Two-decade spatiotemporal dynamics of nutrients in Yangtze-Huaihe lakes revealed by remote sensing.

Total nitrogen (TN) and total phosphorus (TP) serve as key indicators of aquatic eutrophication. While the monitoring stations are continuous in time, there are too few points in some lakes, and many lakes with low attention do not have monitoring stations, making it difficult to reflect the overall pattern of the lake area and multi-lake regions. This study integrated Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data with machine learning techniques to estimate TN and TP concentrations across 26 lakes in the Yangtze-Huaihe Region (YHR) of China over a 20-year period (2003-2023). The Extreme Gradient Boosting (XGB) algorithm was identified as the top performer, achieving coefficient of determination (R2) values of 0.56 (RMSE = 1.14 mg/L) for TN and 0.58 (RMSE = 0.06 mg/L) for TP estimation. The long-term reconstruction revealed that TN levels consistently ranging from 1.2 to 2.0 mg/L, with 70% of the retrieved values exceeding the 1.5 mg/L threshold after 2015. TP concentrations showed a progressive increase, generally fluctuating between 0.10 and 0.16 mg/L levels throughout the study period. Notably, the lakes exhibited a higher trophic state index for phosphorus (mean TSITP = 74.30) was demonstrated compared to nitrogen (mean TSITN = 61.13), with spatial analysis identifying external riverine inputs as the primary phosphorus source. These results align with environmental parameters derived from other remote sensing observations, providing reliable data to support water management decisions in the YHR and offering a transferable framework for global eutrophication monitoring.