Crop-identification using Sentinel-1 and sentinel-2 data for Indian region

Abstract

Real-time monitoring of agricultural crops is an important exercise because of the huge economic impact. Identification of crop during early stage of the crop cycle can help formulate better agriculture 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 identification for an Indian region. A multi-class classification algorithm based on random forest is applied to the features extracted from the above mentioned satellite data sets. Initial experimental suggest that the Sentinel-1 SAR data is promising in achieving high classification accuracy (85%).