Quantitative Remote Sensing Retrieval for Algae in Inland Waters
SONG Yu1, SONG Xiao-dong1, JIANG Hong2, GUO Zhao-bing3, GUO Qing-hai1*
1. Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China 2. International Institute for Earth System Science, Nanjing University, Nanjing 210093, China 3. School of Environment Science and Engineering, Nanjing University of Information & Technology, Nanjing 210044, China
Abstract：Chlorophyll is a very important indictor for the eutrophication status of lake water body. Using remotely sensed data to achieve real-time dynamic monitoring of the spatial distribution of chlorophyll has great importance. This paper aims to find the best band for the hyperspectral ratio model of chlorophyll-a, and take advantage of this model to implement remote sensing retrieval of algae in Taihu Lake. By the analysis of the spectral reflectance and water quality sampling data of the surface water body, the regression model between the ratio of reflectance and chlorophyll-a was built, and it was showed that the ratio model between the wavelengths around 700 and 625 nm had a relatively high coefficient value of determination (R2), while the ratio model constructed with 710 nm and visible wavelengths showed a descended R2 following with the increment of the visible wavelengths. Combined with in-situ water samplings analysis and spectral reflectance measurement, the results showed that it’s possible to retrieve algae water body using the MODIS green index (GI). The spatial distributions of chlorophyll-a and algae in Taihu Lake were extracted successfully using MODIS data with the algorithm developed in this paper.
Key words：Chlorophyll-a;Algae blooms;Hyperspectral;MODIS;Taihu Lake
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