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.
Development and technical paper
| 
07 Nov 2022
Development and technical paper |
| 07 Nov 2022
| 07 Nov 2022
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
Related authors
Philip E. Lewis, Feng Yin, Jose Luis Gómez-Dans, Thomas Weiß, and Elhadi Adam
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-3-2024, 199–206, https://doi.org/10.5194/isprs-annals-X-3-2024-199-2024, https://doi.org/10.5194/isprs-annals-X-3-2024-199-2024, 2024
Jose Luis Gómez-Dans, Philip Edward Lewis, Feng Yin, Kofi Asare, Patrick Lamptey, Kenneth Kobina Yedu Aidoo, Dilys Sefakor MacCarthy, Hongyuan Ma, Qingling Wu, Martin Addi, Stephen Aboagye-Ntow, Caroline Edinam Doe, Rahaman Alhassan, Isaac Kankam-Boadu, Jianxi Huang, and Xuecao Li
Earth Syst. Sci. Data, 14, 5387–5410, https://doi.org/10.5194/essd-14-5387-2022, https://doi.org/10.5194/essd-14-5387-2022, 2022
Short summary
Short summary
We provide a data set to support mapping croplands in smallholder landscapes in Ghana. The data set contains information on crop location on three agroecological zones for 2 years, temporal series of measurements of leaf area index and leaf chlorophyll concentration for maize canopies and yield. We demonstrate the use of these data to validate cropland masks, create a maize mask using satellite data and explore the relationship between satellite measurements and yield.
Philip E. Lewis, Feng Yin, Jose Luis Gómez-Dans, Thomas Weiß, and Elhadi Adam
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-3-2024, 199–206, https://doi.org/10.5194/isprs-annals-X-3-2024-199-2024, https://doi.org/10.5194/isprs-annals-X-3-2024-199-2024, 2024
Dominik Rains, Isabel Trigo, Emanuel Dutra, Sofia Ermida, Darren Ghent, Petra Hulsman, Jose Gómez-Dans, and Diego G. Miralles
Earth Syst. Sci. Data, 16, 567–593, https://doi.org/10.5194/essd-16-567-2024, https://doi.org/10.5194/essd-16-567-2024, 2024
Short summary
Short summary
Land surface temperature and surface net radiation are vital inputs for many land surface and hydrological models. However, current remote sensing datasets of these variables come mostly at coarse resolutions, and the few high-resolution datasets available have large gaps due to cloud cover. Here, we present a continuous daily product for both variables across Europe for 2018–2019 obtained by combining observations from geostationary as well as polar-orbiting satellites.
Jose Luis Gómez-Dans, Philip Edward Lewis, Feng Yin, Kofi Asare, Patrick Lamptey, Kenneth Kobina Yedu Aidoo, Dilys Sefakor MacCarthy, Hongyuan Ma, Qingling Wu, Martin Addi, Stephen Aboagye-Ntow, Caroline Edinam Doe, Rahaman Alhassan, Isaac Kankam-Boadu, Jianxi Huang, and Xuecao Li
Earth Syst. Sci. Data, 14, 5387–5410, https://doi.org/10.5194/essd-14-5387-2022, https://doi.org/10.5194/essd-14-5387-2022, 2022
Short summary
Short summary
We provide a data set to support mapping croplands in smallholder landscapes in Ghana. The data set contains information on crop location on three agroecological zones for 2 years, temporal series of measurements of leaf area index and leaf chlorophyll concentration for maize canopies and yield. We demonstrate the use of these data to validate cropland masks, create a maize mask using satellite data and explore the relationship between satellite measurements and yield.
Anni Zhao, Chris M. Brierley, Zhiyi Jiang, Rachel Eyles, Damián Oyarzún, and Jose Gomez-Dans
Geosci. Model Dev., 15, 2475–2488, https://doi.org/10.5194/gmd-15-2475-2022, https://doi.org/10.5194/gmd-15-2475-2022, 2022
Short summary
Short summary
We describe the way that our group have chosen to perform our recent analyses of the Palaeoclimate Modelling Intercomparison Project ensemble simulations. We document the approach used to obtain and curate the simulations, process those outputs via the Climate Variability Diagnostics Package, and then continue through to compute ensemble-wide statistics and create figures. We also provide interim data from all steps, the codes used and the ability for users to perform their own analyses.
