Application of spectral decomposition algorithm for mapping water quality in a turbid lake (Lake Kasumigaura,Japan) from Landsat TM data |
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| Authors: | Youichi Oyama Bunkei Matsushita Takehiko Fukushima Kazuo Matsushige Akio Imai |
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| Institution: | 1. Department of Oceanography, University of Cape Town, Rondebosch, 7701, Cape Town, South Africa;2. Brockmann Consult GmbH, Max-Planck-Str. 2, 21502 Geesthacht, Germany;3. Odermatt & Brockmann GmbH, c/o the HUB Association, Viaduktstrasse 93-95, 8005 Zurich, Switzerland;1. Ocean Process Analysis Laboratory, University of New Hampshire, Durham, NH 03824, USA;2. European Commission—Joint Research Centre, Institute for Environment and Sustainability, TP 272, via E. Fermi 2749, I-21027 Ispra, VA, Italy;3. Department of Biological Sciences, University of New Hampshire, Durham, NH 03824, USA;4. Image Processing Laboratory (IPL). Universitat de València Catedrático José Beltrán, 2E-46980 Paterna (València), Spain;1. Institute for Great Lakes Research, Central Michigan University, United States;2. Department of Geography, Central Michigan University, United States;3. Department of Geosciences, University of Massachusetts, Amherst, United States;4. Department of Biology, Central Michigan University, United States |
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| Abstract: | The remote sensing of Case 2 water has been far less successful than that of Case 1 water, due mainly to the complex interactions among optically active substances (e.g., phytoplankton, suspended sediments, colored dissolved organic matter, and water) in the former. To address this problem, we developed a spectral decomposition algorithm (SDA), based on a spectral linear mixture modeling approach. Through a tank experiment, we found that the SDA-based models were superior to conventional empirical models (e.g. using single band, band ratio, or arithmetic calculation of band) for accurate estimates of water quality parameters. In this paper, we develop a method for applying the SDA to Landsat-5 TM data on Lake Kasumigaura, a eutrophic lake in Japan characterized by high concentrations of suspended sediment, for mapping chlorophyll-a (Chl-a) and non-phytoplankton suspended sediment (NPSS) distributions. The results show that the SDA-based estimation model can be obtained by a tank experiment. Moreover, by combining this estimation model with satellite-SRSs (standard reflectance spectra: i.e., spectral end-members) derived from bio-optical modeling, we can directly apply the model to a satellite image. The same SDA-based estimation model for Chl-a concentration was applied to two Landsat-5 TM images, one acquired in April 1994 and the other in February 2006. The average Chl-a estimation error between the two was 9.9%, a result that indicates the potential robustness of the SDA-based estimation model. The average estimation error of NPSS concentration from the 2006 Landsat-5 TM image was 15.9%. The key point for successfully applying the SDA-based estimation model to satellite data is the method used to obtain a suitable satellite-SRS for each end-member. |
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