Quantitative remote sensing inversion of constituents in Case II waters has been a difficult and hot issue. It is due to the complex interaction among the constituents (e.g. pure water, phytoplankton, non-phytoplanktonic suspended solids and CDOM). In this paper we analyze the spectral decomposition algorithm of the optically active substances (OAS) in Taihu Lake. It contains two steps. First step is to obtain standard reflectance spectra of end-members (Chl a, NPSS, CDOM and pure Water) by simulation experience using Hydrolight; second step is to establish a number of equations (according to the numbers of end-members) obtained from different wavelengths and derive the decomposition coefficients to be used as the independent variables in the SDA-based estimation model. The results show that the decomposition coefficients obtained from spectral decomposition algorithm are highly independent. Moreover, we build the band ratio model based on the same data as the spectral decomposition algorithm. Then we apply the two models to the experimental data in November, 2008, and get the average relative error of 22.4% and 37.7%, which shows that to some extent the spectral decomposition algorithm has more seasonal versatility than that of band ratio model. The average relative error of chlorophyll concentration estimation model is 31.7%, which shows low inversion accuracy. The reason may be due to the concentration of chlorophyll is too low compared to non-algal suspended solids concentration. The average concentrations were 17.3μg/L and 78.6mg/L. So, to some extent, the spectral information of chlorophyll is concealed by the high suspended solids.
LU Chaoping, LV Heng, LI Yunmei
. Algorithms Based on Spectral Decomposition Algorithm for Retrieval of Constituents in Taihu Lake[J]. Journal of Geo-information Science, 2011
, 13(5)
: 687
-694
.
DOI: 10.3724/SP.J.1047.2011.00687
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