Machine Learning-Based Remote Sensing Inversion and Spatiotemporal Characterization of Chl-a Concentration in the Leizhou Peninsula Coastal Waters

The Leizhou Peninsula, located in the northern South China Sea, features coastal waters with dual functions as both marine ranch demonstration zones and ecological protection areas. Remote sensing monitoring of Chlorophyll-a (Chl-a) concentration in this region holds strategic significance for assessing primary productivity, red tide risk, and the sustainability of the blue food economy. This study integrates in situ survey data from four cruises conducted between 2020 and 2024 with Sentinel-3 OLCI remote sensing imagery, constructing and comparing the performance of six machine learning inversion models. The results show that for the inversion scenarios of the Leizhou Peninsula waters, the GBDT model performs best among the evaluated models (R2 = 0.79, RMSE = 0.36 mg/m3, MAE = 0.30 mg/m3). Based on the GBDT model, pixel-by-pixel inversion maps with 300 m spatial resolution were generated for four seasons in 2024, revealing a spatial gradient of “high nearshore–low offshore, high in the east–low in the west” and a seasonal pattern of “low in spring–rise in summer–stable in autumn–high in winter.” In addition, the study verified the operational potential of machine learning in complex type-II waters, analyzed the distribution characteristics and influencing factors of Chl-a concentration in this region, and provided scientific and technical support for marine ranch carrying capacity assessment, eutrophication early warning, and carbon sink accounting in the Leizhou Peninsula.