Published 2025-06-01
“…Despite advancements in satellite-based radiometry such as NASA’s Soil Moisture Active Passive (SMAP), significant challenges persist in coastal SSS retrieval due to radio frequency interference (RFI), land-sea contamination, and complex interactions of nearshore dynamic processes.MethodThis study proposes a deep neural network (DNN) framework that integrates SMAP L-band brightness temperature data with ancillary oceanographic and geographic parameters such as sea surface temperature, the shortest distance to the coastline (dis) to enhance SSS estimation accuracy in the Yellow and
East China Seas. The framework leverages machine learning interpretability tools (Shapley Additive Explanations, SHAP) to optimize input feature selection and employs a grid
search strategy for hyperparameter tuning.Results and discussionSystematic validation against independent in-situ measurements demonstrates that the baseline DNN model constructed for the entire region and time period outperforms conventional
algorithms including K-Nearest Neighbors,
Random Forest, and XGBoost and the standard SMAP SSS product, achieving a reduction of 36.0%, 33.4%, 40.1%, and 23.2%, respectively in root mean square error (RMSE). …”
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