Remote Sensing Assessment of Trophic State in Reservoir Tributary Embayments Based on Multi-Source Data Fusion
Abstract
1. Introduction
2. Study Area and Data Collection
2.1. Study Area
2.2. Data Collection
2.2.1. Water Quality Indicator Data
2.2.2. Remote Sensing Data
2.2.3. Meteorological Data
3. Methodology
3.1. TSI Inversion Model Framework
3.2. Data Preprocessing
3.2.1. Meteorological and Water Quality Data Preprocessing
3.2.2. Preprocessing of the Trophic State Index (TSI)
3.2.3. Preprocessing of Remote Sensing Image Data
3.3. TSI Inversion Prediction Model Based on BP-NN-GSA
3.3.1. Architecture of the Backpropagation Neural Network Model
3.3.2. Model Parameters
3.3.3. Performance Evaluation of the BP-NN-GSA Model
4. Results
4.1. Comparison of TSI Inversion Accuracy for Different Parameter Combinations
4.2. Evaluation of the Accuracy of the TSI Inversion Model
4.3. Prediction of Spatiotemporal Distribution of TSI
4.3.1. Prediction of TSI for 2009
4.3.2. TSI Prediction for 2019 and 2020
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Shi, Y.; Mao, J.; Liu, X.; Meng, D.; Zhu, J.; Gao, H.; Wang, K. Remote Sensing Assessment of Trophic State in Reservoir Tributary Embayments Based on Multi-Source Data Fusion. Remote Sens. 2025, 17, 2886. https://doi.org/10.3390/rs17162886
Shi Y, Mao J, Liu X, Meng D, Zhu J, Gao H, Wang K. Remote Sensing Assessment of Trophic State in Reservoir Tributary Embayments Based on Multi-Source Data Fusion. Remote Sensing. 2025; 17(16):2886. https://doi.org/10.3390/rs17162886
Chicago/Turabian StyleShi, Yangjie, Jingqiao Mao, Xinbo Liu, Dinghua Meng, Jianing Zhu, Huan Gao, and Kang Wang. 2025. "Remote Sensing Assessment of Trophic State in Reservoir Tributary Embayments Based on Multi-Source Data Fusion" Remote Sensing 17, no. 16: 2886. https://doi.org/10.3390/rs17162886
APA StyleShi, Y., Mao, J., Liu, X., Meng, D., Zhu, J., Gao, H., & Wang, K. (2025). Remote Sensing Assessment of Trophic State in Reservoir Tributary Embayments Based on Multi-Source Data Fusion. Remote Sensing, 17(16), 2886. https://doi.org/10.3390/rs17162886