Remote sensing inversion study of total organic carbon concentration in Karst Plateau Lakes–Taking Pingzhai reservoir as an example

Abstract

Currently, the inversion of remote sensing satellite images of water environment indicators mostly stays in the indicators with active optical characteristics, while there is less research on the inversion of most water quality indicators with non-optical activity properties, weak scattering and absorption of optical radiation, the size of their concentration has little effect on the spectral characteristics of the water body, such as TOC(Total Organic Carbon). In this paper, based on Pingzhai Reservoir, a dammed river in the karst mountainous area, the inversion model of TOC concentration was established based on BP neural network (BPNN) and sentinel-2 satellite remote sensing images. The results showed that the single bands with high correlation with the measured TOC concentration data were two vegetation red-edge bands B6 (740 nm) and B7 (783 nm) and one NIR band B8 (842 nm), and finally b7, b6 + b7, b7 + b8, b7 × b8 were selected as the input layers of BPNN for modeling through the combination of the bands, and their Pearson’s coefficients were −0.667, −0.656, −0.655, − 0.675.The inverse model established could reach a minimum RMSE of 0.235 mg/L and a maximum R² of 0.889, which was superior to that of the conventional empirical model. Demonstrate the feasibility of a TOC inversion method based on Sentinel-2 data and BPNN to monitor TOC concentrations in Pingzhai Reservoir. The study successfully established a BP neural network inversion model of TOC concentration in Pingzhai Reservoir with low error, meanwhile, we analyzed the correlation between common water quality indicators and TOC in the reservoir, in which TOC showed significant positive correlation with WT and significant negative correlation with TN and EC, with Pearson’s coefficients of 0.655, −0.666, and −0.393, respectively. The article provides scientific theoretical foundation and technical support for water quality protection of water sources.

Keywords:

• Water quality remote sensing inversion
• Karst mountain area
• BP neural network
• Sentinel-2
• TOC

Disclosure statement

No potential conflict of interest was reported by the author(s)

Authors’ contributions

Rukai Xie, Zhongfa Zhou, Jie Kong designed the research; Rukai Xie, Kong Jie, Yan Zou, Fuqiang Zhang, Li Li, Yanbi Wang, Cui Wang and Caixia Ding performed the field experiments and analyzed the data; Zhongfa Zhou provided financial and equipment. The first draft of the manuscript was written by Rukai Xie and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Data availability of statement

All data generated or analyzed during this study are included in this published article.

Additional information

Funding

This study was supported financially by National Natural Science Foundation of China (42161048); National Natural Science Foundation of China (41661088).