Water Environment Monitoring Based On Multi-source Remote Sensing Data And Its Influence On Cyanobacteria Blooms In Chao Lake

With the expansion of human activities and urbanization,the load capacity of pollutants in Chaohu Lake has gradually decreased,and the self-recovery function has gradually weakened with the accumulation of domestic and industrial wastewater discharge.Eutrophication directly leads to the occurrence of cyanobacteria bloom,damages the water ecological landscape,affects human production and life,and gradually attracts people’s attention.In the case of algal bloom outbreak,it is often difficult to capture key information such as the scope of the bloom comprehensively in the field ship survey.Satellite remote sensing has the characteristics of large-scale and periodic monitoring,which can just make up for the deficiency and realize the rapid and accurate acquisition of the outbreak scope,extent,duration and other information of algal blooms.Multi-source remote sensing data can not only characterize lake water dynamics,but also be an important bridge connecting lake water quality changes and cyanobacterial bloom distribution.In this paper,CryoSat-2 data was used to obtain the water level of Chaohu Lake from 2017 to 2021,and the water level was verified in combination with the measured water level.The Sentiel-2 data was used to monitor the water quality of Chaohu Lake,and the Sentinel-2 image and the measured data of sampling points were combined to construct the Chaohu Lake water quality inversion model.Spearman correlation coefficient method was used to analyze the correlation between TN and TP water quality indexes and water level of Chaohu Lake.In addition,Sentinel-2 images were used as data sources to extract the cyanobacteria blooms in Chaohu Lake from 2017 to 2021,and the relationship between water quality changes in Chaohu Lake and cyanobacteria bloom distribution was comprehensively analyzed.The results showed as follows:(1)Based on CryoSat-2 data,Chaohu Lake water level in this paper has a good extraction effect,and the consistency between satellite observed water level and measured water level series under different elevation systems is extremely significant.The Pearson correlation coefficients of measured water level and measured satellite observed water level scatter maps in 2018 and 2020 are 0.9688 and 0.9777,respectively.p≤0.001(p is the significance level).From the results of lake water level changes,Chaohu Lake water level was stable from January to May.From May,affected by the rainy season,Chaohu Lake water level began to rise and reached its peak in July and August.After September,the water level gradually decreased.(2)The inversion accuracy of TP and TN in Chaohu Lake from 2017 to 2021.The average error of TP and TN is 15% and 12% respectively,respectively,and the error of the inversion model is both within 30%.Spearman correlation coefficient was used to analyze the correlation between water level change and TN,TP and concentration in Chaohu Lake.The average correlation coefficient was-0.34 and-0.33,respectively,with absolute values < 0.5.In terms of spatial variation,the water quality of Chaohu Lake is generally poor in the west and good in the east.In terms of interannual variation,water quality in wet years is better than that in dry years and normal years.In terms of annual variation,the water quality was generally better in dry season than in wet season.In terms of correlation,the water level of Chaohu Lake is negatively correlated with the concentrations of TN and TP,and the concentrations of TN and TP decrease with the increase of water level.(3)By establishing three extraction models of chlorophyll-A concentration inversion,normalized vegetation index(NDVI)and plankton algae index(FAI),and then using the measured chlorophyll-A concentration data and threshold stability analysis,FAI(B4/B5/B10)is the optimal combination.The extraction of cyanobacteria blooms in Chaohu Lake during 2017-2021 has solved the extraction problem of cyanobacteria blooms.The functional relationship between NDVI and FAI results was established,and the threshold of FAI used for monitoring cyanobacteria blooms in Chaohu Lake was-1.1566.APA was used to accurately calculate the algal bloom area in the mixed pixel of algal bloom and estimate the actual algal bloom area in the whole water area of lake.(4)The change of water quality and the distribution of cyanobacteria blooms in Chaohu Lake from 2017 to 2021 are interrelated.Although the nutrient levels in the inflow rivers and lake bodies of Chaohu Lake have decreased,there is still a large gap between the nutrient threshold to control the occurrence of cyanobacteria blooms,so the intensity of cyanobacteria blooms has not weakened.Based on the spatial distribution characteristics of nitrogen and phosphorus in Chaohu Lake,the distribution of nitrogen and phosphorus concentration in Chaohu Lake determines the spatial pattern of cyanobacterial blooms.Although the response process of algae growth to nitrogen and phosphorus is very complex,the spatial distribution and intensity of cyanobacterial blooms in Chaohu Lake are determined by the spatial distribution of nitrogen and phosphorus in Chaohu Lake.This paper verifies the effectiveness of CryoSat-2 data in monitoring lake level,and CryoSat-2 data can also be used to monitor water quantity and flood.At the same time,Sentinel-2 image data was used to extract cyanobacteria bloom,which greatly improved the extraction accuracy compared with other satellites,and also provided data support for relevant departments to formulate cyanobacteria bloom control.Figure:[55] table:[25] reference:[98]...