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 -- Sensitivity Analysis in Earth Observation Modelling -- Sensitivity Analysis in Earth Observation Modelling -- Copyright -- Dedication -- Contents -- List of Contributors -- Preface -- ABOUT THE COVER -- 1 - INTRODUCTION TO SA IN EARTH OBSERVATION (EO) -- 1 - OVERVIEW OF SENSITIVITY ANALYSIS METHODS IN EARTH OBSERVATION MODELING -- 1. INTRODUCTION -- 1.1 DEFINING THE MODEL OUTPUTS AND INPUTS FOR SENSITIVITY ANALYSIS -- 1.1.1 Defining Factor (or Parametric) Uncertainty -- 2. LOCAL SENSITIVITY ANALYSIS -- 2.1 CORRELATION ANALYSIS -- 2.2 REGRESSION ANALYSIS -- 3. GLOBAL SENSITIVITY ANALYSIS -- 3.1 ONE-AT-A-TIME SENSITIVITY ANALYSIS METHODS -- 3.2 THE MORRIS METHOD FOR FACTOR SCREENING -- 3.3 VARIANCE-BASED SENSITIVITY ANALYSIS -- 3.4 SAMPLING METHODS FOR GLOBAL SENSITIVITY ANALYSIS -- 3.4.1 Random Sampling -- 3.4.2 Stratified Sampling and the Latin Hypercube -- 3.4.3 Sampling for Sensitivity Indices -- 3.5 SURROGATE MODELS FOR GLOBAL SENSITIVITY ANALYSIS -- 3.5.1 Generalized Linear Modeling -- 3.5.2 Neural Networks -- 3.5.3 Direct Sensitivity Analysis of Surrogate Models -- 3.6 POLYNOMIAL CHAOS -- 3.7 GAUSSIAN PROCESS AND BAYES LINEAR EMULATION -- 4. GRAPHICAL METHODS FOR GLOBAL SENSITIVITY ANALYSIS -- 4.1 SCATTER PLOTS -- 4.2 PLOTTING THE RESPONSE SURFACE -- 4.3 PLOTTING THE SENSITIVITY INDICES -- 5. CONCLUSIONS -- REFERENCES -- 2 - MODEL INPUT DATA UNCERTAINTY AND ITS POTENTIAL IMPACT ON SOIL PROPERTIES -- 1. INTRODUCTION -- 2. A WORLD OF MODELS - HOW CAN THEY BE CLASSIFIED? -- 3. CAN WE TRUST MODELS? - MODEL ACCURACY AND THEIR SENSITIVITY TO INPUT DATA UNCERTAINTY -- 4. SELECTING THE MOST APPROPRIATE MODEL -- 5. WHY AND HOW TO ACCOUNT FOR MODELING UNCERTAINTIES CAUSED BY DIFFERENT INPUT DATA SOURCES -- 6. ASSESSING SENSITIVITY OF ENVIRONMENTAL MODELS. , 7. HOW SOIL TEXTURE MEASURED WITH VISIBLE-NEAR-INFRARED SPECTROSCOPY AFFECTS HYDROLOGICAL MODELING: A CASE STUDY -- 7.1 STUDY SITES AND INSTRUMENTS -- 7.2 SOIL SAMPLES -- 7.3 CHEMOMETRICS -- 7.4 IMPACT OF CHEMOMETRIC METHOD ON SOIL PREDICTION -- 7.5 DIFFERENT INSTRUMENTS, DIFFERENT SOIL PREDICTIONS? WHAT WAS FINALLY THE BEST SOIL PREDICTION ACCURACY? -- 7.6 WHAT DOES THIS FINALLY MEAN FOR OUR ENVIRONMENTAL MODELING? -- 8. WHAT DID WE LEARN? -- REFERENCES -- 2 - LOCAL SA METHODS: CASE STUDIES -- 3 - LOCAL SENSITIVITY ANALYSIS OF THE LANDSOIL EROSION MODEL APPLIED TO A VIRTUAL CATCHMENT -- 1. INTRODUCTION -- 2. MATERIALS AND METHODS -- 2.1 MODEL DESCRIPTION -- 2.2 SENSITIVITY ANALYSIS -- 2.2.1 Parameters -- 2.2.2 Virtual Catchment -- 2.2.3 Sensitivity Calculation -- 3. RESULTS AND DISCUSSION -- 3.1 LINEAR HILLSLOPE -- 3.1.1 Aggregated Parameters -- 3.2 COMPLEX HILLSLOPES -- 4. CONCLUDING REMARKS -- Acknowledgments -- REFERENCES -- 4 - SENSITIVITY OF VEGETATION PHENOLOGICAL PARAMETERS: FROM SATELLITE SENSORS TO SPATIAL RESOLUTION AND TEMPORAL CO ... -- 1. INTRODUCTION -- 2. MONITORING VEGETATION PHENOLOGY -- 3. SENSITIVITY ANALYSIS -- 4. SENSITIVITY OF REMOTELY SENSED PHENOLOGICAL PARAMETERS -- 4.1 SATELLITE SENSOR -- 4.2 VEGETATION INDEX -- 4.3 SPATIAL RESOLUTION -- 4.4 COMPOSITE PERIOD, SMOOTHING, AND FILTERING -- 4.4.1 Composite Period -- 4.4.2 Smoothing Techniques -- 5. CASE STUDY -- 5.1 STUDY AREA -- 5.2 DATA AND METHODOLOGY -- 5.3 RESULTS AND DISCUSSION -- 6. CONCLUSION -- REFERENCES -- 5 - RADAR RAINFALL SENSITIVITY ANALYSIS USING MULTIVARIATE DISTRIBUTED ENSEMBLE GENERATOR∗ -- 1. INTRODUCTION -- 2. DATA AND METHODS -- 2.1 STUDY AREA AND DATA SOURCES -- 2.2 THE MULTIVARIATE DISTRIBUTED ENSEMBLE GENERATOR -- 2.3 THE XINANJIANG MODEL -- 3. METHODOLOGY -- 3.1 EXPERIMENTAL DESIGN -- 3.2 VERIFICATION METHOD -- 4. RESULTS AND DISCUSSION. , 4.1 IMPLEMENTATION OF ENSEMBLE FLOW GENERATION -- 4.2 IMPACT OF ERROR DISTRIBUTION ON MODEL OUTPUT -- 4.3 IMPACT OF SPATIOTEMPORAL DEPENDENCE ON MODEL OUTPUT -- 5. CONCLUSIONS -- REFERENCES -- 6 - FIELD-SCALE SENSITIVITY OF VEGETATION DISCRIMINATION TO HYPERSPECTRAL REFLECTANCE AND COUPLED STATISTICS -- 1. INTRODUCTION -- 2. BACKGROUND ON SPECTRAL DISCRIMINATION OF VEGETATION -- 2.1 PARAMETRIC VERSUS NONPARAMETRIC STATISTICAL TESTS -- 2.1.1 Other Discrimination Methods -- 2.2 UNALTERED VERSUS PROCESSED HYPERSPECTRAL REFLECTANCE -- 2.3 CASE STUDIES FOR EFFECTS OF TYPE OF REFLECTANCE AND STATISTICAL TEST ON THE VEGETATION DISCRIMINATION RESULTS -- 3. SENSITIVITY OF SPECTRAL DISCRIMINATION OF VEGETATION TO THE TYPE OF REFLECTANCE AND STATISTICAL TEST -- 3.1 HYPERSPECTRAL DATA AND METHOD DESCRIPTION -- 3.2 SENSITIVITY OF VEGETATION SPECTRAL DISCRIMINATION TO REFLECTANCE TYPE AND STATISTICAL METHOD -- 3.3 