O. Pokrovsky and J. L. Roujean, Land surface albedo retrieval via kernel-based BRDF modeling: II. An optimal design scheme for the angular sampling, Remote Sens. Environ, vol.84, pp.120-142, 2003.

E. Raschke, T. H. Vonder-haar, W. R. Bandeen, and M. Pasternak, The annual radiation balance of the earth-atmosphere system during 1969-70 from Nimbus 3 measurements, J. Atmos. Sci, vol.30, pp.341-364, 1973.

C. Pohl, L. Istomina, S. Tietsche, E. Jäkel, J. Stapf et al., Broadband albedo of Arctic sea ice from MERIS optical data, vol.14, pp.165-182, 2020.

Z. Wang, C. B. Schaaf, Q. Sun, J. Kim, A. M. Erb et al., Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic time series from Landsat and the MODIS BRDF/NBAR/albedo product, Int. J. Appl. Earth Obs. Geoinf, vol.59, pp.104-117, 2017.

X. Zhao, S. Liang, S. Liu, W. Yuan, Z. Xiao et al., The Global Land Surface Satellite (GLASS) remote sensing data processing system and products, Remote Sens, vol.5, pp.2436-2450, 2013.

, Remote Sens, vol.12, p.2500, 2020.

T. Andrews, R. A. Betts, B. B. Booth, C. D. Jones, and G. S. Jones, Effective radiative forcing from historical land use change, vol.48, pp.3489-3505, 2017.

Q. Li, M. Ma, X. Wu, and H. Yang, Snow cover and vegetation-induced decrease in global albedo from 2002 to 2016, J. Geophys. Res. Atmos, vol.123, pp.124-138, 2018.

S. Boussetta, G. Balsamo, E. Dutra, A. Beljaars, and C. Albergel, Assimilation of surface albedo and vegetation states from satellite observations and their impact on numerical weather prediction, Remote Sens. Environ, vol.163, pp.111-126, 2015.

T. He, S. Liang, D. Wang, X. Chen, D. X. Song et al., Land surface albedo estimation from Chinese HJ satellite data based on the direct estimation approach. Remote Sens, vol.7, pp.5495-5510, 2015.

C. L. Brest and S. N. Goward, Deriving surface albedo measurements from narrow band satellite data, Int. J. Remote Sens, vol.8, pp.351-367, 1987.

I. Csiszar and G. Gutman, Mapping global land surface albedo from NOAA AVHRR, J. Geophys. Res. Atmos, vol.104, pp.6215-6228, 1999.

T. Manninen, T. Aalto, T. Markkanen, M. Peltoniemi, K. Böttcher et al., Monitoring changes in forestry and seasonal snow using surface albedo during 1982-2016 as an indicator, Biogeosciences, vol.16, pp.223-240, 2019.

C. Zhou, T. Zhang, and L. Zheng, The characteristics of surface albedo change trends over the Antarctic sea ice region during recent decades

M. Zhou, G. Chen, Z. Dong, B. Xie, S. Gu et al., Estimation of surface albedo from meteorological observations across China, Agric. For. Meteorol, 2020.

Y. Qu, S. Liang, Q. Liu, T. He, S. Liu et al., Mapping surface broadband albedo from satellite observations: A review of literatures on algorithms and products. Remote Sens, vol.7, pp.990-1020, 2015.

A. Riihelä, T. Manninen, J. Key, Q. Sun, M. Sütterlin et al., A Multisensor Approach to Global Retrievals of Land Surface Albedo, Remote Sens, vol.10, 2018.

Z. Wang, C. B. Schaaf, Q. Sun, Y. Shuai, and M. O. Román, Capturing Rapid Land Surface Dynamics with Collection V006 MODIS BRDF/NBAR/Albedo (MCD43) Products. Remote Sens. Environ, vol.207, pp.50-64, 2018.

