Note: Front Cover -- Satellite Soil Moisture Retrieval: Techniques and Applications -- Copyright -- Dedication -- Contents -- List of Contributors -- Author Biographies -- Preface -- Acknowledgments -- About the Cover -- Section I: Introduction -- Chapter 1: Soil Moisture from Space: Techniques and Limitations -- 1. Introduction -- 2. Means of Measuring Soil Moisture -- 2.1. Remotely Sensed Soil Moisture, The Main Approaches -- 2.2. Microwave as a Tool for Soil Moisture Monitoring: Current Status -- 3. Satellite Missions -- 4. Soil Moisture Retrieval From Space Using Passive Microwaves -- 4.1. Surface Soil Moisture -- 4.2. Root-Zone Soil Moisture -- 5. Way Forward -- 6. Caveats -- 7. Conclusions and Perspectives -- References -- Chapter 2: Available Data Sets and Satellites for Terrestrial Soil Moisture Estimation -- 1. Introduction -- 2. In Situ Data Sets for Soil Moisture -- 2.1. International Soil Moisture Network -- 2.2. Field Campaigns -- 2.2.1. Soil Moisture Experiments Series -- 2.2.1.1. Soil Moisture Experiment 2002 -- 2.2.1.2. Soil Moisture Experiment 2003 -- 2.2.1.3. Soil Moisture Experiment 2004 -- 2.2.1.4. Soil Moisture Experiment 2005 -- 2.2.2. Canadian Experiment for Soil Moisture in 2010 -- 2.2.3. Soil Moisture Active Passive Validation Experiment -- 2.2.3.1. SMAPVEX08 -- 2.2.3.2. SMAPVEX12 -- 2.2.4. Soil Moisture Active Passive Experiments -- 2.3. FLUXNET sites -- 3. Satellite Data Sets for Soil Moisture -- 3.1. The Scanning Multichannel Microwave Radiometer (SMMR) -- 3.2. Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E/2) -- 3.3. Advanced Scatterometer (ASCAT) -- 3.4. Soil Moisture and Ocean Salinity (SMOS) -- 3.5. Soil Moisture Active and Passive (SMAP) Mission -- 4. Conclusion -- References -- Section II: Optical and Infrared Techniques & -- Synergies Between them. , Chapter 3: Soil Moisture Retrievals Using Optical/TIR Methods -- 1. Introduction -- 2. Optical/TIR Model History and Concept -- 2.1. History -- 2.2. The Ts/VI Concept -- 3. Optical/TIR Models Used for SM Estimation -- 3.1. Direct Estimation of SM From Ts/VI Space -- 3.2. Models Based on Ts/VI and Empirical Equations -- 4. Case Study: Estimation of SM Using Optical/TIR RS in the Canadian Prairies -- 4.1. Introduction -- 4.2. Materials and Methods -- 4.2.1. Study Area and Data -- 4.2.2. Methodology -- 4.3. Results -- 4.3.1. Comparing EF Estimations Retrieved From Three Different Approaches -- 4.3.2. SM Estimation From Evaporative Fraction -- 4.3.3. Correlations Between Estimated SM and Field Data -- 5. Summary and Future Outlook -- References -- Chapter 4: Optical/Thermal-Based Techniques for Subsurface Soil Moisture Estimation -- 1. Introduction -- 2. Methodology -- 2.1. Study Area -- 2.2. Satellite Data, Estimation of TVDI, and Measurements -- 3. Results and Discussion -- 3.1. TVDI Parameters -- 3.2. Comparison of TVDI and Subsurface Soil Moisture Measurements -- 4. Conclusions -- References -- Chapter 5: Spatiotemporal Estimates of Surface Soil Moisture from Space Using the Ts/VI Feature Space -- 1. Introduction -- 2. The Ts/VI Domain -- 3. Experimental Set Up and Data Sets -- 3.1. CarboEurope In Situ Measurements -- 3.2. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Imagery -- 3.3. The Advanced Along Track Scanning Radiometer (AATSR) Imagery -- 3.4. The SimSphere Land Biosphere Model -- 4. Methodology -- 4.1. Preprocessing -- 4.2. ``Triangle´´ Implementation -- 4.3. Coupling EO With the SVAT Model to Retrieve SMC -- 5. Results -- 6. Discussion -- 7. Conclusions -- Acknowledgments -- References -- Chapter 6: Spatial Downscaling of Passive Microwave Data With Visible-to-Infrared Information for High-Resolution Soil Mo. , 1. Introduction -- 2. A Semiempirical Model to Capture the