Articles | Volume 15, issue 21
Geosci. Model Dev., 15, 7933–7976, 2022
https://doi.org/10.5194/gmd-15-7933-2022
Geosci. Model Dev., 15, 7933–7976, 2022
https://doi.org/10.5194/gmd-15-7933-2022
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 et al.

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Cited articles

AERONET: Aerosol Robotic Network (AERONET) Homepage, https://aeronet.gsfc.nasa.gov/ (last access: 21 October 2022), 2021. a, b
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Baldridge, A., Hook, S., Grove, C., and Rivera, G.: The ASTER spectral library version 2.0, Remote Sens. Environ., 113, 711–715, https://doi.org/10.1016/j.rse.2008.11.007, 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, https://doi.org/10.1080/22797254.2018.1507613, 2018a. a
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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.