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|Title:||A Statistical Method for Generating Cross-Mission Consistent Normalized Water-Leaving Radiances|
|Authors:||D ALIMONTE Davide; ZIBORDI GIUSEPPE; MELIN FREDERIC|
|Citation:||IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING vol. 46 no. 12 p. 4075-4093|
|Publisher:||IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC|
|Type:||Articles in Journals|
|Abstract:||Accurate merging of primary radiometric ocean color products like 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 multi-linear regression algorithm is here used to remove systematic differences between in situ and remote sensing measurements. The application of the correction scheme to SeaWiFS and MODIS primary radiometric products has shown an increased 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 Lwn at 412 nm has shown values of 12% and 2% for corrected with respect to values of 29% and -20% for uncorrected data, respectively. Similarly, the scatter and bias for SeaWiFS derived Lwn at 412 nm has shown values of 11% and 2% for corrected with respect to 27% and -11% 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 8% with respect to in situ values. Differently, the correction scheme does not improve Lwn values at the other center-wavelengths were the agreement between remote sensing and in situ data is already comparable to the intrinsic uncertainty of the in situ data. Finally, the study has shown the need of restricting the application of the correction scheme to data with spectral features represented within the reference dataset used for defining the correction parameters.|
|JRC Institute:||Institute for Environment and Sustainability|
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