SENSITIVITY OF VEGETATION SPECTRAL DISCRIMINATION TO THE NUMBER OF OBSERVATIONS -- 4. FINAL REMARKS -- REFERENCES -- 3 - GLOBAL (OR VARIANCE)-BASED SA METHODS: CASE STUDIES -- 7 - A MULTIMETHOD GLOBAL SENSITIVITY ANALYSIS APPROACH TO SUPPORT THE CALIBRATION AND EVALUATION OF LAND SURFACE MODELS -- 1. INTRODUCTION -- 2. MODEL AND METHODS -- 2.1 REGIONAL SENSITIVITY ANALYSIS -- 2.2 VARIANCE-BASED SENSITIVITY ANALYSIS -- 2.3 THE PAWN DENSITY-BASED METHOD -- 2.4 THE JULES MODEL -- 2.5 THE SANTA RITA CREOSOTE STUDY SITE -- 2.6 EXPERIMENTAL SETUP: DEFINITION OF INPUT FACTORS AND OUTPUTS -- 2.7 DEFINITION OF THE RANGE OF VARIATION OF THE INPUT FACTORS -- 3. RESULTS -- 3.1 RESULTS OF REGIONAL SENSITIVITY ANALYSIS -- 3.2 RESULTS OF VARIANCE-BASED SENSITIVITY ANALYSIS -- 3.3 RESULTS OF PAWN -- 3.4 OVERALL SENSITIVITY ASSESSMENT FROM THE MULTIMETHOD APPROACH -- 4. CONCLUSIONS -- Acknowledgments -- REFERENCES. , 8 - GLOBAL SENSITIVITY ANALYSIS FOR SUPPORTING HISTORY MATCHING OF GEOMECHANICAL RESERVOIR MODELS USING SATELLITE I ... -- 1. INTRODUCTION -- 2. CASE STUDY -- 2.1 SURFACE DEFORMATION AT THE KB-501 WELL OF IN SALAH SITE -- 2.2 THREE-DIMENSIONAL HYDROMECHANICAL MODEL OF KB-501 -- 3. METHODS -- 3.1 VARIANCE-BASED GLOBAL SENSITIVITY ANALYSIS -- 3.2 PRINCIPLES OF METAMODELING -- 3.3 INTRODUCTION TO KRIGING METAMODEL -- 3.4 DESCRIPTION OF THE WORKFLOW -- 4. APPLICATION -- 4.1 REDUCING THE NUMBER OF UNCERTAINTY INPUT PARAMETERS -- 4.2 CALIBRATION OF THE RESERVOIR YOUNG'S MODULUS -- SUMMARY AND FUTURE WORK -- Acknowledgments -- REFERENCES -- 9 - ARTIFICIAL NEURAL NETWORKS FOR SPECTRAL SENSITIVITY ANALYSIS TO OPTIMIZE INVERSION ALGORITHMS FOR SATELLITE-BAS ... -- 1. INTRODUCTION -- 2. DATA AND METHODS -- 2.1 ARTIFICIAL NEURAL NETWORKS: OVERVIEW -- 2.1.1 Artificial Neural Networks for the Inversion of Satellite Measurements of Spectral Radiation for the Observation of the Ear ... -- 2.2 NEURAL NETWORK-BASED TECHNIQUES TO REDUCE THE INPUT VECTOR DIMENSIONALITY -- 2.2.1 Extended Pruning -- 2.2.2 Autoassociative Neural Networks -- 2.3 SAMPLE DATA SET -- 2.3.1 Sulfate Aerosols and Their Extinction Coefficient -- 2.3.2 Thermal Infrared Satellite Pseudo-Observations -- 3. RESULTS -- 3.1 TRAINING AND TESTING THE MAXIMUM DIMENSIONALITY NEURAL NETWORK -- 3.2 SELECTION OF THE INPUT WAVELENGTHS AND SPECTRAL SENSITIVITY ANALYSIS -- 3.3 COMPARING THE PERFORMANCES OF REDUCED DIMENSIONALITY NEURAL NETWORK -- 4. CONCLUSIONS -- Acknowledgments -- REFERENCES -- 10 - GLOBAL SENSITIVITY ANALYSIS FOR UNCERTAIN PARAMETERS, MODELS, AND SCENARIOS -- 1. INTRODUCTION -- 2. MORRIS METHOD -- 3. SOBOL' METHOD -- 3.1 FIRST-ORDER AND TOTAL-EFFECT SENSITIVITY INDICES -- 3.2 MONTE CARLO IMPLEMENTATION AND TWO APPROXIMATION METHODS. , 3.2.1 Sparse-Grid Collocation for Evaluating Mean and Variance -- 3.2.2 Distributed Evaluation of Local Sensitivity Analysis -- 4. SOBOL' METHOD FOR MULTIPLE MODELS AND SCENARIOS -- 4.1 HIERARCHICAL FRAMEWORK FOR UNCERTAINTY QUANTIFICATION -- 4.2 GLOBAL SENSITIVITY INDICES FOR SINGLE MODEL AND SINGLE SCENARIO -- 4.3 GLOBAL SENSITIVITY INDICES FOR MULTIPLE MODELS BUT SINGLE SCENARIO -- 4.4 GLOBAL SENSITIVITY INDICES FOR MULTIPLE MODELS AND MULTIPLE SCENARIOS -- 5. SYNTHETIC STUDY WITH MULTIPLE SCENARIOS AND MODELS -- 5.1 SYNTHETIC CASE OF GROUNDWATER REACTIVE TRANSPORT MODELING -- 5.2 UNCERTAIN SCENARIOS, MODELS, AND PARAMETERS -- 5.3 TOTAL SENSITIVITY INDEX FOR HEAD UNDER INDIVIDUAL MODELS AND SCENARIOS -- 5.4 TOTAL SENSITIVITY INDEX FOR HEAD UNDER MULTIPLE MODELS BUT INDIVIDUAL SCENARIOS -- 5.5 TOTAL SENSITIVITY INDEX FOR HEAD UNDER MULTIPLE MODELS AND MULTIPLE SCENARIOS -- 5.6 TOTAL SENSITIVITY INDEX FOR ETHENE CONCENTRATION -- 6. USING GLOBAL SENSITIVITY ANALYSIS FOR SATELLITE DATA AND MODELS -- 7. CONCLUSIONS AND PERSPECTIVES -- Acknowledgments -- REFERENCES -- 4 - OTHER SA METHODS: CASE STUDIES -- 11 - SENSITIVITY AND UNCERTAINTY ANALYSES FOR STOCHASTIC FLOOD HAZARD SIMULATION -- 1. INTRODUCTION -- 2. BASIC PRINCIPLES OF STOCHASTIC APPROACH TO FLOOD HAZARD -- 2.1 STOCHASTIC SIMULATION OF RESERVOIR INFLOWS -- 2.1.1 Storm Seasonality -- 2.1.2 Precipitation Magnitude-Frequency Relationship -- 2.1.3 Temporal and Spatial Distribution of Storms -- 2.1.4 Air Temperature and Freezing Level Temporal Patterns -- 2.1.5 The 1000-mb Air Temperature Simulation -- 2.1.6 Air Temperature Lapse Rates -- 2.1.7 Freezing Level -- 2.1.8 Watershed Model Antecedent Conditions Sampling -- 2.1.9 Initial Reservoir Level -- 2.2 SIMULATION OF RESERVOIR OPERATION-FLOOD ROUTING -- 2.3 SIMULATION PROCEDURE. , 3. UNCERTAINTY ASSOCIATED WITH STOCHASTICALLY DERIVED FLOOD QUANTILES.

