Radar Remote Sensing for Crop Biophysical Parameter Estimation (Springer Remote Sensing/Photogrammetry)

✍ Scribed by Dipankar Mandal, Avik Bhattacharya, Yalamanchili Subrahmanyeswara Rao

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 252
• Category: Library

✦ Synopsis

This book presents a timely investigation of radar remote sensing observations for agricultural crop monitoring and advancements of research techniques and their applicability for crop biophysical parameter estimation. It introduces theoretical background of radar scattering from vegetation volume and semi-empirical modelling approaches that are the foundation for biophysical parameter inversion. The contents will help readers explore the state-of-the-art crop monitoring and biophysical parameter estimation using approaches radar remote sensing. It is useful guide for academicians, practitioners and policymakers.

✦ Table of Contents

Foreword
Preface
Acknowledgements
Contents
About the Authors
1 Introduction
1.1 Background
1.2 Motivation
1.3 Key Objectives
1.4 Book Organization
References
2 Basic Theory of Radar Polarimetry
2.1 SAR Imaging Principles
2.2 Polarization of Electromagnetic Wave
2.3 Stokes Vector
2.4 Scattering Polarimetry
2.4.1 Scattering Matrix
2.4.2 Covariance and Coherency Matrices
2.4.3 Kennaugh Matrix
2.5 Polarimetric SAR Imaging Modes
2.5.1 Full-Pol or Quad-Pol Mode
2.5.2 Dual-Pol Mode in Linear Basis
2.5.3 Compact-Pol Mode
2.6 Radar Backscatter Coefficient
2.7 Target Decompositions Techniques
2.7.1 Full-Pol Decompositions
2.7.2 Compact-Pol Decomposition
2.7.3 Dual-Pol Decomposition
2.8 SAR Missions
2.9 Summary
References
3 Vegetation Models: Empirical and Theoretical Approaches
3.1 Vegetation Descriptors
3.1.1 Crop Phenology
3.1.2 Leaf Area Index (LAI) and Plant Area Index (PAI)
3.1.3 Crop Geometry
3.1.4 Vegetation Biomass
3.2 Evidence of Radar Response to Vegetation
3.3 Empirical Models
3.4 Theoretical Models
3.4.1 Wave Theory Approach
3.4.2 Radiative Transfer Theory Approach
3.5 Summary and Practical Considerations
References
4 Evolution of Semi-empirical Approach: Modeling and Inversion
4.1 Semi-empirical Models
4.1.1 Dielectric Slab Model
4.1.2 Water Cloud Model (WCM)
4.1.3 Modified Forms of Water Cloud Model
4.2 Theoretical Evaluation of WCM Parametrization
4.2.1 WCM Parameters for Spherical Particles
4.2.2 WCM Parameters for Non-spherical Particles
4.2.3 Validity of WCM with Respect to S2RT
4.3 Water Cloud Model Parameterization
4.4 Inverse Problem for Crop Parameter Estimation
4.4.1 Iterative Optimization (IO)
4.4.2 Look-Up Table (LUT) Search
4.4.3 Support Vector Regression (SVR)
4.4.4 Random Forest Regression (RFR)
4.5 Summary
References
5 Biophysical Parameter Retrieval Using Full- and Dual-Pol SAR Data
5.1 Emerging Trends in Model Inversion Approaches
5.2 Joint Estimation of Biophysical Parameterspg with MTRFR
5.2.1 Study Area and Data Set
5.2.2 Vegetation Modeling
5.2.3 Model Inversion with MTRFR
5.2.4 WCM Calibration Results
5.2.5 Validation of PAI and WB Estimates with MTRFR
5.2.6 Comparison of Inversion Methodologies
5.2.7 Relationship Between PAI and WB
5.3 Joint Estimation of Biophysical Parameters with MSVR
5.3.1 Study Area and Data Set
5.3.2 Multi-output Support Vector Regression (MSVR)-Based Inversion
5.3.3 Validation for Crop Biophysical Parameter Estimation
5.3.4 Comparison of Inversion Results for MSVR and SVR
5.4 Investigation of Inversion Methodologies: Cross-Site Experiment
5.4.1 Study Area and Data Set
5.4.2 Vegetation Modeling
5.4.3 Experiment Setting for Inter-comparison of WCM Inversion
5.4.4 WCM Calibration Results
5.4.5 LAI Estimation and Comparison of Inversion Approaches
5.4.6 Comparison of Memory-Time Performances
5.5 Crop Inventory Mapping with Dual-Pol SAR Data: GEE4Bio
5.5.1 Study Area and Data Set
5.5.2 Sentinel-1 Data Processing Chain in GEE for Biophysical Parameter Estimation
5.5.3 Validation of Biophysical Parameter Inversion and Mapping
5.6 AWS4AgriSARmap: Mapping Biophysical Parameter on AWS
5.6.1 Configuring SNAP Processing in AWS
5.6.2 Sentinel-1 Preprocessing with SNAP Graph Processing Tool (GPT)
5.6.3 PAI Map Generation
5.7 Summary
References
6 Biophysical Parameter Retrieval Using Compact-Pol SAR Data
6.1 Compact-Pol SAR Data for Crop Monitoring
6.2 Vegetation Modeling with Compact-Pol Descriptors
6.2.1 MWCM Formulation
6.3 Experiment Design for Inversion
6.4 Study Area and Data Sets
6.4.1 Vijayawada Test Site
6.4.2 Carman Test Site
6.5 Results and Discussion
6.5.1 Temporal Analysis of Scattering Powers
6.5.2 Vegetation Modeling
6.5.3 Validation of PAI Estimates for Rice
6.6 Validation of PAI Estimates for Soybean
6.7 Summary
References
7 Radar Vegetation Indices for Crop Growth Monitoring
7.1 State of the Art Polarimetric Radar Vegetation Indices
7.1.1 Radar Vegetation Index (RVI)
7.1.2 Scattering Power Decomposition-Based Vegetation Indices
7.2 Generalized Radar Vegetation Index (GRVI)
7.2.1 GRVI Formulation
7.2.2 Study Area and Data Set
7.2.3 Preprocessing SAR Data
7.2.4 Results and Discussion
7.3 Compact-Pol Radar Vegetation Index–CpRVI
7.3.1 Formulation of CpRVI
7.3.2 Study Area and Data Set
7.3.3 Results and Discussion
7.4 Dual-Pol Radar Vegetation Index–DpRVI
7.4.1 DpRVI Formulation
7.4.2 Study Area and Data Set
7.4.3 Data Analysis and Comparison
7.4.4 Results and Discussion
7.5 Comparison of DpRVI for Multi-frequency SAR Data
7.5.1 Study Area and Data Sets
7.5.2 Results and Analysis
7.6 Inter-comparison of Radar Vegetation Indices
7.6.1 Study Area and Data Sets
7.6.2 Comparison Results
7.7 Summary
References
8 Summary and Conclusions
8.1 Summary and Conclusions of the Research Work
8.2 Scope for Future Development and Perspectives
References
Index