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Open AccessArticle

Remote Estimation of Water Quality Parameters of Medium- and Small-Sized Inland Rivers Using Sentinel-2 Imagery

1
School of Water Conservancy Science & Engineering, Zhengzhou University, Zhengzhou 450001, China
2
School of the Geo-Science & Technology, Zhengzhou University, Zhengzhou 450001, China
*
Author to whom correspondence should be addressed.
Water 2020, 12(11), 3124; https://doi.org/10.3390/w12113124
Received: 27 August 2020 / Revised: 5 November 2020 / Accepted: 5 November 2020 / Published: 7 November 2020
(This article belongs to the Section Hydrology and Hydrogeology)
In the application of quantitative remote sensing in water quality monitoring, the existence of mixed pixels greatly affects the accuracy of water quality parameter inversion, especially for narrow inland rivers. Improving the image spatial resolution and weakening the interference of mixed pixels in the image are some of the urgent problems to be solved in the study of water quality monitoring of medium- and small-sized inland rivers. We processed Sentinel-2 multispectral images using the super-resolution algorithm and generated a set of 10 m spatial resolution images with basically unchanged reflection characteristics. Both qualitative and quantitative evaluation results show that the super-resolution algorithm can weaken the influence of mixed pixels while maintaining spectral invariance. Before the application of the super-resolution algorithm, the inversion accuracy of water quality parameters in this study were as follows: for NH3-N, the R2 was 0.61, the root mean squared error (RMSE) was 0.177 and the mean absolute percentage error (MAPE) was 29.33%; for Chemical Oxygen Demand (COD), the R2 was 0.26, the RMSE was 0.756 and the MAPE was 4.62%; for Total Phosphorus (TP), the R2 was 0.69, the RMSE was 0.032 and the MAPE was 30.58%. After the application of the super-resolution algorithm, the inversion accuracy of water quality parameters in this study were as follows: for NH3-N, the R2 was 0.67, the RMSE was 0.161 and the MAPE was 25.88%; for COD, the R2 was 0.53, the RMSE was 0.546 and the MAPE was 3.36%; for TP, the R2 was 0.60, the RMSE was 0.034 and the MAPE was 24.28%. Finally, the spatial distribution of NH3-N, COD and TP was obtained by using a machine learning model. The results showed that the application of the super-resolution algorithm can effectively improve the retrieval accuracy of NH3-N, COD and TP, which illustrates the application potential of the super-resolution algorithm in water quality remote sensing quantitative monitoring. View Full-Text
Keywords: super-resolution algorithm; total phosphorus; chemical oxygen demand; water quality monitoring; Sentinel-2; NH3-N; remote sensing super-resolution algorithm; total phosphorus; chemical oxygen demand; water quality monitoring; Sentinel-2; NH3-N; remote sensing
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MDPI and ACS Style

Huangfu, K.; Li, J.; Zhang, X.; Zhang, J.; Cui, H.; Sun, Q. Remote Estimation of Water Quality Parameters of Medium- and Small-Sized Inland Rivers Using Sentinel-2 Imagery. Water 2020, 12, 3124. https://doi.org/10.3390/w12113124

AMA Style

Huangfu K, Li J, Zhang X, Zhang J, Cui H, Sun Q. Remote Estimation of Water Quality Parameters of Medium- and Small-Sized Inland Rivers Using Sentinel-2 Imagery. Water. 2020; 12(11):3124. https://doi.org/10.3390/w12113124

Chicago/Turabian Style

Huangfu, Kuan; Li, Jian; Zhang, Xinjia; Zhang, Jinping; Cui, Hao; Sun, Quan. 2020. "Remote Estimation of Water Quality Parameters of Medium- and Small-Sized Inland Rivers Using Sentinel-2 Imagery" Water 12, no. 11: 3124. https://doi.org/10.3390/w12113124

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