<figure><img src="image_22.png" /><figcaption>Figure A3. Snapshot of SALVAL configuration step 3.</figcaption></figure>

(4) Visualize the validation results for different criteria, or generate a standardized validation report in PDF.

<figure><img src="image_23.png" /><figcaption>Figure A4. Snapshot of SALVAL configuration step 4.</figcaption></figure>

(5) Enjoy the interactive validation process (see below Direct Validation type results).

## References

1. Dickinson, R.E. Land processes in climate models. *Remote Sens. Environ.* **1995**, *51*, 27–38. [CrossRef]

2. Liang, S. A direct algorithm for estimating land surface broadband albedos from MODIS imagery. *IEEE Trans. Geosci. Remote Sens.* **2003**, *41*, 136–145. [CrossRef]

3. WMO; United Nations Educational, Scientific and Cultural Organization; Intergovernmental Oceanographic Commission; United Nations Environment Programme; International Science Council. The 2022 GCOS Implementation Plan (GCOS-244). Available online: https://gcos.wmo.int/en/publications/gcos-implementation-plan2022 (accessed on 10 November 2022).
<figure><img src="image_26.png" /><figcaption>Figure A3. Snapshot of SALVAL configuration step 3.</figcaption></figure>

(4) Visualize the validation results for different criteria, or generate a standardized validation report in PDF.

<figure><img src="image_27.png" /><figcaption>Figure A4. Snapshot of SALVAL configuration step 4.</figcaption></figure>

(5) Enjoy the interactive validation process (see below Direct Validation type results).

## References

1. Dickinson, R.E. Land processes in climate models. *Remote Sens. Environ.* **1995**, *51*, 27–38. [CrossRef]

2. Liang, S. A direct algorithm for estimating land surface broadband albedos from MODIS imagery. *IEEE Trans. Geosci. Remote Sens.* **2003**, *41*, 136–145. [CrossRef]

3. WMO; United Nations Educational, Scientific and Cultural Organization; Intergovernmental Oceanographic Commission; United Nations Environment Programme; International Science Council. The 2022 GCOS Implementation Plan (GCOS-244). Available online: https://gcos.wmo.int/en/publications/gcos-implementation-plan2022 (accessed on 10 November 2022).
4. Zhou, Y.; Wang, D.; Liang, S.; Yu, Y.; He, T. Assessment of the Suomi NPP VIIRS Land Surface Albedo Data Using Station Measurements and High-Resolution Albedo Maps. *Remote Sens.* **2016**, *8*, 137. [CrossRef]

5. Wang, D.; Liang, S.; Zhou, Y.; He, T.; Yu, Y. A New Method for Retrieving Daily Land Surface Albedo from VIIRS Data. *IEEE Trans. Geosci. Remote Sens.* **2017**, *55*, 1765–1775. [CrossRef]

6. Wang, D.; Liang, S.; He, T.; Yu, Y. Direct estimation of land surface albedo from VIIRS data: Algorithm improvement and preliminary validation. *J. Geophys. Res. Atmos.* **2013**, *118*, 12577–12586. [CrossRef]

7. Balsamo, G.; Agusti-Panareda, A.; Albergel, C.; Arduini, G.; Beljaars, A.; Bidlot, J.; Bousserez, N.; Boussetta, S.; Brown, A.; Buizza, R.; et al. Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review. *Remote Sens.* **2018**, *10*, 2038. [CrossRef]

8. Bayat, B.; Camacho, F.; Nickeson, J.; Cosh, M.; Bolten, J.; Vereecken, H.; Montzka, C. Toward operational validation systems for global satellite-based terrestrial essential climate variables. *Int. J. Appl. Earth Obs. Geoinf.* **2021**, *95*, 102240. [CrossRef]

9. Leroy, M.; Deuzé, J.L.; Bréon, F.M.; Hautecoeur, O.; Herman, M.; Buriez, J.C.; Tanré, D.; Bouffiès, S.; Chazette, P.; Roujean, J.L. Retrieval of atmospheric properties and surface bidirectional reflectances over land from POLDER/ADEOS. *J. Geophys. Res. Atmos.* **1997**, *102*, 17023–17037. [CrossRef]

10. Schaaf, C.B.; Gao, F.; Strahler, A.H.; Lucht, W.; Li, X.; Tsang, T.; Strugnell, N.C.; Zhang, X.; Jin, Y.; Muller, J.P.; et al. First operational BRDF, albedo nadir reflectance products from MODIS. *Remote Sens. Environ.* **2002**, *83*, 135–148. [CrossRef]

11. Strahler, A.H.; Muller, J.-P.; MODIS Science Team Members. MODIS BRDF/Albedo Product: Algorithm Theoretical Basis Document Version 5.0. Available online: https://modis.gsfc.nasa.gov/data/atbd/atbd_mod09.pdf (accessed on 15 February 2023).

