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RSS FeedsRemote Sensing, Vol. 11, Pages 2167: Ocean Color Quality Control Masks Contain the High Phytoplankton Fraction of Coastal Ocean Observations (Remote Sensing)

 
 

19 september 2019 10:02:32

 
Remote Sensing, Vol. 11, Pages 2167: Ocean Color Quality Control Masks Contain the High Phytoplankton Fraction of Coastal Ocean Observations (Remote Sensing)
 


Satellite estimation of oceanic chlorophyll-a content has enabled characterization of global phytoplankton stocks, but the quality of retrieval for many ocean color products (including chlorophyll-a) degrades with increasing phytoplankton biomass in eutrophic waters. Quality control of ocean color products is achieved primarily through the application of masks based on standard thresholds designed to identify suspect or low-quality retrievals. This study compares the masked and unmasked fractions of ocean color datasets from two Eastern Boundary Current upwelling ecosystems (the California and Benguela Current Systems) using satellite proxies for phytoplankton biomass that are applicable to satellite imagery without correction for atmospheric aerosols. Evaluation of the differences between the masked and unmasked fractions indicates that high biomass observations are preferentially masked in National Aeronautics and Space Administration (NASA) ocean color datasets as a result of decreased retrieval quality for waters with high concentrations of phytoplankton. This study tests whether dataset modification persists into the default composite data tier commonly disseminated to science end users. Further, this study suggests that statistics describing a dataset’s masked fraction can be helpful in assessing the quality of a composite dataset and in determining the extent to which retrieval quality is linked to biological processes in a given study region.


 
198 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 11, Pages 2168: Validation of AERONET-Estimated Upward Broadband Solar Fluxes at the Top-Of-The-Atmosphere with CERES Measurements (Remote Sensing)
Remote Sensing, Vol. 11, Pages 2186: Automatic Methodology to Detect the Coastline from Landsat Images with a New Water Index Assessed on Three Different Spanish Mediterranean Deltas (Remote Sensing)
 
 
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