This study presents an integrated multi-satellite assessment of water quality in small high-Andean lakes (SHAL) and a reservoir in the Central Andes. Sentinel-2 MSI and Landsat 8 and 9 OLI imagery were used to evaluate multiple atmospheric correction methods and develop a framework for monitoring remote mountain lakes. Water quality and trophic state were investigated in seven SHAL and one reservoir within the Misicuni and Chocaya watersheds, Bolivia. The study systems are located between 4000 and 4600 m a.s.l. and range in area from 0.01 to 10 km2, whereas the reservoir has an elevation of 3774 m a.s.l. Field samples were combined with multi-satellite observations to estimate chlorophyll-a, transparency, turbidity, and chromophoric dissolved organic matter. Random Forest models were developed using spectral data alone and in combination with landscape and hydroclimatic predictors, while XGBoost was evaluated as a complementary algorithm. Integrating catchment descriptors improved model performance, yielding cross-validated predictions for water-quality variables, while uncertainty estimates were generated for mapped products. Atmospheric correction methods performed differently among water-quality variables, indicating that no single method performs consistently best across variables. Predictions revealed spatial and temporal patterns associated with bofedales (high-Andean wetlands), antecedent precipitation, elevation, and anthropogenic land cover. Remote sensing-derived trophic state and dissolved organic carbon estimates were consistent with field observations, enabling assessment of ecological condition and the spatial distribution of carbon pools across SHAL. The proposed framework provides a scalable approach for monitoring high-Andean lakes, supporting ecosystem-based management, land-use planning, and watershed decision-making where water quality observations remain sparse.
