Publications

Melin, F; Sclep, G; Jackson, A; Sathyendranath, S (2016). Uncertainty estimates of remote sensing reflectance derived from comparison of ocean color satellite data sets. REMOTE SENSING OF ENVIRONMENT, 177, 107-124.

Abstract
Assigning uncertainty to ocean-color satellite products is a requirement to allow informed use of these data. Here, uncertainty estimates are derived using the comparison on a 12th-degree grid of coincident daily records of the remote-sensing reflectance R-RS obtained with the same processing chain from three satellite missions, MERIS, MODIS and SeaWiFS. The approach is spatially resolved and produces sigma, the part of the R-RS uncertainty budget associated with random effects. The global average of sigma decreases with wavelength from approximately 0.7-0.910(-3) sr(-1) at 412 nm to 0.05-0.1 10(-3) sr(-1) at the red band, with uncertainties on sigma evaluated as 20-30% between 412 and 555 nm, and 30-40% at 670 nm. The distribution of sigma shows a restricted spatial variability and small variations with season, which makes the multi-annual global distribution of sigma-an estimate applicable to all retrievals of the considered missions. The comparison of sigma with other uncertainty estimates derived from field data or with the support of algorithms provides a consistent picture. When translated in relative terms, and assuming a relatively low bias, the distribution of sigma suggests that the objective of a 5% uncertainty is fulfilled between 412 and 490 nm for oligotrophic waters (chlorophyll-a concentration below 0.1 mg m(-3)). This study also provides comparison statistics. Spectrally, the mean absolute relative difference between R-RS from different missions shows a characteristic U-shape with both ends at blue and red wavelengths inversely related to the amplitude of R-RS. On average and for the considered data sets, SeaWiFS R-RS tend to be slightly higher than MODIS R-RS, which in turn appear higher than MERIS R-RS. Biases between mission-specific R-RS may exhibit a seasonal dependence, particularly in the subtropical belt. (C) 2016 The Authors. Published by Elsevier Inc.

DOI:
10.1016/j.rse.2016.02.014

ISSN:
0034-4257