12. Geiger, B.; Carrer, D.; Franchistéguy, L.; Roujean, J.L.; Meurey, C. Land surface albedo derived on a daily basis from meteosat second generation observations. *IEEE Trans. Geosci. Remote Sens.* **2008**, *46*, 3841–3856. [CrossRef]

13. Carrer, D.; Roujean, J.L.; Meurey, C. Comparing operational MSG/SEVIRI Land Surface albedo products from Land SAF with ground measurements and MODIS. *IEEE Trans. Geosci. Remote Sens.* **2010**, *48*, 1714–1728. [CrossRef]

14. Carrer, D.; Pinault, F.; Lellouch, G.; Trigo, I.F.; Benhadj, I.; Camacho, F.; Ceamanos, X.; Moparthy, S.; Munoz-Sabater, J.; Schüller, L.; et al. Surface Albedo Retrieval from 40-Years of Earth Observations through the EUMETSAT/LSA SAF and EU/C3S Programmes: The Versatile Algorithm of PYALUS. *Remote Sens.* **2021**, *13*, 372. [CrossRef]

15. Lellouch, G.; Carrer, D.; Vincent, C.; Pardé, M.; Frietas, S.C.; Trigo, I.F. Evaluation of Two Global Land Surface Albedo Datasets Distributed by the Copernicus Climate Change Service and the EUMETSAT LSA-SAF. *Remote Sens.* **2020**, *12*, 1888. [CrossRef]

16. Carrer, D.; Moparthy, S.; Lellouch, G.; Ceamanos, X.; Pinault, F.; Freitas, S.C.; Trigo, I.F. 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.* **2018**, *10*, 1262. [CrossRef]

17. Trigo, I.F.; Dacamara, C.C.; Viterbo, P.; Roujean, J.-L.; Olesen, F.; Barroso, C.; Camacho-de-Coca, F.; Carrer, D.; Freitas, S.C.; García-Haro, J.; et al. The Satellite Application Facility for Land Surface Analysis. *Int. J. Remote Sens.* **2011**, *32*, 2725–2744. [CrossRef]

18. Land Surface Analysis (LSA-SAF) of EUMETSAT. Available online: https://landsaf.ipma.pt/en/ (accessed on 10 November 2022).

19. Copernicus Global Land Service (CGLS) portal. Available online: https://land.copernicus.eu/global/index.html (accessed on 10 November 2022).

20. Copernicus Climate Change Service (C3S). Available online: https://climate.copernicus.eu/ (accessed on 10 November 2022).

21. Sanchez-Zapero, J.; Camacho, F.; Leon-Tavares, J.; Martinez-Sanchez, E.; Gorrono, J.; Benhadj, I.; Tote, C.; Swinnen, E.; Munoz-Sabater, J. Prototype for Surface Albedo Retrieval Based on Sentinel-3 OLCI and SLSTR Data in the Framework of Copernicus Climate Change. In Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 11–16 July 2021; pp. 2377–2380.

22. Liu, Q.; Wang, L.; Qu, Y.; Liu, N.; Liu, S.; Tang, H.; Liang, S. Preliminary evaluation of the long-term GLASS albedo product. *Int. J. Digit. Earth* **2013**, *6*, 69–95. [CrossRef]

23. Liu, Y.; Wang, Z.; Sun, Q.; Erb, A.M.; Li, Z.; Schaaf, C.B.; Zhang, X.; Román, M.O.; Scott, R.L.; Zhang, Q.; 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. Environ.* **2017**, *201*, 256–274. [CrossRef]

24. Sánchez-Zapero, J.; Camacho, F.; Martínez-Sánchez, E.; Lacaze, R.; Carrer, D.; Pinault, F.; Benhadj, I.; Muñoz-Sabater, J. Quality Assessment of PROBA-V Surface Albedo V1 for the Continuity of the Copernicus Climate Change Service. *Remote Sens.* **2020**, *12*, 2596. [CrossRef]

25. Zeng, Y.; Su, Z.; Calvet, J.C.; Manninen, T.; Swinnen, E.; Schulz, J.; Roebeling, R.; Poli, P.; Tan, D.; Riihelä, A.; et al. Analysis of current validation practices in Europe for space-based climate data records of essential climate variables. *Int. J. Appl. Earth Obs. Geoinf.* **2015**, *42*, 150–161. [CrossRef]

