Simultaneous Subpixel Retrieval of Lake Ice and Snow From MODIS Data: Resolving Spectral Ambiguity via a Physics-Informed Framework

Abstract

Lake ice and snow cover on the Tibetan Plateau play a critical role in regulating lake-surface radiative, thermodynamic, and hydrological processes, yet accurate monitoring remains challenging at moderate spatial resolutions. Over frozen lakes, widely used snow products (e.g., MOD10A1) frequently misclassify lake ice as snow and cannot simultaneously retrieve subpixel fractional ice cover (FIC) and fractional snow cover (FSC), leading to systematic biases in snow estimation and surface albedo. To address this limitation, we develop a retrieval framework that explicitly resolves lake ice–snow ambiguity through simultaneous subpixel estimation of lake ice and snow fractions by integrating multisource remote sensing observations. High-resolution Sentinel-2 data are used to construct subpixel reference fractions, which are fused with daily moderate-resolution imaging spectroradiometer (MODIS) observations within a physics-informed extreme gradient boosting (XGBoost) regression framework incorporating spectral, thermal, and spatiotemporal information. Model transferability is rigorously evaluated using spatially independent lakes across the Tibetan Plateau. Results demonstrate robust generalization across independent lakes, achieving a root-mean-square error (RMSE) of 0.122 for both FSC and FIC. Compared with MOD10A1, the proposed approach reduces snow-cover RMSE by approximately 40% and substantially improves consistency with Sentinel-2 reference data. Physically interpretable predictors mitigate ice–snow spectral confusion and reduce albedo overestimation in conventional snow products. By explicitly differentiating lake ice and snow at the subpixel scale, this framework provides a physically consistent approach for characterizing lake–atmosphere interactions, with strong potential for advancing hydrological modeling and generating long-term fractional ice and snow datasets over the Tibetan Plateau.