. • Crop analysts use a continuous GPS-derived dataset to analyze the pest- or weedinfested spots in the field and monitor the increase or decrease of pests/weeds in the field. • Precision farming is mostly used on arable lands with GNSS technology and decision support system (DSS) to optimize the use of fertilizers. Even collection of field data samples, such as soil samples for nutrients, soil moisture, alkalinity, salinity, pH, and other parameters, also location of growing weed and their pressure ratings, on some types of spatial grid is a basic task in PA. To map spatial variability patterns based on the field measured samples, precise spatial locations of the recorded points must be determined accurately as much as possible using GPS or GPS-assisted instruments. The use of different types of GPS instruments made by several companies helps in the above steps and supports the above procedures for spatial mapping using geotagging or georeferencing process for better accuracy [46]. Hitachi Ltd. company [56] conducted a research to eliminate the weeds from the agricultural fields in Western Australia, through a combination of image recognition, AI, data analytics, and satellite positioning with reliable accuracy provided by QZSS. Weed control is just one of a number of agricultural applications, employing the GPS techniques. UAVs are equipped with precise GPS instruments and mounted with camera (thermal, multispectral) to collect imagery of the fields or advanced General Packet Radio Service (GPRS)ebased equipment to transfer the location to farmers (either to mobile or their computers) for analysis. The transmitted spatial location enables the precise location of weeds or targeted features in the field, thereby saving time and cutting the expense due to selective applications of the weedicide or related agricultural inputs in the fields. Furthermore, those application of inputs will be fully automatic and if required farmers can administer manual inputs. Hitachi Ltd. company [56] also experimented the advanced techniques for PA that provide precise location and navigation, target identification come location, automatic tractor control, and optimal path and route for different applications such as spraying fertilizers, pesticides,

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and herbicides. Together these technologies are transforming the agricultural practices and changing their looks from traditional to SMART farming. Thus, advanced technologies can help large-scale farmers to exploit the PA and move toward the SMART farming for better sustainable and resilience methods. Borgelt [46] employed GPS-based instrument called 8-row R62 COMBINE for soybean crop yield mapping, which was attached with an impact-based Ag Leader Yield Monitor 2000 sensor. This activity was performed with the help of Ashtech GPS Sensor II. The instrument employed provides the instantaneous values and cumulative totals of yield, grain moisture, grain flow, speed, distance, and other parameters by measuring the force of grain impacting against a plate situated at the top of the instrument (clean grain elevator). Grain force, elevator speed, and other measured parameters were used to estimate the mass grain flow rates [57] and thus ultimately provide the crop yield saving labor time and covering a large spatial area. Employing GIS along with GPS for PA will enhance the field information to farmers and help them to monitor different variables affecting their crops. These variables include soil types, soil moisture, surface temperature, photosynthetic activities, or pest locations. With prior information about the field variables, farmers can utilize the inputs accordingly, i.e., it can reduce excess water use by monitoring and measuring the variables (irrigational water use, soil moisture of the field), measure exactly how much water is being used by crops, or find the soil moisture of the field, instead of inferring their potential need from weather variables [58]. During past 10 years, GPS has been used successfully for numerous operations throughout most aspects of agriculture in the United States. In comparison to conventional methods, the PA is much more efficient, such as by using GPS on the tractors, the leveling the land to planting the seed, and irrigating the crop have become more efficient in the United States. The GPS-based applications in PA allows farmers to navigate particular sites in the field, collect soil samples, and monitor crop conditions more accurately year after year. Earlier it was not easy for the farmers to correlate production techniques and crop yields with land variability, but through PA, nowadays, more precise application of pesticides, herbicides, and fertilizers and better control of the dispersion of those chemicals are possible and by this way reducing expenses, producing a higher yield, and creating a more environmentally friendly farm. The developed countries are employing PA and SMART farming in the agricultural activities and proving the farm as global bread baskets in coming years, even developing countries have now started using PA but still will need more technical supports. Even through RS data and GPS location, crop analysts can monitor the crop's health, water stress, normalized differential vegetation index, and other vegetation indices of particular vegetation in the field [54]. Some other important applications of GPS-based precision farming are guidance point and swath guidance, which assist in mapping and guiding tractors during plowing. Moreover, using pointdline guidancedthe instruments can be employed in boundary delineation or utilizing the functioning for optimal route applications. Even it assists in soil sampling using grid sampling or directed sampling. 2.1.1 SMART farming The advancements in GPS, GIS, and RS technologies are changing the vision of precise farming and aiding the improvement of site-specific crop and soil management. SMART (Scientific, Marketable, Affordable, Reliable, Time saving) farming or agriculture is replacing the