Synergy of Passive Microwaves With Optical Data at Different Spatial Scales -- 3. High-Resolution Soil Moisture Mapping From Space -- 3.1. Long-Term Validation Over the Central Part of the Duero Basin -- 3.2. Exploring the Use of SWIR-Based Vegetation Indices to Disaggregate SMOS Observations to 500m -- 4. Airborne Field Experiments -- 4.1. Airborne Platform for Simultaneous Thermal, VNIR Hyperspectral and Microwave L-Band Acquisions: Proof-of-Concept and... -- 4.2. Airborne GNSS-R and Landsat 8 for Soil Moisture Estimation -- 5. Future Lines and Recommendations -- Acknowledgments -- References -- Section III: Microwave Soil Moisture Retrieval Techniques -- Chapter 7: Soil Moisture Retrieved From a Combined Optical and Passive Microwave Approach: Theory and Applications -- 1. Introduction -- 2. Radiative Transfer Equation -- 2.1. Tau-Omega Model -- 2.2. Effective Temperature, Single Dispersion and Vegetation Optical Depth -- 2.3. A Combined Optical-Passive Microwave Approach -- 2.4. Case Study-Optical Passive Microwave at in-situ level -- 2.5. Case Study-Optical Passive Microwave at Regional Scale -- 2.5.1. Study Area -- 2.5.2. Data -- 2.5.3. Results -- 2.5.4. Discussion -- 2.6. Toward a Thermal Infrared Contribution in the Optical-Passive Microwave Approach -- 3. Conclusions -- Acknowledgments -- References -- Chapter 8: Nonparametric Model for the Retrieval of Soil Moisture by Microwave Remote Sensing -- 1. Introduction -- 2. Material and Methods -- 2.1. Instrumentation Setup and Observations -- 2.2. Radial Basis Function Artificial Neural Network -- 2.3. Performance Indices -- 3. Results and Discussion -- 3.1. Assessment of Data Sets -- 3.2. Estimation of Soil Moisture Using the RBFANN -- 4. Conclusions -- References. , Chapter 9: Temperature-Dependent Spectroscopic Dielectric Model at 0.05-16 GHz for a Thawed and Frozen Alaskan Organic Soil -- 1. Introduction -- 2. Soil Samples and Measurement Procedures -- 3. Concept of a Multirelaxation Spectroscopic Dielectric Model -- 4. Retrieving the Parameters of the Multirelaxation Spectroscopic Dielectric Model -- 5. The Temperature-Dependent Multirelaxation Spectroscopic Dielectric Model -- 6. Evaluation of the TD MRSDM -- 7. Conclusions -- References -- Chapter 10: Active and Passive Microwave Remote Sensing Synergy for Soil Moisture Estimation -- 1. Introduction -- 1.1. Measurement Spatial Resolution -- 1.2. Soil Moisture Sensitivity and Estimation Accuracy -- 2. SR CAP Soil Moisture Retrieval -- 3. MR CAP Soil Moisture Retrieval -- 3.1. Multi-Temporal and Multi-Scale Method -- 3.2. Probabilistic and Machine Learning Techniques -- 4. Forward Electromagnetic Scattering and Emission Model Considerations -- 5. Further Discussions -- References -- Chapter 11: Intercomparison of Soil Moisture Retrievals From In Situ, ASAR, and ECV SM Data Sets Over Different European ... -- 1. Introduction -- 2. Materials and Methods -- 2.1. In Situ Soil Moisture Observations -- 2.2. ECV Soil Moisture Observations -- 2.3. ASAR Soil Moisture Observations -- 2.4. Characterization of Errors -- 3. Results and Discussion -- 3.1. Time Series Temporal Analysis -- 3.2. Seasonal Analysis -- 4. Conclusions -- Acknowledgments -- References -- Section IV: Advanced Applications of Soil Moisture -- Chapter 12: Use of Satellite Soil Moisture Products for the Operational Mitigation of Landslides Risk in Central Italy -- 1. Introduction -- 2. PRESSCA Early Warning System -- 3. Case Study and Data Sets -- 3.1. Study Area and Ground Meteorological Observations -- 3.2. Satellite Soil Moisture Observations -- 4. Results and Discussion. , 4.1. Comparison Between Satellite, In Situ, and Modeled Soil Moisture Data -- 4.2. Impact of Soil Moisture Condition on Landslide Hazard -- 5. Conclusions and Future Perspectives -- Acknowledgments -- References -- Chapter 13: Remotely Sensed Soil Moisture as a Key Variable in Wildfires Prevention Services: Towards New Prediction Tool... -- 1. Introduction -- 2. Remotely Sensed Soil Moisture, Climate Change, and Fire Risk -- 2.1. Remote Sensing of the Earth's Soil Moisture -- 2.2. Remotely Sensed Soil Moisture, Climate Change, and Fire Risk -- 3. The Role of Soil Moisture in Forest Fires -- 3.1. Droughts and High Temperatures Lead to Extreme Forest Fires Events -- 3.2. Dead Fuels Moisture Is a Key Variable in Forest Fire Risk Indices -- 4. Linking Remotely Sensed Soil Moisture With Forest Fires Ignition and Propagation -- 5. Fire Risk Assessment in the Iberian Peninsula Using SMOS Data -- 5.1. New Fire Risk Maps Over the Iberian Peninsula Based on SMOS Data -- 5.2. Fire Risk Maps Availability and Operational Applications -- 6. Conclusions -- Acknowledgments -- References -- Chapter 14: Integrative Use of Near-Surface Satellite Soil Moisture and Precipitation for Estimation of Improved Irrigati... -- 1. Introduction -- 2. Material and Methods -- 2.1. Study Area -- 2.2. Ground Validation Data -- 2.3. Satellite Data -- 2.4. Numerical Modeling -- 2.4.1. Initial and Boundary Conditions -- 2.4.2. Soil Parameters -- 2.5. Evaluation Criteria -- 3. Results and Discussion -- 3.1. Comparison of TRMM Rainfall With Ground-Based Rainfall Measurements -- 3.2. Soil Hydraulic Parameters -- 3.3. Soil Moisture Calibration and Validation -- 4. Conclusions -- Acknowledgments -- References -- Chapter 15: A Comparative Study on SMOS and NLDAS-2 Soil Moistures Over a Hydrological Basin-With Continental Climate -- 1. Introduction -- 2. Data and Methodology. , 2.1. Study Area and Data Sets.

Note: Front Cover -- GPS and GNSS Technology in Geosciences -- GPS and GNSS Technology in Geosciences -- Contents -- Contributors -- Foreword -- I - General introduction to GPS/GNSS technology -- 1 - Introduction to GPS/GNSS technology -- 1. Background -- 2. Major segments of GPS -- 3. Functioning of GPS -- 3.1 Pseudorange -- 3.2 Carrier-phase measurement -- 3.3 GPS broadcast message, ephemeris, and almanac -- 4. GPS errors -- 4.1 Satellite and receiver clock errors -- 4.2 Multipath error -- 4.3 Ionospheric delay -- 4.4 Tropospheric delay -- 4.5 GPS ephemeris errors -- 4.6 Other limitations -- 5. GPS technologies -- 6. Global Navigation Satellite System -- 6.1 NAVSTAR -- 6.2 GLONASS -- 6.3 Galileo -- 6.4 Compass/BeiDou -- 6.5 Quasi-Zenith Satellite System -- 6.6 IRNSS/NavIC -- 7. Applications of GPS/GNSS -- 7.1 Navigation -- 7.2 Military services -- 7.3 Geodetic control surveys -- 7.4 Cadastral survey -- 7.5 Photogrammetry, remote sensing, and GIS -- 7.6 Ground truthing and validation -- 7.7 Disaster, response, and mitigation -- 7.8 Integration of GPS with mobile and google maps and GPS -- 8. Conclusions -- References -- Further reading -- 2 - Fundamentals of structural and functional organization of GNSS -- 1. GNSS structural organization -- 1.1 Introduction -- 1.2 Some notation and definitions -- 1.3 GNSS global coverage -- 1.4 GNSS regional coverage -- 1.5 Three main GNSS segments -- 1.6 Navigation using one satellite -- 1.7 2D navigation using two satellites -- 1.8 2D navigation using three satellite -- 1.8.1 The main idea of an iterative algorithm to compensate for the systematic error Δρ -- 1.8.2 Inaccurate vehicle clock synchronization -- 1.9 3D GNSS using N satellites -- 1.10 Summary and conclusions on the topic structural organization of GNSS -- 2. GNSS functional organization -- 2.1 GNSS functional principle -- 2.1.1 Systems of coordinates. , 2.1.2 Time systems -- 2.1.3 Factors affecting accuracy -- 2.1.4 GNSS accuracy improvement -- 