Radar Remote Sensing

Radar Remote Sensing

Applications and Challenges

1st Edition - August 27, 2022

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  • Editors: Prashant Srivastava, Dileep Gupta, Tanvir Islam, Dawei Han, Rajendra Prasad
  • Paperback ISBN: 9780128234570
  • eBook ISBN: 9780128235942

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Radar Remote Sensing: Applications and Challenges advances the scientific understanding, development, and application of radar remote sensing using monostatic, bistatic and multi-static radar geometry. This multidisciplinary reference pulls together a collection of the recent developments and applications of radar remote sensing using different radar geometry and platforms at local, regional and global levels.  Radar Remote Sensing is for researchers and practitioners with earth and environmental and meteorological sciences, who are interested in radar remote sensing in ground based scatterometer and SAR systems; air borne scatterometer and SAR systems; space borne scatterometer and SAR systems.

Key Features

  • Covers monostatic, bistatic and multi-static radar geometry
  • Features case studies, including experimental investigations, for practical application
  • Includes geophysical, oceanographical, and meteorological Synthetic Aperture Radar data


Postgraduates, PhD Research scholars, professors and scientists in the microwave remote sensing and space community. Hydrologists, modelers, agricultural scientists, ocean scientists, hydro-ecologists and meteorologists, environmental consultants, and oil and gas/mining industries

