J. Vidot and É. Borbás, Land surface VIS/NIR BRDF atlas for RTTOV-11: Model and validation against SEVIRI land SAF albedo product: Land Surface VIS/NIR BRDF Atlas for RTTOV-11, Q. J. R. Meteorol. Soc, vol.140, pp.2186-2196, 2014.

W. Hou, J. Wang, X. Xu, and J. S. Reid, An algorithm for hyperspectral remote sensing of aerosols: 2. Information content analysis for aerosol parameters and principal components of surface spectra, J. Quant. Spectrosc. Radiat. Transf, vol.192, pp.14-29, 2017.

J. I. Peltoniemi, S. Kaasalainen, J. Naranen, L. Matikainen, and J. Piironen, Measurement of directional and spectral signatures of light reflectance by snow, IEEE Trans. Geosci. Remote Sens, vol.43, pp.2294-2304, 2005.

B. Franch, E. F. Vermote, J. A. Sobrino, and E. Fédèle, Analysis of directional effects on atmospheric correction, Remote Sens. Environ, vol.128, pp.276-288, 2013.

E. F. Vermote and S. Kotchenova, Atmospheric correction for the monitoring of land surfaces, J. Geophys. Res. Atmos, vol.113, 2008.

Y. Wang, A. I. Lyapustin, J. L. Privette, R. B. Cook, S. K. Santhanavannan et al., Assessment of biases in MODIS surface reflectance due to Lambertian approximation, Remote Sens. Environ, vol.114, pp.2791-2801, 2010.

H. Zhang, Z. Jiao, L. Chen, Y. Dong, X. Zhang et al., Quantifying the Reflectance Anisotropy Effect on Albedo Retrieval from Remotely Sensed Observations Using Archetypal BRDFs, Remote Sens, vol.10, 1628.

R. C. Levy, S. Mattoo, L. A. Munchak, L. A. Remer, A. M. Sayer et al., The Collection 6 MODIS aerosol products over land and ocean, Atmos. Meas. Tech, 2013.

A. Lyapustin, Y. Wang, S. Korkin, and D. Huang, MODIS Collection 6 MAIAC algorithm, Atmos. Meas. Tech, vol.11, 2018.

W. Von-hoyningen-huene, M. Freitag, and J. B. Burrows, Retrieval of aerosol optical thickness over land surfaces from top-of-atmosphere radiance, J. Geophys. Res. Atmos, vol.108, 2003.

L. Mei, V. Rozanov, M. Vountas, J. P. Burrows, R. C. Levy et al., Retrieval of aerosol optical properties using MERIS observations: Algorithm and some first results. Remote Sens, vol.197, pp.125-140, 2017.

J. M. Jackson, H. Liu, I. Laszlo, S. Kondragunta, L. A. Remer et al., Suomi-NPP VIIRS aerosol algorithms and data products, J. Geophys. Res. Atmos, vol.118, pp.12-673, 2013.

K. F. Boersma, H. J. Eskes, R. J. Dirksen, J. P. Veefkind, P. Stammes et al., An improved tropospheric NO 2 column retrieval algorithm for the Ozone Monitoring Instrument, Atmos. Meas. Tech, 1905.

E. J. Bucsela, N. A. Krotkov, E. A. Celarier, L. N. Lamsal, W. H. Swartz et al., A new stratospheric and tropospheric NO 2 retrieval algorithm for nadir-viewing satellite instruments: Applications to OMI, Atmos. Meas. Tech, vol.6, pp.2607-2626, 2013.

, Remote Sens, vol.12, pp.1679-1701, 2020.

J. R. Acarreta, J. F. De-haan, and P. Stammes, Cloud pressure retrieval using the O 2 -O 2 absorption band at 477 nm, J. Geophys. Res. Atmos, vol.109, 2004.

R. B. Koelemeijer, P. Stammes, J. W. Hovenier, and J. De-haan, A fast method for retrieval of cloud parameters using oxygen A band measurements from the Global Ozone Monitoring Experiment, J. Geophys. Res. Atmos, vol.106, pp.3475-3490, 2001.

J. Joiner and A. P. Vasilkov, First results from the OMI rotational Raman scattering cloud pressure algorithm, IEEE Trans. Geosci. Remote Sens, vol.44, pp.1272-1282, 2006.

K. Noguchi, A. Richter, V. Rozanov, A. Rozanov, J. P. Burrows et al., Effect of surface BRDF of various land cover types on geostationary observations of tropospheric NO 2, Atmos. Meas. Tech, vol.7, pp.3497-3508, 2014.

A. Lorente, K. Boersma, P. Stammes, L. Gijsbert-tilstra, A. Richter et al., The importance of surface reflectance anisotropy for cloud and NO 2 retrievals from GOME-2 and OMI, Atmos. Meas. Tech, vol.11, pp.4509-4529, 2018.

Y. Zhou, D. Brunner, R. J. Spurr, K. F. Boersma, M. Sneep et al., Accounting for surface reflectance anisotropy in satellite retrievals of tropospheric NO 2, Atmos. Meas. Tech, 1185.