Cited articles
Baetens, L. and Hagolle, O.: Sentinel-2 reference cloud masks generated by an
active learning method, Zenodo [data set], https://doi.org/10.5281/ZENODO.1460961, 2018. a
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
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, 2018b. a
Benedetti, A., Morcrette, J.-J., Boucher, O., Dethof, A., Engelen, R. J., Fisher,
M., Flentje, H., Huneeus, N., Jones, L., Kaiser, J. W, and Kinne, S.: Aerosol analysis
and forecast in the European centre for medium-range weather forecasts
integrated forecast system: 2. Data assimilation, J. Geophys. Res.-Atmos., 114, D13205, https://doi.org/10.1029/2008jd011115, 2009. a, b, c
Bouvet, M., Thome, K., Berthelot, B., Bialek, A., Czapla-Myers, J., Fox, N. P.,
Goryl, P., Henry, P., Ma, L., Marcq, S., and Meygret, A.: RadCalNet: A radiometric
calibration network for earth observing imagers operating in the visible to
shortwave infrared spectral range, Remote Sens., 11, 2401, https://doi.org/10.3390/rs11202401, 2019. a, b
Briggs, W. L., Henson, V. E., and McCormick, S. F.: A Multigrid Tutorial,
Second Edition, Society for Industrial and Applied Mathematics,
https://doi.org/10.1137/1.9780898719505, 2000. a
Byrd, R. H., Lu, P., Nocedal, J., and Zhu, C.: A Limited Memory Algorithm for
Bound Constrained Optimization, SIAM J. Sci. Comput., 16,
1190–1208, https://doi.org/10.1137/0916069, 1995. a
Capderou, M.: Satellites: Orbits and Missions, Springer, https://doi.org/10.1007/b139118,
2005. a
CEOS: CEOS Analysis Ready Data Strategy,
http://ceos.org/ard/files/CEOS_ARD_Strategy_v1.0_1-Oct-2019.pdf
(last access: 3 March 2020), 2019. a
CEOS: CEOS Analysis Ready Data Surface Reflectance Specification,
https://ceos.org/ard/files/PFS/SR/v5.0/CARD4L_Product_Family_Specification_Surface_Reflectance-v5.0.pdf,
last access: 25 January 2020. a
CEOS: WGCV CARD4L Review Panel evaluation (SR PFS v5),
CEOS,
https://ceos.org/ard/files/Self%20Assessments/SR/v5.0/WGCV_CARD4L_Review_Panel_Assessment_USGS_SR_PFS_v5.pdf (last access: 21 October 2022), 2021a. a
CEOS: CEOS Analysis Ready Data, https://ceos.org/ard,
last access: 21 September 2021b. a
CEOS: Analysis Ready Data For Land,
https://ceos.org/ard/files/PFS/SR/v5.0/CARD4L_Product_Family_Specification_Surface_Reflectance-v5.0.pdf (last access: 21 October 2022),
2021c. a
Chatterjee, A., Michalak, A. M., Kahn, R. A., Paradise, S. R., Braverman,
A. J., and Miller, C. E.: A geostatistical data fusion technique for merging
remote sensing and ground-based observations of aerosol optical thickness,
J. Geophys. Res.-Atmos., 115, D20207, https://doi.org/10.1029/2009JD013765, 2010. a
Che, X., Zhang, H. K., and Liu, J.: Making Landsat 5, 7 and 8 reflectance
consistent using MODIS nadir-BRDF adjusted reflectance as reference, Remote Sens. Environ., 262, 112517, https://doi.org/10.1016/j.rse.2021.112517, 2021. a
Chen, J. and Zhu, W.: Comparing Landsat-8 and Sentinel-2 top of atmosphere and
surface reflectance in high latitude regions: case study in Alaska, Geocarto
International, 37, 6052–6071, https://doi.org/10.1080/10106049.2021.1924295, 2021. a
Clerc, S. and MPC Team: Sentinel-2 L1C data quality report, ESA, Tech. Rep, 59, 2021. a
Doxani, G., Vermote, E., Roger, J.-C., Gascon, F., Adriaensen, S., Frantz, D.,
Hagolle, O., Hollstein, A., Kirches, G., Li, F., Louis, J., Mangin, A.,
Pahlevan, N., Pflug, B., and Vanhellemont, Q.: Atmospheric Correction
Inter-Comparison Exercise, Remote Sens., 10, 352, https://doi.org/10.3390/rs10020352,
2018. a, b, c
Doxani, G., Vermote, E., Roger, J.-C., Skakun, S., Gascon, F., Collison, A.,
Keukelaere, L. D., Desjardins, C., Frantz, D., Hagolle, O., Kim, M., , Louis,
J., Pacifici, F., Pflug, B., Poilvé, H., Ramon, D., Richter, R., and
Yin, F.: Atmospheric Correction Inter-Comparison eXercise (ACIX II Land): an
atmospheric correction processors assessment for Landsat 8 and Sentinel-2
over land, Remote Sens. Environ., in review, 2022. a, b, c, d
Dubovik, O., Herman, M., Holdak, A., Lapyonok, T., Tanré, D., Deuzé, J. L., Ducos, F., Sinyuk, A., and Lopatin, A.: Statistically optimized inversion algorithm for enhanced retrieval of aerosol properties from spectral multi-angle polarimetric satellite observations, Atmos. Meas. Tech., 4, 975–1018, https://doi.org/10.5194/amt-4-975-2011, 2011. a, b, c
Dubovik, O., Lapyonok, T., Litvinov, P., Herman, M., Fuertes, D., Ducos, F.,