Table of Contents

  • Cover image
  • Title page
  • Table of Contents
  • Copyright
  • Contributors
  • Foreword
  • Section 1. Basis of radar remote sensing
  • Chapter 1. Introduction to RADAR remote sensing
  • 1. Brief history of RADAR remote sensing
  • 2. Optical versus RADAR remote sensing
  • 3. Fundamentals of RADAR
  • 4. Types of RADAR
  • 5. Operational frequencies of RADAR
  • 6. Backscatter mechanisms
  • 7. Radar image characteristics
  • 8. Application of microwave-based remote sensing
  • Chapter 2. Microwave components and devices for RADAR systems
  • 1. Introduction
  • 2. Transmission line
  • 3. Antennas
  • 4. Microwave filters
  • 5. Absorbers
  • 6. Microwave sources
  • 7. Mode converter
  • 8. Network analyzer
  • 9. Some other important microwave components
  • 10. Summary
  • Chapter 3. Theory of monostatic and bistatic radar systems
  • 1. Introduction
  • 2. Bistatic and monostatic radar system configuration
  • 3. Radar equation
  • 4. Radar cross-section per unit area/scattering coefficient system and measurement concepts
  • 5. Measurement procedures
  • 6. Procedure of bistatic specular scatterometer measurement and its calibration over natural terrain
  • 7. Summary
  • Chapter 4. Review of microwave fundamentals and its applications
  • 1. Introduction
  • 2. Theory of radiative transfer
  • 3. Electromagnetic interaction with discrete objects
  • 4. Interaction with inhomogeneous media
  • 5. Interaction with a smooth surface
  • 6. Interaction with rough surfaces
  • 7. Microwave interaction with natural surfaces
  • 8. Summary
  • Section 2. Conventional methods for radar remote sensing
  • Chapter 5. Comparative flood area analysis based on change detection and binarization methods using Sentinel-1 synthetic aperture radar data
  • 1. Introduction
  • 2. Study area
  • 3. Materials and methods
  • 4. Results
  • 5. Discussion
  • 6. Conclusions
  • Chapter 6. Subsurface feature identification using L Band Synthetic Aperture Radar (SAR) data over Jaisalmer, India
  • 1. Introduction
  • 2. Study area
  • 3. Data used
  • 4. Methodology
  • 5. Result
  • 6. Conclusion
  • Chapter 7. Terrestrial water budget through radar remote sensing
  • 1. Introduction
  • 2. Precipitation from radar remote sensing
  • 3. Soil moisture from radar remote sensing
  • 4. Water levels from radar altimetry
  • 5. Summary and conclusions
  • Chapter 8. Application of synthetic aperture radar remote sensing in forestry
  • 1. Introduction
  • 2. Polarimetric matrix generation
  • 3. Polarimetric speckle filtering
  • 4. Orientation angle correction
  • 5. Polarimetric decomposition
  • 6. Terrain correction
  • 7. Polarimetric classification
  • 8. Summary and final remarks
  • Chapter 9. Classification of Radar data using Bayesian optimized two-dimensional Convolutional Neural Network
  • 1. Introduction
  • 2. Background
  • 3. Dataset and ground data collection
  • 4. Dataset preparation for classification
  • 5. Methodology
  • 6. Results and discussion
  • 7. Conclusion
  • Chapter 10. Modeling and simulation of synthetic aperture radar dataset for retrieval of soil surface parameters
  • 1. Introduction
  • 2. Study area and collection of field data
  • 3. Collection and processing of satellite data
  • 4. Soil moisture modeling
  • 5. Results and discussion
  • 6. Conclusion
  • Chapter 11. Flood inundation mapping from synthetic aperture radar and optical data using support vector machine: a case study from Kopili River basin during Cyclone Amphan
  • 1. Introduction
  • 2. Study area
  • 3. Material and methods
  • 4. Result and discussion
  • 5. Conclusion
  • Chapter 12. Performance assessment of phased array type L-band Synthetic Aperture Radar and Landsat-8 used in image classification
  • 1. Introduction
  • 2. Datasets
  • 3. Methodology
  • 4. Results and discussion
  • 5. Conclusions and future work
  • Chapter 13. Evaluation of speckle filtering methods using polarimetric Sentinel-1A data
  • 1. Introduction
  • 2. Study site and data used
  • 3. Methodology
  • 4. Results and discussion
  • 5. Conclusion
  • Section 3. Advanced methods for radar remote sensing
  • Chapter 14. Emerging techniques of polarimetric interferometric synthetic aperture radar for scattering-based characterization
  • 1. Introduction
  • 2. Synthetic aperture radar polarimetry
  • 3. Polarimetric decomposition
  • 4. Polarization orientation angle
  • 5. Probability distributions
  • 6. Polarimetric synthetic aperture radar interferometry
  • 7. Polarimetric synthetic aperture radar interferometry coherence-based decomposition
  • 8. Polarimetric synthetic aperture radar interferometry decorrelation-based decomposition model
  • Chapter 15. Advanced method for radar remote sensing: circularly polarized synthetic aperture radar
  • 1. Introduction
  • 2. Circularly polarized scattering for remote sensing
  • 3. Specification of circular polarized synthetic aperture radar for microsatellite
  • 4. Radio-frequency system of circular polarized synthetic aperture radar
  • 5. Flight test and images
  • 6. Summary and future research
  • Chapter 16. A processing chain for estimating crop biophysical parameters using temporal Sentinel-1 synthetic aperture radar data in cloud computing framework
  • 1. Introduction
  • 2. Methodology
  • 3. Results and discussion
  • 4. Conclusion
  • Chapter 17. Fuzzy logic for the retrieval of kidney bean crop growth variables using ground-based scatterometer measurements
  • 1. Introduction
  • 2. Method and observations
  • 3. Fuzzy inference system
  • 4. Results and discussion
  • 5. Conclusion
  • Chapter 18. Monitoring tropical peatlands subsidence by time-series interferometric synthetic aperture radar (InSAR) technique
  • 1. Introduction
  • 2. Interferometry synthetic aperture radar for tropical peatlands
  • 3. Case study: Sintang, Indonesia
  • 4. Summary
  • Chapter 19. Toward a North American continental wetland map from space: wetland classification using satellite imagery and machine learning algorithms on Google Earth Engine
  • 1. Introduction
  • 2. Wetland classification systems
  • 3. Wetland field data
  • 4. Remote sensing data
  • 5. Cloud computing platforms and machine learning algorithms
  • 6. Wetland classification results for Canada
  • 7. Conclusion
  • Section 4. Future challenges in radar remote sensing
  • Chapter 20. Challenges in Radar remote sensing
  • 1. Introduction
  • 2. Conclusion
  • Chapter 21. The study of Indian Space Research Organization's Ku-band based scatterometer satellite (SCATSAT-1) in agriculture: applications and challenges
  • 1. Introduction
  • 2. Background of SCATSAT-1
  • 3. Applications in agriculture
  • 4. Summary and conclusions
  • Chapter 22. Radar remote sensing of soil moisture: fundamentals, challenges & way-out
  • 1. Introduction
  • 2. Effect of target parameters on SAR sensitivity toward soil moisture
  • 3. Addressing the effect of target parameters on SAR sensitivity toward soil moisture
  • 4. Effect of the sensor parameters on SAR sensitivity toward soil moisture
  • 5. To identify sensitive polarimetric parameters derived from fully and hybrid polarimetric SAR for soil moisture
  • 6. Addressing the various challenges involved in ground truth planning and ground truth data collection for radar remote sensing of soil moisture
  • 7. Addressing the challenges involved in development of a soil moisture retrieval model using radar remote sensing
  • 8. Addressing challenges involved in SAR data processing due to a huge data volume
  • 9. Addressing the issue of interval and scale of a soil moisture map
  • 10. Conclusion
  • Index

Product details

  • No. of pages: 484
  • Language: English
  • Copyright: © Elsevier 2022
  • Published: August 27, 2022
  • Imprint: Elsevier
  • Paperback ISBN: 9780128234570
  • eBook ISBN: 9780128235942

