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

Abstract

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%.

Keywords:

• Crop classification
• maize
• SAR
• polarimetry
• SVM
• RF
• DT

Acknowledgements

Authors would like to thank and acknowledge the SUFALAM project, SAC and ISRO Headquarters for the funds and support rendered throughout in carrying out the project.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study was supported by the grants from Indian Space Research Organization.