RETRIEVAL OF CHLOROPHYLL-A CONCENTRATION IN PINGZHAI RESERVOIR BASED ON SENTINEL-2

To realize remote sensing monitoring of chlorophyll-a in Pingzhai Reservoir, the measured chlorophyll-a concentration and quasi-synchronized Sentinel-2 data of Pingzhai Reservoir on November 17th and 18th, 2017 were selected. The BP neural network model was established by selecting the best band combination to invert the chlorophyll-a of Pingzhai Reservoir, and its spatial distribution characteristics was analyzed. The Sentinel-2 red edge band was more sensitive to chlorophyll-a than the visible light band and had greater potential for chlorophyll-a concentration inversion. The band combination method with the largest correlation coefficient were: B5/B4, [1/B4-1/B5]*B6, [1/B4-1/B5]*B7, and [1/B4-1/B5]*B8; the resolvable coefficient R2 of BP neural network model was 0.9160 and the average relative error was 29.87%. The inversion accuracy of BP neural network model was better than that of three-band model; the concentration distribution of chlorophyll-a in Pingzhai Reservoir was obviously different. The concentration of the central reservoir in the open water was higher, and the concentration in the upper reaches of each tributary was lower. The research showed that Sentinel-2 data could be well applied to the retrieval of chlorophyll-a concentration in karst plateau lakes. The prediction results of BP neural network model was reasonable and reliable. The research results could provide a scientific basis for the water environment management of Pingzhai Reservoir.