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The research aims to develop an effective remote sensing framework for assessing water quality in fragmented river networks.

Water Quality Inversion and Spatiotemporal Analysis of Changshu City Based on Multi-source Remote Sensing Data

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Randomized trial analyzes water quality in Changshu City using multi-source remote sensing data, suggesting improved monitoring methods.

The research aims to develop an effective remote sensing framework for assessing water quality in fragmented river networks.
Utilized multi-source remote sensing data (Gaofen-2, Sentinel-2) and field measurements in 2024.
Adopted a modified Normalized Difference Water Index (mNDWI) with spatially weighted adaptive threshold for water body extraction.
Developed statistical regression and PCA-RF models for quantitative water quality inversion.
Achieved accurate extraction of complex river networks with improved R² and RMSE metrics from model validation.
Successfully assessed eutrophication using a weighted modified Carlson Trophic State Index, highlighting significant pollution levels.
Provided reliable water quality evaluations aligning with national environmental standards.

Liu et al. (2026) studied this question.

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