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traditional or conventional farming practices, assisted by RS, GIS, and GPS technologies. RS provides a large volume of data with large regional coverage, whereas GIS-assisted techniques help in the mapping, output of data, and GPS enables farmers to acquire the accurate and reliable spatial positional information about the field. The rapid data acquisition and processing are prerequisites, and UAVs can play a key role in future precision farming. 2.1.2 IoT-based smart farming The IoT (Internet of things) fundamental is the data any one can draw from things (T) and transmit over the Internet (as illustrated in Fig. 16.2). IoT is believed to be emerging solutions to agricultural problems and is considered useful in all areas of farming, starting from growing crops to harvesting. IoT devices installed on a farm are very important as they collect and process data in a repetitive cycle that enables farmers to take decision fast to emerging issues and changes in ambient situations [59]. PA is considered an umbrella concept for IoT-based approaches that make farming more controlled and accurate, where plants and cattle get precisely the treatment they require. Besides, PA allows decisions to be made per meter square or even per plant/animal rather than for a field [59]. In PA, large farm owners can use wireless IoT applications to monitor the location, well-being, and health of their growing crops as well as cattle easily [59].

FIGURE 16.2

SMART farming and fundamentals of Internet of things in precision agriculture.

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3. Challenges and future work This section deals with the challenges and hurdles associated with GPS in farming, such as handling sophisticated instruments, human-associated device-handling errors, and less awareness about the device or technologies. The main challenges faced are the errors associated with satellite-based, signal propagation, and instruments/equipment-based errors. They are orbit perturbations, satellite clock errors (atomic clock of satellite vs. quartz clock of GPS equipment), deviation in orbital path, tropospheric and ionospheric delay, multipath, and receiver noise. Most of these errors are satellite based and can be removed, but signal propagationebased errors are reduced by employing dual-frequency receiver. In developing countries, the biggest challenge for precision farming is the local culture and perception of the farmers. Additionally, farm sizes in developing countries are relatively small due to high population density, making crop monitoring of each farm quite challenging for the agroscientists. A sufficient number of technical experts are needed in order to instruct and guide local farmers; however, that is not possible in most areas. Publicly available data are still not an option for every part of each country or their cost makes it forbidding for the farmers. Since local farmers lack both instrumentation and expertise, the government needs to focus its efforts on the farmers' education on basic precision farming principles. The most important about the GPS-assisted PA is about the free GPS signals to be used by all across the globe with reliable accuracy. SMART farming can be productive when used wisely along with GPSassisted instruments or equipment. They can be employed to take several works in large farms and perform several works. They are listed as • Real-time image processing and a DSS is needed in precision farming to fill the gap between RS and variable air surveys. • Aerial spraying technology is one of the critical aerial technologies that can be used for pest control; it can control pest outbreaks in the field and also minimize the damages caused by the hovers while sprinkling pesticides. • Multisensor, multitemporal, and multispectral-hyperspectral & data fusion technique can enhance mapping precision, and different analyses can be performed to obtain thematic and numerical outputs [64]. • Cloud computing technology can transform the future of farming practices as centralized and automated shares infrastructure that can share the information regarding weather, soil types, etc., to the farmers through smartphones.