Lopatin, A., Chaikovsky, A., Torres, B., Derimian, Y., Huang, X.,
Aspetsberger, M., and Federspie, C.: GRASP: a versatile algorithm for
characterizing the atmosphere, sPIE: Newsroom,
https://doi.org/10.1117/2.1201408.005558, 2014. a
Duveiller, G., Baret, F., and Defourny, P.: Crop specific green area index
retrieval from MODIS data at regional scale by controlling pixel-target
adequacy, Remote Sens. Environ., 115, 2686–2701,
https://doi.org/10.1016/j.rse.2011.05.026, 2011. a, b
Eck, T. F., Holben, B., Reid, J., Dubovik, O., Smirnov, A., O'neill, N.,
Slutsker, I., and Kinne, S.: Wavelength dependence of the optical depth of
biomass burning, urban, and desert dust aerosols, J. Geophys. Res.-Atmos., 104, 31333–31349, 1999. a
Eilers, P. H.: A perfect smoother, Anal. Chem., 75, 3631–3636, 2003. a
Eilers, P. H., Pesendorfer, V., and Bonifacio, R.: Automatic smoothing of
remote sensing data, in: 2017 9th International Workshop on the Analysis of
Multitemporal Remote Sensing Images (MultiTemp), IEEE, 1–3, 2017. a
ESA: Sentinel-2,
https://sentinel.esa.int/web/sentinel/missions/sentinel-2 (last access: 21 October 2022),
2015. a
ESA: Gearing up for third Sentinel-2 satellite,
ESA, https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-2/Gearing_up_for_third_Sentinel-2_satellite (last access: 21 October 2022),
2021a. a
ESA: S2 MPC Level-2A Algorithm Theoretical Basis Document,
https://sentinel.esa.int/documents/247904/4363007/Sentinel-2-Level-2A-Algorithm-Theoretical-Basis-Document-ATBD.pdf/fe5bacb4-7d4c-9212-8606-6591384390c3 (last access: 21 October 2022),
2021b. a
ESA: S2 MPC Level-2A Algorithm Theoretical Basis Document,
https://step.esa.int/thirdparties/sen2cor/2.10.0/docs/S2-PDGS-MPC-L2A-ATBD-V2.10.0.pdf (last access: 21 October 2022),
2021c. a
Feng, M., Sexton, J. O., Huang, C., Masek, J. G., Vermote, E. F., Gao, F.,
Narasimhan, R., Channan, S., Wolfe, R. E., and Townshend, J. R.: Global
surface reflectance products from Landsat: Assessment using coincident
MODIS observations, Remote Sens. Environ., 134, 276–293,
https://doi.org/10.1016/j.rse.2013.02.031, 2013. a
Flood, N.: Comparing Sentinel-2A and Landsat 7 and 8 using surface reflectance
over Australia, Remote Sens., 9, 659, https://doi.org/10.3390/rs9070659, 2017. a
Foga, S., Scaramuzza, P. L., Guo, S., Zhu, Z., Dilley Jr., R. D., Beckmann, T.,
Schmidt, G. L., Dwyer, J. L., Hughes, M. J., and Laue, B.: Cloud detection
algorithm comparison and validation for operational Landsat data products,
Remote Sens. Environ., 194, 379–390, 2017. a
Franch, B., Vermote, E., Sobrino, J., and Fédèle, E.: Analysis of
directional effects on atmospheric correction, Remote Sens. Environ.,
128, 276–288, https://doi.org/10.1016/j.rse.2012.10.018, 2013. a, b
Francis, A., Mrziglod, J., Sidiropoulos, P., and Muller, J.-P.: Sentinel-2
Cloud Mask Catalogue, Zenodo [data set], https://doi.org/10.5281/ZENODO.4172871, 2020. a
Gascon, F., Bouzinac, C., Thépaut, O., Jung, M., Francesconi, B., Louis,
J., Lonjou, V., Lafrance, B., Massera, S., Gaudel-Vacaresse, A., and Languille, F.:
Copernicus Sentinel-2A calibration and products validation status, Remote Sens., 9, 584, https://doi.org/10.3390/rs9060584, 2017. a
GCOS: Albedo ESSENTIAL CLIMATE VARIABLE (ECV) FACTSHEET,
https://gcos.wmo.int/en/essential-climate-variables/albedo
(last access: 12 September 2022), 2019. a
Giles, D. M., Sinyuk, A., Sorokin, M. G., Schafer, J. S., Smirnov, A., Slutsker, I., Eck, T. F., Holben, B. N., Lewis, J. R., Campbell, J. R., Welton, E. J., Korkin, S. V., and Lyapustin, A. I.: Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements, Atmos. Meas. Tech., 12, 169–209, https://doi.org/10.5194/amt-12-169-2019, 2019. a, b
Gómez-Dans, J. L., Lewis, P. E., and Disney, M.: Efficient Emulation of
Radiative Transfer Codes Using Gaussian Processes and Application to Land
Surface Parameter Inferences, Remote Sens., 8, 119, https://doi.org/10.3390/rs8020119, 2016. a, b
Govaerts, Y. and Luffarelli, M.: Joint retrieval of surface reflectance and aerosol properties with continuous variation of the state variables in the solution space – Part 1: theoretical concept, Atmos. Meas. Tech., 11, 6589–6603, https://doi.org/10.5194/amt-11-6589-2018, 2018. a, b, c
Guanter, L., Del Carmen González-Sanpedro, M., and Moreno, J.: A
method for the atmospheric correction of ENVISAT/MERIS data over land
targets, Int. J. Remote Sens., 28, 709–728,
