Revolutionizing agricultural and environmental analytics with synthetic aperture radar (SAR): innovations, challenges, and future directions
-
Neha Bhati
,
Narayan Vyas
,
Rajesh Yadav
,
Rakesh Sharma
Abstract
Synthetic aperture radar (SAR) has made significant advances in exploring different scattering mechanisms and target geometries, as its evolution has opened numerous opportunities to enhance agricultural classificationagricultural classification and environmental monitoring. SAR is independent of weather conditions and natural light, guaranteeing high-quality images at any time and playing a crucial role in effective monitoring. This chapter explains important methods such as Polarimetric SAR (PolSAR), Interferometric SAR (InSAR), and multi-temporal SAR techniques, used to improve crop classification, soil-moisture estimation, and crop-disease detection. Moreover, the synergistic role of SAR with machine learning (ML) and artificial intelligence (AI) is discussed highlighting how it enables multi-dimensional data fusion, predictive analysis, and improved decision-makingdecision-making outcomes. It also explores the application of SAR in land-use change, urbanization, and environmental degradation. The chapter proposes future research directions, such as combining SAR dataSAR data with optical and thermal imagery and downsizing SAR systems to enable their operation on a larger scale at a reasonable cost.
Abstract
Synthetic aperture radar (SAR) has made significant advances in exploring different scattering mechanisms and target geometries, as its evolution has opened numerous opportunities to enhance agricultural classificationagricultural classification and environmental monitoring. SAR is independent of weather conditions and natural light, guaranteeing high-quality images at any time and playing a crucial role in effective monitoring. This chapter explains important methods such as Polarimetric SAR (PolSAR), Interferometric SAR (InSAR), and multi-temporal SAR techniques, used to improve crop classification, soil-moisture estimation, and crop-disease detection. Moreover, the synergistic role of SAR with machine learning (ML) and artificial intelligence (AI) is discussed highlighting how it enables multi-dimensional data fusion, predictive analysis, and improved decision-makingdecision-making outcomes. It also explores the application of SAR in land-use change, urbanization, and environmental degradation. The chapter proposes future research directions, such as combining SAR dataSAR data with optical and thermal imagery and downsizing SAR systems to enable their operation on a larger scale at a reasonable cost.
Kapitel in diesem Buch
- Frontmatter I
- Preface V
- Contents VII
- Integrating Sentinel-1 satellite data with machine learning for land use classification 1
- A systematic review of deep learning techniques in microwave remote sensing: challenges, applications, and future directions 17
- Fundamentals of active and passive microwave remote sensing: principles and applications 31
- Comprehensive overview of active and passive microwave remote sensing satellite sensors 55
- Essentials of RADAR remote sensing and AI integration 73
- Fusion of scatterometer and optical remote sensing: enhanced classification and change detection 91
- AI-powered urban infrastructure monitoring using RADAR-based remote sensing 103
- Fusion of the optical and microwave images for cloud removal 123
- Integrating AI in RADAR remote sensing: enhancing data processing, interpretation, and decision-making 141
- Revolutionizing precision agriculture: the synergy of RADAR, Internet of things (IoT), and satellite technology 155
- Integrating AI with RADAR remote sensing: applications in disaster mitigation, defense, and climate change 171
- Computational techniques in RADAR remote sensing from a machine and deep learning perspective 189
- Deep learning-based water body segmentation in SAR imagery: enhancing accuracy with CNN-U-Net and EfficientNet 205
- Artificial intelligence in RADAR remote sensing: advances, challenges, and future prospects 215
- Revolutionizing agricultural and environmental analytics with synthetic aperture radar (SAR): innovations, challenges, and future directions 229
- Editors’ biographies
- Index 247
Kapitel in diesem Buch
- Frontmatter I
- Preface V
- Contents VII
- Integrating Sentinel-1 satellite data with machine learning for land use classification 1
- A systematic review of deep learning techniques in microwave remote sensing: challenges, applications, and future directions 17
- Fundamentals of active and passive microwave remote sensing: principles and applications 31
- Comprehensive overview of active and passive microwave remote sensing satellite sensors 55
- Essentials of RADAR remote sensing and AI integration 73
- Fusion of scatterometer and optical remote sensing: enhanced classification and change detection 91
- AI-powered urban infrastructure monitoring using RADAR-based remote sensing 103
- Fusion of the optical and microwave images for cloud removal 123
- Integrating AI in RADAR remote sensing: enhancing data processing, interpretation, and decision-making 141
- Revolutionizing precision agriculture: the synergy of RADAR, Internet of things (IoT), and satellite technology 155
- Integrating AI with RADAR remote sensing: applications in disaster mitigation, defense, and climate change 171
- Computational techniques in RADAR remote sensing from a machine and deep learning perspective 189
- Deep learning-based water body segmentation in SAR imagery: enhancing accuracy with CNN-U-Net and EfficientNet 205
- Artificial intelligence in RADAR remote sensing: advances, challenges, and future prospects 215
- Revolutionizing agricultural and environmental analytics with synthetic aperture radar (SAR): innovations, challenges, and future directions 229
- Editors’ biographies
- Index 247