J. L. Roujean, J. Leon-tavares, B. Smets, P. Claes, F. C. De-coca et al., Surface albedo and toc-r 300 m products from PROBA-V instrument in the framework of, Copernicus Global Land Service. Remote Sens. Environ, vol.215, pp.57-73, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02354384

T. He, D. Wang, and Y. Qu, Land surface albedo, Comprehensive Remote Sensing, vol.5, pp.140-162, 2018.

S. Liang, D. Wang, Y. Zhou, Y. Yu, and J. Peng, VIIRS NDE Surface Albedo Algorithm Theoretical Basis Document, p.12, 2018.

B. Pinty, F. Roveda, M. Verstraete, N. Gobron, Y. Govaerts et al., Surface albedo retrieval from METEOSAT: Part 1, Theory. J. Geophys. Res, vol.105, pp.18099-18112, 2000.

A. Lattanzio, J. Schulz, J. Matthews, A. Okuyama, B. Theodore et al.,

, Bull. Am. Meteorol, vol.94, pp.205-214, 2013.

B. Geiger, D. Carrer, L. Franchisteguy, J. L. Roujean, and C. Meurey, Land surface albedo derived on a daily basis from Meteosat second generation observations, IEEE Trans. Geosci. Remote Sens, vol.46, pp.3841-3856, 2008.

S. R. Proud, M. O. Rasmussen, R. Fensholt, I. Sandholt, C. Shisanya et al., Improving the smac atmospheric correction code by analysis of meteosat second generation ndvi and surface reflectance data, Remote Sens. Environ, vol.114, pp.1687-1698, 2010.

T. He, S. L. Liang, D. Wang, H. Wu, Y. Yu et al., Estimation of surface albedo and directional reflectance from Moderate Resolution Imaging Spectroradiometer (MODIS) observations. Remote Sens, vol.119, pp.286-300, 2012.

T. He, Y. Zhang, S. Liang, Y. Yu, and D. Wang, Developing Land Surface Directional Reflectance and Albedo Products from Geostationary GOES-R and Himawari Data: Theoretical Basis, Operational Implementation, and Validation

, Remote Sens, vol.12, pp.2500-2526, 2020.

C. B. Schaaf, F. Gao, A. H. Strahler, W. Lucht, X. Li et al., First operational BRDF, albedo nadir reflectance products from MODIS, Remote Sens. Environ, vol.83, pp.135-148, 2002.

T. He, S. Liang, and D. Wang, Direct Estimation of Land Surface Albedo from Simultaneous MISR Data, IEEE Trans. Geosci. Remote Sens, vol.55, pp.2605-2617, 2017.

D. Carrer, B. Smets, X. Ceamanos, and J. L. Roujean, Copernicus Global Land Operations "Vegetation and Energy, 2017.

D. Carrer, S. Moparthy, G. Lellouch, X. Ceamanos, F. Pinault et al., Land surface albedo derived on a ten daily basis from Meteosat Second Generation Observations: The NRT and climate data record collections from the EUMETSAT LSA SAF, Remote Sens, vol.10, p.1262, 2018.

G. P. Asner, C. A. Wessman, D. S. Schimel, and S. Archer, Variability in leaf and litter optical properties: Implications for BRDF model inversions using AVHRR, MODIS, and MISR. Remote Sens. Environ, vol.63, pp.243-257, 1998.

C. S. Lee, K. S. Han, J. M. Yeom, K. S. Lee, M. Seo et al., Surface albedo from the geostationary Communication, Ocean and Meteorological Satellite (COMS)/Meteorological Imager (MI) observation system, GISci. Remote Sens, vol.55, pp.38-62, 2018.

S. Liang, A direct algorithm for estimating land surface broadband albedos from MODIS imagery, IEEE Trans. Geosci. Remote Sens, vol.41, pp.136-145, 2003.

K. Bessho, K. Date, M. Hayashi, A. Ikeda, T. Imai et al., An introduction to Himawari-8/9-Japan's new-generation geostationary meteorological satellites, J. Meteorol. Soc. Jpn, vol.94, pp.151-183, 2016.

