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

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 opti...

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Veröffentlicht in:Algorithms Jg. 16; H. 6; S. 301
Hauptverfasser: Pachniak, Elliot, Fan, Yongzhen, Li, Wei, Stamnes, Knut
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.06.2023
Schlagworte:
Aerosols
Algorithms
Bayesian analysis
Bayesian inversion
Chlorophyll
Coasts
Conditional probability
Data processing
Datasets
Estimates
Machine learning
Neural networks
Ocean color
Optical properties
Probability density functions
Radiative transfer
Remote sensing
Sensors
Simulation
Uncertainty
Canada
ISSN:1999-4893, 1999-4893
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1999-4893
1999-4893
DOI:10.3390/a16060301