Mind the Cloud: Propagation of Cloud‐Induced Bias in Lake Surface Water Temperature Remote Sensing and Modeling

Satellite remote sensing is widely used to monitor lake surface water temperature (LSWT) due to its global coverage and relatively long‐term record. A common practice in previous studies is to exclude observations during cloudy periods, as most satellite‐based LSWT products rely on optical sensors that cannot penetrate cloud cover. Using synthetic LSWT data sets, we demonstrate that cloud cover tends to coincide with specific thermal conditions, introducing geographically structured biases in annual mean LSWT estimates. We further conducted global‐scale synthetic numerical experiments to examine the impact of cloud cover propagates into model predictions when satellite‐derived LSWTs are used for model development. Two modeling approaches were evaluated: a physically‐based model (Air2water) and a data‐driven model based on a Long Short‐Term Memory (LSTM) neural network. While the LSTM model achieved an overall higher predictive accuracy, it exhibited significantly amplified biases when trained on cloud‐affected data sets, particularly in the warmest‐month temperature and ice duration. In contrast, Air2water showed relatively stable bias patterns, reflecting its resistance to data gaps due to its physical constraints. Our findings underscore the importance of accounting for cloud‐induced selection bias in optical satellite remote sensing and environmental modeling.