Discrimination of maize crop in a mixed Kharif crop scenario with synergism of multiparametric SAR and optical data

Accurate mapping of coarse cereals envisaged maize to be discriminated from co-existing Kharif crops using multiplatform, multiparametric SAR and Optical data in various polarization combination from Sentinel-1 and RADARSAT-2 in synergism with Sentinel-2, under machine learning algorithms viz. support vector machine (SVM) and random forest (RF) and knowledge-based Decision Tree (DT). SVM over-estimates the area of maize whereas RF and DT performed similarly (60–75%) but DT being closest to field conditions (68–78%). A 4 date Sentinel-1 under DT classified Soybean to 62% accuracy and Cotton to 76% on a regional scale in Maharashtra. Inclusion of 1-date optical image improved sugarcane accuracy. The similar-structured crops, such as Maize and Pearl Millet were discriminated with their unique phenological response in crop-calendar using quad-pol RADARSAT-2. With inclusion of crop responsive model-based polarimetric decomposition (Yamaguchi Volume and Double-Bounce) and Eigen-based parameters (Entropy and Alpha-Angle), maize classification accuracy elevated to 84%.