SLC Coherence Feature for Enhancing Cropland Extent Mapping: A Case Study on Maharashtra Region, India

Land-use and land-cover (LULC) plays a crucial role in various agricultural applications, including crop identification, crop acreage estimation, and yield estimation. However, accurately mapping cropland extent can be challenging, particularly when grassland areas are misclassified as cropland due to similar temporal signatures during the rainy season. To address this issue, this study explores the use of interferometric coherence, a measure of correlation between two measurements of the same resolution cell, as a distinguishing feature between cropland and grassland. The main objective is to improve the classification accuracy of cropland and grassland areas. The methodology involves computation of coherence, and generation and selection of statistical features based on NDVI, VH, and coherence-VV. Seasonal statistics are computed for these features, and the separability index is used to evaluate their discriminatory power. The results demonstrate that coherence features, particularly coherence-VV have high separability indices, indicating their effectiveness in distinguishing between cropland and grassland. The proposed methodology achieves promising results with an F1-score of 0.95. The study highlights the potential of coherence features for improving the accuracy of land-use/land-cover classification and provides insights for agricultural applications in different geographical regions.