Time-Series PolSAR and Multispectral Fusion for Enhanced Hypersaline Water Body Classification

Classification of hypersaline water bodies, for example, salt fields and salt lakes, presents unique challenges due to the similar spectral and scattering characteristics of various saline water bodies. To address this issue, we propose an innovative classification method tailored for such environments, integrating Sentinel-1 multitemporal polarimetric synthetic aperture radar (PolSAR) data with Landsat multispectral imagery. The method introduces a novel PolSAR-based feature, termed scattering mechanism entropy, to quantify variations in salt crystal precipitation processes. Additionally, the blue band from multispectral imagery is leveraged to represent ion concentrations in hypersaline water bodies. By deriving the statistical relationship between scattering mechanism entropy and blue band data for each pixel, we amplify the separability of salt features and mitigate the influence of spectral similarity. These derived features are then concatenated into a Chernoff distance-based classifier for improved classification performance. To validate the robustness and effectiveness of this method, we apply it to the classification of salt fields and salt lakes on four major salt lake sites in China: Qarhan salt lake (2018–2020), Yiliping salt lake (2021–2023), Taijnar salt lake (2019–2023), and Gasikule salt lake (2019–2023). The proposed approach achieves classification accuracies of 90.91%, 92.73%, 97.40%, and 97.65%, respectively, significantly outperforming existing water body classification methods.