A Deep Learning Inversion Method for 3D Temperature Structures in the South China Sea with Physical Constraints

The South China Sea, a vital marginal sea in tropical–subtropical Southeast Asia, plays a globally significant role in marine biodiversity and climate system dynamics. The accurate monitoring of its thermal structure is essential for ecological and climatic studies, yet retrieving subsurface temperature remains challenging due to complex ocean–atmosphere interactions. This study develops a Convolutional Long Short-Term Memory (ConvLSTM) neural network, integrating multi-source satellite remote sensing data, to reconstruct the Ocean Subsurface Temperature Structure (OSTS). To address the multiparameter complexity of temperature retrieval, physical constraints—particularly the heat budget balance of water bodies—are incorporated into the loss function. Experiments demonstrate that the physics-informed ConvLSTM model significantly improves the temperature estimation accuracy by simultaneously optimizing the physical consistency and predictive performance. The proposed approach advances ocean remote sensing by synergizing data-driven learning with thermodynamic principles, offering a robust framework for understanding the South China Sea’s thermal variability.