Note: Cover -- Half Title -- Title Page -- Copyright Page -- Preface -- Table of Contents -- List of Contributors -- Section I: General -- 1: Introduction to Geospatial Technology for Water Resources -- Section II: Geographical Information System Based Approaches -- 2: GIS Supported Water Use Master Plan: A Planning Tool for Integrated Water Resources Management in Nepal -- 3: Spatial Integration of Rice-based Cropping Systems for Soil and Water Quality Assessment Using Geospatial Tools and Techniques -- 4: A Geographic Information System (GIS) Based Assessment of Hydropower Potential within the Upper Indus Basin Pakistan -- 5: Flood Risk Assessment for Kota Tinggi, Johor, Malaysia -- 6: Delineation and Zonation of Flood Prone Area Using Geo-hydrological Parameters: A Case Study of Lower Ghaghara River Valley -- 7: Geospatial Technology for Water Resource Development in WGKKC2 Watershed -- Section III: Satellite Based Approaches -- 8: Predicting Flood-vulnerability of Areas Using Satellite Remote-sensing Images in Kumamoto City, Japan -- 9: Validation of Hourly GSMaP and Ground Base Estimates of Precipitation for Flood Monitoring in Kumamoto, Japan -- 10: Appraisal of Surface and Groundwater of the Subarnarekha River Basin, Jharkhand, India: Using Remote Sensing, Irrigation Indices and Statistical Technique -- 11: Spatial and Temporal Variability of Sea Surface Height Anomaly and its Relationship with Satellite Derived Chlorophyll a Pigment Concentration in the Bay of Bengal -- 12: Monitoring Soil Moisture Deficit Using SMOS Satellite Soil Moisture: Correspondence through Rainfall-runoff Model -- Section IV: Artificial Intelligence and Hybrid Approaches -- 13: A Deterministic Model to Predict Frost Hazard in Agricultural Land Utilizing Remotely Sensed Imagery and GIS. , 14: A Statistical Approach for Catchment Calibration Data Selection in Flood Regionalisation -- 15: Prediction of Caspian Sea Level Fluctuations Using Artificial Intelligence -- 16: Spatio-temporal Uncertainty Model for Radar Rainfall -- 17: Soil Moisture Retrieval from Bistatic Scatterometer Measurements using Fuzzy Logic System -- Section V: Challenges in Geospatial Technology For Water Resources Development -- 18: Challenges in Geospatial Technology for Water Resources Development -- Index -- About the Editors.

Note: Cover -- Title Page -- Copyright Page -- CONTENTS -- CONTRIBUTORS -- PREFACE -- Section I Introduction -- Chapter 1 Concepts and Methodologies of Environmental Hazards and Disasters -- 1.1. INTRODUCTION -- 1.2. HYDROMETEOROLOGICAL HAZARDS IN AGRICULTURE -- 1.3. BIOPHYSICAL HAZARDS IN AGRICULTURE -- 1.4. SUMMARY -- REFERENCES -- Chapter 2 Indigenous Knowledge for Disaster Solutions in the Hilly State of Mizoram, Northeast India -- 2.1. INTRODUCTION -- 2.2. TRADITIONAL PRACTICES FOR LANDSLIDE PREVENTION -- 2.3. BAMBOO‐BASED HOUSING IN EARTHQUAKE PRONE ZONE -- 2.4. COMMUNITY-BASED EFFORTS TO COMBAT FOREST FIRES -- 2.5. LOCAL WATER HARVESTING SYSTEMS IN MIZORAM -- 2.6. CONCLUSIONS -- REFERENCES -- Chapter 3 Urban Risk and Resilience to Climate Change and Natural Hazards: A Perspective from Million‐Plus Cities on the Indian Subcontinent -- 3.1. BACKGROUND -- 3.2. URBAN SYSTEM IN CHANGING CLIMATIC CONDITIONS -- 3.3. URBAN HAZARDS AND RISK -- 3.4. URBAN RESILIENCE AND ADAPTATION -- 3.5. CONCLUSION -- ACKNOWLEDGMENTS -- REFERENCES -- Chapter 4 The Contribution of Earth Observation in Disaster Prediction, Management, and Mitigation: A Holistic View -- 4.1. INTRODUCTION -- 4.2. EARTH OBSERVATION (EO) FOR NATURAL DISASTER PREDICTION -- 4.3. EARTH OBSERVATION (EO) FOR NATURAL DISASTER MANAGEMENT AND MITIGATION -- 4.4. CONCLUSIONS -- ACKNOWLEDGMENTS -- REFERENCES -- Section II Atmospheric Hazards and Disasters -- Chapter 5 Tropical Cyclones Over the North Indian Ocean in Changing Climate -- 5.1. INTRODUCTION -- 5.2. DATA AND METHODOLOGY -- 5.3. RESULT AND ANALYSIS -- 5.4. CONCLUSIONS -- ACKNOWLEDGMENTs -- REFERENCES -- Chapter 6 Simulation of Intensity and Track of Tropical Cyclones Over the Arabian Sea Using the Weather Research and Forecast (WRF) Modeling System: with Different Initial Conditions (ICs) -- 6.1. INTRODUCTION. , 6.2. SYNOPTIC CONDITIONS ASSOCIATED WITH THE TROPICAL CYCLONES -- 6.3. DESCRIPTION OF THE WEATHER RESEARCH AND FORECAST MODEL AND NUMERICAL EXPERIMENTS -- 6.4. RESULTS AND DISCUSSION -- 6.5. CONCLUSIONS -- ACKNOWLEDGMENTS -- REFERENCES -- Chapter 7 Development of a Soft Computing Model from the