https://doi.org/10.1080/01431160600815525, 2007. a, b, c
Hagolle, O., Huc, M., Pascual, D., and Dedieu, G.: A Multi-Temporal and
Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for
the Atmospheric Correction of FormoSat-2, LandSat, VENS and
Sentinel-2 Images, Remote Sens., 7, 2668–2691, https://doi.org/10.3390/rs70302668,
2015a. a
Hall, D. K. and Riggs, G. A.: Normalized-Difference Snow Index (NDSI), in:
Encyclopedia of Earth Sciences Series, Springer Netherlands, 779–780,
https://doi.org/10.1007/978-90-481-2642-2_376, 2011. a
Hecht-Nielsen, R.: Theory of the backpropagation neural network, in: Neural networks for perception, Academic Press, 65–93, https://doi.org/10.1109/IJCNN.1989.118638, 1992. a
Helder, D., Markham, B., Morfitt, R., Storey, J., Barsi, J., Gascon, F., Clerc,
S., LaFrance, B., Masek, J., Roy, D., Lewis, A., and Pahlevan, N.:
Observations and Recommendations for the Calibration of Landsat 8 OLI and
Sentinel 2 MSI for Improved Data Interoperability, Remote Sens., 10,
1340, https://doi.org/10.3390/rs10091340, 2018. a
Hilker, T.: Chapter 3.02 – Surface Reflectance/Bidirectional Reflectance
Distribution Function, in: Comprehensive Remote Sensing, edited by: Liang,
S., Elsevier, Oxford, 2–8,
https://doi.org/10.1016/B978-0-12-409548-9.10347-1, 2018. a
Hou, W., Wang, J., Xu, X., Reid, J. S., Janz, S. J., and Leitch, J. W.: An
algorithm for hyperspectral remote sensing of aerosols: 3. Application to the
GEO-TASO data in KORUS-AQ field campaign, J. Quant.
Spectrosc. Ra., 253, 107161,
https://doi.org/10.1016/j.jqsrt.2020.107161, 2020. a, b
Hsu, N., Tsay, S.-C., King, M., and Herman, J.: Aerosol Properties Over
Bright-Reflecting Source Regions, IEEE T. Geosci.
Remote, 42, 557–569, https://doi.org/10.1109/tgrs.2004.824067, 2004. a
Hsu, N., Tsay, S.-C., King, M., and Herman, J.: Deep Blue Retrievals of Asian
Aerosol Properties During ACE-Asia, IEEE T. Geosci.
Remote, 44, 3180–3195, https://doi.org/10.1109/tgrs.2006.879540, 2006. a
Hsu, N. C., Jeong, M.-J., Bettenhausen, C., Sayer, A. M., Hansell, R., Seftor,
C. S., Huang, J., and Tsay, S.-C.: Enhanced Deep Blue aerosol retrieval
algorithm: The second generation, J. Geophys. Res.-Atmos., 118, 9296–9315, https://doi.org/10.1002/jgrd.50712, 2013. a, b
Hughes, M. J. and Hayes, D. J.: Automated detection of cloud and cloud shadow
in single-date Landsat imagery using neural networks and spatial
post-processing, Remote Sens., 6, 4907–4926, 2014. a
Ilehag, R., Schenk, A., Huang, Y., and Hinz, S.: KLUM: An Urban VNIR and
SWIR Spectral Library Consisting of Building Materials, Remote Sens., 11,
2149, https://doi.org/10.3390/rs11182149, 2019. a, b
Jacquemoud, S., Verhoef, W., Baret, F., Bacour, C., Zarco-Tejada, P. J., Asner,
G. P., François, C., and Ustin, S. L.: PROSPECT + SAIL models: A
review of use for vegetation characterization, Remote Sens. Environ.,
113, S56–S66, https://doi.org/10.1016/j.rse.2008.01.026, 2009. a
Justice, C. O., Román, M. O., Csiszar, I., Vermote, E. F., Wolfe, R. E.,
Hook, S. J., Friedl, M., Wang, Z., Schaaf, C. B., Miura, T., and Tschudi, M.: Land and
cryosphere products from Suomi NPP VIIRS: Overview and status, J. Geophys. Res.-Atmos., 118, 9753–9765, 2013. a
Kaiser, G. and Schneider, W.: Estimation of sensor point spread function by
spatial subpixel analysis, Int. J. Remote Sens., 29,
2137–2155, https://doi.org/10.1080/01431160701395310, 2008. a
Kaminski, T., Pinty, B., Voßbeck, M., Lopatka, M., Gobron, N., and Robustelli, M.: Consistent retrieval of land surface radiation products from EO, including traceable uncertainty estimates, Biogeosciences, 14, 2527–2541, https://doi.org/10.5194/bg-14-2527-2017, 2017. a
Kaufman, Y. J.: Aerosol optical thickness and atmospheric path radiance,
J. Geophys. Res.-Atmos., 98, 2677–2692, 1993. a
Kaufman, Y. J., Tanré, D., Remer, L. A., Vermote, E. F., Chu, A., and
Holben, B. N.: Operational remote sensing of tropospheric aerosol over land
from EOS moderate resolution imaging spectroradiometer, J. Geophys. Res.-Atmos., 102, 17051–17067,
https://doi.org/10.1029/96jd03988, 1997. a
Kokaly, R. F., Clark, R. N., Swayze, G. A., Livo, K. E., Hoefen, T. M., Pearson, N. C., Wise, R. A., Benzel, W. M., Lowers, H. A., Driscoll, R. L., and Klein, A. J.: USGS Spectral Library Version 7 Data: U.S. Geological Survey data release [data set], https://doi.org/10.5066/F7RR1WDJ, 2017. a, b
Ku, H.: Notes on the use of propagation of error formulas, J. Res.
