Mapping fractional vegetation cover in heterogeneous-vegetated regions using SAR images and DBO-optimized ensemble learning algorithms

Fractional vegetation cover (FVC) is a crucial biophysical indicator for monitoring vegetation abundance and distribution. Existing FVC estimation methods based on synthetic aperture radar (SAR) images often overlook the heterogeneity of scattering mechanisms across vegetation types, limiting accuracy in complex and topographically varied regions. To address this, this study proposes a vegetation-type-adaptive approach that integrates tailored feature selection with ensemble learning algorithms optimized by the dung beetle optimizer (DBO), based on dual-polarized SAR images. The study area is classified into cropland, woodland, and wetland using the European Space Agency’s (ESA’s) WorldCover 10-m product to address vegetation heterogeneity. Following this classification, 25 SAR features – including backscatter coefficients, polarimetric decomposition parameters, and radar vegetation indices – are extracted from Sentinel-1 SAR data over the Dongting Lake region, China. The minimum redundancy maximum relevance (mRMR) algorithm identifies optimal feature subsets for each vegetation type, effectively capturing unique scattering characteristics. Five ensemble models, adaptive boosting (Adaboost), categorical boosting (CatBoost), extreme gradient boosting (XGBoost), random forest (RF), and light gradient boosting machine (LightGBM), are trained separately for each vegetation type, with hyperparameters tuned via DBO. To mitigate terrain-induced layover effects, an adaptive OTSU-based thresholding strategy is applied. Experimental results demonstrate high-precision FVC estimation, with R2 values of 0.8916, 0.8313, and 0.9303 for cropland, woodland, and wetland, respectively. Compared with conventional uniform models, this vegetation-type-specific approach significantly improves accuracy by addressing feature heterogeneity and geometric distortions. This method provides a robust and physically consistent solution for FVC mapping in heterogeneous-vegetated regions, significantly advancing SAR-based vegetation monitoring under complex terrain conditions.