Abstract
Atmospheric correction (AC) algorithms for ocean color (OC) data processing usually rely on ancillary data documenting the atmosphere and the sea state to help the calculation of the remote sensing reflectance <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{\text {RS}}$ </tex-math></inline-formula> from the radiance measured by a space sensor. This study aims at assessing the impact that the uncertainties associated with these ancillary data have on the AC outputs. For this objective, a full year of global Sea-viewing Wide Field-of-view Sensor (SeaWiFS) imagery is processed with the standard AC algorithm <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l2gen</i> of the National Aeronautics and Space Administration with different sets of ancillary data, the reference case with National Centers for Environmental Prediction (NCEP) Reanalysis-2 meteorological data and satellite ozone products, as well as with ten ensemble members from the European Centre for Medium-Range Weather Forecast (ECMWF) CERA-20C data. The spread within the ensemble data and the differences with respect to the reference case are taken as a measure of the uncertainties associated with ancillary data. The impact on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{\text {RS}}$ </tex-math></inline-formula> of perturbations in ancillary variables vary in space, the variables having the largest effects being wind speed and relative humidity, and ozone at bands where ozone absorption is largest, while sea-level pressure and precipitable water have the smallest effect. Sensitivity coefficients quantifying the relationship between perturbations in ancillary variables and effects on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{\text {RS}}$ </tex-math></inline-formula> change with variable and wavelength. At the global scale, the variations found on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{\text {RS}}$ </tex-math></inline-formula> when ancillary data are perturbed are usually small but not negligible and should be considered in the ocean color (OC) data uncertainty budget.
Highlights
O CEAN colour (OC) provides access to marine biological quantities, foremost the concentration of chlorophyll-a [1], a major and universal phytoplankton pigment
Even though there is growing emphasis on the provision of uncertainties for satellite data [4], [5], this practice is in its infancy in the field of OC, which can be partly explained by the fact that this is challenging: OC remote sensing is affected by a large and complex ensemble of error sources [6], including errors associated with the topof-atmosphere (TOA) signal and the numerous assumptions and approximations needed to solve the remote sensing problem and find a solution for RRS
Positive δdif for relative humidity (RH) in the Indian Ocean, the tropical Pacific or the Mediterranean Sea are associated with negative δdif for RRS(443); along the Atlantic African shores, δdif for RH is seen positive in the north and south and negative in the equatorial region, contrary to δdif for RRS(443)
Summary
O CEAN colour (OC) provides access to marine biological quantities, foremost the concentration of chlorophyll-a [1], a major and universal phytoplankton pigment. Even though there is growing emphasis on the provision of uncertainties for satellite data [4], [5], this practice is in its infancy in the field of OC, which can be partly explained by the fact that this is challenging: OC remote sensing is affected by a large and complex ensemble of error sources [6], including errors associated with the topof-atmosphere (TOA) signal and the numerous assumptions and approximations needed to solve the remote sensing problem and find a solution for RRS This requires a proper metrological treatment of the OC data processing to model sources of uncertainties and their propagation in the various processing steps [7]
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