Affiliations: [a] College of Environment and Ecology, Chongqing University, Chongqing | [b] Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Online Monitoring Center of Ecological and Environmental of the Three Gorges Project, Chongqing | [c] College of Materials Science and Engineering, Chongqing University, Chongqing, Chongqing | [d] Environmental Sciences Research Institute, Chongqing Collaborative Innovation Center of Big Data Application in Eco-Environmental Remote Sensing, Chongqing | [e]
Big Data Application Center of Ecological and Environment of Chongqing, Chongqing
Correspondence:
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Corresponding author. Fang Fang, College of Environment and Ecology, Chongqing University, Chongqing 400044, china. E-mail: fangfangcq@cqu.edu.cn.
Note: [1] The authors contributed equally to this work
Abstract: In recent years, with the rapid development of satellite technology, remote sensing inversion has been used as an important part of environmental monitoring. Remote sensing inversion has been prepared for large-scale water environment monitoring in the watershed that is difficult for the traditional water environment monitoring methods. This paper will discuss some shortcomings of traditional remote sensing inversion methods, and proposes a remote sensing inversion method based on convolutional neural network, which realizes large-scale remote sensing smart and automatic inversion monitoring of the water environment. The results show that the method is practical and effective, and can achieve high recognition accuracy for water blooms.
Keywords: Water quality monitoring, water environment, data mining, deep learning, remote sensing; artificial intelligence