4. Conclusions and recommendations The previous sections discussed the challenges and future scope of the GPS applications in PA. Therefore, overall in this chapter, a holistic method was discussed to explore the scope and applicability of GPS in agricultural practices. Therefore, in order to achieve the most out of the advanced GPS-assisted technology for PA, and ultimately benefit to farmers and environment, it should be combined with all parameters and input required during agricultural practices. Inclusion of other technologies in the PA will improve the accuracy and reliability, making it to SMART farming with precision. These technologies include IoT, wireless

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sensors to measure plant parameters (pest, soil parameters, disease), and for transferring data, which may be one-time or real-time information communications using GPRS-based monitoring [60,61]. When SMART farming and PA incorporated together, making it the technology of SMART GeoFarmatics, it is possible that future farming will be more precise, accurate, reliable, timely, and productive based on innovative data sciences PA [60], authors discussed about the GrIDSense informative technology for SMART farming. Even precision brings economic and environmental benefits to the farmers and improves the large-scale farming efficiently. Employing PA and SMART farming can increase the income of agricultural people up to 20% [62]. These benefits are being availed by the large-scale farmers in most countries, while enjoying the minimal input with increased crop yields. Looking at the advantages of multidisciplinary involvement in the agricultural sectors will definitely boom the farming, which already turned traditional farming to technology-based precision and SMART geofarmatics, in near future for more yields and production with minimal or optimal agricultural inputs and monitoring.

Acknowledgment Authors are thankful to Prof. Rupamanjari Ghosh, Vice-Chancellor, Shiv Nadar University, Greater Noida for support and encouragement to carry out the research work.

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Use of GPS, remote sensing imagery, and GIS in soil organic carbon mapping Dimitris Triantakonstantis1, Zoi Papadopoulou2, Nikolaos Katsenios1, Panagiotis Sparangis1, Aspasia Efthimiadou1 1

Department of Soil Science, Institute of Soil and Water Resources, Hellenic Agricultural Organization e Demeter, Lycovrisi, Attiki, Greece; 2Department of Natural Resources and Agricultural Engineering, Agricultural University of Athens, Athens, Greece

1. Introduction Global Positioning System (GPS) and other Global Navigation Satellite Systems (GNSSs) (such as BeiDou, Galileo, GLONASS, NaviC, QZSS) have made a critical impact to improve the agriculture practices. In the 21st century, they have been two of the most important tools along with geographic information system (GIS) and other remote sensing (RS) utilities for precision agriculture, which is of increasing importance among the farmers and the agronomists [1,2]. These new technologies offer more accurate mapping of soil properties [3]. From delineating soil boundaries of each field to navigating through it, GPS receivers allow the satellite signals to calculate their position. Before these technologies, farmers used signs inside of the field to remember every location with different characteristics and practices. This information now is provided by the GPS and other GNSS tools almost in real time, which means that these allow all the measurements of crop, soil, and water to be mapped with precise locations [2,4]. Applications of GPS varied; vehicle tracking, aviation, and place location were among the most common use of the GPS; however, since precision agriculture concept emerged, it gained a crucial role in agriculture. Soil mapping, monitoring of the crops, and even guiding with precision in the field helped the agricultural sector [5]. Long et al. [4] used GPS to

