Abstract
This study evaluated the ability to improve Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) chl-a retrieval from optically shallow coastal waters by applying algorithms specific to the pixels’ benthic class. The form of the Ocean Color (OC) algorithm was assumed for this study. The operational atmospheric correction producing Level 2 SeaWiFS data was retained since the focus of this study was on establishing the benefit from the alternative specification of the bio-optical algorithm. Benthic class was determined through satellite image-based classification methods. Accuracy of the chl-a algorithms evaluated was determined through comparison with coincident in situ measurements of chl-a. The regionally-tuned models that were allowed to vary by benthic class produced more accurate estimates of chl-a than the single, unified regionally-tuned model. Mean absolute percent difference was approximately 70% for the regionally-tuned, benthic class-specific algorithms. Evaluation of the residuals indicated the potential for further improvement to chl-a estimation through finer characterization of benthic environments. Atmospheric correction procedures specialized to coastal environments were recognized as areas for future improvement as these procedures would improve both classification and algorithm tuning.
Highlights
As an index for phytoplankton, remotely-sensed chlorophyll a has been recognized as useful for establishing a baseline for water quality conditions and for assessing current status, even in optically-complex nearshore [1,2] and inland [3] environments
The present study evaluated the ability to improve satellite chl-a retrieval from optically shallow global use of the Ocean Color (OC) algorithm, and because band-ratio algorithms have been shown to have the coastal waters by employing algorithms specific to the pixels’ benthic class
Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) satellite Rrs and satellite-derived bottom classifications were utilized for evaluation of potential reduction in the uncertainty of satellite-based chl-a classification
Summary
As an index for phytoplankton, remotely-sensed chlorophyll a (chl-a) has been recognized as useful for establishing a baseline for water quality conditions and for assessing current status, even in optically-complex nearshore [1,2] and inland [3] environments. The Ocean Color (OC) Chlorophyll algorithm, the chl-a algorithm currently operational for SeaWiFS, is known to overestimate chl-a in nearshore waters [4,5]. The OC algorithm uses empirical correlations derived from global in situ data and, cannot account for systematic differences in the bio-optical relationship that may temporarily or permanently exist in certain geographic zones [6]. Compared to satellite-derived chl-a for oceanic waters, nearshore environments pose challenges from colored dissolved organic matter (CDOM), suspended sediments, bottom reflectance and atmospheric conditions which, like chl-a, absorb blue light preferentially.
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