Synopsis
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.
- 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
About the Authors
Prashant K. Srivastava is working at IESD, Banaras Hindu University, as a faculty and was affiliated with Hydrological Sciences, NASA Goddard Space Flight Center, as research scientist on SMAP satellite soil mois ture retrieval algorithm development, instrumentation, and simulation for various applications. He received his PhD degree from the Department of Civil Engineering, University of Bristol, Bristol, United Kingdom. Prashant was the recipient of several awards such as NASA Fellowship, USA; University of Maryland Fellowship, USA; Commonwealth Fellowship, UK; Early Career Research Award (ECRA, DST, India), CSIR, as well as UGC JRF-NET (2005, 2006). He is leading a number of projects funded from reputed agencies in India as well as world. He was also a collaborator with NASA JPL on SMAP soil mois ture calibration and validation as well as Scatsat-1, NISAR, AVIRIS-NG missions of India. Prashant made more than 200+ publications in peer-reviewed journals and published 14 books with reputed publishing house such as Springer, Taylor and Francis, AGU-Wiley, and Elsevier, and several book chapters with good cita tion index. He presented his work in several conferences and workshops and is acting as a convener for the last few years in EGU, Hydroinformatics (HIC), and other conferences. He is also acting as Regional Editor Asia-Geocarto International (T & F), Associate Editor-Journal of Hydrology (Elsevier), GIScience and Remote Sensing (T & F), Remote Sensing Applications: Society and Environment (Elsevier), Sustainable Environment (T & F), Water Resources Management (Springer), Frontiers Remote Sensing, Associate Editor- Remote Sensing-MDPI, Associate Editor- Environment, Development and Sustainability (Springer), Environmental Processes (Springer), Bull of Env and Sci Res.
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.
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.
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
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.
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