Research on Chlorophyll-a Concentration Retrieval Based on BP Neural Network Model—Case Study of Dianshan Lake, China

Wei-Dong Zhu, Chu-Yi Qian, Nai-Ying He, Yu-Xiang Kong, Zi-Ya Zou and Yu-Wei Li
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Wei-Dong Zhu: School of Marine Science, Shanghai Ocean University, Shanghai 201306, China
Chu-Yi Qian: School of Marine Science, Shanghai Ocean University, Shanghai 201306, China
Nai-Ying He: School of Marine Science, Shanghai Ocean University, Shanghai 201306, China
Yu-Xiang Kong: School of Marine Science, Shanghai Ocean University, Shanghai 201306, China
Zi-Ya Zou: School of Marine Science, Shanghai Ocean University, Shanghai 201306, China
Yu-Wei Li: School of Marine Science, Shanghai Ocean University, Shanghai 201306, China

Sustainability, 2022, vol. 14, issue 14, 1-15

Abstract: The Chlorophyll-a (Chl-a) concentration is an important indicator of water environmental conditions; thus, the simultaneous monitoring of large-area water bodies can be realized through the remote sensing-based retrieval of the Chl-a concentrations. The back propagation (BP) neural network learning method has been widely used for the remote sensing retrieval of water quality in first and second-class water bodies. However, many Chl-a concentration measurements must be used as learning samples with this method, which is constrained by the number of samples, due to the limited time and resources available for simultaneous measurements. In this paper, we conduct correlation analysis between the Chl-a concentration data measured at Dianshan Lake in 2020 and 2021 and synchronized Landat-8 data. Through analysis and study of the radiative transfer model and the retrieval method, a BP neural network retrieval model based on multi-phase Chl-a concentration data is proposed, which allows for the realization of remote sensing-based Chl-a monitoring in third-class water bodies. An analysis of spatiotemporal distribution characteristics was performed, and the method was compared with other constructed models. The research results indicate that the retrieval performance of the proposed BP neural network model is better than that of models constructed using multiple regression analysis and curve estimation analysis approaches, with a coefficient of determination of 0.86 and an average relative error of 19.48%. The spatial and temporal Chl-a distribution over Dianshan Lake was uneven, with high concentrations close to human production and low concentrations in the open areas of the lake. During the period from 2020 to 2021, the Chl-a concentration showed a significant upward trend. These research findings provide reference for monitoring the water environment in Dianshan Lake.

Keywords: remote sensing; chlorophyll-a; BP neural network; Dianshan Lake; third-class water bodies (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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