C. Popp, P. Wang, D. Brunner, P. Stammes, Y. Zhou et al., MERIS albedo climatology for FRESCO+ O 2 A-band cloud retrieval, Atmos. Meas. Tech, vol.4, pp.463-483, 2011.

A. Vasilkov, W. Qin, N. Krotkov, L. Lamsal, R. Spurr et al., Accounting for the effects of surface BRDF on satellite cloud and trace-gas retrievals: A new approach based on geometry-dependent Lambertian equivalent reflectivity applied to OMI algorithms, Atmos. Meas. Tech, vol.10, p.333, 2017.

J. R. Herman and E. A. Celarier, Earth surface reflectivity climatology at 340-380 nm from TOMS data, J. Geophys. Res. Atmos, vol.102, pp.28003-28011, 1997.

R. B. Koelemeijer, J. F. De-haan, and P. Stammes, A database of spectral surface reflectivity in the range 335-772 nm derived from 5.5 years of GOME observations, J. Geophys. Res. Atmos, vol.108, 2003.

Q. L. Kleipool, M. R. Dobber, J. De-haan, and P. F. Levelt, Earth surface reflectance climatology from 3 years of OMI data, J. Geophys. Res. Atmos, vol.113, 2008.

L. G. Tilstra, P. Wang, and P. Stammes, Surface reflectivity climatologies from UV to NIR determined from Earth observations by GOME-2 and SCIAMACHY, J. Geophys. Res. Atmos, vol.122, pp.4084-4111, 2017.

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.

J. V. Martonchik, D. J. Diner, B. Pinty, M. M. Verstraete, R. B. Myneni et al., Determination of land and ocean reflective, radiative, and biophysical properties using multiangle imaging, IEEE Trans. Geosci. Remote Sens, vol.36, pp.1266-1281, 1998.

J. Muller, G. López, G. Watson, N. Shane, T. Kennedy et al., The ESA GlobAlbedo Project for mapping the Earth's land surface albedo for 15 Years from European Sensors, Proceedings of the EGU, vol.13, p.10969, 2012.

L. Gonzalez, F. Bréon, K. Caillault, and X. Briottet, A sub km resolution global database of surface reflectance and emissivity based on 10-years of MODIS data, ISPRS J. Photogramm. Remote Sens, vol.122, pp.222-235, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01445824

P. Bicheron and M. Leroy, Bidirectional reflectance distribution function signatures of major biomes observed from space, J. Geophys. Res. Atmos, vol.105, pp.26669-26681, 2000.

F. Bréon and F. Maignan, A BRDF-BPDF database for the analysis of Earth target reflectances, Earth Syst. Sci. Data, vol.9, pp.31-45, 2017.

S. Kharbouche, J. Muller, and P. E. Lewis, A 15 Year Climatology of Spectral BRDF Derived from MODIS for a Priori Optimal Estimation of Global Surface Albedo within the EU-FP7 QA4ECV Project, 2014.

G. Schaepman-strub, M. E. Schaepman, T. H. Painter, S. Dangel, and J. V. Martonchik, Reflectance quantities in optical remote sensing-Definitions and case studies, Remote Sens. Environ, vol.103, pp.27-42, 2006.

F. Maignan, F. Bréon, and R. Lacaze, Bidirectional reflectance of Earth targets: Evaluation of analytical models using a large set of spaceborne measurements with emphasis on the Hot Spot, Remote Sens. Environ, vol.90, pp.210-220, 2004.

, Remote Sens, vol.12, pp.1679-1702, 2020.

A. A. Kokhanovsky and E. P. Zege, Scattering optics of snow, Appl. Opt, vol.43, pp.1589-1602, 2004.

C. Bacour, L. Gonzalez, and F. Bréon, Improvement and/or Expension of Existing Surface Datasets-Algorithmic Theoretical Basis Document. Technical Note 4 for ESA Study Contract Nr C4000102979/CCN No6, NOVELTIS, vol.109, p.15, 2019.

I. E. Bell and G. V. Baranoski, Reducing the dimensionality of plant spectral databases, IEEE Trans. Geosci. Remote Sens, vol.42, pp.570-576, 2004.

L. Liu, B. Song, S. Zhang, and X. Liu, A novel principal component analysis method for the reconstruction of leaf reflectance spectra and retrieval of leaf biochemical contents

W. Hou, Y. Mao, C. Xu, Z. Li, D. Li et al., Study on the spectral reconstruction of typical surface types based on spectral library and principal component analysis, Proceedings of the Fifth Symposium on Novel Optoelectronic Detection Technology and Application, vol.11023, p.110232, 2018.

Z. Jiao, Y. Dong, C. B. Schaaf, J. M. Chen, M. Román et al., An algorithm for the retrieval of the clumping index (CI) from the MODIS BRDF product using an adjusted version of the kernel-driven BRDF model, Remote Sens. Environ, vol.209, pp.594-611, 2018.

F. Bréon, F. Maignan, M. Leroy, and I. Grant, Analysis of hot spot directional signatures measured from space, J. Geophys. Res. Atmos, vol.107, p.1, 2002.

