Abstract
Remote sensing has long been an effective method for water quality monitoring because of its advantages such as high coverage and low consumption. For non-optically active parameters, traditional empirical and analytical methods cannot achieve quantitative retrieval. Machine learning has been gradually used for water quality retrieval due to its ability to capture the potential relationship between water quality parameters and satellite images. This study is based on Sentinel-2 images and compared the ability of four machine learning algorithms (eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Random Forest (RF), and Artificial Neural Network (ANN)) to retrieve chlorophyll-a (Chl-a), dissolved oxygen (DO), and ammonia-nitrogen (NH3-N) for inland reservoirs. The results indicated that XGBoost outperformed the other three algorithms. We used XGBoost to reconstruct the spatial-temporal patterns of Chl-a, DO, and NH3-N for the period of 2018–2020 and further analyzed the interannual, seasonal, and spatial variation characteristics. This study provides an efficient and practical way for optically and non-optically active parameters monitoring and management at the regional scale.
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Data availability
The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.
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Funding
This work was supported by the National Key R&D Program of China (2021YFC3200400).
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Shang Tian: put forward ideas, performed the experiments, and wrote the draft.
Hongwei Guo: performed the experiments and revised the draft.
Jinhui Jeanne Huang: supervised research activity and revised draft.
Xiaotong Zhu: trained machine learning models.
Bo Wang: processed data.
Wang Xu: provided study materials.
Qinghuai Zeng: provided study materials.
Youquan Mai: provided study materials.
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Tian, S., Guo, H., Xu, W. et al. Remote sensing retrieval of inland water quality parameters using Sentinel-2 and multiple machine learning algorithms. Environ Sci Pollut Res 30, 18617–18630 (2023). https://doi.org/10.1007/s11356-022-23431-9
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DOI: https://doi.org/10.1007/s11356-022-23431-9