A deep neural network framework for estimating coastal salinity from SMAP brightness temperature data
Year: 2025
Type: article
Abstract: Introduction Sea surface salinity (SSS) is a critical parameter for understanding ocean circulation, marine ecosystem processes, and climate change. 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. Method This 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 discussion Systematic 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). Compared with SMAP SSS products, the baseline DNN demonstrates a reduction of 33.8% and 7.3% in RMSE in nearshore (dis ≤ 50 km) and offshore regions (50 km
Source: Frontiers in Marine Science
Authors: Yidi Wei, Qing Xu, Xiaobin Yin, Yan Li, Kaiguo Fan
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,801
Award: G6703551828
OpenAlex ID: W4411464300