Abstract
Total phosphorus (TP) in water is an important indicator reflecting water environment and water ecology. If the concentration exceeds the standard, it will directly lead to eutrophication. The daily monitoring of total phosphorus in water bodies has already mentioned the important agenda of environmental protection, while the routine testing has a large workload and heavy tasks. We used satellite remote sensing technology to extract image data and establish a mathematical models, what was used to invert the total phosphorus concentration in water. Taking the Ring River as an example, we selected different time nodes to sample and measure the TP value, and use the landsat-8 image data to establish a semi-empirical regression model. The model structure, the calculation results found that the error with the measured data is within the controllable range. The method is simple in operation, saves resources, manpower and financial resources, and can accurately reflect the actual situation of the water body TP.
Highlights
In recent years, with the rapid development of China's economy, urbanization construction is getting faster and faster
Correlation analysis was performed between the single-band reflectance and the reflectance obtained by combining the bands with the measured values of Total phosphorus (TP)
It is concluded that the B2-B5 band has the best correlation with total phosphorus, and the binary linear regression model is obtained
Summary
With the rapid development of China's economy, urbanization construction is getting faster and faster. Urban closed water bodies are more susceptible to external disturbances and water quality is more susceptible to pollution [1,2]. The stench of water bodies and the frequent occurrence of cyanobacteria seriously affect the urban water ecological environment and the quality of life of residents. Due to the above advantages, Landsat-8 data can be better monitored by remote sensing than other satellite data. It is the reason why Landsat-8 satellite OLI data is selected as the remote sensing inversion [8]. We downloaded the quasi-synchronized Landsat-8 satellite OLI data on the US Geological Survey website (http://glovis.usgs.gov/) based on field sampling time. According to the above principles, satellite images on March 12, 2019 and April 13, 2019 were selected for modeling and verification [9]
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