Object-based machine learning approach for soybean mapping using temporal sentinel-1/sentinel-2 data
Mamta Kumari, Varun Pandey, Karun Kumar Choudhary, C. S. Murthy
Abstract
Soybean mapping in Indian context is challenging owing to its short growing period coinciding with the monsoon clouds, inter-cropping and smallholders’ land. This study proposes an approach for mapping soybean by integrating object-based image analysis with machine learning (ML) based classification using temporal Sentinel-1 SAR (S-1) and Sentinel-2 optical (S-2) data. Field objects were delineated with scale-optimized multi-resolution segmentation using historical S-2 data. Object-based temporal VH-backscatter and NDVI were extracted for training, validation and testing of the three ML models. Validation results showed the outperformance of Extreme gradient boosting (OB-XGBoost) over Random Forest (OB-RF) and Support vector machine (OB-SVM) with an overall accuracy (OA) of 92.50, 91.08 and 90.1, respectively. Testing of OB-XGBoost model resulted in OA, kappa statistics, and F-score (soybean) of 86.12%, 0.82, and 87.23%, respectively. The soybean map produced by the proposed methodology has shown better representation in terms of homogeneity and uniformity than the pixel-based classification.