A Statistical Method for Generating Cross-Mission Consistent Normalized Water-Leaving Radiances

The accurate merging of primary radiometric ocean color products such as the normalized water-leaving radiance requires combining data from various space missions, which may be affected by different uncertainties as resulting from absolute calibration and minimization of the atmospheric effects. A statistical correction scheme based on a multilinear regression algorithm is used here to remove systematic differences between in situ and remote-sensing measurements. The application of the correction scheme to Sea-viewing Wide Field-of-View Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MOINS) primary radiometric products improves the convergence between remote-sensing and in situ measurements, with the largest effects at 412 and 443 nm. Specifically, the scatter and bias of MODIS derived with respect to in situ L-wn at 412 nm have shown values of 12% and 3% for corrected with respect to values of 34% and -28% for uncorrected data, respectively. Similarly, the scatter and bias for SeaWiFS-derived L-wn at 412 nm have shown values of 14% and 4% for corrected with respect to 32% and -20% for uncorrected data. Results at 667 nm for MODIS and at 670 nm for SeaWiFS, although displaying a reduction in the scatter of data, have shown a significant residual bias of about 11% and 17% with respect to in situ values. Finally, it was shown the need for restricting the application of the correction scheme to data with atmospheric and marine optical features represented within the reference data set used to define the correction coefficients.