Monthly suspended particulate matter reconstruction in ebinur lake using satellite data fusion and machine learning

Suspended particulate matter (SPM) monitoring in arid lakes is limited by gaps in satellite observations and field measurements. We reconstructed monthly SPM in Ebinur Lake, China, during the ice-free months (May–October) of 2014–2024 using imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat 8, and Sentinel-2, together with spatiotemporal fusion and machine-learning models calibrated with 220 in situ samples. The Spatial and Temporal Nonlocal Filter-Based Fusion Model (STNLFFM) outperformed the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) in reconstructing reflectance, achieving validation R2 values of 0.769 (Landsat 8) and 0.861 (Sentinel-2). Adding environmental predictors, especially wind-related variables, improved SPM retrieval. The selected Extra Trees model achieved R2 = 0.816 and a root mean square error (RMSE) of 113.39 mg/L in five-fold cross-validation while better preserving observed SPM variation than alternative models. Reconstructed monthly mean SPM ranged from 167 mg/L in July 2022 to 413 mg/L in September 2015, with seasonal and interannual variability but no consistent monotonic trend. These findings show that combining image fusion with spectral and environmental predictors supports long-term SPM monitoring in data-scarce arid lakes.