26. Nightingale, J.; Mittaz, J.P.D.; Douglas, S.; Dee, D.; Ryder, J.; Taylor, M.; Old, C.; Dieval, C.; Fouron, C.; Duveau, G.; et al. Ten Priority Science Gaps in Assessing Climate Data Record Quality. *Remote Sens.* **2019**, *11*, 986. [CrossRef]

27. LPV (Land Product Validation). Subgroup CEOS Validation Hierarchy 2019. Available online: https://lpvs.gsfc.nasa.gov/ (accessed on 2 March 2022).
4. Zhou, Y.; Wang, D.; Liang, S.; Yu, Y.; He, T. Assessment of the Suomi NPP VIIRS Land Surface Albedo Data Using Station Measurements and High-Resolution Albedo Maps. *Remote Sens.* **2016**, *8*, 137. [CrossRef]

5. Wang, D.; Liang, S.; Zhou, Y.; He, T.; Yu, Y. A New Method for Retrieving Daily Land Surface Albedo from VIIRS Data. *IEEE Trans. Geosci. Remote Sens.* **2017**, *55*, 1765–1775. [CrossRef]

6. Wang, D.; Liang, S.; He, T.; Yu, Y. Direct estimation of land surface albedo from VIIRS data: Algorithm improvement and preliminary validation. *J. Geophys. Res. Atmos.* **2013**, *118*, 12577–12586. [CrossRef]

7. Balsamo, G.; Agusti-Panareda, A.; Albergel, C.; Arduini, G.; Beljaars, A.; Bidlot, J.; Bousserez, N.; Boussetta, S.; Brown, A.; Buizza, R.; et al. Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review. *Remote Sens.* **2018**, *10*, 2038. [CrossRef]

8. Bayat, B.; Camacho, F.; Nickeson, J.; Cosh, M.; Bolten, J.; Vereecken, H.; Montzka, C. Toward operational validation systems for global satellite-based terrestrial essential climate variables. *Int. J. Appl. Earth Obs. Geoinf.* **2021**, *95*, 102240. [CrossRef]

9. Leroy, M.; Deuzé, J.L.; Bréon, F.M.; Hautecoeur, O.; Herman, M.; Buriez, J.C.; Tanré, D.; Bouffiès, S.; Chazette, P.; Roujean, J.L. Retrieval of atmospheric properties and surface bidirectional reflectances over land from POLDER/ADEOS. *J. Geophys. Res. Atmos.* **1997**, *102*, 17023–17037. [CrossRef]

10. Schaaf, C.B.; Gao, F.; Strahler, A.H.; Lucht, W.; Li, X.; Tsang, T.; Strugnell, N.C.; Zhang, X.; Jin, Y.; Muller, J.P.; et al. First operational BRDF, albedo nadir reflectance products from MODIS. *Remote Sens. Environ.* **2002**, *83*, 135–148. [CrossRef]

11. Strahler, A.H.; Muller, J.-P.; MODIS Science Team Members. MODIS BRDF/Albedo Product: Algorithm Theoretical Basis Document Version 5.0. Available online: https://modis.gsfc.nasa.gov/data/atbd/atbd_mod09.pdf (accessed on 15 February 2023).

12. Geiger, B.; Carrer, D.; Franchistéguy, L.; Roujean, J.L.; Meurey, C. Land surface albedo derived on a daily basis from meteosat second generation observations. *IEEE Trans. Geosci. Remote Sens.* **2008**, *46*, 3841–3856. [CrossRef]

13. Carrer, D.; Roujean, J.L.; Meurey, C. Comparing operational MSG/SEVIRI Land Surface albedo products from Land SAF with ground measurements and MODIS. *IEEE Trans. Geosci. Remote Sens.* **2010**, *48*, 1714–1728. [CrossRef]

14. Carrer, D.; Pinault, F.; Lellouch, G.; Trigo, I.F.; Benhadj, I.; Camacho, F.; Ceamanos, X.; Moparthy, S.; Munoz-Sabater, J.; Schüller, L.; et al. Surface Albedo Retrieval from 40-Years of Earth Observations through the EUMETSAT/LSA SAF and EU/C3S Programmes: The Versatile Algorithm of PYALUS. *Remote Sens.* **2021**, *13*, 372. [CrossRef]