Reanalyzed Atmospheric Data to Detect Severe Weather Conditions -- 7.1. INTRODUCTION -- 7.2. METHODOLOGY -- 7.3. RESULTS AND DISCUSSION -- 7.4. CONCLUSIONS -- ACKNOWLEDGMENTS -- REFERENCES -- Chapter 8 Lightning, the Global Electric Circuit, and Climate -- 8.1. INTRODUCTION -- 8.2. LIGHTNING DISCHARGES -- 8.3. LIGHTNING DISCHARGES AND THE GLOBAL ELECTRIC CIRCUIT -- 8.4. ATMOSPHERIC DISCHARGES AND CLIMATE -- 8.5. CONCLUSIONS AND RECOMMENDATION -- ACKNOWLEDGMENTS -- REFERENCES -- Chapter 9 An Exploration of the Panther Mountain Crater Impact Using Spatial Data and GIS Spatial Correlation Analysis Techniques -- 9.1. INTRODUCTION -- 9.2. MATERIALS -- 9.3. METHODOLOGY -- 9.4. RESULTS -- 9.5. DISCUSSION -- 9.6. CONCLUSIONS -- ACKNOWLEDGMENTS -- REFERENCES -- Section III Land Hazards and Disasters -- Chapter 10 Satellite Radar Interferometry Processing and Elevation Change Analysis for Geoenvironmental Hazard Assessment -- 10.1. INTRODUCTION -- 10.2. STUDY AREA -- 10.3. GEOENVIRONMENTAL MONITORING -- 10.4. RADAR INTERFEROMETRY -- 10.5. MULTITEMPORAL ELEVATIONS CHANGE ANALYSIS -- 10.6 CONCLUSIONS -- REFERENCES -- Chapter 11 Assessing the Use of Sentinel-2 in Burnt Area Cartography: Findings from a Case Study in Spain -- 11.1. INTRODUCTION -- 11.2. MATERIALS AND METHODS -- 11.3. RESULTS -- 11.4. DISCUSSION -- 11.5. CONCLUSIONS -- ACKNOWLEDGMENTS -- REFERENCES -- Chapter 12 Assimilating SEVIRI Satellite Observation into the Name-III Dispersion Model to Improve Volcanic Ash Forecast -- 12.1. INTRODUCTION -- 12.2. NAME DISPERSION MODEL -- 12.3. DATA AND METHODOLOGY. , 12.4. RESULTS AND DISCUSSIONS -- 12.5. CONCLUSIONS -- REFERENCES -- Chapter 13 Geoinformation Technology for Drought Assessment -- 13.1. INTRODUCTION -- 13.2. STUDY AREA -- 13.3. MATERIALS AND METHODS -- 13.4. METHODOLOGY -- 13.5. RESULTS AND DISCUSSIONS -- 13.6. CONCLUSIONS -- ACKNOWLEDGMENTS -- REFERENCES -- Chapter 14 Introduction to Landslides -- 14.1. INTRODUCTION -- 14.2. MORPHOLOGY OF LANDSLIDES -- 14.3. SLOPE FAILURE -- 14.4. CAUSES OF LANDSLIDES -- 14.5. TYPES OF LANDSLIDES -- 14.6. CLASSIFICATION OF LANDSLIDES -- 14.7. IDENTIFICATION OF LANDSLIDES -- 14.8. GROUND OBSERVATION -- 14.9. MEASUREMENT OF LANDSLIDES -- 14.10. LANDSLIDE HAZARD ZONATION MAPPING -- 14.11. LANDSLIDE HAZARD MITIGATION -- 14.12. SUMMARY -- REFERENCES -- Chapter 15 Probabilistic Landslide Hazard Assessment using Statistical Information Value (SIV) and GIS Techniques: A Case Study of Himachal Pradesh, India -- 15.1. INTRODUCTION -- 15.2. STATISTICAL INFORMATION VALUE (SIV) MODEL -- 15.3. LANDSLIDE CONDITIONING FACTORS -- 15.4. METHODOLOGY -- 15.5. RESULTS AND DISCUSSION -- 15.6. CONCLUSION -- REFERENCES -- Chapter 16 One-Dimensional Hydrodynamic Modeling of the River Tapi: The 2006 Flood, Surat, India -- 16.1. INTRODUCTION -- 16.2. STUDY AREA AND DATA USED -- 16.3. METHODOLOGIES -- 16.4. RESULTS -- 16.5. VALIDATION OF SIMULATED FLOW -- 16.6. SENSITIVITY OF STAGE RELATION WITH TIDAL CONDITION -- 16.7. RECOMMENDATIONS AND SUGGESTION FOR FLOOD PREVENTION -- 16.8. DISCUSSIONS -- 16.9. CONCLUSIONS -- ACKNOWLEDGMENTs -- REFERENCES -- Section IV Ocean Hazards and Disasters -- Chapter 17 Tropical Cyclone-Induced Storm Surges and Wind Waves in the Bay of Bengal -- 17.1. INTRODUCTION -- 17.2. METHODOLOGY -- 17.3. TROPICAL CYCLONE ACTIVITY OVER THE NORTH INDIAN OCEAN -- 17.4. STUDIES ON TROPICAL CYCLONE-INDUCED STORM SURGES FOR THE BAY OF BENGAL. , 17.5. CHARACTERISTICS OF OCEAN WIND WAVES AND THEIR ROLE DURING EXTREME WEATHER EVENTS -- 17.6. COUPLED WAVE‐HYDRODYNAMIC MODELS -- 17.7 STORM SURGE AND INUNDATION MODELING FOR CYCLONE THANE -- 17.8. STORM SURGE AND INUNDATION MODELING FOR CYCLONE AILA -- 17.9. COUPLED MODELING SYSTEM FOR CYCLONE PHAILIN -- 17.10. COUPLED MODELING SYSTEM FOR CYCLONE HUDHUD -- 17.11. SUMMARY AND CONCLUSIONS -- REFERENCES -- Chapter 18 Space-Based Measurement of Rainfall Over India and Nearby Oceans Using Remote Sensing Application -- 18.1. INTRODUCTION -- 18.2. RAINFALL ESTIMATION USING INFRARED OBSERVATIONS -- 18.3. RAINFALL ESTIMATION USING MICROWAVE OBSERVATIONS -- 18.4. RAINFALL ESTIMATION USING MERGED IR AND MICROWAVE OBSERVATIONS -- 18.5. CONCLUSION -- ACKNOWLEDGMENT -- REFERENCES -- Chapter 19 Modeling Tsunami Attenuation and Impacts on Coastal Communities -- 19.1. INTRODUCTION -- 19.2. MODELING TSUNAMIS -- 19.3. TSUNAMI DEFENSE STRUCTURES -- 19.4. TSUNAMI-BORNE DEBRIS -- 19.5. INFRASTRUCTURE -- 19.6. SUMMARY -- REFERENCES -- INDEX -- EULA.

