Estimation of higher chlorophylla concentrations using field spectral measurement and HJ-1A hyperspectral satellite data in Dianshan Lake,China |
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| Institution: | 1. INSERM-IRSET n° 1085, University of Rennes 1, F-35000 Rennes, France;2. INRA, Agrocampus Ouest, n° 1069 SAS, F-35000 Rennes, France;3. Faculty of Engineering, Universidad Distrital Francisco José de Caldas, Bogota, Colombia;4. Department of Epidemiology and Public Health, University Hospital, F-35000 Rennes, France;1. State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China;2. Dipartimento Farmaco Chimico Tecnologico, CSGI, University of Siena, 53100 Siena, Italy;1. Sino-US Global Logistics Institute, Shanghai Jiaotong University, Shanghai 200030, China;2. School of Management, Shanghai University, Shanghai 200444, China;3. School of Management, Shanghai University of Engineering Science, Shanghai 201620, China;1. State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, Jiangsu 210008, China;2. Department of Environmental Biology, The University of Adelaide, Adelaide, South Australia 5005, Australia;3. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, Jiangsu 210098, China;4. Department of Environmental Science and Engineering, School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China |
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| Abstract: | Based on in situ water sampling and field spectral measurements in Dianshan Lake, a semi-analytical three-band algorithm was used to estimate Chlorophylla (Chla) content in case II waters. The three bands selected to estimate Chla for high concentrations included 653, 691 and 748 nm. An equation, based on the difference in reciprocal reflectance between 653 and 691 nm, multiplied by reflectance at 748 nm as Rrs?1(653) ? Rrs?1 (691)] Rrs(748), explained 85.57% of variance in Chla concentration with a root mean square error (RMSE) of <6.56 mg/m3. In order to test the utility of this model with satellite data, HJ-1A Hyperspectral Imager (HSI) data were analyzed using comparable wavelengths selected from the in situ data B67?1(656) ? B80?1(716)] B87(753). This model accounted for 84.3% of Chla variation, estimating Chla concentrations with an RMSE of <4.23 mg/m3. The results illustrate that, based on the determined wavelengths, the spectrum-based model can achieve a high estimation accuracy and can be applied to hyperspectral satellite imagery especially for higher Chla concentration waters. |
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| Keywords: | Hyperspectral HJ-1A satellite Three-band model Chla Dianshan lake |
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