J. Yang, Z. Zhang, C. Wei, F. Lu, and Q. Guo, Introducing the new generation of Chinese geostationary weather satellites, Fengyun-4. Bull. Am. Meteorol, vol.98, pp.1637-1658, 2017.

S. J. Goodman, GOES-R Series Introduction, In The GOES-R Series, pp.1-3, 2020.

M. Descheemaecker, M. Plu, V. Marécal, M. Claeyman, F. Olivier et al., Monitoring aerosols over Europe: An assessment of the potential benefit of assimilating the VIS04 measurements from the future MTG/FCI geostationary imager, Atmos. Meas. Tech, vol.12, pp.1251-1275, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02051475

S. M. Oh, R. Borde, M. Carranza, and I. C. Shin, Development and Intercomparison Study of an Atmospheric Motion Vector Retrieval Algorithm for GEO-KOMPSAT-2A

K. S. Lee, C. S. Lee, M. Seo, S. Choi, N. H. Seong et al., Improvements of 6S Look-Up-Table Based Surface Reflectance Employing Minimum Curvature Surface Method, Asia Pac. J. Atmos. Sci, vol.2020, pp.1-14

N. H. Seong, D. Jung, J. Kim, and K. S. Han, Evaluation of NDVI Estimation Considering Atmospheric and BRDF Correction through Himawari-8/AHI, Asia-Pac. J. Atmos. Sci, vol.2020, pp.1-10

M. He, D. Wang, W. Ding, Y. Wan, Y. Chen et al., A Validation of Fengyun4A Temperature and Humidity Profile Products by Radiosonde Observations

J. Wei, Z. Li, L. Sun, Y. Peng, Z. Zhang et al., Evaluation and uncertainty estimate of next-generation geostationary meteorological Himawari-8/AHI aerosol products, Sci. Total Environ, vol.692, pp.879-891, 2019.

W. Zhang, H. Xu, and L. Zhang, Assessment of Himawari-8 AHI Aerosol Optical Depth Over Land

K. S. Lee, D. Jin, J. M. Yeom, M. Seo, S. Choi et al., New Approach for Snow Cover Detection through Spectral Pattern Recognition with MODIS Data, J. Sens, 2017.

C. S. Lee, J. M. Yeom, H. L. Lee, J. J. Kim, and K. S. Han, Sensitivity analysis of 6S-based look-up table for surface reflectance retrieval, Asia-Pac. J. Atmos. Sci, vol.51, pp.91-101, 2015.

, Remote Sens, vol.12, pp.2500-2527, 2020.

K. Wang, J. Liu, X. Zhou, M. Sparrow, M. Ma et al., Validation of the MODIS global land surface albedo product using ground measurements in a semidesert region on the Tibetan Plateau, J. Geophys. Res. Atmos, vol.109, p.5107, 2004.

B. N. Holben, T. F. Eck, I. A. Slutsker, D. Tanre, J. P. Buis et al., AERONET-A federated instrument network and data archive for aerosol characterization, Remote Sens. Environ, vol.66, pp.1-16, 1998.

M. Sicard, Validation of AERONET-Estimated Upward Broadband Solar Fluxes at the Top-Of-The-Atmosphere with CERES Measurements

O. E. García, A. M. Díaz, F. J. Expósito, J. P. Díaz, O. Dubovik et al., Validation of AERONET estimates of atmospheric solar fluxes and aerosol radiative forcing by ground-based broadband measurements, J. Geophys. Res. Atmos, vol.113, 2008.

B. N. Holben, T. F. Eck, I. Slutsker, A. Smirnov, A. Sinyuk et al., Aeronet's Version 2.0 quality assurance criteria, Proc. SPIE, p.64080, 2006.

Y. Hwang, Y. Ryu, Y. Huang, J. Kim, H. Iwata et al., Comprehensive assessments of carbon dynamics in an intermittently-irrigated rice paddy, Agric. For. Meteorol, vol.285, p.107933, 2020.

