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An improved analytical algorithm for remote estimation of chlorophyll-a in highly turbid waters
An analytical three-band algorithm for spectrally estimating chlorophyll-a (Chl-a) has been proposed recently and the model does not need to be trained. However, the model did not consider the effects of the absorption due to colored detritus matter (CDM) and backscattering of the water column, resulting in an overestimation when Chl-a < 50 mg m− 3 and an underestimation when Chl-a ≥ 50 mg m− 3. In this letter, an improved three-band algorithm is proposed by integrating both backscattering and CDM absorption coefficients into the model. The results demonstrate that the improved three-band model resulted in more accurate estimation of Chl-a than the previously used three-band model when they were applied to water samples collected from five highly turbid water bodies with Chl-a ranging from 2.54 to 285.8 mg m− 3. The best results, after model modification, were observed in three Indiana reservoirs with R2 = 0.905 and relative root mean square error of 20.7%, respectively.
An improved analytical algorithm for remote estimation of chlorophyll-a in highly turbid waters
An analytical three-band algorithm for spectrally estimating chlorophyll-a (Chl-a) has been proposed recently and the model does not need to be trained. However, the model did not consider the effects of the absorption due to colored detritus matter (CDM) and backscattering of the water column, resulting in an overestimation when Chl-a < 50 mg m− 3 and an underestimation when Chl-a ≥ 50 mg m− 3. In this letter, an improved three-band algorithm is proposed by integrating both backscattering and CDM absorption coefficients into the model. The results demonstrate that the improved three-band model resulted in more accurate estimation of Chl-a than the previously used three-band model when they were applied to water samples collected from five highly turbid water bodies with Chl-a ranging from 2.54 to 285.8 mg m− 3. The best results, after model modification, were observed in three Indiana reservoirs with R2 = 0.905 and relative root mean square error of 20.7%, respectively.
An improved analytical algorithm for remote estimation of chlorophyll-a in highly turbid waters
An improved analytical algorithm for remote estimation of chlorophyll-a in highly turbid waters
Linhai Li (author) / Lin Li (author) / Kaishan Song (author) / Yunmei Li (author) / Kun Shi (author) / Zuchuan Li (author)
Environmental Research Letters ; 6 ; 034037
2011-07-01
7 pages
Article (Journal)
Electronic Resource
English
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