Satellite-based flood assessment for extent and severity is very crucial input before, during, and after a flood event has occurred. Though optical remote sensing data has been widely used for flood hazard mapping, Synthetic Aperture Radar (SAR) data is preferred for detecting inundated areas and providing reliable information during a flood event due to its capability to operate in all weather and day/night time. Availability of cloud-free optical images during monsoon over north eastern India is a rarity. SAR data also has the advantage of detecting inundation under vegetated areas due to its penetration capabilities and sensitivity to soil moisture. The present study is an attempt to use SAR data for flood monitoring of the Kaziranga National Park (KNP) during monsoon, 2017. Every year, animals are washed away by floods and most of them migrate to higher grounds in order to escape from the rising water levels. Flooding events are common in the study area during the monsoon season due to high rainfall and its close proximity to the Brahmaputra River. Dual polarized (VV and VH) Sentinel-1 SAR images obtained for the entire monsoon period in 2017 were used to create inundation maps of the KNP. Two flood waves were observed in July and August, the second of which is considered to be one of the worst flooding events inundating most areas of the park. The use of SAR data for monitoring of flood events can be very crucial for identifying locations for building animal shelters and finding routes for rescue and relief operations during the disaster.

There are enormous advantages of a review article in the field of emerging technology like radar remote sensing applications in agriculture. This paper aims to report select recent advancements in the field of Synthetic Aperture Radar (SAR) remote sensing of crops. In order to make the paper comprehensive and more meaningful for the readers, an attempt has also been made to include discussion on various technologies of SAR sensors used for remote sensing of agricultural crops viz. basic SAR sensor, SAR interferometry (InSAR), SAR polarimetry (PolSAR) and polarimetric interferometry SAR (PolInSAR). The paper covers all the methodologies used for various agricultural applications like empirically based models, machine learning based models and radiative transfer theorem based models. A thorough literature review of more than 100 research papers indicates that SAR polarimetry can be used effectively for crop inventory and biophysical parameters estimation such are leaf area index, plant water content, and biomass but shown less sensitivity towards plant height as compared to SAR interferometry. Polarimetric SAR Interferometry is preferable for taking advantage of both SAR polarimetry and SAR interferometry. Numerous studies based upon multi-parametric SAR indicate that optimum selection of SAR sensor parameters enhances SAR sensitivity as a whole for various agricultural applications. It has been observed that researchers are widely using three models such are empirical, machine learning and radiative transfer theorem based models. Machine learning based models are identified as a better approach for crop monitoring using radar remote sensing data. It is expected that the review article will not only generate interest amongst the readers to explore and exploit radar remote sensing for various agricultural applications but also provide a ready reference to the researchers working in this field. Keywords-biophysical parameters retrieval; crop inventory; synthetic aperture radar (SAR); synthetic aperture radar interferometry (InSAR); SAR polarimetry (PolSAR); polarimetric SAR interferometry (PolInSAR).

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