Articles | Volume 15, issue 21
Development and technical paper
07 Nov 2022
Development and technical paper |  | 07 Nov 2022

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

Feng Yin, Philip E. Lewis, and Jose L. Gómez-Dans

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Cited articles

AERONET: Aerosol Robotic Network (AERONET) Homepage, (last access: 21 October 2022), 2021. a, b
Anderson, T. L., Charlson, R. J., Winker, D. M., Ogren, J. A., and Holmén, K.: Mesoscale variations of tropospheric aerosols, J. Atmos. Sci., 60, 119–136, 2003. a, b
Baetens, L. and Hagolle, O.: Sentinel-2 reference cloud masks generated by an active learning method, Zenodo [data set],, 2018. a
Baldridge, A., Hook, S., Grove, C., and Rivera, G.: The ASTER spectral library version 2.0, Remote Sens. Environ., 113, 711–715,, 2009. a, b
Barsi, J. A., Alhammoud, B., Czapla-Myers, J., Gascon, F., Haque, M. O., Kaewmanee, M., Leigh, L., and Markham, B. L.: Sentinel-2A MSI and Landsat-8 OLI radiometric cross comparison over desert sites, Eur. J. Remote Sens., 51, 822–837,, 2018a. a
Short summary
The proposed SIAC atmospheric correction method provides consistent surface reflectance estimations from medium spatial-resolution satellites (Sentinel 2 and Landsat 8) with per-pixel uncertainty information. The outputs from SIAC have been validated against a wide range of ground measurements, and it shows that SIAC can provide accurate estimations of both surface reflectance and atmospheric parameters, with meaningful uncertainty information.