A Spatio-Temporal Machine Learning Framework for High-Accuracy Thermal Mapping of Dynamic Coastal Waters Using UAV Imagery

Unmanned aerial vehicle (UAV) thermal imagery is a powerful tool for monitoring coastal waters, but achieving high-accuracy temperature retrieval remains a major challenge due to complex, non-linear biases introduced by dynamic environmental conditions. This study presents a novel spatio-temporal machine learning framework to overcome this limitation. The core innovation is a multi-layer perceptron (MLP) model that decouples sensor readings from environmental effects by integrating UAV-retrieved temperatures with crucial contextual features: geographical coordinates (spatial) and tidal status (temporal). Applied to the challenging scenario of a nuclear power plant’s thermal plume, the framework demonstrated exceptional performance. On the test set, the model achieved a coefficient of determination (R2) of 0.998 and a mean absolute error (MAE) of only 0.15 °C, significantly outperforming traditional regression approaches. Ultimately, this method successfully generated high-resolution, high-accuracy sea surface temperature (SST) maps under 24 distinct tidal conditions spanning both summer and winter seasons. This work provides a robust and transferable framework for the quantitative thermal mapping of any complex and dynamic aquatic environment using UAVs.