Machine Learning-Based Remote Sensing Retrieval of Nutrient Concentrations in China’s Coastal Waters

Dissolved inorganic nitrogen (DIN) and dissolved inorganic phosphorus (DIP) are the two dominant nutrients influencing seawater quality. Due to the non-optically active nature of nutrients and their regional variability, relying solely on optical data is inadequate for achieving high-precision remote sensing retrieval in complex marine environments. We developed a novel remote sensing algorithm for DIN and DIP using MODIS remote sensing reflectance (Rrs) products and the XGBoost machine learning framework. Beyond optical inputs, our model integrates sea surface temperature (SST) and spatiotemporal information, including a shoreline-based pixel location descriptor, which significantly enhances model performance. We generated monthly average distributions of DIN and DIP concentrations across China’s coastal waters from 2012 to 2022. The findings highlight extensive high-nutrient zones in the Bohai Bay and Changjiang River (Yangtze River) Estuary−Hangzhou Bay regions, with a notable declining trend in nutrient concentrations. The Zhujiang River (Pearl River) Estuary also exhibits elevated nutrient levels, albeit with minimal changes. This study pioneers the incorporation of dual-coordinate information in nutrient retrieval for complex marine environments, significantly improving model accuracy and addressing stripping artifacts associated with single-coordinate systems. Moreover, the results provide unprecedented spatiotemporal insights into nutrient distributions in China’s coastal waters, offering valuable support for marine environmental management and policy-making.