Developing a two-step method for retrieving cyanobacteria abundance from inland eutrophic lakes using MERIS data |
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| Institution: | 1. Agricultural & Biological Engineering, Purdue University, 225 S University St, West Lafayette, IN 47906, USA;2. Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, 550 Stadium Mall Dr, West Lafayette, IN 47906, USA;3. Lyles School of Civil Engineering, Purdue University, 550 Stadium Mall Dr, West Lafayette, IN 47907, USA;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. PML—Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth PL1 3DH, UK;2. IFREMER, Laboratoire d''écologie pélagique, DYNECO PELAGOS, BP 70, 29280 Plouzané, France;3. MARE—Marine and Environmental Sciences Centre, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal;4. CIMA—Centro de Investigação Marinha e Ambiental, FCT, Universidade do Algarve, Campus de Gambelas, 8005-139 Faro, Portugal;5. SAGREMARISCO Lda, Apartado 21, 8650-999 Vila do Bispo, Portugal;6. Brockmann Consult, Max-Planck-Str. 2, 21502 Geesthacht, Germany;1. National Oceanic and Atmospheric Administration, National Centers for Coastal Ocean Science, Silver Spring, MD 20910, United States;2. National Center for Water Quality Research, Heidelberg University, Tiffin, OH 44883, United States |
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| Abstract: | Cyanobacteria are the main dominant species in inland eutrophic lakes during algae blooms, and measures of cyanobacteria abundance can be used for monitoring and early detection of algal blooms by remote sensing. During May 2013 and August 2016, a total 137 water samples were collected from Lake Taihu and Lake Chaohu. Remote-sensing reflectance was measured, surface water was collected in the field, and chlorophyll-a concentration, phycocyanin concentration, suspended-matter concentration and phytoplankton pigment absorption parameters were measured in the laboratory. The composition and density of planktonic algae were also detected by microscope examination. The remote-sensing reflectance at 15 MERIS bands was simulated based on our measured spectral data, and a two-step method for detecting cyanobacteria abundance using the partial least squares model based on 5 MERIS bands was developed. The results showed that the estimation algorithm can predict cyanobacteria abundance in inland eutrophic lakes with satisfactory accuracy, with RMSE of 7.56 and MAPE of 13.44 %. This algorithm was successfully applied to the MERIS image acquired on August 12, 2010, and showed a reasonable spatial distribution of cyanobacteria abundance in Lake Taihu. It demonstrated that the developed estimation method was an effective way to monitor cyanobacteria abundance in water with a potential to be successfully applied to Sentinel-3 images. |
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| Keywords: | Cyanobacteria abundance MERIS bands Inland lake Remote sensing |
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