2.2 GNSS signal structure, encoding, and frequency -- 2.3 Pseudoranges -- 2.4 GNSS positioning -- 2.5 Differential GNSS architecture -- 2.5.1 Local Area Differential GNSS positioning -- 2.5.2 Regional Area Differential GNSS positioning -- 2.5.3 Wide Area Differential GNSS positioning -- 2.6 Summary and conclusions on the topic functional organization of GNSS -- References -- References additional -- 3 - Security of GNSS -- 1. Introduction -- 2. GNSS interference -- 3. GNSS jamming -- 4. GNSS self-jamming -- 5. GNSS meaconing -- 6. GNSS spoofing -- 6.1 The cloud-based GNSS positioning -- 7. The cloud-based GNSS spoofing detection -- 8. Some notation and definitions for detection of spoofing -- 8.1 Dual-antenna spoofing detector -- 8.2 Measuring the distance between antennas in normal navigation mode -- 8.3 Measurement the distance between antennas in spoofing mode -- 8.3.1 The decisive rule 1 -- 8.4 Spoofing detection by the dispersion of the pseudorange difference of two antennas -- 8.4.1 The decisive rule 2 -- 8.4.2 Discussion of the decisive rules -- 8.5 Single-antenna spoofing detector -- 8.6 Measuring the distance between two positions of single antenna in normal mode -- 8.7 Measurement of spacing between two positions of single antenna in spoofing mode -- 8.8 The decisive rule -- 9. GNSS spoofer DIY (Do It Yourself) -- 10. GNSS self-spoofing -- 11. Briefly about antispoofing -- 12. Summary and conclusions -- 13. Postscript -- References -- II - GPS/GNSS concept and algorithms -- 4 - GNSS multipath errors and mitigation techniques -- 1. Introduction -- 2. Multipath errors and their characteristics -- 2.1 Code multipath error -- 2.2 Phase multipath error, SNR/CNR, and their relationship -- 2.2.1 Case 1: no (constructive) carrier-phase multipath effect. , 2.2.2 Case 2: maximum carrier-phase multipath effect -- 2.2.3 Case 3: no (destructive) carrier-phase multipath effect -- 2.3 Characteristics of multipath errors -- 3. Multipath mitigation techniques -- 3.1 Hardware-based multipath mitigation techniques -- 3.2 Software-based multipath mitigation techniques -- 3.2.1 Stochastic modeling -- 3.2.1.1 Elevation angle of satellite -- 3.2.1.2 Signal-to-noise ratio or carrier-to-noise ratio -- 3.2.2 Carrier-phase multipath reconstruction using the correlations of carrier-phase multipath with SNR -- 3.2.3 Sidereal day-to-day repeatability analysis -- 3.2.4 Antenna array -- 3.2.5 Ray tracing method -- 3.2.6 Comparison and discussion on software-based multipath mitigation techniques -- 4. Summary -- References -- 5 - Antenna technology for GNSS -- 1. Introduction -- 1.1 Line-of-Sight and reflected signals -- 1.2 Circular polarization-mitigating the multipath -- 2. Key antenna parameters for GNSS receivers -- 2.1 Radiation pattern and antenna gain -- 2.2 Axial ratio -- 2.3 Performance versus cost -- 3. Antennas for GNSS -- 3.1 Microstrip patch antennas -- 3.2 Sequentially rotated arrays -- 3.3 3D antenna structures: quadrifilar helix and electromagnetic dipole -- 3.4 Omnidirectional GNSS antennas -- 3.5 Choke rings, EBG, and low-angle signals -- 4. Final remarks -- References -- 6 - Probing the tropospheric water vapor using GPS -- 1. Introduction -- 1.1 Motivation for water vapor study -- 1.2 Navigation satellite system and GPS -- 2. GPS error sources -- 2.1 Atmospheric errors -- 3. Water vapor retrieval using GPS -- 3.1 Network GPS data processing -- 3.2 PPP GPS data processing -- 3.3 GPS datasets used for perceptible water vapor estimation -- 3.4 Computation of PWV from ZTD -- 3.5 Future scope and challenges -- 4. Conclusions -- Acknowledgment -- References -- 7 - Probing the upper atmosphere using GPS. , 1. Introduction -- 1.1 Quiescent ionosphere -- 1.2 Geomagnetic storms -- 1.3 