Note: Front Cover -- Agricultural Water Management -- Agricultural Water Management -- Copyright -- Dedication -- Contents -- Contributors -- Preface -- I - Introduction -- 1 - Concepts and methodologies for agricultural water management -- 1. Introduction -- 2. Irrigation water management techniques -- 2.1 HYDRA -- 2.2 Decision support system for agrotechnology transfer (DSSAT) -- 2.3 Hydrus -- 2.4 MOPECO model -- 2.5 Linear optimisation model for efficient use of irrigation water -- 2.6 A network flow model for irrigation water management -- 2.6.1 Minimum cost network flow problem -- 2.7 Bayesian data-driven models for irrigation water management -- 2.8 CropSyst model -- 2.9 Water balance method -- 2.10 Irrigation practices -- 3. Conclusion -- References -- 2 - Traditional water management in India -- 1. Introduction -- 2. The role of traditional knowledge -- 3. Revisiting some traditional practices -- 3.1 Bamboo drip irrigation system -- 3.2 Zabo system -- 3.3 Katta -- 3.4 Madakas/Johads/Pemghara -- 3.5 Eri -- 3.6 Kuhl -- 3.7 Surangam -- 3.8 Jackwells -- 3.9 Ramtek model -- 3.10 Pat system -- 3.11 Zings -- 3.12 Khadins -- 3.13 Swing basket -- 3.14 Moat (rope-and-bucket lift) -- 3.15 Dhekli -- 3.16 Persian wheel -- 3.17 Don -- 3.18 Archimedean screw -- 3.19 Paddle wheel -- 4. Conclusion -- References -- 3 - Application of geospatial technology in agricultural water management -- 1. Introduction -- 1.1 Conventional geospatial techniques for water resource management -- 1.2 Advanced techniques -- 2. Way forward -- References -- II - Convetional technqiues -- 4 - Treated waste water as an alternative to fresh water irrigation with improved crop production -- 1. Introduction -- 2. Materials and methods -- 2.1 Experimental sites -- 2.2 Materials -- 2.3 Experimental design -- 2.4 Methods -- 2.4.1 Physicochemical analysis of soil and effluent. , 2.4.2 Heavy metal and biochemical analysis -- 2.4.3 Germination and vegetative growth study -- 3. Results and discussion -- 3.1 Physicochemical properties of soil -- 3.2 Physicochemical properties of water samples -- 3.3 Heavy metal analysis -- 3.4 Germination and vegetative growth of seedlings -- 3.5 Biochemical/nutritional studies -- 3.5.1 Protein and Ascorbic acid -- 3.5.2 Phosphorus (P) and Potassium (K) -- 3.5.3 Sodium (Na), Magnesium (Mg) and Calcium (Ca) -- 4. Conclusion -- Acknowledgements -- References -- 5 - Assessing the suitability of Ghaghra River water for irrigation purpose in India -- 1. Introduction -- 2. Study area -- 3. Material and methods -- 4. Results and discussion -- 4.1 Physicochemical characteristics (pH, EC, TDS and TH) -- 4.2 Irrigation indices -- 4.2.1 Sodium percentage (Na%) -- 4.2.2 United States Salinity Laboratory's (USSL's) diagram -- 4.2.3 Residual sodium carbonate (RSC) -- 4.2.4 Permeability index (PI) -- 4.2.5 Magnesium hazard (MH) -- 4.2.6 Kelly's index (KI) -- 5. Management plan -- 6. Conclusions -- Acknowledgements -- References -- 6 - Desiccation-tolerant rhizobacteria: a possible approach for managing agricultural water stress -- 1. Introduction -- 1.1 Drought -- 1.2 Effect of drought stress on plant growth and development -- 1.3 Concept behind drought adaptations -- 1.4 Bacterial response against water stress in soil -- 1.5 Bacterial-mediated drought tolerance in crop plants -- 2. Rhizobacteria -- 3. Plant-microbe interaction -- 4. Screening and characterization of rhizobacteria for water-stress management in agriculture system -- 5. Rhizobacteria as a tool for water-stress management in agriculture system -- 5.1 Improve root system architecture for water uptake -- 5.2 Improve shoot growth -- 5.3 Relative water content -- 5.4 Osmotic adjustment -- 5.5 Plant growth regulators -- 6. Conclusion. , Acknowledgement -- References -- 7 - Irrigation water quality appraisal using statistical methods and WATEQ4F geochemical model -- 1. Introduction -- 2. Materials and methods -- 2.1 Study area -- 2.2 Collection of sample and analysis -- 2.3 Irrigation indices -- 2.3.1 Sodicity -- 2.3.2 Sodium adsorption ratio (SAR) -- 2.3.3 Residual sodium carbonate (RSC)/Residual alkalinity (RA) -- 2.3.4 Percent sodium (%Na) or sodium hazard -- 2.3.5 Kelly's ratio (KR) or Kelly's index (KI) -- 2.3.6 Permeability index (PI) -- 2.3.7 Magnesium adsorption ratio (MAR) -- 2.3.8 Potential salinity (PS) -- 2.3.9 Chloroalkaline indices (CAI1 and CAI2) -- 2.3.10 Corrosivity ratio (CR) -- 2.3.11 Total dissolved salts (TDS) -- 2.3.12 Total hardness (TH) -- 2.3.13 Classification of groundwater -- 2.4 Statistical analysis -- 2.5 WATEQ4F model -- 3. Result and discussions -- 3.1 Hydrogeochemistry of water -- 3.2 Correlation matrix -- 3.3 Principal component analysis -- 3.4 Cluster analysis -- 3.5 Irrigation indices -- 3.6 Saturation index -- 4. Conclusion -- References -- III - Earth observation techniques -- 8 - Estimation of potential evapotranspiration using INSAT-3D satellite data over an agriculture area -- 1. Introduction -- 2. Materials and methodology -- 2.1 Study area -- 2.2 INSAT-3D -- 2.2.1 Instrument detail -- 2.2.1.1 Imager -- 2.2.1.2 Sounder -- 2.2.1.3 DRT (Data Relay Transponder) -- 2.2.1.4 SAS& -- R (Satellite Aided Search & -- Rescue) -- 2.3 Hamon's method -- 3. Performance analysis -- 4. Results and discussions -- 4.1 Evaluation of temperature data from INSAT-3D and ground base -- 4.2 Comparative assessment of evapotranspiration products -- 5. Conclusions -- References -- 9 - Estimation of evapotranspiration using surface energy balance system and satellite datasets -- 1. Introduction -- 2. Materials and methods -- 2.1 Study area. , 3. Data used and methodology -- 4. Theory of SEBS -- 4.1 Surface radiation balance equation -- 4.1.1 Surface albedo (α) -- 4.1.2 Incoming shortwave radiation (RS↓) -- 4.1.3 Outgoing long wave radiation (RL↑) -- 4.1.4 Incoming long wave