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determine how prominent was its application in soil survey and found that GPS methods were accurate for navigating and positioning through the field and to digitizing soil boundaries with greater efficiency than conventional methods. In 2018, Bhunia et al. [6] tested various interpolation techniques using GIS in order to estimate the spatial variation of soil organic carbon (SOC) in three soil depths. To collect accurately the coordinates of every sample site, they used portable GPS. Another study estimated the concentration of organic carbon in topsoil combining RS images with field survey data that were gathered form a GPS unit and a multivariate regression model [7]. Srivastava et al. [8] also used GPS-aided information recording coordinates to help in GIS map processing along with RS to compare different interpolation methods that were used to estimate soil moisture and SOC. As it is already mentioned, GPS/GNSS systems can work and improve their results working along with GIS and RS tools. These systems provide high-resolution images of the field, which can be used to calculate some very important indices like normalized difference vegetation index (NDVI), soil color index (SCI), etc. With the help of statistical software, some very reliable models of prediction can be created for parameters like SOC, soil moisture vegetation parameters, and other water, soil, and crop information. GPS/GNSS tools are able to accurately and rapidly navigate through the fields, record, and plot soil boundaries from their signal receivers. Moreover, knowing the exact position offers the ability to return to the specific site to make more samples, for replication reasons [1,2,4,9,10]. Satellite images have been systematically used by researchers to estimate SOC mainly because of their low or even zero cost. Factors such as the roughness of the soil, the moisture content, and the vegetation cover reduce the accuracy of the estimations [11]. Nowadays, researchers have access to free-of-charge images of high resolution from satellites, and in the near future, these are expected to be more qualitative and more frequent [12]. Several space-borne platforms like Sentinel-2 (S2), Landsat ETMþ, Hyperion, EnMAP, PRISMA, and HyspIRI have been used to estimate SOC [11]. Data from Landsat TM were used for a model in order to predict topsoil organic carbon in an Alpine environment in China [13]. Except SOC, satellite images have been used to make models to estimate various soil properties like pH, cation exchange capacity, texture, iron, calcium, CaCO3, and soil salinity [14,15]. SOC constitutes an important component of soil organic matter (SOM) and is a key factor to all soil processes. Soil is the second largest natural carbon sink behind the oceans; this is due to the fact that through the plants bounds the carbon dioxide (CO2) from the atmospheric air. In the majority of the terrestrial ecosystems, SOC composes the largest carbon pool [16]. SOC is an important soil characteristic with an intense impact on soil quality and therefore on plant growth. Soil fertility is significantly affected by SOC considering the correlation that it has with the structure of the soil, carbon sequestration, soil resilience, and nutrient retention [17]. In general, SOC is considered one major factor of soil quality because it stands as the primary source of nutrients and, at the same time, increases the water storage capacity, a very important mission considering the effect of climate change on the availability of water. SOC has an immediate relationship with the atmospheric carbon. In global ecosystems, the carbon cycle can be deeply affected by even a minor change in SOC over wide areas [18,19]. It is also a very dependable indicator of observing possible soil degradation generated by accelerated erosion [20].

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2. Materials and methods

353

The traditional method to measure SOC in a soil sample is by using typical chemical analysis methods in the laboratory, which are highly accurate. However, these conventional methods for estimating soil parameters are time-consuming and costly procedures. The implementation of alternative approaches is imperative. GIS and RS are fast techniques and cost effective in identifying a vast variety of soil properties, including SOC. For the mapping of SOC or SOM, various vegetation indices such as NDVI and other soil indices like SCI and bare soil index (BSI) have been frequently used, after all the major source of SOM is vegetation [21,22]. Vegetation parameters such as vegetation indices and type can be used productively for mapping SOC as great indicators of primary productivity. Predictions of SOC concentration and stock can be measured by these indices on both larger and smaller scale [23,24]. A combination of multispectral images and bare soil and vegetation spectral characteristics can be used to predict SOC in field scale [25]. One of the most useful characteristics of soil is its color. It is a useful tool for the estimation of SOC. Darker soils tend to have more organic matter and SOC and thus are more fertile. However, they are often not well drained, unlike red soils. Bare soil refers to soil not covered by grass or other coverings such as pebbles, rocky areas, etc. The BSI is a numerical indicator that combines channelsdblue, red, green, and near infrareddto record soil changes. It is an indicator that enhances the recognition of the bare soil. Healthy and fertile soils are of significant value not only for food production as mentioned above but also in order to prevent the serious and extreme effects of climate change (e.g., increasing of the global average temperatures). Increasing SOC can improve soil productivity and thus increase food production while having a key role in the mitigation of greenhouse gas emissions, carbon dioxide, nitrous oxide, methane [26,27]. Changes in the land use and soil can affect climate change in various ways, either by stimulating or mitigating the transition of the environment as we know it. Without sustainable land and soil management, we cannot cope with the climate change. However, there are some methods in crop production in order to slow down these changes. Cover crops improve soil fertility while increasing water retention and penetration and reducing SOC depletion and soil degradation. In addition to cover crops, minimizing the application of chemical fertilizers and the deep plows and integrating crop residues are practices that can help the mitigation of climate change [16]. The aim of this study was to develop a multiple linear regression model using remotely sensed imagery that can reliably estimate SOC. The ability to predict soil quality properties like SOC in large areas with the contribution of satellite data is a powerful tool in the hands of scientists, framers, stakeholders, and policymakers for climate change mitigation.