Equatorial spread-F -- 1.4 Solar eclipse -- 1.5 Earthquake -- 2. Conclusions -- 3. Recommendations -- Acknowledgment -- References -- 8 - Video-based navigation using convolutional neural networks -- 1. Introduction -- 2. Proposed Super Navigation method -- 2.1 Navigation problem as an image classification problem -- 2.2 Collecting the data -- 2.3 Generating the Super Navigation image -- 2.3.1 Super Navigation image design option: number of frames -- 2.3.2 Super Navigation image design option: frame selection -- 2.4 Selecting the CNN model -- 2.5 Training the CNN model -- 2.6 Inferencing to predict the navigation direction -- 3. Implementation on low-power CNN accelerators -- 3.1 GnetFC model -- 4. Experimental results -- 4.1 Indoor navigation -- 4.1.1 Data collection and labeling -- 4.1.2 Generating Super Navigation images -- 4.1.3 Model training and accuracy comparison -- 4.2 Outdoor navigation -- 4.2.1 Data collection and labeling -- 4.2.2 Generating Super Navigation images -- 4.2.3 Model training and accuracy comparison -- 5. Conclusion and future work -- References -- III - Applications of GPS/GNSS -- 9 - GNSS monitoring natural and anthropogenic phenomena -- 1. Introduction -- 2. Earthquakes -- 3. Landslides monitoring -- 4. Crustal deformations -- 5. Challenges -- 6. Summary -- References -- 10 - Environmental sensing: a review of approaches using GPS/GNSS -- 1. Introduction -- 2. Data collection -- 2.1 Smartphones as sensors -- 2.2 Specialized devices -- 3. Data organization/analysis -- 4. Data visualization -- 5. Applications -- 5.1 Water and soil monitoring/pollution -- 5.2 Air monitoring/pollution -- 5.3 Noise monitoring/pollution -- 6. Discussion and concluding remarks -- References -- 11 - GNSS-derived data for the study of the ionosphere -- 1. The ionosphere. , 2. Ionosphere monitoring -- 3. Ionosphere modeling -- 4. TEC from GNSS -- 5. GNSS TEC for ionosphere studies -- 6. Final remarks -- Acknowledgement -- References -- 12 - Automatic pattern recognition and GPS/GNSS technology in marine digital terrain model -- 1. Introduction -- 2. Datasets description -- 3. Methodology implementation -- 4. The application of pattern recognition in marine pollution and structural studies -- 5. Conclusions -- Acknowledgment -- References -- 13 - Monitoring ionospheric scintillations with GNSS in South America: scope, results, and challenges -- 1. Introduction -- 1.1 Monitoring networks -- 2. Aspects of the climatology of ionospheric scintillations and their effects on GNSS-based applications in South America -- 2.1 Aspects of the climatology of scintillations in South America -- 2.1.1 Summary remarks of the climatology of scintillations in South America -- 2.2 Experimental setup to demonstrate effects of scintillations on field applications -- 3. Statistical modeling of amplitude scintillation -- 3.1 Discussions on application of statistical modeling of amplitude scintillation to mitigate effects of scintillations on GNSS ... -- 4. Low-cost instrumentation for ionospheric plasma bubbles monitoring -- 4.1 Experimental validation -- 4.2 Other initiatives for low-cost receiver design -- 5. Discussion -- 6. Final remarks & -- future outlook -- Acknowledgments -- References -- 14 - The versatility of GNSS observations in hydrological studies -- 1. Introduction -- 2. Materials and methods -- 2.1 Study area -- 2.2 Datasets -- 2.2.1 GNSS datasets -- 2.2.2 Global Land Data Assimilation System -- 2.2.3 GRACE mascon solution -- 2.2.4 Global Precipitation Climatology Centre -- 2.3 Methodology -- 2.3.1 Hydrologic loading -- 2.3.2 GNSS-derived integrated water vapor -- 2.3.3 GNSS-based drought indicator -- 3. Results. , 3.1 Land water storage prediction using observed radial displacements.