An Enhanced Machine Learning Approach for Regional Total Suspended Matter Concentration Retrieval Using Multispectral Imagery

Accurate monitoring of total suspended matter (TSM) concentration is essential for aquatic ecosystem protection and water quality assessment. Multispectral remote sensing provides an effective approach for large-scale TSM monitoring. However, robust retrieval models are difficult to develop due to limited in situ data. This study presents a Deep Feature Extraction–Machine Learning fusion framework that integrates a pre-trained back-propagation neural network (BPNN) with support vector regression (SVR) to enhance TSM retrieval. High-level spectral features extracted by BPNN are used as inputs to SVR (termed DFE-SVR) for regional TSM retrieval, using in situ measurements from five inland lakes in Jiangsu and Anhui Provinces, China. The generated TSM maps showed spatial patterns consistent with TSM concentration distributions visually observed in true-color imagery. Validation results demonstrated that DFE-SVR outperformed BPNN and SVR models, achieving R2 of 0.85 and 0.90 and RMSE of 7.95 and 4.76 mg/L for GF-1 and Sentinel-2 imagery, respectively. Compared with SVR models using principal component analysis or band combinations, DFE-SVR reduced RMSE by over 20%. Under reduced training samples, the DFE-SVR model also maintained higher stability and accuracy. These findings showed its potential for multispectral water quality monitoring with limited in situ data.