2. Materials and methods 2.1 Study area and soil samples Sixty-seven soil samples were collected form the area of Malandrino, in the prefecture of Phocis, in the region of Central Greece (Fig. 17.1). The area is southwest of Mount Giona and is included in the wider basin of lake Mornos. Every soil sample was collected at the depth of 0e30 cm. Locations were recorded using a handheld GPS. The device Garmin GPSMAP 64 was used. The exact coordinates of each soil sample were recorded in the

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17. Use of GPS, remote sensing imagery, and GIS in soil organic carbon mapping

FIGURE 17.1

Distribution of soil samples in the study area of Malandrino, Central Greece.

GPS memory of the device with the name of the number of the sample (1e69). All positions stored at the device were retrieved and connected to the soil samples with high accuracy. The sampling approach that was used was that of soil units. Initially, a soil-type map has been created, and then the soil sampling has been conducted depending on the size of the soil units. The Garmin GPSMAP 64 is a GPS receiver that is generally used for recreational GPS activities. The GPS location is accurate to within 3.65 m, assuming WAAS correction (Wide Area Augmentation System, http://www8.garmin.com/aboutGPS/waas.html) and a clear view of the sky. Considering the limitations of data captured, for many types of studies where high accuracy and precision are not required, researchers effectively utilize those GPS data. Therefore, for our study needs where no high accuracy and precision are required, this GPS receiver provided us the corresponding geodata. After all sampling points were navigated, these recorded data were downloaded from GPS device using DNRGPS application. The data were extracted as Google Keyhole Markup Language format (*.kml) and imported to the freely available QGIS software package.

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2. Materials and methods

355

Soil sample analyses were conducted in the Department of Soil Science of Athens, which is accredited according to ISO 17025. As a pretreatment, soil samples are oven-dried at 70 C for 24 h, according the direction OE-10-S (ISO 11464). Then the samples were crushed and sieved to 2 mm. Some laboratory analyses are described below: Organic carbon of soil samples is oxidized by K2Cr2O7/H2SO4 solution at 1352 C (ISO 14235:1998). The solution of Crþ6 ions that has an orange-red color after the reduction becomes Crþ3 and the color changes to green. The intensity of the green color is measured using spectrophotometer. In order to make a calibration curve, we use glucose as a source of directly oxidized carbon. Soil texture was determined using the method of Bouyoucos [28] and then the soil taxonomy of United States Department of Agriculture (1999). Total salts were calculated using the results of electrical conductivity and the saturation percentage of the soil samples. Electrical conductivity was determined in an aqueous extract of soil according to ISO 1265:1994. Saturation percentage has been measured in the soil samples of known moisture content by adding a measured volume of water [29].

2.2 Remotely sensed images and geographic data Governments are required to report their national greenhouse gas inventories by estimating greenhouse gas emissions and removals from croplands areas using Intergovernmental Panel on Climate Change Guidelines. Despite that, most European national greenhouse gases inventory reports do not report SOC stock changes due to the difficulty related to the monitoring SOC over time and the high cost related to a monitoring network for SOC inventory at national level [30]. Therefore, supplying a scientific-based methodology will be helpful to the preparation and continuous improvement of national greenhouse gas inventories. The application of RS data to derive indirect measurement of SOC in space and time has the potential to supply reliable and cost-effective estimates of SOC [31]. More specifically, remotely sensed imagery has been used widely for the retrieval and hence monitoring of SOC across the Visible-Near Infrared (VNIR) - Short wave Infrared (SWIR) spectral range, thus providing a tool for the generation of spatial maps of the upper soil horizon [32e34]. Recent studies proved the advantages of S2 imagery to derive high-quality information on variations in SOC [12,15,35]. In this regard, we aim to apply the capability of S2 data to predict SOC in croplands areas. A high spatial resolution image from S2 was selected in the visible and in the near-infrared (10 m resolution). The image was taken on August 22, 2018 because at this time, the largest amount of bare ground was expected. Data were obtained from the European Space Agency (ESA) SciHub as a L1C level (product on top-of-atmosphere level) and corrected to SNAP implementation by the SEN2COR module at L2A level. The soil sampling campaigns for ground truthing were collected in soils that will be bare at S2 acquisition date. The SOC content measured in each soil sample in the laboratory is to be paired to the spectrum extracted from the S2 at the sampling point. These data will be used to calibrate an SOC predictive model.

