Affiliations: [a] College of Mathematics and Information Technology, Hebei Normal University of Science and Technology, Hebei Innovation Center for Smart Perception and Applied Technology of Agricultural Data, Qinhuangdao, Hebei, China | [b] School of Computer Science and Technology, Donghua University, Shanghai, China
Correspondence:
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Corresponding author: Jinling Song, College of Mathematics and Information Technology, Hebei Normal University of Science & Technology, Hebei Innovation Center for Smart Perception and Applied Technology of Agricultural Data, Qinhuangdao, Hebei 066004, China. E-mail: songjinling99@163.com.
Abstract: Remote sensing inversion technology can be used for water quality parameter inversion to realize water quality monitoring in large scale space. The current research on water quality parameter inversion is only for a single satellite. In order to make full use of satellite image resources, the remote sensing images of GF-1B\C\D satellite group are taken as the research object. The Mulan River is taken as the research area. The linear regression method is used to construct the regression equations of total phosphorus and ammonia nitrogen, and the inversion model of total phosphorus and ammonia nitrogen is determined according the evaluation parameters. The MSE of the total phosphorus inversion model is 0.049, and the correlation between the inversion value and the measured value is 0.701. The MSE of the ammonia nitrogen inversion model is 0.063, and the correlation between the inversion value and the measured value is 0.813. These data show that the inversion effect is good. The inversion models are applied to the GF-1D satellite remote sensing image on March 15, 2021 to obtain the large-scale spatial distribution maps of total phosphorus concentration and ammonia nitrogen concentration. The water quality classification maps of the the Mulan River in Putian urban area are obtained too, which are convenient for further analysis and evaluation of the water quality.
Keywords: Satellite group, remote sensing image, water quality parameters, inversion model, linear regression