15. Lellouch, G.; Carrer, D.; Vincent, C.; Pardé, M.; Frietas, S.C.; Trigo, I.F. Evaluation of Two Global Land Surface Albedo Datasets Distributed by the Copernicus Climate Change Service and the EUMETSAT LSA-SAF. *Remote Sens.* **2020**, *12*, 1888. [CrossRef]

16. Carrer, D.; Moparthy, S.; Lellouch, G.; Ceamanos, X.; Pinault, F.; Freitas, S.C.; Trigo, I.F. 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.* **2018**, *10*, 1262. [CrossRef]

17. Trigo, I.F.; Dacamara, C.C.; Viterbo, P.; Roujean, J.-L.; Olesen, F.; Barroso, C.; Camacho-de-Coca, F.; Carrer, D.; Freitas, S.C.; García-Haro, J.; et al. The Satellite Application Facility for Land Surface Analysis. *Int. J. Remote Sens.* **2011**, *32*, 2725–2744. [CrossRef]

18. Land Surface Analysis (LSA-SAF) of EUMETSAT. Available online: https://landsaf.ipma.pt/en/ (accessed on 10 November 2022).

19. Copernicus Global Land Service (CGLS) portal. Available online: https://land.copernicus.eu/global/index.html (accessed on 10 November 2022).

20. Copernicus Climate Change Service (C3S). Available online: https://climate.copernicus.eu/ (accessed on 10 November 2022).

21. Sanchez-Zapero, J.; Camacho, F.; Leon-Tavares, J.; Martinez-Sanchez, E.; Gorrono, J.; Benhadj, I.; Tote, C.; Swinnen, E.; Munoz-Sabater, J. Prototype for Surface Albedo Retrieval Based on Sentinel-3 OLCI and SLSTR Data in the Framework of Copernicus Climate Change. In Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 11–16 July 2021; pp. 2377–2380.

22. Liu, Q.; Wang, L.; Qu, Y.; Liu, N.; Liu, S.; Tang, H.; Liang, S. Preliminary evaluation of the long-term GLASS albedo product. *Int. J. Digit. Earth* **2013**, *6*, 69–95. [CrossRef]

23. Liu, Y.; Wang, Z.; Sun, Q.; Erb, A.M.; Li, Z.; Schaaf, C.B.; Zhang, X.; Román, M.O.; Scott, R.L.; Zhang, Q.; 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. Environ.* **2017**, *201*, 256–274. [CrossRef]

24. Sánchez-Zapero, J.; Camacho, F.; Martínez-Sánchez, E.; Lacaze, R.; Carrer, D.; Pinault, F.; Benhadj, I.; Muñoz-Sabater, J. Quality Assessment of PROBA-V Surface Albedo V1 for the Continuity of the Copernicus Climate Change Service. *Remote Sens.* **2020**, *12*, 2596. [CrossRef]

25. Zeng, Y.; Su, Z.; Calvet, J.C.; Manninen, T.; Swinnen, E.; Schulz, J.; Roebeling, R.; Poli, P.; Tan, D.; Riihelä, A.; et al. Analysis of current validation practices in Europe for space-based climate data records of essential climate variables. *Int. J. Appl. Earth Obs. Geoinf.* **2015**, *42*, 150–161. [CrossRef]

26. Nightingale, J.; Mittaz, J.P.D.; Douglas, S.; Dee, D.; Ryder, J.; Taylor, M.; Old, C.; Dieval, C.; Fouron, C.; Duveau, G.; et al. Ten Priority Science Gaps in Assessing Climate Data Record Quality. *Remote Sens.* **2019**, *11*, 986. [CrossRef]

27. LPV (Land Product Validation). Subgroup CEOS Validation Hierarchy 2019. Available online: https://lpvs.gsfc.nasa.gov/ (accessed on 2 March 2022).
28. Justice, C.; Belward, A.; Morisette, J.; Lewis, P.; Privette, J.; Baret, F. Developments in the 'validation' of satellite sensor products for the study of the land surface. *Int. J. Remote Sens.* **2000**, *21*, 3383-3390. [CrossRef]

29. Wang, Z.; Schaaf, C.; Lattanzio, A.; Carrer, D.; Grant, I.; Roman, M.; Camacho, F.; Yang, Y.; Sánchez-Zapero, J. Global Surface Albedo Product Validation Best Practices Protocol. Version 1.0. In *Good Practices for Satellite-Derived Land Product Validation* (p. 45): Land Product Validation Subgroup (WGCV/CEOS); Wang, Z., Nickeson, J., Román, M., Eds.; Available online: https://lpvs.gsfc.nasa.gov/PDF/CEOS_ALBEDO_Protocol_20190307_v1.pdf (accessed on 1 March 2022).

