Soil moisture retrieval from dual-polarized Sentinel-1 SAR data over agricultural regions using a water cloud model.

The accurate retrieval of soil moisture plays a pivotal role in agriculture, particularly in effective irrigation water management, as it significantly affects crop growth and yield. The present study mainly focuses on the robustly investigated capability of dual-polarized Sentinel-1 SAR-derived vegetation descriptors in the water cloud model (WCM) in surface soil moisture (SSM) retrieval over wheat crops. The vegetation descriptors used in the study are radar vegetation index (RVI), backscattering ratio, polarimetric radar vegetation index (PRVI), dual polarization SAR vegetation index (DPSVI), and dual polarimetric radar vegetation index (DpRVI).

Comprehensive analysis of spatiotemporal variability of rainfall-based extremes and their implications on agriculture in the Upper Ganga Command Area.

The agricultural sector in India primarily relies on monsoon rains, making it necessary for agricultural water managers to study variations in water availability by analyzing rainfall patterns. This information is critical for deciding cropping patterns and irrigation approaches, managing water resources, and improving crop water productivity. The long-term spatiotemporal variability over the command area, India Meteorological Department (IMD) based selection of thresholds for extremes indices, and thorough analysis of the consequences of extreme on agriculture make the study unique.