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Modeling chlorophyll-a concentration in Taihu Lake based on different trophic state

In this paper, we want to search for the hyperspectral remote sensing bands most sensitive to chlorophyll-a concentration in different trophic states. Through repeated measurements in Taihu Lake, a large quantity of hyperspectral reflectance data and chlorophyll-a concentration data of lake were obtained from June to September of 2004 and 2005. The eligible hyperspectral data were analyzed to calculate remote sensing reflectance of water in Taihu Lake, and the data of chlorophyll-a concentration obtained from laboratory analysis were used to calculate Trophic State Index. According to the actual condition of Taihu Lake, the hyperspectral data were divided into three groups: mesotropher, eutropher and hyper eutropher. In each trophic state, chlorophyll-a concentration was then regressed against to identify the most sensitive hyperspectral bands. From the established regression models, we can get the conclusion: chlorophyll-a concentration is correspondingly lower under mesotrophic state, badly influenced by suspended matter, the spectral feature of chlorophyll-a is not evident, and the accuracy of regression model in this trophic state is not so satisfactory; in eutrophic state, differential algorithm has better retrieval result, the linear model based on this algorithm has the best estimation result; under hyper eutrophic state, the estimation accuracy is higher than the other two states as a whole. The fitting precision is the highest using the band ratio R719/R670 as independent variable in the quadratic equation model.