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Algorithms

   
Inicio  /  Algorithms  /  Vol: 16 Par: 6 (2023)  /  Artículo
ARTÍCULO
TITULO

Quantifying Uncertainties in OC-SMART Ocean Color Retrievals: A Bayesian Inversion Algorithm

Elliot Pachniak    
Yongzhen Fan    
Wei Li and Knut Stamnes    

Resumen

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" role="presentation">??rsRrs R rs 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" role="presentation">??rsRrs R rs retrievals are provided to demonstrate the approach by applying it to MODIS, OLCI Sentinel-3, and VIIRS sensor data.

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Aerospace
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