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.
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 Chl-a concentrations. Together with hyperspectral remote sensing data, a BP neural network model was used to invert chlorophyll-a concentration, with remote sensing reflectance as the input factor. Given the presence of many bands in the hyperspectral data, selecting an appropriate band reflectance as the input factor is crucial to improving inversion accuracy. In this study, a Pearson correlation analysis method was proposed to select bands. A normality test was performed on the reflectance of each band of the Zhuhai-1 hyperspectral remote sensing data, and the significance index was p < 0.05. The absolute kurtosis value was less than 10, and the absolute skewness value was less than 3, indicating that the Pearson method was applicable. Pearson correlation analysis was utilised to calculate the correlation coefficient between the reflectance data and chlorophyll-a concentration. Five reflectance data with high correlation were selected as the input factors, and chlorophyll-a concentration was adopted as the output factor. An error backpropagation network model was constructed to predict chlorophyll-a concentration, and a Garson function was added to clarify the connection weights of the input factors in the model construction process. Model 12 was determined as the optimal model on the basis of the criteria of the coefficient of determination, the average relative variance, and the minimum mean square error. The chlorophyll-a concentration was predicted for July and November 2020 in the study area, and the results showed that the predicted values had a small error compared with the measured values. The root-mean-square error and mean relative error of the chlorophyll-a concentration predicted and measured values were 2.12 μg/L and 9.66%, respectively. Significant spatial differences in the Chl-a concentration were observed in the study area due to the influence of islands and land; the Chl-a concentration in July was generally higher than that in November. The results of these studies provide a reference for monitoring the water environment in the study area.
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