Abstract
Atmospheric corrections introduce uncertainties in bottom-of-atmosphere Ocean Colour (OC) products. In this paper, we analyse the uncertainty budget of the SeaDAS atmospheric correction algorithm. A metrological approach is followed, where each of the error sources are identified in an uncertainty tree diagram and briefly discussed. Atmospheric correction algorithms depend on ancillary variables (such as meteorological properties and column densities of gases), yet the uncertainties in these variables were not studied previously in detail. To analyse these uncertainties for the first time, the spread in the ERA5 ensemble is used as an estimate for the uncertainty in the ancillary data, which is then propagated to uncertainties in remote sensing reflectances using a Monte Carlo approach and the SeaDAS atmospheric correction algorithm. In an example data set, wind speed and relative humidity are found to be the main contributors (among the ancillary parameters) to the remote sensing reflectance uncertainties.
Highlights
Ocean colour remote sensing opens a window onto ocean biology through the calculation of chlorophyll-a concentration from radiometric remote sensing reflectance measurements
In addition to the two scenes that are the focus of this study, we studied some additional scenes to check whether the conclusions made for our two main scenes hold for other cases
When inspecting the measurement function, we see that a number of correction factors are applied to the water-leaving radiance to get the remote-sensing reflectance normalized for bi-directional effects (e.g., [22]), which is the fundamental measurement from which Ocean Colour (OC)
Summary
Ocean colour remote sensing opens a window onto ocean biology through the calculation of chlorophyll-a concentration from radiometric remote sensing reflectance (normalised water-leaving radiances) measurements. There are many software packages (e.g., the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Data Analysis System, SeaDAS, developed by NASA) which successfully implement these standard approach algorithms and use reanalysis datasets for obtaining the ancillary parameters necessary in the atmospheric correction (mostly information about meteorological conditions and ozone concentration). Reanalysis datasets such as the one produced by National Centers for Environmental Prediction (NCEP) or by the European. In a companion paper to this work (Mélin et al, submit.), we perform a comprehensive analysis of the effects of the ancillary data as well as their uncertainties, spanning the whole globe and all of 2003 using consistent methods
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