radiation (RL↓) -- 4.2 Soil heat flux (G) -- 4.3 Sensible heat flux (H) -- 4.4 Latent heat flux (λ·ET), instantaneous ET (ETinst) -- 5. Results and discussion -- 5.1 Land-use/Land-cover classification (LULC) -- 5.2 Albedo -- 5.3 Normalized difference vegetation index (NDVI) -- 5.4 Land surface temperature (LST) -- 5.5 Net radiation flux (Rn) -- 5.6 Soil heat flux (G) -- 5.7 Instantaneous evapotranspiration (ET) -- 6. Conclusion -- References -- 10 - Large-scale soil moisture mapping using Earth observation data and its validation at selected agricultural sit ... -- 1. Introduction -- 2. Study area -- 3. Data and methodology -- 3.1 SMAP-derived soil moisture data products (SAC-ISRO) -- 3.2 In-situ soil moisture data -- 4. Validation of soil moisture (SAC-ISRO) products using in-situ data -- 4.1 Accuracy assessment based on metrics computation -- 4.2 Validation approach for coarse-scale satellite-derived data products based on scaling method -- 4.2.1 Cumulative Density Function (CDF) matching approach -- 5. Results and discussion -- 6. Conclusion -- Acknowledgements -- References -- 11 - A preliminary evaluation of the 'simplified triangle' with Sentinel-3 images for mapping surface soil moisture ... -- 1. Introduction -- 2. Materials -- 2.1 Study sites and in-situ data -- 2.2 Sentinel data: acquisition and pre-processing -- 3. Methods -- 3.1 Statistical analysis -- 4. Preliminary results -- 5. Discussion -- 6. Conclusions -- Acknowledgements -- References -- IV - Computational intelligence techniques -- 12 - Artificial neural network for the estimation of soil moisture using earth observation datasets -- 1. Introduction. , 2. Materials and methods -- 2.1 Data used -- 2.2 Performance indices -- 2.3 Artificial neural network architecture and modelling -- 3. Results and discussions -- 3.1 Optimization of ANN models -- 3.2 Performance analysis of ANN models -- 4. Conclusion -- Acknowledgement -- References -- 13 - Soil moisture retrieval from the AMSR-E -- 1. Introduction -- 2. Microwave theory -- 3. Surface roughness and vegetation effects -- 4. Atmospheric effects -- 5. Soil moisture retrieval for the Brue catchment using AMSR-E -- 5.1 Modelling approach -- 5.2 LPRM calibration based on event-based water balance equation -- 5.3 LPRM calibration based on continuous PDM storage predictions -- 6. Conclusions -- References -- 14 - Bistatic scatterometer for the retrieval of soil moisture -- 1. Introduction -- 2. Materials and methods -- 2.1 Bistatic scatterometer measurement and computation of bistatic scattering coefficients -- 2.2 Soil moisture measurement -- 2.2.1 Materials -- 2.2.2 Procedure -- 2.2.3 Computation of soil moisture -- 2.3 Soil surface roughness measurement -- 2.3.1 RMS surface height -- 2.3.2 Autocorrelation function -- 2.3.3 Correlation length -- 2.4 Statistical performance indices -- 3. Result and discussions -- 3.1 Soil surface roughness analysis -- 3.2 Angular response of bistatic scattering coefficient -- 3.3 Estimation of soil moisture using ANN -- 4. Conclusion -- References -- 15 - Soil water content influence on pesticide persistence and mobility -- 1. Introduction -- 2. Methods -- 3. Results and discussion -- 4. Conclusion -- Acknowledgements -- References -- V - Geospatial techniques -- 16 - Irrigation water demand estimation in Bundelkhand region using the variable infiltration capacity model -- 1. Introduction -- 2. Study site -- 3. Materials and methods -- 3.1 Datasets -- 3.1.1 VIC input datasets. , 3.1.2 ESA Climate Change Initiative soil moisture (CCI-SM).

Note: Intro -- Preface -- Contents -- Contributors -- About the Editors -- Part I: General -- Chapter 1: Computational Intelligence Techniques and Applications -- 1.1 Introduction -- 1.2 Neural Networks -- 1.2.1 Basic Principles -- 1.2.2 Applications -- 1.3 Evolutionary Computation -- 1.3.1 Basic Principles -- 1.3.2 Applications -- 1.4 Swarm Intelligence -- 1.4.1 Basic Principles -- 1.4.2 Applications -- 1.5 Artificial Immune Systems -- 1.5.1 Basic Principles -- 1.5.2 Applications -- 1.6 Fuzzy Systems -- 1.6.1 Basic Principles -- 1.6.2 Applications -- 1.7 Conclusions -- References -- Part II: Classical Intelligence Techniques in Earth and Environmental Sciences -- Chapter 2: Vector Autoregression (VAR) Modeling and Forecasting of Temperature, Humidity, and Cloud Coverage -- 2.1 Introduction -- 2.2 Materials and Methods -- 2.2.1 Data -- 2.2.2 Test of Stationarity -- 2.2.2.1 Augmented Dickey-Fuller Test -- 2.2.2.2 Phillips-Perron Test -- 2.2.2.3 Kwiatkowski-Phillips-Schmidt-Shin Test -- 2.2.3 Vector Autoregression Model -- 2.2.3.1 Selection of Variables Under Study -- 2.2.3.2 Making a Model of Order p (Arbitrary) -- 2.2.3.3 Determining the Value of Order p -- 2.2.3.4 Estimating Parameters -- 2.2.3.5 Diagnostic Checking -- 2.2.3.6 Cross Validity of the Fitted VAR Models -- 2.2.3.7 Forecasting -- 2.2.3.8 Forecast Error Variance Decomposition -- 2.2.3.9 Impulse Response Function -- 2.3 Results and Discussion -- 2.3.1 Descriptive Statistics -- 2.3.2 Tests for Stationarity -- 2.3.3 Selection of Variables Under Study -- 2.3.4 Selection of Order (p) -- 2.3.5 Estimation of Parameters -- 2.3.6 Diagnostic Checking -- 2.3.7 Cross Validity of the Fitted VAR Models -- 2.3.8 Forecast Error Variance Decomposition -- 2.3.9 Impulse Response Function -- 2.3.10 Forecasting Using VAR(8) Model -- 2.4 Conclusion -- References. , Chapter 3: Exploring the Behavior and Changing Trends of Rainfall and Temperature Using Statistical Computing Techniques -- 3.1 Introduction -- 3.2 Materials and Methods -- 3.2.1 K-Means Clustering -- 3.2.2 Mann-Kendall Trend Test -- 3.2.3 Seasonal Mann-Kendall Trend Test -- 3.2.4 Sen´s Slope Estimator -- 3.2.5 Naive Model -- 3.2.6 Autoregressive Integrated Moving Average Model -- 3.2.7 Random Walk with Drift -- 3.2.8 Theta Model -- 3.2.9 Wilcoxon