A. A. Kokhanovsky and F. Breon, Validation of an analytical snow BRDF model using PARASOL multi-angular and multispectral observations, IEEE Geosci. Remote Sens. Lett, vol.9, pp.928-932, 2012.

Z. Jiao, A. Ding, A. Kokhanovsky, C. Schaaf, F. Bréon et al.,

X. Zhang and S. Yin, Development of a snow kernel to better model the anisotropic reflectance of pure snow in a kernel-driven BRDF model framework. Remote Sens. Environ, vol.221, pp.198-209, 2019.

A. Ding, Z. Jiao, Y. Dong, X. Zhang, J. I. Peltoniemi et al., Evaluation of the Snow Albedo Retrieved from the Snow Kernel Improved the Ross-Roujean BRDF Model

A. Morel and S. Maritorena, Bio-optical properties of oceanic waters: A reappraisal, J. Geophys. Res. Oceans, vol.106, pp.7163-7180, 2001.

Z. Jin, T. P. Charlock, K. Rutledge, K. Stamnes, and Y. Wang, Analytical solution of radiative transfer in the coupled atmosphere-ocean system with a rough surface, Appl. Opt, vol.45, pp.7443-7455, 2006.

, Coupled Ocean and Atmosphere Radiative Transfer (COART), p.15, 2020.

F. M. Bréon and N. Henriot, Spaceborne observations of ocean glint reflectance and modeling of wave slope distributions, J. Geophys. Res. Oceans, vol.111, 2006.

R. Frouin, M. Schwindling, and P. Deschamps, Spectral reflectance of sea foam in the visible and near-infrared: In situ measurements and remote sensing implications, J. Geophys. Res. Oceans, vol.101, pp.14361-14371, 1996.

P. Koepke, Effective reflectance of oceanic whitecaps, Appl. Opt, vol.23, pp.1816-1824, 1984.

A. A. Kokhanovsky, Spectral reflectance of whitecaps, J. Geophys. Res. Oceans, vol.109, 2004.

E. F. Vermote and A. Vermeulen, Atmospheric correction algorithm: Spectral reflectances (MOD09). Version 4.0. Algorithm Theor, Basis Doc. NASA EOS-ID, vol.4, pp.1-107, 1999.

, Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Ocean Color Data. NASA OB.DAAC: Greenbelt, MD, USA, Maintained by NASA Ocean Biology Distibuted Active Archive Center (OB.DAAC), p.15, 2014.

L. Ricciardulli and F. Wentz, QuikSCAT (V04) wind vectors with Ku-2011 geophysical model function, Remote Sens. Syst. Tech. Rep, p.43011, 2011.

, Remote Sensing Systems QuikScat/SeaWinds Page, p.15, 2020.

, Remote Sens, vol.12, pp.1679-1703, 2020.

, CryoClim Service Documentation Page, p.15, 2020.

. Cryoclim-data-portal, , 2020.

C. Goyens, S. Marty, E. Leymarie, D. Antoine, M. Babin et al., High angular resolution measurements of the anisotropy of reflectance of sea ice and snow, Earth Space Sci, vol.5, pp.30-47, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01727419

. Dlr-spectral-archive, , p.15, 2020.

. Aster-spectral-library, , p.15, 2020.

, USGS Spectral Database, p.15, 2020.

F. Gerber, R. Marion, A. Olioso, S. Jacquemoud, B. R. Da-luz et al., Modeling directional-hemispherical reflectance and transmittance of fresh and dry leaves from 0.4 µm to 5.7 µm with the PROSPECT-VISIR model, Remote Sens. Environ, vol.115, pp.404-414, 2011.
URL : https://hal.archives-ouvertes.fr/hal-01337522

S. G. Warren, Optical constants of ice from the ultraviolet to the microwave, Appl. Opt, vol.23, pp.1206-1225, 1984.

. Refractive-index-database, , p.15, 2020.

D. J. Segelstein, The Complex Refractive index of Water, 1981.

G. M. Hale and M. R. Querry, Optical constants of water in the 200-nm to 200-µm wavelength region, Appl. Opt, vol.12, pp.555-563, 1973.

. Refractive-index-database, , p.15, 2020.

, ESA Earth Observation Portal Database, p.15, 2020.

A. Portal, , p.15, 2020.

D. Tanré, F. M. Bréon, J. L. Deuzé, O. Dubovik, F. Ducos et al., Remote sensing of aerosols by using polarized, directional and spectral measurements within the A-Train: The PARASOL mission, Atmos. Meas. Tech, vol.4, pp.1383-1395, 2011.

K. Kobayashi and M. U. Salam, Comparing simulated and measured values using mean squared deviation and its components, Agron. J, vol.92, pp.345-352, 2000.

H. G. Gauch, J. T. Hwang, and G. W. Fick, Model evaluation by comparison of model-based predictions and measured values, Agron. J, vol.95, pp.1442-1446, 2003.

D. M. Winker, M. A. Vaughan, A. Omar, Y. Hu, K. A. Powell et al., Overview of the CALIPSO mission and CALIOP data processing algorithms, J. Atmos. Ocean. Technol, vol.26, pp.2310-2323, 2009.