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17. Use of GPS, remote sensing imagery, and GIS in soil organic carbon mapping

3. Methodology 3.1 Soil properties model Different model techniques can be used in order to develop the model with the best performance [36]. Numerous digital soil mapping techniques have been employed, such as multiple linear regression [37], linear mixed models [38], random forest model [39], support vector machines [40], artificial neural networks [41], kriging [42], and the boosted regression tree model [43] for soil properties calculation. In our study, the methodology steps are explained in the flowchart of Fig. 17.2. Except the results of laboratory analysis (pH, total salts, texture, etc.), the following indices (Eqs. 17.1e17.3) derived by satellite images were also considered. Soil color cndexðSCIÞ ¼

ðRed  GreenÞ ðRed þ GreenÞ

Normalized difference vegetation indexðNDVIÞ ¼

Bare Soil Index  BSI ¼

ðNIR  RedÞ ðNIR þ RedÞ

ðRed þ GreenÞ  ðRed þ BlueÞ  100 þ 100 ðNIR þ GreenÞ þ ðRed þ BlueÞ

(17.1)

(17.2)

(17.3)

The indices as given in Eqs. (17.1)e(17.3) are graphically presented in Figs. 17.3e17.5. Multiple linear regression is a statistical analysis technique that creates a model to predict the values of a response variable using one or more explanatory variables (Eq. 17.4). The equation for multiple linear regression is Y ¼ a þ b1 X1 þ b2 X2 þ . þ bk Xk þ e

(17.4)

where Y is a dependent variable, X is explanatory variables; a is the constant term, b is slope coefficients for each explanatory variable, and e is the model's error term. For the regression analysis, IBM SPSS software version 24 (IBM Corp., Armonk, N.Y., USA) was used. For our study, a multiple linear regression model was applied to the soileRS data in order to estimate the SOC value. Our application is restricted to point data spatial analysis, where sampling locations were considered. Moreover, geostatistical analysis was applied using spatial interpolation so as to estimate SOC in places where no data are available. More specifically, the inverse distance weighting method was used to assign values of SOC in each cell of a grid (Fig. 17.6). The performance of the model will be evaluated using cross-validation between predicted and observed soil properties values. The measurements of performance of the model will be defined as vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u N u1 X pffiffiffiffiffiffiffiffiffiffi  2 RMSE ¼ MSE ¼ t yi  b (17.5) yi N i¼1

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3. Methodology

FIGURE 17.2 Flowchart of the methodology steps.

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357

358

17. Use of GPS, remote sensing imagery, and GIS in soil organic carbon mapping

FIGURE 17.3

Soil color index (SCI) of Malandrino area.

III. Applications of GPS/GNSS

3. Methodology

FIGURE 17.4

Normalized difference vegetation index (NDVI) of Malandrino area.

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359

360

17. Use of GPS, remote sensing imagery, and GIS in soil organic carbon mapping

FIGURE 17.5

Bare soil index (BSI) of Malandrino area.

III. Applications of GPS/GNSS

3. Methodology

FIGURE 17.6

Spatial interpolation (IDW) of soil organic carbon (SOC) in Malandrino area.

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361

362

17. Use of GPS, remote sensing imagery, and GIS in soil organic carbon mapping

2 yi yi  b R ¼ 1  P 2 yi  yi P

2

(17.6)

where b y is the predicted value of y and y is the mean value of y. Moreover, the predictive accuracy of the method applied was also evaluated using Percent bias (PBIAS). This index measures the average tendency of the simulated data to be larger or smaller than their observed data [44]. The optimal value of PBIAS is 0. If PBIAS is positive, the model is underestimated, while negative values indicate model overestimation bias [44].