Test -- 3.2.10 RMS Error -- 3.2.11 SMAPE -- 3.2.12 Materials Used During the Study -- 3.2.13 Data Used in the Study -- 3.3 Results and Discussion -- 3.3.1 Change in Temperature -- 3.3.2 Changes in Monthly Total Rainfall -- 3.3.3 Clustering of Rainfall -- 3.3.4 Comparison of Different Time Series Forecasting Models -- 3.4 Concluding Remarks -- References -- Chapter 4: Time Series Model Building and Forecasting on Maximum Temperature Data -- 4.1 Introduction -- 4.2 Materials and Methods -- 4.2.1 Time Series Data on Temperature -- 4.2.2 Testing Stationarity of the Time Series -- 4.2.3 Box-Jenkins Modeling Strategy -- 4.2.3.1 Identification of Order for the SARIMA Structure -- 4.2.3.2 Parameter Estimation of the SARIMA Model -- 4.2.3.3 Diagnostic Checking of the Fitted Model -- 4.2.3.4 Forecasting of the Study Variable -- 4.3 Results and Discussion -- 4.3.1 Testing Stationarity Status of Temperature -- 4.3.2 Model Building and Forecasting -- 4.3.2.1 Identification of Parameters Value of the SARIMA Structure -- 4.3.2.2 Estimation of Parameters for Selected SARMA Models -- 4.3.2.3 Diagnostic Checking for Estimated SARMA Models -- 4.3.2.4 Forecasting of Temperature Using Selected SARMA Models -- 4.4 Conclusion -- References -- Chapter 5: GIS Visualization of Climate Change and Prediction of Human Responses -- 5.1 Introduction -- 5.2 Method and Materials. , 5.3 Wet Bulb (Twb) and Globe (Tg) Temperature as Physical Model to Predict WBGT -- 5.4 WBGT and Tolerance Time a Tool for Climate Change Assessment -- 5.4.1 Tolerance Time -- 5.5 Discussion -- 5.6 Conclusion -- References -- Part III: Probabilistic and Transforms Intelligence Techniques in Earth and Environmental Sciences -- Chapter 6: Markov Chain Analysis of Weekly Rainfall Data for Predicting Agricultural Drought -- 6.1 Introduction -- 6.2 Study Area and Method -- 6.2.1 Climatic Condition of the Barind Region -- 6.2.2 Markov Chain Agricultural Drought Index -- 6.2.3 Test of Null Hypothesis -- 6.2.4 Mapping the Spatial Extent of Agricultural Drought -- 6.3 Results and Discussion -- 6.3.1 Temporal and Spatial Characteristics of Agricultural Drought -- 6.3.2 Probability of Wet Spell -- 6.3.3 Relative Frequency of Occurrence of Agricultural Drought -- 6.3.4 Result of Hypothesis Testing -- 6.4 Conclusions -- References -- Chapter 7: Forecasting Tropical Cyclones in Bangladesh: A Markov Renewal Approach -- 7.1 Introduction -- 7.2 Data Sources and Description -- 7.3 Methods -- 7.3.1 Markov Renewal Process and Its Properties -- 7.3.2 Likelihood Construction and Parameter Estimation -- 7.3.3 Cross-State Prediction -- 7.3.4 Asymptotic Behavior: Mean Recurrence Time -- 7.4 Results and Discussion -- 7.5 Conclusion -- References -- Chapter 8: Performance of Wavelet Transform on Models in Forecasting Climatic Variables -- 8.1 Introduction -- 8.2 Wavelet Transformation -- 8.3 Data Processing and Forecasting Framework -- 8.3.1 Approach-1 -- 8.3.2 Approach-2 -- 8.4 Comparison of Forecasting Performance -- 8.5 Empirical Results -- 8.5.1 Forecasting Based on Original Series -- 8.5.2 Forecasting Based on Decomposed Series Using Wavelet Transformation -- 8.5.2.1 Approach-1 -- 8.5.2.2 Approach-2 -- 8.5.3 Comparison -- 8.6 Conclusions -- References. , Chapter 9: Analysis of Inter-Annual Climate Variability Using Discrete Wavelet Transform -- 9.1 Introduction -- 9.2 Multiband Decomposition of Climate Signals -- 9.2.1 Fourier-Based Filter Bank and Its Limitations -- 9.2.2 Wavelet-Based Filter Bank -- 9.3 Annual Cycle Extraction -- 9.4 Results and Discussion -- 9.5 Conclusions -- References -- Part IV: Hybrid Intelligence Techniques in Earth and Environmental Sciences -- Chapter 10: Modeling of Suspended Sediment Concentration Carried in Natural Streams Using Fuzzy Genetic Approach -- 10.1 Introduction -- 10.2 Methods -- 10.2.1 Fuzzy Logic -- 10.2.2 Genetic Algorithm -- 10.2.3 Fuzzy Genetic Approach -- 10.2.4 Adaptive Network-Based Fuzzy Inference System -- 10.2.5 Neural Networks -- 10.2.6 Sediment Rating Curve -- 10.3 Applications and Results -- 10.4 Conclusion -- References -- Chapter 11: Prediction of Local Scour Depth Downstream of Bed Sills Using Soft Computing Models -- 11.1 Introduction -- 11.2 Material and Methods -- 11.2.1 Physical Definition of Scouring -- 11.2.2 Scouring Prediction at Bed Sills -- 11.2.2.1 Empirical Equations -- 11.2.2.2 Genetic Algorithm -- 11.2.2.3 Gene Expression Programming -- 11.2.2.4 M5 Tree Model -- 11.2.2.5 Data Set -- 11.2.2.6 Selection of Input and Output Parameters -- 11.2.3 Experimental Setup -- 11.3 Results -- 11.3.1 GA Model -- 11.3.2 GEP Model -- 11.3.3 M5 Tree Equations -- 11.4 Performance Analysis of Results -- 11.5 Conclusions -- References -- Chapter 12: Evaluation of Wavelet-Based De-noising Approach in Hydrological Models Linked to Artificial Neural Networks -- 12.1 Introduction -- 12.2 Study Area and Data -- 12.3 Materials and Methods -- 12.3.1 Wavelet De-noising Procedure -- 12.3.2 Artificial Neural Network and Efficiency Criteria -- 12.4 Results and Discussion -- 12.4.1 Ad Hoc ANN -- 12.4.1.1 Stream-Flow Forecasting -- 12.4.1.2 SSL Forecasting. , 12.4.2 Hybrid ANN-Wavelet -- 12.4.2.1 Wavelet-Based De-noising Approach -- 12.4.2.2 Stream-Flow Forecasting -- 12.4.2.3 SSL Forecasting -- 12.4.3 Comparison of Models -- 12.5 Concluding Remarks -- References -- Chapter 13: Evaluation of Mathematical Models with Utility Index: A Case Study from Hydrology -- 13.1 Introduction -- 13.2 Study Area and Data Used -- 13.3 Models -- 13.3.1 LLR Model -- 13.3.2 ANN and ANFIS Models -- 13.3.3 Support Vector Machines -- 13.3.4 Wavelet Hybrid Models -- 13.3.5 Index of Model Utility (U) -- 13.4 Results and Discussions -- 13.4.1 Comparison of Data Models Using Utility Index -- 13.4.2 Comparison of Data Models Using Statistical Indices -- 13.5 Conclusions -- References -- Index.