Nat. Bur. Stand., 70C, 263, https://doi.org/10.6028/jres.070c.025,
1966. a
Lamquin, N., Woolliams, E., Bruniquel, V., Gascon, F., Gorroño, J.,
Govaerts, Y., Leroy, V., Lonjou, V., Alhammoud, B., Barsi, J. A., and Czapla-Myers, J. S.: An
inter-comparison exercise of Sentinel-2 radiometric validations assessed by
independent expert groups, Remote Sens. Environ., 233, 111369,
2019. a, b, c, d
Levy, R. C., Remer, L. A., and Dubovik, O.: Global aerosol optical properties
and application to Moderate Resolution Imaging Spectroradiometer aerosol
retrieval over land, J. Geophys. Res.-Atmos., 112,
D13210, https://doi.org/10.1029/2006jd007815, 2007a. a
Levy, R. C., Remer, L. A., Mattoo, S., Vermote, E. F., and Kaufman, Y. J.:
Second-generation operational algorithm: Retrieval of aerosol properties over
land from inversion of Moderate Resolution Imaging Spectroradiometer spectral
reflectance, J. Geophys. Res.-Atmos., 112, D13211,
https://doi.org/10.1029/2006jd007811, 2007b. a
Levy, R. C., Mattoo, S., Munchak, L. A., Remer, L. A., Sayer, A. M., Patadia, F., and Hsu, N. C.: The Collection 6 MODIS aerosol products over land and ocean, Atmos. Meas. Tech., 6, 2989–3034, https://doi.org/10.5194/amt-6-2989-2013, 2013. a, b
Li, Q., Li, C., and Mao, J.: Evaluation of atmospheric aerosol optical depth
products at ultraviolet bands derived from MODIS products, Aerosol Sci. Technol., 46, 1025–1034, 2012. a
Li, Y., Chen, J., Ma, Q., Zhang, H. K., and Liu, J.: Evaluation of Sentinel-2A
Surface Reflectance Derived Using Sen2Cor in North America, IEEE J.
Sel. Top. Appl., 11,
1997–2021, https://doi.org/10.1109/jstars.2018.2835823, 2018. a
Liang, S.: Narrowband to broadband conversions of land surface albedo I:
Algorithms, Remote Sens. Environ., 76, 213–238,
https://doi.org/10.1016/S0034-4257(00)00205-4, 2001. a
Lipponen, A., Mielonen, T., Pitkänen, M. R. A., Levy, R. C., Sawyer, V. R., Romakkaniemi, S., Kolehmainen, V., and Arola, A.: Bayesian aerosol retrieval algorithm for MODIS AOD retrieval over land, Atmos. Meas. Tech., 11, 1529–1547, https://doi.org/10.5194/amt-11-1529-2018, 2018. a
Louis, J., Debaecker, V., Pflug, B., Main-Knorn, M., Bieniarz, J.,
Mueller-Wilm, U., Cadau, E., and Gascon, F.: Sentinel-2 Sen2Cor: L2A
processor for users, in: Proceedings Living Planet Symposium 2016,
Spacebooks Online, 1–8, https://elib.dlr.de/107381/ (last access: 22 October 2022), 2016. a
Lyapustin, A., Martonchik, J., Wang, Y., Laszlo, I., and Korkin, S.: Multiangle
implementation of atmospheric correction (MAIAC): 1. Radiative transfer
basis and look-up tables, J. Geophys. Res.-Atmos.,
116, D03211, https://doi.org/10.1029/2010JD014986, 2011. a
Lyapustin, A., Wang, Y., Korkin, S., and Huang, D.: MODIS Collection 6 MAIAC algorithm, Atmos. Meas. Tech., 11, 5741–5765, https://doi.org/10.5194/amt-11-5741-2018, 2018. a
Masek, J., Vermote, E., Saleous, N., Wolfe, R., Hall, F., Huemmrich, F., Gao,
F., Kutler, J., and Lim, T.: LEDAPS Landsat Calibration, Reflectance,
Atmospheric Correction Preprocessing Code, ORNL DAAC [code],
https://doi.org/10.3334/ornldaac/1080, 2012. a
Masek, J. G., Wulder, M. A., Markham, B., McCorkel, J., Crawford, C. J.,
Storey, J., and Jenstrom, D. T.: Landsat 9: Empowering open science and
applications through continuity, Remote Sens. Environ., 248,
111968, https://doi.org/10.1016/j.rse.2020.111968, 2020. a
McGill, R., Tukey, J. W., and Larsen, W. A.: Variations of box plots,
Am. Stat., 32, 12–16, 1978. a
Merchant, C. J., Paul, F., Popp, T., Ablain, M., Bontemps, S., Defourny, P., Hollmann, R., Lavergne, T., Laeng, A., de Leeuw, G., Mittaz, J., Poulsen, C., Povey, A. C., Reuter, M., Sathyendranath, S., Sandven, S., Sofieva, V. F., and Wagner, W.: Uncertainty information in climate data records from Earth observation, Earth Syst. Sci. Data, 9, 511–527, https://doi.org/10.5194/essd-9-511-2017, 2017. a
Morcrette, J.-J., Boucher, O., Jones, L., Salmond, D., Bechtold, P., Beljaars,
