Validation of ocean color remote sensing reflectance data: Analysis of results at European coastal sites

Abstract

From its initial measurements 20 years ago, the Ocean Color component of the Aerosol Robotic Network (AERONET-OC) has produced large validation data sets to assess the ocean color satellite data records. This study, applied to the standard atmospheric correction algorithm l2gen of the National Aeronautics and Space Administration, analyses the populations of residuals (differences between matching satellite and field data) of remote sensing reflectance RRS and, secondarily, aerosol optical thickness τa, and their validation statistics associated with data collected at seven AERONET-OC sites located in European coastal regions for six satellite missions. Validation statistics do not appear particularly sensitive to observation conditions, represented by viewing and solar geometry, aerosol optical thickness τa, Ångström exponent or water properties indexed by RRS, the most obvious exceptions being a systematic increase of the residuals for high solar zenith angles for all missions and sites. Residuals of RRS and τa tend to be inversely correlated as deviations in aerosol radiance and water-leaving radiance compensate each other. The spectra of a RRS uncertainty estimate σ decrease in a monotonous manner from 0.7 to 1.5 10−3 sr−1 for wavelengths around 412nm to 0.1–0.35 10−3 sr−1 around 667nm, with σ usually relatively high for waters associated with high amplitudes of RRS. At 412nm, σ for τa is mostly in the interval 0.03–0.04. For RRS, variations across missions of σ or of the relative differences between satellite and field data are overall smaller than variations across sites, which suggests that the products derived from various missions are rather consistent at a given location when processed with the considered algorithm. For a given mission, RRS residuals are well correlated between bands (inter-band correlation coefficient mostly larger than 0.5), which has implications on the propagation of uncertainties through bio-optical algorithms. For a given wavelength, residuals are correlated between missions to various degrees (most often with inter-mission correlation coefficients larger than 0.5). Besides implications as far as uncertainties of multi-mission merged products are concerned, this inter-mission correlation among residuals suggest that the residuals are not random and could be reduced.