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 (MODIS) 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.