A., Benedetti, A., Bonet, A., Kaiser, J. W., Razinger, M., and Schulz, M.: Aerosol
analysis and forecast in the European Centre for medium-range weather
forecasts integrated forecast system: Forward modeling, J. Geophys. Res.-Atmos., 114, D06206, https://doi.org/10.1029/2008JD011235, 2009. a, b, c
MPC Team: Sentinel-2 L1C Data Quality Report Issue 67 (September 2021) –
Sentinel Online,
https://sentinels.copernicus.eu/documents/247904/685211/Sentinel-2_L1C_Data_Quality_Report.pdf/6ad66f15-48ca-4e65-b304-59ef00b7f0e0?t=1631086843717,
last access: 24 September 2021. a
NCEO: Dataset Record: NCEO Analysis Ready Data,
CEDA, https://catalogue.ceda.ac.uk/uuid/ad7de4e3b3b34cc0adca86c68e94d3a1 (last access: 21 October 2022),
2021. a
Nie, Z., Chan, K. K. Y., and Xu, B.: Preliminary Evaluation of the Consistency
of Landsat 8 and Sentinel-2 Time Series Products in An Urban Area – An
Example in Beijing, China, Remote Sens., 11, 2957, https://doi.org/10.3390/rs11242957, 2019. a
Niro, F., Goryl, P., Dransfeld, S., Boccia, V., Gascon, F., Adams, J., Themann,
B., Scifoni, S., and Doxani, G.: European Space Agency (ESA)
Calibration/Validation Strategy for Optical Land-Imaging Satellites and
Pathway towards Interoperability, Remote Sens., 13, 3003, https://doi.org/10.3390/rs13153003, 2021. a
Pahlevan, N., Sarkar, S., Franz, B., Balasubramanian, S., and He, J.:
Sentinel-2 MultiSpectral Instrument (MSI) data processing for aquatic
science applications: Demonstrations and validations, Remote Sens. Environ., 201, 47–56, https://doi.org/10.1016/j.rse.2017.08.033, 2017. a
Pahlevan, N., Chittimalli, S. K., Balasubramanian, S. V., and Vellucci, V.:
Sentinel-2/Landsat-8 product consistency and implications for monitoring
aquatic systems, Remote Sens. Environ., 220, 19–29,
https://doi.org/10.1016/j.rse.2018.10.027, 2019. a
Pérez-Ramírez, D., Whiteman, D. N., Smirnov, A., Lyamani, H., Holben,
B. N., Pinker, R., Andrade, M., and Alados-Arboledas, L.: Evaluation of
AERONET precipitable water vapor versus microwave radiometry, GPS, and
radiosondes at ARM sites, J. Geophys. Res.-Atmos., 119,
9596–9613, 2014. a
Pflug, B., Louis, J., Debraecker, V., Müller-Wilm, U., Quang, C., Gascon,
F., and Boccia, V.: Next updates for atmospheric correction processor
Sen2Cor, in: SPIE 11533, Image and Signal Processing for Remote Sensing
XXVI, p. 1153304, https://doi.org/10.1117/12.2574035, 2020. a
RadCalNet: RadCalNet Guidance Site Selection, Tech. rep., RadCalNet,
2018a. a
RadCalNet: RadCalNet Guidance Site Selection, Tech. rep., RadCalNet,
2018b. a
Remer, L. A., Kaufman, Y. J., Tanré, D., Mattoo, S., Chu, D. A., Martins,
J. V., Li, R.-R., Ichoku, C., Levy, R. C., Kleidman, R. G., Eck, T. F.,
Vermote, E., and Holben, B. N.: The MODIS Aerosol Algorithm, Products,
and Validation, J. Atmos. Sci., 62, 947–973,
https://doi.org/10.1175/jas3385.1, 2005. a
Rodgers, C. D.: Inverse methods for atmospheric sounding: theory and practice,
vol. 2, World scientific, 256 pp., https://doi.org/10.1142/3171, 2000. a, b, c
Rouquié, B., Hagolle, O., Bréon, F.-M., Boucher, O., Desjardins, C.,
and Rémy, S.: Using Copernicus Atmosphere Monitoring Service Products to
Constrain the Aerosol Type in the Atmospheric Correction Processor MAJA,
Remote Sens., 9, 1230, https://doi.org/10.3390/rs9121230, 2017. a
Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C. E., Allen, R. G.,
Anderson, M. C., Helder, D., Irons, J. R., Johnson, D. M., Kennedy, R., and Scambos, T. A.: Landsat-8: Science and product vision for terrestrial global change
research, Remote Sens. Environ., 145, 154–172, 2014. a
Runge, A. and Grosse, G.: Comparing Spectral Characteristics of Landsat-8 and
Sentinel-2 Same-Day Data for Arctic-Boreal Regions, Remote Sens., 11, 1730, https://doi.org/10.3390/rs11141730,
2019. a
