Quantifying Uncertainties in OC-SMART Ocean Color Retrievals: A Bayesian Inversion Algorithm
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Published:2023-06-16
Issue:6
Volume:16
Page:301
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ISSN:1999-4893
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Container-title:Algorithms
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language:en
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Short-container-title:Algorithms
Author:
Pachniak Elliot1ORCID, Fan Yongzhen2, Li Wei1, Stamnes Knut1ORCID
Affiliation:
1. Light and Life Laboratory, Physics Department, Stevens Institute of Technology, Hoboken, NJ 07030, USA 2. Earth System Science Interdisciplinary Center, Cooperative Institute for Satellite Earth System Studies, University of Maryland, College Park, MD 20742, USA
Abstract
The Ocean Color—Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART) is a robust data processing platform utilizing scientific machine learning (SciML) in conjunction with comprehensive radiative transfer computations to provide accurate remote sensing reflectances (Rrs estimates), aerosol optical depths, and inherent optical properties. This paper expands the capability of OC-SMART by quantifying uncertainties in ocean color retrievals. Bayesian inversion is used to relate measured top of atmosphere radiances and a priori data to estimate posterior probability density functions and associated uncertainties. A framework of the methodology and implementation strategy is presented and uncertainty estimates for Rrs retrievals are provided to demonstrate the approach by applying it to MODIS, OLCI Sentinel-3, and VIIRS sensor data.
Funder
NASA’s OBB program NOAA
Subject
Computational Mathematics,Computational Theory and Mathematics,Numerical Analysis,Theoretical Computer Science
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