Sentinel-1-derived coherence time-series for crop monitoring in Indian agriculture region

Abstract

Synthetic aperture radar (SAR) remote sensing has been widely used for crop monitoring due to its high-resolution imaging and all-weather data acquisition capabilities. In this study, time-series interferometric SAR (InSAR) coherence products generated from Sentinel-1 data were employed to monitor phenological stages, determine total cropping duration and estimate sowing as well as harvest dates for the Bengal-gram crop. The Bengal-gram crop was cultivated in the study region lies in Chhattisgarh, India, during the Rabi 2018–2019 season. Upon analysis, the temporal fluctuations in InSAR coherence were observed as a function of crop phenological stages as well as sowing and harvesting events. The lowest coherence value (i.e. 0.29) represents the peak vegetation. Sowing and harvesting dates were determined by observing the sudden shifts in coherence trend i.e. from high to low and low to high, respectively. The temporal analysis performed using InSAR coherence was also cross-verified with the time-series normalized difference vegetation index (NDVI) index derived using Sentinel-2 bands. Based on detailed analysis, we found that the time-series analysis of InSAR coherence provides reliable information on the crop growth stages and this approach can be used to monitor other crops as well.