Sayer, A. M., Govaerts, Y., Kolmonen, P., Lipponen, A., Luffarelli, M., Mielonen, T., Patadia, F., Popp, T., Povey, A. C., Stebel, K., and Witek, M. L.: A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing, Atmos. Meas. Tech., 13, 373–404, https://doi.org/10.5194/amt-13-373-2020, 2020. a, b, c, d
Schaaf, C. and Wang, Z.: MCD43A4 MODIS/Terra+Aqua BRDF/Albedo Nadir
BRDF Adjusted Refectance Daily L3 Global – 500 m V006, USGS [data set],
https://doi.org/10.5067/MODIS/MCD43A4.006, 2015. a, b, c
Schaaf, C., Strahler, A., Chopping, M., Gao, F., Hall, D., Jin, Y., Liang, S.,
Nightingale, J., Román, M., Roy, D., and Zhang, X.: MODIS MCD43 Product
User Guide V005,
https://lpdaac.usgs.gov/documents/441/MCD43_User_Guide_V5.pdf,
last access: 22 September 2021. a
Schaaf, C. B., Gao, F., Strahler, A. H., Lucht, W., Li, X., Tsang, T.,
Strugnell, N. C., Zhang, X., Jin, Y., Muller, J.-P., Lewis, P., Barnsley, M.,
Hobson, P., Disney, M., Roberts, G., Dunderdale, M., Doll, C., d'Entremont,
R. P., Hu, B., Liang, S., Privette, J. L., and Roy, D.: First operational
BRDF, albedo nadir reflectance products from MODIS, Remote Sens. Environ., x83, 135–148,
https://doi.org/10.1016/S0034-4257(02)00091-3, 2002. a, b
Schowengerdt, R. A.: Remote sensing: models and methods for image processing,
Elsevier, ISBN-13 978-0123694072, 2006. a
Schulz, M., Christophe, Y., Ramonet, M., Wagner, A., Eskes, H. J., Basart, S.,
Benedictow, A., Bennouna, Y., Blechschmidt, A.-M., Chabrillat, S., Cuevas,
E., El-Yazidi, A., Flentje, H., Hansen, K. M., Im, U., Kapsomenakis, J.,
Langerock, B., Richter, A., Sudarchikova, N., Thouret, V., Warneke, T., and
Zerefos, C.: Validation report of the CAMS near-real-time global atmospheric
composition service: Period December 2019–February 2020,
https://doi.org/10.24380/322N-JN39, 2020. a
Shen, J., Jiang, J., Du, Y., and Liu, Y.: Impact of aerosol type on atmospheric
correction of case II waters, in: IOP Conference Series: Earth and
Environmental Science, IOP Publishing, 234, 012019, https://doi.org/10.1088/1755-1315/234/1/012019, 2019. a
Skakun, S., Justice, C. O., Vermote, E., and Roger, J.-C.: Transitioning from
MODIS to VIIRS: an analysis of inter-consistency of NDVI data sets for
agricultural monitoring, Int. J. Remote Sens., 39,
971–992, 2018. a
Tachikawa, T., Kaku, M., Iwasaki, A., Gesch, D. B., Oimoen, M. J., Zhang, Z.,
Danielson, J. J., Krieger, T., Curtis, B., Haase, J., Abrams, M.: ASTER global
digital elevation model version 2-summary of validation results, Tech. rep.,
NASA, 2011. a
Tan, B., Woodcock, C., Hu, J., Zhang, P., Ozdogan, M., Huang, D., Yang, W.,
Knyazikhin, Y., and Myneni, R.: The impact of gridding artifacts on the local
spatial properties of MODIS data: Implications for validation, compositing,
and band-to-band registration across resolutions, Remote Sens. Environ., 105, 98–114, 2006. a
Tanré, D., Bréon, F. M., Deuzé, J. L., Dubovik, O., Ducos, F., François, P., Goloub, P., Herman, M., Lifermann, A., and Waquet, F.: Remote sensing of aerosols by using polarized, directional and spectral measurements within the A-Train: the PARASOL mission, Atmos. Meas. Tech., 4, 1383–1395, https://doi.org/10.5194/amt-4-1383-2011, 2011. a
Tirelli, C., Curci, G., Manzo, C., Tuccella, P., and Bassani, C.: Effect of the
Aerosol Model Assumption on the Atmospheric Correction over Land: Case
Studies with CHRIS/PROBA Hyperspectral Images over Benelux, Remote Sens.,
7, 8391–8415, https://doi.org/10.3390/rs70708391, 2015. a
USGS: L8 Biome Cloud Validation Masks – data.doi.gov,
https://data.doi.gov/dataset/l8-biome-cloud-validation-masks (last access: 21 October 2022),
2015. a
USGS: L8 SPARCS Cloud Validation Masks, USGS [data set], https://doi.org/10.5066/F7FB5146, 2016. a
USGS: Landsat 9 Commissioning and Operations Phases after Launch,
https://www.usgs.gov/media/images/landsat-9-commissioning-and-operations-phases-after-launch (last access: 21 October 2022),
2021. a
Vermote, E., Tanré, D., Deuze, J., Herman, M., and Morcette, J.-J.: Second
Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview,
