Effect of bio-optical parameter variability on the remote estimation of chlorophyll-a concentration in turbid productive waters: experimental results
G Dall'Olmo, AA Gitelson - Applied optics, 2005 - opg.optica.org
Applied optics, 2005•opg.optica.org
The analytical development and underlying hypothesis of a three-band algorithm for
estimating chlorophyll-a concentration ([Chla]) in turbid productive waters are presented.
The sensitivity of the algorithm to the spectral location of the bands used is analyzed. A large
set of experimental observations ([Chla] varied between 4 and 217 mg m^− 3 and turbidity
between 2 and 78 nephelometric turbidity units) was used to calibrate and validate the
algorithm. It was found that the variability of the chlorophyll-a fluorescence quantum yield …
estimating chlorophyll-a concentration ([Chla]) in turbid productive waters are presented.
The sensitivity of the algorithm to the spectral location of the bands used is analyzed. A large
set of experimental observations ([Chla] varied between 4 and 217 mg m^− 3 and turbidity
between 2 and 78 nephelometric turbidity units) was used to calibrate and validate the
algorithm. It was found that the variability of the chlorophyll-a fluorescence quantum yield …
The analytical development and underlying hypothesis of a three-band algorithm for estimating chlorophyll-a concentration ([Chla]) in turbid productive waters are presented. The sensitivity of the algorithm to the spectral location of the bands used is analyzed. A large set of experimental observations ([Chla] varied between 4 and 217 mg m^−3 and turbidity between 2 and 78 nephelometric turbidity units) was used to calibrate and validate the algorithm. It was found that the variability of the chlorophyll-a fluorescence quantum yield and of the chlorophyll-a specific absorption coefficient can reduce considerably the accuracy of remote predictions of [Chla]. Instead of parameterizing these interferences, their effects were minimized by tuning the spectral regions used in the algorithm. This allowed us to predict [Chla] with a relative root-mean-square error of less than 30%.
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