Retrieval of chlorophyll a concentration based on HJ-1 data and environmental variables: a case study of marine areas in Shenzhen
WU Jian-sheng1,2, WANG Wei1
1. Key Laboratory of Urban Habitant Environment Science and Technology, School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China;
2. Laboratory of Earth Surface Processes of Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
Abstract：The concentration of chlorophyll a(Chl a) is an important indicator of eutrophication in coastal marine environments.This research retrieved the Chl a concentration of marine areas in Shenzhen using environmental variables such as the kernel density of land-based pollution outfall, distance to the harbor approach, and the distance to marine aquaculture zones.The environmental in situ data was then combined with HJ-1 multispectral data to derive two back-propagation(BP) neural-network models using Matlab.The BP neural-network models of this study were used to test whether the introduction of environmental variables could improve the accuracy of Chl a concentration estimates obtained with BP neural networks.In addition, the sensitivity of input parameters was analyzed.Results showed that:(1) The introduction of environmental variables could greatly improve the retrieval accuracy of a BP neural-network.The retrievable accuracy of the BP neural-network model modified with environmental variables was better than that of the original BP neural-network model.The MSEs(mean squared errors) of training and verification of the BP neural-network model with environmental variables were 4.71 μg/L and 3.50 μg/L, respectively.The original BP neural-network model had MSEs of training and verification of 10.98 μg/L and 12.61 μg/L, respectively.(2) The approach of using a BP neural-network model with environmental variables was investigated further.The input layer had seven variables including blue reflectance, green reflectance, red reflectance, near-infrared reflectance, the kernel density of land-based pollution outfall, distance to the harbor approach, and the distance to marine aquaculture zones.The hidden layer had five nodes.The output layer was the Chl a concentration.(3) The Chl a concentration was most sensitive to the kernel density of land-based pollution outfall, followed sequentially by the near-infrared reflectance, red reflectance, distance to the harbor approach, blue reflectance, green reflectance, and the distance to marine aquaculture zones.
WU Jian-sheng,WANG Wei. Retrieval of chlorophyll a concentration based on HJ-1 data and environmental variables: a case study of marine areas in Shenzhen[J]. Marine Environmental Science, 2018, 37(3): 424-431.
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