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Interpretable geospatial machine learning for seasonal land surface temperature prediction using multi-source earth observation data
An interpretable machine learning framework to predict seasonal MODIS clear-sky daytime LST across Rostov Oblast, Russia, using multi-source Earth observation data from 2015 to 2024 provides a spatially explicit and interpretable approach for seasonal clear-sky daytime LST prediction in understudied temperate continent...