Note: Intro -- Foreword -- Contents -- About the Editors -- Introduction -- Part IClassical Remote Sensing Applications -- 1 Remote Sensing-Based Determination of Conifer Needle Flushing Phenology over Boreal-Dominant Regions -- Abstract -- 1…Introduction -- 2…Materials and Methods -- 2.1 Description of the Study Area and Data Requirements -- 2.2 Generation of AGDD Maps -- 2.3 Determination of AGDD and NDWI Thresholds for CNG Occurrence -- 2.4 Integration of both AGDD and NDWI Threshold for CNG Occurrence -- 2.5 Mapping of CNF Using the Best Prediction Criteria -- 3…Results and Discussion -- 3.1 Determination of AGDD Threshold for CNF Occurrence -- 3.2 Determination of NDWI Thresholds for CNF Occurrence -- 3.3 Integration of both AGDD and NDWI Thresholds -- 3.4 Spatial Dynamics of CNF Across the Landscape -- 4…Concluding Remarks -- Acknowledgements -- References -- 2 Information System for Integrated Watershed Management Using Remote Sensing and GIS -- Abstract -- 1…Introduction -- 1.1 Why Management of Natural Resources on Watershed Basis? -- 1.2 Role of Geographic Information System (GIS) and Remote Sensing (RS) in Watershed Management -- 1.3 Decision Support System in Watershed Management -- 1.4 Need for Advanced and Augmented Techniques for Watershed Management -- 2…Study Area -- 3…Conceptual Design -- 3.1 Data Used -- 3.2 Tools and Technologies Used -- 3.3 System Architecture -- 4…Online Generation and Implementation of WATMIS -- 5…Conclusion -- References -- 3 Sensitivity Exploration of SimSphere Land Surface Model Towards Its Use for Operational Products Development from Earth Observation Data -- Abstract -- 1…Introduction -- 2…Sensitivity Analysis: An Overview -- 3…Materials and Methods -- 3.1 SimSphere Model -- 3.2 The Bayesian GSA Method -- 3.3 BACCO Implementation -- 4…Results -- 4.1 Emulator Validation -- 4.2 SA Results -- 5…Discussion. , 6…Conclusions -- Acknowledgments -- References -- 4 Remote Estimation of Land Surface Temperature for Different LULC Features of a Moist Deciduous Tropical Forest Region -- Abstract -- 1…Introduction -- 2…Materials and Methods -- 2.1 Study Area and Datasets -- 2.2 Image Interpretation for LULC -- 2.3 Surface Temperature Estimation -- 3…Results and Discussions -- Acknowledgments -- References -- 5 Geospatial Strategy for Estimation of Soil Organic Carbon in Tropical Wildlife Reserve -- Abstract -- 1…Introduction -- 2…Materials and Methods -- 2.1 Study Area -- 2.2 Data Used -- 2.3 Image Interpretation -- 2.4 Estimation of Soil Organic Carbon -- 3…Results and Discussion -- 3.1 Land Use Land Cover Classification -- 3.2 Bare Soil Index -- 3.3 Soil Type Map -- 3.4 Soil Organic Carbon and Regression Analysis -- 4…Conclusion -- Acknowledgment -- References -- Part IIAdvanced Remote Sensing Applications -- 6 A Comparative Assessment Between the Application of Fuzzy Unordered Rules Induction Algorithm and J48 Decision Tree Models in Spatial Prediction of Shallow Landslides at Lang Son City, Vietnam -- Abstract -- 1…Introduction -- 2…Study Area and Spatial Database -- 2.1 Study Area Characteristics -- 2.2 Spatial Database -- 2.2.1 Landslide Inventory -- 2.2.2 Digital Elevation Model and Derivatives -- 2.3 Lithology and Distance to Faults -- 2.4 Land Use and Soil Type -- 3…Methodology -- 3.1 Training and Validation Dataset -- 3.2 Fuzzy Unordered Rules Induction Algorithm -- 3.3 Decision Tree -- 3.4 Bagging -- 3.5 Generation of Landslide Susceptibility Maps -- 4…Validation and Comparison of Landslide Susceptibility Models -- 4.1 Model Performance and Evaluation -- 4.2 Model Validation -- 4.3 Relative Contribution of the Conditioning Factors -- 5…Conclusion -- Acknowledgement -- References. , 7 Application of Geo-Spatial Technique for Flood Inundation Mapping of Low Lying Areas -- Abstract -- 1…Introduction -- 2…Study Area and Datasets -- 2.1 Tapi Basin -- 2.2 Surat City -- 2.2.1 Geology and Soil Conditions -- 2.2.2 Ground Water Table -- 2.2.3 Climate -- 2.2.4 Temperature and Rainfall -- 2.2.5 Demography/Population in the Study Area -- 2.3 Hydraulic Structures at LTB -- 2.3.1 Ukai Dam -- 2.3.2 Kakrapar Weir -- 2.3.3 Singanpur Weir -- 2.4 Flood Event 2006 -- 2.5 Data Collection -- 3…Methodology -- 4…Results and Discussion -- 5…Validation -- 6…Conclusion -- Acknowledgments -- References -- 8 Spatial Variations in Vegetation Fires and Carbon Monoxide Concentrations in South Asia -- Abstract -- 1…Introduction -- 2…Data Sets and Methodology -- 2.1 Vegetation Fires -- 2.2 MOPITT CO Retrievals -- 2.3 Aerosol Optical Depth (AOD) and Aerosol Small Mode Fraction (SMAF) -- 2.4 Spatial Gridding, Ordinary Linear Regression (OLR) and Locally Weighted regression -- 3…Results and Discussion -- Acknowledgements -- References -- 9 Land Use Fragmentation Analysis Using Remote Sensing and Fragstats -- Abstract -- 1…Introduction -- 2…Study Area -- 3…Materials and Methods -- 3.1 Classification of Satellite Data -- 3.2 Fragmentation Analysis -- 4…Result and Discussion -- 4.1 Land Use and Land Cover Distribution -- 4.2 Landscape Level Metrics -- 4.3 Class Level Metrics -- 4.4 Class Level Metric Analysis -- 4.5 Land Fragmented Class Analysis -- 4.6 Estimating Effects on Water Quality -- 5…Conclusion -- Acknowledgments -- References -- 10 Chlorophyll Retrieval Using Ground Based Hyperspectral Data from a Tropical Area of India Using Regression Algorithms -- Abstract -- 1…Introduction -- 2…Materials and Methods -- 2.1 Field Experiment and Canopy Spectral Measurements -- 2.2 Data Analysis -- 2.3 Chlorophyll Estimation -- 2.4 Statistical Analysis. , 2.4.1 Descriptive Statistics -- 2.4.2 Regression Analysis -- 2.4.3 Paired t Test -- 3…Results and Discussion -- 3.1 Descriptive Statistics of the Data -- 3.2 Continuum Removal and REIP Evaluation -- 3.3 Calculation of Vegetation Indices -- 3.4 Relationship of Chlorophyll with Vegetation Indices -- 3.5 Sensitivity Analysis of the Empirical Relationships Developed -- 4…Conclusion -- Acknowledgment -- References -- 11 Remote Sensing Based Identification of Painted Rock Shelter Sites: Appraisal Using Advanced Wide Field Sensor, Neural Network and Field Observations -- Abstract -- 1…Introduction -- 2…Material and Methodology -- 2.1 Study Area -- 2.2 Satellite and GIS Datasets -- 2.3 Classifiers/Algorithms Implemented in this Study -- 2.3.1 Artificial Neural Network (ANN) -- 2.3.2 Maximum Likelihood Classification (MLC) -- 3…Accuracy Assessment of the Classified Images -- 4…Results and Discussion -- 4.1 Land Covers Distribution and Accuracy Assessment -- 4.2 Interpretation of the Results and Archaeological Relevance -- 5…Conclusion -- Acknowledgment -- References.

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.