Comprehensive overview of active and passive microwave remote sensing satellite sensors
-
Nitin Arora
Abstract
With technological advancements, microwave remote sensingmicrowave remote sensing has become indispensable for environmental monitoring and resource management. Real-time microwave remote sensing involves the use of radar and entails the transmission of signals to obtain highly accurate information on environmental parameters such as precipitation, soil moisturesoil moisture, plant structure, and terrain features for accurate management. On the other hand, active microwave remote sensing requires emitted energy that bounces back and is sensed by the system. In contrast, passive microwave remote sensing is based on natural microwave emissions from the Earth’s surfaces, which is very useful for measuring soil moisture and atmospheric conditions and is free from any interference from emitted signals. This chapter explains the technical prospects, working procedures, and comparatively better features of both approaches to highlight their importance in agricultural research supervision, resource management, and most importantly, sustainability. Readers will be able to learn and understand from real cases, applications, and real-world examples that demonstrate how these technologies work and can be applied to improve crop yields, optimize water use, and manage the effects of climate change.
Abstract
With technological advancements, microwave remote sensingmicrowave remote sensing has become indispensable for environmental monitoring and resource management. Real-time microwave remote sensing involves the use of radar and entails the transmission of signals to obtain highly accurate information on environmental parameters such as precipitation, soil moisturesoil moisture, plant structure, and terrain features for accurate management. On the other hand, active microwave remote sensing requires emitted energy that bounces back and is sensed by the system. In contrast, passive microwave remote sensing is based on natural microwave emissions from the Earth’s surfaces, which is very useful for measuring soil moisture and atmospheric conditions and is free from any interference from emitted signals. This chapter explains the technical prospects, working procedures, and comparatively better features of both approaches to highlight their importance in agricultural research supervision, resource management, and most importantly, sustainability. Readers will be able to learn and understand from real cases, applications, and real-world examples that demonstrate how these technologies work and can be applied to improve crop yields, optimize water use, and manage the effects of climate change.
Chapters in this book
- 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
Chapters in this book
- 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