About the Editors

Prashant Srivastava

Prashant Kumar Srivastava is working at IESD, Banaras Hindu University as a faculty and is also a visiting scholar with the Hydrological Sciences division at NASA Goddard Space Flight Center. He received his doctoral degree from the Department of Civil Engineering, University of Bristol, Bristol, UK. He received several awards such as NASA Fellowship, University of Maryland Fellowship, Commonwealth Fellowship, Early Career Research Award (ECRA, DST, India), CSIR as well as UGC– JRF-NET (2005, 2006). He is leading number of projects funded from reputed agencies in India as well as world. He is also a collaborator with NASA JPL on SMAP soil moisture calibration and validation as well as Scatsat-1, NISAR, AVIRIS-NG missions of India. He has published 130+ peer-reviewed journals and seven books. He is also acting as Associate Editor of Journal of Hydrology, Hydrological Sciences Journal, Remote Sensing (MDPI), Journal of Earth System Science, Environment Development and Sustainability, Environmental Processes, Cogent Environmental Sciences, Co-editor-in-Chief Bulletin of Environmental and Scientific Research and Guest Editor of Physics and Chemistry of the Earth, IJSLUP and JSRS.

Affiliations and Expertise

Remote Sensing Laboratory, IESD, Banaras Hindu University, Varanasi, India

Dileep Gupta

Dileep Kumar Gupta received his doctoral degree from the Department of Physics, Indian Institute of Technology (Banaras Hindu University), Varanasi, India. Dr. Dileep received several reputed awards like UGC-NET, GATE, UGC research fellowship and DST international travel support. He has published 30+ research articles in different peer reviewed journals/conference proceedings/book chapters. He is an expert in algorithm development for soil moisture and crop variables retrieval using different ground based and space borne active and passive microwave sensor. He is also an expert of different machine learning algorithms for remote sensing data processing.

Affiliations and Expertise

IESD, Banaras Hindu University, India

Tanvir Islam

Dr. Tanvir Islam is presently with the NASA Jet Propulsion Laboratory, and specializes in remote sensing observations. Currently, he is engaged with the development of advanced microwave calibration and retrieval algorithms for NASA’s Earth observing missions. Prior to joining NASA/JPL in 2015, he was with the NOAA/NESDIS/STAR, and worked on the development of satellite remote sensing algorithms, with an emphasis on microwave variational inversion techniques (2013-2015). He also held visiting scientist positions at the University of Tokyo, as part of the NASA/JAXA precipitation measurement missions (PMM) algorithm development team, in 2012, and at the University of Calgary, in 2015. He received the Ph.D. degree in remote sensing from the University of Bristol, Bristol, UK, in 2012. Dr. Islam was the recipient of the Faculty of Engineering Commendation from the University of Bristol (nominated for a University Prize for his outstanding Ph.D. thesis), in 2012, the JAXA visiting fellowship award, in 2012, the CIRA postdoctoral fellowship award, in 2013, the Calgary visiting fellowship award, in 2015, and the Caltech postdoctoral scholar award, in 2015. He has served as a lead guest editor for a special issue on “Microwave Remote Sensing” for the Physics and Chemistry of the Earth (Elsevier), and currently serving on the editorial board of Atmospheric Measurement Techniques (EGU) and Scientific Reports (Nature). He has published four books and more than 60 peer-reviewed papers in leading international journals. His primary research interests include microwave remote sensing, radiometer calibration, retrieval algorithms, radiative transfer theory, data assimilation, mesoscale modeling, cloud and precipitation system, and artificial intelligence in geosciences.

Affiliations and Expertise

NASA Jet Propulsion Laboratory, Pasadena, CA, USA

Dawei Han

Dawei Han is professor of Hydroinformatics in the Department of Civil Engineering, University of Bristol. His research interests include hydrological modelling, Real-time Flood Forecasting, Flood Risk Assessment and Management, Climate Change impact, Remote Sensing and Geographic Information System, natural hazards, and microwave remote sensing applications

Affiliations and Expertise

Professor of Hydroinformatics, Department of Civil Engineering, University of Bristol, UK

Rajendra Prasad

Rajendra Prasad is a professor at Indian Institute of Technology (Banaras Hindu University), Varanasi, India. He earned his doctoral degree from the Department of Electronics Engineering, Indian Institute of Technology (Banaras Hindu University). He has published 60+ research articles in different peer reviewed journals/conference proceedings/book chapters. He is an expert in the bistatic scatterometer measurement and studying scattering mechanism of crop/vegetation and soil surface parameters and their monitoring using soft computational techniques. He also has experience with various remote sensing datasets like active and passive microwave, optical and hyperspectral for the accurate retrieval of soil moisture, and different vegetation properties.

Affiliations and Expertise

Professor, Indian Institute of Technology (Banaras Hindu University), Varanasi, India

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