Machine learning-based retrieval of chlorophyll-a and total suspended matter from HY-3A CZI: Model development, validation, and application

作者：Lan Zhang, Chen Zhang, Chaofei Ma, Xi Chen, Qi Li, Xiaomin Ye, Zhifeng Yu, Liqiao Tian · 发表于：ISPRS Journal of Photogrammetry and Remote Sensing · 年份：2025 · DOI：10.1016/j.isprsjprs.2025.06.032 · 被引用次数：12 · 研究领域：Water Quality Monitoring and Analysis、Air Quality Monitoring and Forecasting、Marine and coastal ecosystems

Water quality monitoring via optical remote sensing provides essential insights for large-scale and long-term assessment and management of aquatic ecosystems. However, the inherent complexity of biogeochemical optical processes presents challenges in developing robust and generalizable retrieval algorithms. Machine learning (ML) has emerged as an effective alternative to handle the nonlinear relationships between apparent optical properties (AOPs) and water quality parameters (WQPs). Here, we introduced ML models to retrieve two key WQPs, chlorophyll-a (Chla) and total suspended matter (TSM), from the second-generation Coastal Zone Imager (CZI) onboard the HY-3A satellite. First, we compiled a global in-situ dataset (N = 7,535) comprising co-located remote sensing reflectance ( R r s ), Chla (range: 0.01–360.02 mg m −3 ) and TSM (range: 0.1–2626.82 g m −3 ) measurements, spanning diverse aquatic environments including oceans, coasts, estuaries, and inland waters. Based on this comprehensive dataset, we developed and evaluated five ML models, Random Forest (RF), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), Deep Neural Network (DNN) and Mixture Density Network (MDN), to retrieve Chla and TSM from HY-3A CZI. Model testing showed that ML models outperformed the traditional retrieval algorithms, with MDN achieving the best performance, with Median Absolute Percentage Error (MAPE) of 33.64% for Chla and 30.79% for TSM. To further assess model applicability ...