Remote sensing inversion of chlorophyll-a in Poyang Lake based on BP neural network

Abstract

[Objective] By comparing the inversion result of chlorophyll-a from two models in Poyang Lake, the model with higher inversion accuracy is selected, enabling more accurate and efficient application in water quality monitoring and management of shallow lakes. [Methods] Chlorophyll-a is a key indicator for water quality monitoring and a critical parameter for eutrophication assessment in aquatic environments. Poyang Lake was selected as a representative study area. Chlorophyll-a concentration in Poyang Lake was inverted based on measured chlorophyll-a concentration and Landsat-8 OLI satellite remote sensing data. After preprocessing the remote sensing images, correlation analysis was conducted between single band and band combination data and chlorophyll-a concentration data. A chlorophyll-a band ratio model was developed, and relevant band combinations were selected to further establish a back propagation( BP) neural network model. The correlation between the measured chlorophyll-a concentrations and the inversion result from the BP neural network model was compared. [Results] The result showed that the developed BP neural network model led to an improvement in the coefficient of determination(R2) between the predicted and measured values, from 0. 624~0. 855 to 0. 745~0. 921, compared to the band ratio model. The mean absolute percentage error(MAPE) and root mean square error(RMSE) were reduced by more than 46% compared to the band ratio model. [Conclusion] The BP neural network model outperforms the band ratio model in inversion accuracy. Temporally, chlorophyll-a concentrations inverted by the BP neural network model are higher during wet seasons and lower during dry seasons, with chlorophyll-a concentrations increasing in summer and decreasing in winter. Spatially, chlorophyll-a concentration is lower in the central lake and areas with high water flow, and higher along the shoreline and in regions with intense human activities, with the southern lake area showing higher concentrations than the northern area. The established BP neural network model demonstrates excellent performance in chlorophyll-a inversion in shallow lakes, providing important support for the conservation of ecological environment in lakes.

Keywords

remote sensing image / chlorophyll-a / neural network / shallow lakes / influencing factors