IEEE T. Geoscience Remote, 35, 675–686,
https://doi.org/10.1109/36.581987, 1997a. a
Vermote, E., Justice, C., Claverie, M., and Franch, B.: Preliminary analysis of
the performance of the Landsat 8/OLI land surface reflectance product, Remote Sens. Environ., 185, 46–56, 2016. a
Vermote, E. F. and Kotchenova, S.: Atmospheric correction for the monitoring of
land surfaces, J. Geophys. Res.-Atmos., 113, D23S90,
https://doi.org/10.1029/2007JD009662, 2008. a, b, c
Vermote, E. F. and Saleous, N.: Operational atmospheric correction of MODIS
visible to middle infrared land surface data in the case of an infinite
Lambertian target, in: Earth science satellite remote sensing,
Springer, 123–153, https://doi.org/10.1007/978-3-540-37293-6_8, 2006. a
Vermote, E. F., Tanré, D., Deuzé, J. L., Herman, M., Morcrette, J. J., and Kotchenova, S. Y.: Second
Simulation of a Satellite Signal in the Solar Spectrum-vector (6SV). 6S
User Guide Version, 3, Tech. rep., Department of Geography, University of
Maryland, 2006. a
Wang, Z., Schaaf, C. B., Sun, Q., Shuai, Y., and Román, M. O.: Capturing
rapid land surface dynamics with Collection V006 MODIS BRDF/NBAR/Albedo
(MCD43) products, Remote Sens. Environ., 207, 50–64,
https://doi.org/10.1016/j.rse.2018.02.001, 2018. a
Wang, Z., Schaaf, C., Lattanzio, A., Carrer, D., Grant, I., Román, M.,
Camacho, F., Yu, Y., Sánchez-Zapero, J., and Nickeson, J.: Global Surface
Albedo Product Validation Best Practices Protocol Version 1.0, in: Best Practice for Satellite Derived Land Product Validation, edited by: Wang, Z., Nickeson, J., and
Román, M., Land Product Validation Subgroup (WGCV/CEOS),
45,
https://doi.org/10.5067/DOC/CEOSWGCV/LPV/ALBEDO.001, 2019. a
Wanner, W., Strahler, A. H., Hu, B., Lewis, P., Muller, J.-P., Li, X., Schaaf,
C. L. B., and Barnsley, M. J.: Global retrieval of bidirectional reflectance
and albedo over land from EOS MODIS and MISR data: Theory and
algorithm, J. Geophys. Res.-Atmos., 102,
17143–17161, https://doi.org/10.1029/96jd03295, 1997. a
Wenny, B. N. and Thome, K.: Look-up table approach for uncertainty
determination for operational vicarious calibration of Earth imaging sensors,
Appl. Optics, 61, 1357–1368, 2022. a
Wieland, M., Li, Y., and Martinis, S.: Multi-sensor cloud and cloud shadow
segmentation with a convolutional neural network, Remote Sens. Environ., 230, 111203, https://doi.org/10.1016/j.rse.2019.05.022, 2019.
a, b
Wulder, M. A., Hilker, T., White, J. C., Coops, N. C., Masek, J. G.,
Pflugmacher, D., and Crevier, Y.: Virtual constellations for global
terrestrial monitoring, Remote Sens. Environ., 170, 62–76, 2015. a
Xiong, X. and Butler, J. J.: MODIS and VIIRS calibration history and future
outlook, Remote Sens., 12, 2523, https://doi.org/10.3390/rs12162523, 2020. a
Yin, F.: SIAC-v2.3.5, Zenodo [code], https://doi.org/10.5281/zenodo.6651964, 2022a. a
Yin, F.: SIAC validation data, Zenodo [data set], https://doi.org/10.5281/zenodo.6652892,
2022b. a
Zhang, T., Zang, L., Mao, F., Wan, Y., and Zhu, Y.: Evaluation of
Himawari-8/AHI, MERRA-2, and CAMS Aerosol Products over China, Remote Sens., 12, 1684, https://doi.org/10.3390/rs12101684, 2020. a
Zhu, C., Byrd, R. H., Lu, P., and Nocedal, J.: Algorithm 778: L-BFGS-B:
Fortran subroutines for large-scale bound-constrained optimization, ACM
T. Math. Softw., 23, 550–560,
https://doi.org/10.1145/279232.279236, 1997. a
Zhu, Z. and Woodcock, C. E.: Object-based cloud and cloud shadow detection in
Landsat imagery, Remote Sens. Environ., 118, 83–94, 2012. a
Zhu, Z., Zhang, J., Yang, Z., Aljaddani, A. H., Cohen, W. B., Qiu, S., and
Zhou, C.: Continuous monitoring of land disturbance based on Landsat time
series, Remote Sens. Environ., 238, 111116,
https://doi.org/10.1016/j.rse.2019.03.009, 2020. 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.
The proposed SIAC atmospheric correction method provides consistent surface reflectance...