S. Lee, Y. Ryu, and C. Jiang, Urban heat mitigation by roof surface materials during the East Asian summer monsoon, Environ. Res. Lett, vol.10, p.124012, 2015.

A. Strahler, J. Muller, W. Lucht, C. Schaaf, T. Tsang et al., , 2020.

J. M. Yeom, J. L. Roujean, K. S. Han, K. S. Lee, and H. W. Kim, Thin cloud detection over land using background surface reflectance based on the BRDF model applied to Geostationary Ocean Color Imager (GOCI) satellite data sets

W. Choi, H. Lee, J. Kim, J. Y. Ryu, S. S. Park et al., Effects of spatiotemporal O4 column densities and temperature-dependent O4 absorption cross-section on an aerosol effective height retrieval algorithm using the O4 air mass factor from the ozone monitoring instrument, Remote Sens. Environ, vol.229, pp.223-233, 2019.

X. Zhang, M. A. Friedl, C. B. Schaaf, A. H. Strahler, J. C. Hodges et al., Monitoring vegetation phenology using MODIS, Remote Sens. Environ, vol.84, pp.471-475, 2003.

S. Y. Kotchenova, E. F. Vermote, R. Levy, and A. Lyapustin, Radiative transfer codes for atmospheric correction and aerosol retrieval: Intercomparison study, Appl. Opt, vol.47, pp.2215-2226, 2008.

E. F. Vermote, D. Tanré, J. L. Deuze, M. Herman, and J. J. Morcette, Second simulation of the satellite signal in the solar spectrum, 6S: An overview, IEEE Trans. Geosci. Remote. Sens, vol.35, pp.675-686, 1997.

Y. M. Darge, B. T. Hailu, A. A. Muluneh, and T. Kidane, Detection of geothermal anomalies using Landsat 8 TIRS data in Tulu Moye geothermal prospect, Main Ethiopian Rift, Int. J. Appl. Earth Obs. Geoinf, vol.74, pp.16-26, 2019.

E. Vermote, D. Tanré, J. L. Deuzé, M. Herman, J. J. Morcrette et al., Second Simulation of A Satellite Signal in the Solar Spectrum-Vector (6SV)

, 6S User Guide Version 3, p.11, 2020.

J. F. Calleja, C. Recondo, J. Peón, S. Fernández, F. De-la-cruz et al., A New Method for the Estimation of Broadband Apparent Albedo Using Hyperspectral Airborne Hemispherical Directional Reflectance Factor Values

S. I. Kim, D. S. Ahn, K. S. Han, and J. M. Yeom, Improved Vegetation Profiles with GOCI Imagery Using Optimized BRDF Composite, J. Sens, issue.7, 2016.

J. L. Roujean, M. Leroy, and P. Y. Deschamps, A bidirectional reflectance model of the Earth's surface for the correction of remote sensing data, J. Geophys. Res. Atmos, vol.97, pp.20455-20468, 1992.

B. Duchemin and P. Maisongrande, Normalisation of directional effects in 10-day global syntheses derived from VEGETATION/SPOT: I. Investigation of concepts based on simulation, Remote Sens. Environ, vol.81, pp.90-100, 2002.

, Remote Sens, vol.12, pp.2500-2528, 2020.

K. S. Han, J. L. Champeaux, and J. L. Roujean, A land cover classification product over France at 1 km resolution using SPOT4/VEGETATION data, Remote Sens. Environ, vol.92, pp.52-66, 2004.

J. Roujean, Inversion of Lumped Parameters Using BRDF Kernels, Comprehensive Remote Sensing

, , vol.3, pp.23-34, 2018.

Y. Wang, X. Li, Z. Nashed, F. Zhao, H. Yang et al., Regularized kernel-based brdf model inversion method for ill-posed land surface parameter retrieval, Remote Sens. Environ, vol.111, pp.36-50, 2007.

