Estimation of Particulate Organic Carbon Concentration and Its Driving Factors in Lakes of the Yangtze–Huaihe Plain, China

Inland lake ecosystems function as active nodes in the global carbon cycle, yet reliable basin-scale estimates of particulate organic carbon concentration (CPOC) remain scarce owing to the optical complexity of these environments. In this study, POC denotes the total particulate organic carbon pool, including organic matter from living and non-living particles. Here we present a semi-analytical retrieval scheme tailored to Sentinel-2 Multispectral Instrument (MSI) data and applied to 90 large eutrophic lakes (each >10 km2) within the Yangtze–Huaihe Plain (YHP) of eastern China. The scheme reconstructs the particulate absorption coefficient at 665 nm, ap(665), from reflectance measurements at 665, 705, and 740 nm, reducing the interference of colored dissolved organic matter (aCDOM) absorption and particulate backscattering (bb). When evaluated against 60 field observations in the predefined concentration-stratified holdout, the algorithm achieved MAE = 0.59 mg/L, RMSE = 0.73 mg/L, and MAPE = 46.89%—outperforming three widely cited benchmark algorithms—and showed preliminary transfer potential in the two estuarine datasets. MSI-derived CPOC maps from 2018 to 2024 revealed systematic east-to-west and center-to-periphery concentration gradients across the 90-lake assemblage, shaped by catchment nutrient delivery, basin hydrodynamics, and phytoplankton standing stock. Correlation and attribution analyses identified chlorophyll-a concentration (CChla), atmospheric pressure, and total phosphorus (CTP) as the three dominant environmental modulators of CPOC variability. The framework provides a field-calibrated approach for examining regional lake CPOC patterns. Its satellite application and ability to distinguish small concentration differences require further independent validation and uncertainty assessment.