Evaluation of Multi-Temporal Sentinel-1 Dual Polarization SAR Data for Crop Type Classification

Abstract

India is one of the highly populated countries, and its economy mainly depends on agriculture. The crop type classification is an essential requirement for ensuring food security, crop monitoring, and to understand the environmental consequences of cultivated ecosystems. This study exploits freely available multi-temporal SAR data for discriminating crop types, such as wheat, gram, and mustard, over Ashok Nagar district, Madhya Pradesh, India. Nine Sentinel-1 dual-polarized data acquired from January 2018 to April 2018 in interferometric wide swath mode are used. Class separability analysis using Bhattacharyya Distance (BD) has been performed for multi-temporal VV and VH backscatter, log-ratio, and Radar Vegetation Index (RVI) to quantify the ability to distinguish temporal profiles of crops. RVI has shown the significant result in class separability analysis in comparison with other parameters. Crop type classification map has been generated using a support vector machine classifier with overall accuracy and Kappa coefficient of 96.32% and 0.95, respectively.