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Remote sensing is useful technique to not only detect harmful algal bloom but also quantify the concentration of the harmful algae by using surface reflectance data. The hyperspectral image allows to investigate detail spatial information of harmful algae. Atmospheric correction is critical process in order to achieve an accurate optical signal in water surface. However, the influence of atmospheric correction performance on the estimation of PC concentration has rarely been studied. Thus, this study investigated the influence of three atmospheric correction method on the spatial distribution and concentration of phycocyanin (PC) concentration from estimation of the bio-optical algorithms. Atmospheric correction is an important image processing technique of hyperspectral image because the result of the correction is expected to influence bio-optical algorithms for quantifying phycocyanin (PC) concentration. From August to October, four field and airborne monitoring campaigns in the Baekje Weir in South Korea were implemented to measure water surface reflectance. PC concentrations in the surface water were analyzed using freezing and thawing method. The two band ratio algorithm, the three band ratio algorithm, the Li algorithm, and the Simis algorithm were utilized to estimate PC concentration. And, atmospheric correction of hyperspectral image was conducted by MODTRAN 6, ATCOR 4, and an artificial neural network (ANN). The ANN model utilized atmospheric parameters which generated from MODTRAN 6 to simulate water surface reflectance. Bio-optical algorithms were applied to the atmospherically corrected image for generating PC distribution map. Even though the atmospheric correction result from the ANN showed Nash-Sutcliffe efficiency (NSE) values of 0.80 and 0.76 for the training and validation steps, respectively, the PC concentration from the bio-optical algorithms showed NSE values ranged from 0.17 to 0.57. The ANN model was turned out to be required having large quantities of input data in order to have precise simulation performances. Atmospheric correction from MODTRAN 6 showed an NSE value over 0.8, whereas the correction from ATCOR 4 had a negative NSE value. However, the accuracy in certain regions of the reflectance spectra (λ<500 nm and λ>700 nm) was relatively low compared to the other spectra region because of insufficient atmospheric observation data during monitoring period. The relationship between atmospheric correction and bio-optical algorithm performance for MODTRAN 6, ATCOR 4, and ANN simulation showed different estimation of PC concentration in the atmospherically corrected images. The precise atmospheric correction by MODTRAN 6 enabled an accurate bio-optical algorithm performance and thus was critical to generating a satisfactory spatial distribution map of PC. In addition, the Li and Simis algorithms were revealed to be much more sensitive to the performance of the atmospheric correction than the other algorithms. Therefore, this study provides a useful tool for understanding importance of atmospheric correction influence of hyperspectral images on quantification of harmful algal blooms.
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