Assessment Of Sentinel-1 And Sentinel-2 Satellite Imagery For Crop Classification In Indian Region During Kharif And Rabi Crop Cycles

Real-time monitoring of agricultural crops is an important exercise because of it's huge impact on agri-business and agricultural policy management. Identification of crops during multiple crop growth stages can help formulate better agricultural policies and management strategies. In this context, the objective of this article is to evaluate the potential of Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery in crop classification for an Indian region. A multi-class classification algorithm based on the support vector machine (SVM) is applied to the temporal features extracted from the above mentioned satellite data sets. The experiments are conducted for Kharif and Rabi crop cycles with major crops in the region. The experiments suggest that the joint use of optical and radar imagery results in better classification accuracy compared to using them individually. An overall accuracy of 89% and 96% is obtained for Kharif and Rabi crops, respectively.