The Dongting Lake Basin is a critical ecological zone in the middle reaches of the Yangtze River, playing a pivotal role in safeguarding regional ecological security and supporting socio-economic development. To investigate the spatiotemporal patterns and underlying drivers of water quality in Dongting Lake, this study developed a Spectral-Attention CNN (SA-CNN) inversion model integrated with the Efficient Channel Attention (ECA) mechanism, utilizing multi-source remote sensing data and convolutional neural networks. Results indicate that the proposed SA-CNN model significantly outperforms traditional machine learning approaches in predicting key water quality parameters, including total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH3–N), and turbidity. Notably, the model achieved its highest predictive accuracy for TP, with an R2 value of 0.94. By incorporating spectral weight prior knowledge, the model was successfully transferred and trained on Landsat imagery. The validated model was subsequently applied to reconstruct and analyze the spatiotemporal trends from 2015 to 2025, revealing that water quality in Dongting Lake exhibits a fluctuating decline during winter months, while summer periods show an increasing trend in turbidity and TP concentrations. Further analysis suggests that water quality parameters are positively correlated with temperature and negatively correlated with precipitation, with anthropogenic activities also exerting a considerable influence.
Guo et al. (Thu,) studied this question.