4. Results The descriptive statistics of the satellite image channels and soil properties that were used to make the model are presented in Table 17.1. The mean value of pH in the study area of TABLE 17.1

Descriptive statistics of the satellite image channels and soil properties that were used in the model. Mean

Standard Deviation

Minimum

Maximum

pH

6.71

0.75

4.60

7.70

SOM

4.40

2.37

1.30

11.30

CaCo3

1.10

1.50

0.00

8.60

Clay

23.37

7.65

10.00

48.00

Total salts

0.02

0.01

0.01

0.06

B1

625.93

113.02

424.00

942.00

B5

1.500.03

302.04

769.00

2.446.00

B8

2.438.68

277.22

1.896.00

3.344.00

B12

2.422.78

424.31

1.211.00

3.636.00

B3

938.69

219.48

497.00

1.580.00

B4

1.143.90

318.35

536.00

2.124.00

B8A

2.567.57

239.40

2.119.00

3.523.00

B6

2.056.47

234.37

1.572.00

2.969.00

B2

689.40

162.19

378.00

1.090.00

B11

3.307.78

451.73

1.871.00

4.229.00

B9

2.565.41

185.17

2.167.00

2.996.00

B7

2.305.81

250.49

1.862.00

3.268.00

SCI

0.09

0.05

0.07

0.24

NDVI

0.37

0.11

0.13

0.63

BSI

104.74

0.93

101.26

108.19

BSI, bare soil index; NDVI, normalized difference vegetation index; SCI, soil color index; SOM, soil organic matter.

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5. Discussion and conclusions

Malandrino was 6.71 with a standard deviation value of 0.75, while the range of pH values was 4.60e7.70. The mean percentage of SOM was 4.40 with a standard deviation value of 2.37, while the range of values was wide (1.30e11.30). The mean percentage of CaCO3 was 1.10 with a standard deviation value of 1.50, while the range of values was 0e8.60. The clay percentage was 23.37 with a standard deviation value of 7.65, while the range was 10%e48%. The total salt percentage mean value was 0.02 with a standard deviation value of 0.01, while the range of values was 0.01e0.06. Regarding the indices, SCI mean value was 0.09 (standard deviation: 0.05), with a range of values from 0.07 to 0.24; NDVI mean value was 0.37 (standard deviation: 0.11), with a range of values from 0.13 to 0.63; and BSI value was 104.74 (standard deviation: 0.93), with a range of values from 101.26 to 108.19. In addition to these indicators, the model showed a better fit when all the satellite image channels were included as well as the soil properties: texture and salts. pH and CaCO3 were not included into linear regression model because they are significantly correlated with other variables at significance level P < .05 (Table 17.2). The multiple linear regression model that was used to estimate the spatial distribution of SOC is as follows (Eq. 17.7): Soil organic carbon ¼  39:527 þ 0:069  soil texture þ 58:645  total salts þ 0:002  B1 þ 0:008  B2 þ 0:011  B3  0:009  B4 þ 0:006  B5  0:006  B6 þ 0:002  B7 þ 0:003  B8 þ 3:526e4  B8A þ 1:456e4  B9  3:828e4  B11  0:002  B12 þ 22; 498  NDVI þ 37:526  SCI þ 0:281  BSI (17.7) In this study, the accuracy and robustness of the linear regression model for SOC calculation were evaluated. The studied model results were discussed in terms of their assumptions and applicability using statistical performance indices such as root-mean-square error (RMSE) and %BIAS. Therefore, the results of regression analysis produced the following indices: R ¼ 0.825, R2 ¼ 0.681, and RMSE ¼ 0.903. These results indicated an effective performance of the model. The %BIAS is 0.008, which is extremely low, and therefore, the SOC was effectively predicted by linear regression model.

5. Discussion and conclusions The agriculture sector has been trying to deal with the constant demands of the global market while trying to adapt to climate change. These two missions usually have contradictive practices in order to achieve their purpose. Agricultural practices have a major role in this matter: the role of mitigating climate change while being cost effective for everyone involved. The contribution of agriculture to climate change mitigation is important since the implementation of appropriate farming practices has an impact on carbon sequestration and therefore

III. Applications of GPS/GNSS

Correlation matrix of Pearson's r and p-values of the satellite image channels and soil properties of the study area. C

C

III. Applications of GPS/GNSS

B12

B4

B8A

B6

B2

0.020

B3

B4

B8A

B6

B2

e 0.005

e

p-value

0.965

e

0.872

Pearson's r 0.055 0.015 0.670

e

0.657

0.906