Mapping Dissolved Organic Carbon and Identifying Drivers in Chaohu Lake: A Novel Convolutional Multi-Head Attention Fusion Network with Hyperspectral Data

Dissolved organic carbon (DOC) maintains the ecological balance of inland lake systems and contributes significantly to the global carbon cycle. This study aims to develop a novel deep learning algorithm to predict DOC concentrations and explore its modeling performance in nonlinear relationships. We used hyperspectral imagery (HSI) from the Chinese Ziyuan-1 satellite series alongside in situ water sample data to construct a Convolutional Multi-Head Attention Fusion Network (CMAF-Net) for prediction of DOC in Chaohu Lake, China. For comparison, we tested its performance against support vector regression (SVR), random forest (RF), and convolutional neural network (CNN) models. The spatial distribution patterns of the DOC were analyzed to explore the primary environmental drivers. The results demonstrate that CMAF-Net significantly outperforms the best-performing baseline CNN model, achieving an R2 of 0.88, RMSE of 0.29 mg/L, and RPD of 2.79. Furthermore, environmental factor analysis reveals strong correlations between DOC concentrations and water temperature, total nitrogen (TN), and total phosphorus (TP), identifying them as dominant drivers of the spatial variability of DOC. Hyperspectral remote sensing integrated with CMAF-Net, under the synergistic optimization of local band feature extraction and global band-dependency modeling to screen characteristic water spectra, significantly improves DOC prediction accuracy and enhances multidimensional feature learning. The proposed approach establishes a novel pathway for the quantitative monitoring of DOC in inland aquatic lakes.