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.