Articles | Volume 15, issue 21
https://doi.org/10.5194/gmd-15-7933-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-7933-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Bayesian atmospheric correction over land: Sentinel-2/MSI and Landsat 8/OLI
Feng Yin
CORRESPONDING AUTHOR
Department of Geography, University College London, Gower Street, London WC1E 6BT, United Kingdom
National Centre for Earth Observation (NCEO), Space Park Leicester, Leicester LE4 5SP, United Kingdom
Philip E. Lewis
Department of Geography, University College London, Gower Street, London WC1E 6BT, United Kingdom
National Centre for Earth Observation (NCEO), Space Park Leicester, Leicester LE4 5SP, United Kingdom
Jose L. Gómez-Dans
Department of Geography, University College London, Gower Street, London WC1E 6BT, United Kingdom
National Centre for Earth Observation (NCEO), Space Park Leicester, Leicester LE4 5SP, United Kingdom
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- GPP of a Chinese Savanna Ecosystem during Different Phenological Phases Simulated from Harmonized Landsat and Sentinel-2 Data X. Zhang et al. 10.3390/rs16183475
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- Need and vision for global medium-resolution Landsat and Sentinel-2 data products V. Radeloff et al. 10.1016/j.rse.2023.113918
- Harmonized Landsat and Sentinel-2 Data with Google Earth Engine E. Berra et al. 10.3390/rs16152695
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- Quantifying Forest Cover Loss during the COVID-19 Pandemic in the Lubumbashi Charcoal Production Basin (DR Congo) through Remote Sensing and Landscape Analysis Y. Useni Sikuzani et al. 10.3390/resources13070095
- Detecting Coseismic Landslides in GEE Using Machine Learning Algorithms on Combined Optical and Radar Imagery S. Peters et al. 10.3390/rs16101722
- Improved prediction of rice yield at field and county levels by synergistic use of SAR, optical and meteorological data W. Yu et al. 10.1016/j.agrformet.2023.109729
- MANGLEE: A Tool for Mapping and Monitoring MANgrove Ecosystem on Google Earth Engine—A Case Study in Ecuador L. Caiza-Morales et al. 10.1007/s41651-024-00175-3
- A framework for improved predictions of the climate impacts on potential yields of UK winter wheat and its applicability to other UK crops G. Hayman et al. 10.1016/j.cliser.2024.100479
- LinkNet-Spectral-Spatial-Temporal Transformer Based on Few-Shot Learning for Mangrove Loss Detection with Small Dataset I. Panuntun et al. 10.3390/rs16061078
- Early-Stage Mapping of Winter Canola by Combining Sentinel-1 and Sentinel-2 Data in Jianghan Plain China T. Liu et al. 10.3390/rs16173197
- Climate change impacts on streamflow, sediment load and carbon fluxes in the Lena River delta S. Chalov et al. 10.1016/j.ecolind.2023.111252
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Latest update: 30 Jan 2025
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.
The proposed SIAC atmospheric correction method provides consistent surface reflectance...