Bayesian atmospheric correction over land: Sentinel-2/MSI and Landsat 8/OLI

Mitigating the impact of atmospheric effects on optical remote sensing data is critical for monitoring intrinsic land processes and developing Analysis Ready Data (ARD). This work develops an approach to this for the NERC NCEO medium resolution ARD Landsat 8 (L8) and Sentinel 2 (S2) products, called...

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Bibliographic Details
Published inGeoscientific Model Development Vol. 15; no. 21; pp. 7933 - 7976
Main Authors Yin, Feng, Lewis, Philip E, Gómez-Dans, Jose L
Format Journal Article
LanguageEnglish
Published Katlenburg-Lindau Copernicus GmbH 07.11.2022
Copernicus Publications
Subjects
Aerosols
Albedo
Atmosphere
Atmospheric correction
Atmospheric effects
Atmospheric monitoring
Bayesian analysis
Bayesian theory
Correlation coefficient
Correlation coefficients
Earth resources technology satellites
Estimates
Landsat
MODIS
Monitoring
Optical thickness
Point spread functions
Probability theory
Radiative transfer
Reflectance
Remote sensing
Resolution
Sensors
Spatial discrimination
Spatial resolution
Uncertainty
Water vapor
Water vapour
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Summary:Mitigating the impact of atmospheric effects on optical remote sensing data is critical for monitoring intrinsic land processes and developing Analysis Ready Data (ARD). This work develops an approach to this for the NERC NCEO medium resolution ARD Landsat 8 (L8) and Sentinel 2 (S2) products, called Sensor Invariant Atmospheric Correction (SIAC). The contribution of the work is to phrase and solve that problem within a probabilistic (Bayesian) framework for medium resolution multispectral sensors S2/MSI and L8/OLI and to provide per-pixel uncertainty estimates traceable from assumed top-of-atmosphere (TOA) measurement uncertainty, making progress towards an important aspect of CEOS ARD target requirements.
ISSN:1991-9603
1991-959X
1991-962X
1991-9603
1991-962X
DOI:10.5194/gmd-15-7933-2022