J. M. Yeom and H. O. Kim, Feasibility of using Geostationary Ocean Colour Imager (GOCI) data for land applications after atmospheric correction and bidirectional reflectance distribution function modelling, Int. J. Remote Sens, vol.34, pp.7329-7339, 2013.

S. Peng, J. Wen, Q. Xiao, D. You, B. Dou et al., Multi-Staged NDVI Dependent Snow-Free Land-Surface Shortwave Albedo Narrowband-to-Broadband (NTB) Coefficients and Their Sensitivity Analysis

C. B. Schaaf, J. Martonchik, B. Pinty, Y. Govaerts, F. Gao et al., Retrieval of surface albedo from satellite sensors, Advances in Land Remote Sensing: System, Modeling, Inversion and Application

S. Liang, . Ed, and . Springer, , pp.219-243, 2008.

S. Liang, Narrowband to broadband conversions of land surface albedo I: Algorithms. Remote Sens. Environ, vol.76, pp.213-238, 2001.

Y. Liu, Z. Wang, Q. Sun, A. M. Erb, Z. Li et al., Evaluation of the VIIRS BRDF, Albedo and NBAR products suite and an assessment of continuity with the long term MODIS record. Remote Sens, vol.201, pp.256-274, 2017.

A. Gruber, W. A. Dorigo, S. Zwieback, A. Xaver, and W. Wagner, Characterizing Coarse-Scale Representativeness of in situ Soil Moisture Measurements from the International Soil Moisture Network, Vadose Zone J, vol.12, pp.1-16, 2013.

A. Stoffelen, Toward the true near-surface wind speed: Error modeling and calibration using triple collocation, J. Geophys. Res. Oceans, vol.103, pp.7755-7766, 1998.

K. A. Mccoll, J. Vogelzang, A. G. Konings, D. Entekhabi, M. Piles et al., Extended triple collocation: Estimating errors and correlation coefficients with respect to an unknown target, Geophys. Res. Lett, vol.41, pp.6229-6236, 2014.

A. Gruber, C. H. Su, S. Zwieback, W. Crow, W. Dorigo et al., Recent advances in (soil moisture) triple collocation analysis, Int. J. Appl. Earth Obs. Geoinf, vol.45, pp.200-211, 2016.

X. Wu, Q. Xiao, J. Wen, and D. You, Direct Comparison and Triple Collocation: Which Is More Reliable in the Validation of Coarse-Scale Satellite Surface Albedo Products, J. Geophys. Res. Atmos, vol.124, pp.5198-5213, 2019.

N. P. Molotch and R. C. Bales, Comparison of ground-based and airborne snow surface albedo parameterizations in an alpine watershed: Impact on snowpack mass balance, Water Resour. Res, vol.42, 2006.

F. Wu and C. Fu, Assessment of GEWEX/SRB version 3.0 monthly global radiation dataset over China, Meteorol. Atmos. Phys, vol.112, 2011.

S. Kraatz, R. Khanbilvardi, and P. Romanov, A comparison of MODIS/VIIRS cloud masks over ice-bearing river: On achieving consistent cloud masking and improved river ice mapping

Y. Govaerts and A. Lattanzio, Estimation of surface albedo increase during the eighties Sahel drought from Meteosat observations, Glob. Planet. Chang, vol.64, pp.139-145, 2008.

J. H. Jeppesen, R. H. Jacobsen, F. Inceoglu, and T. S. Toftegaard, A cloud detection algorithm for satellite imagery based on deep learning, Remote Sens. Environ, vol.229, pp.247-259, 2019.

, Remote Sens, vol.12, pp.2500-2529, 2020.

Y. J. Lim, K. Y. Byun, T. Y. Lee, H. Kwon, J. Hong et al., A land data assimilation system using the MODIS-derived land data and its application to numerical weather prediction in East Asia, Asia-Pac. J. Atmos. Sci, vol.48, pp.83-95, 2012.

M. Seo, H. Kim, M. Huh, J. Yeom, C. S. Lee et al., Long-Term Variability of Surface Albedo and Its Correlation with Climatic Variables over Antarctica