30. Nightingale, J.; Schaepman-Strub, G.; Nickeson, J.; Baret, F.; Herold, M. Assessing Satellite-Derived Land Product Quality for Earth System Science Applications: Overview of the CEOS LPV Sub-Group. In Proceedings of the 34th International Symposium on Remote Sensing of Environment, Sydney, NSW, Australia, 10–15 April 2011.

31. Mayr, S.; Kuenzer, C.; Gessner, U.; Klein, I.; Rutzinger, M. Validation of Earth Observation Time-Series: A Review for Large-Area and Temporally Dense Land Surface Products. *Remote Sens.* **2019**, *11*, 2616. [CrossRef]

32. Liang, S.; Fang, H.; Chen, M.; Shuey, C.J.; Walthall, C.; Daughtry, C.; Morisette, J.; Schaaf, C.; Strahler, A. Validating MODIS land surface reflectance and albedo products: Methods and preliminary results. *Remote Sens. Environ.* **2002**, *83*, 149–162. [CrossRef]

33. Cescatti, A.; Marcolla, B.; Santhana Vannan, S.K.; Pan, J.Y.; Román, M.O.; Yang, X.; Ciais, P.; Cook, R.B.; Law, B.E.; Matteucci, G.; et al. Intercomparison of MODIS albedo retrievals and in situ measurements across the global FLUXNET network. *Remote Sens. Environ.* **2012**, *121*, 323–334. [CrossRef]

34. EOLAB. Surface ALbedo VALidation (SALVAL) Tool. Available online: https://salval.eolab.es/ (accessed on 10 November 2022).

35. JCGM-GUM Joint Committee for Guides in Metrology (JCGM)—Guides to the Expression of Uncertainty in Measurement (GUM). [ISO/IEC Guide 98—Part 3, 2008]. Available online: https://www.iso.org/sites/JCGM/GUM-introduction.htm (accessed on 10 November 2022).

36. GCOS-154 Systematic Observation Requirements for Satellite-Based Data Products for Climate. *Supplemental Details to the Satellite-Based Component of the "Implementation Plan for the GCOS in Support of the UNFCCC"*. Available online: https://library.wmo.int/doc_num.php?explnum_id=3710 (accessed on 10 April 2022).

37. SALVAL Sampling—CalValPortal. Available online: https://calvalportal.ceos.org/sampling (accessed on 19 December 2022).

38. Camacho, F.; Cernicharo, J.; Lacaze, R.; Baret, F.; Weiss, M. GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 2: Validation and intercomparison with reference products. *Remote Sens. Environ.* **2013**, *137*, 310–329. [CrossRef]

39. Sánchez, J.; Camacho, F.; Lacaze, R.; Smets, B. Early validation of PROBA-V GEOV1 LAI, FAPAR and FCOVER products for the continuity of the copernicus global land service. *Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.*—ISPRS Arch. **2015**, *40*, 93–100. [CrossRef]

40. Lewis, P.; Barnsley, M. Influence of the sky radiance distribution on various formulations of the earth surface albedo. In Proceedings of the 6th International Symposium on Physical Measurements and Signatures in Remote Sensing, ISPRS, Val d'Isère, France, 17–22 January 1994; pp. 707–715.

41. Román, M.O.; Schaaf, C.B.; Woodcock, C.E.; Strahler, A.H.; Yang, X.; Braswell, R.H.; Curtis, P.S.; Davis, K.J.; Dragoni, D.; Goulden, M.L.; et al. The MODIS (Collection V005) BRDF/albedo product: Assessment of spatial representativeness over forested landscapes. *Remote Sens. Environ.* **2009**, *113*, 2476–2498. [CrossRef]

42. Román, M.O.; Schaaf, C.B.; Lewis, P.; Gao, F.; Anderson, G.P.; Privette, J.L.; Strahler, A.H.; Woodcock, C.E.; Barnsley, M. Assessing the coupling between surface albedo derived from MODIS and the fraction of diffuse skylight over spatially-characterized landscapes. *Remote Sens. Environ.* **2010**, *114*, 738–760. [CrossRef]

43. MODIS Data Products. Available online: https://modis.gsfc.nasa.gov/data/dataprod/ (accessed on 10 November 2022).

44. Global LAnd Surface Satellite (GLASS). Available online: http://www.glass.umd.edu/ (accessed on 1 April 2022).

45. Globalbedo Portal. Available online: http://www.globalbedo.org/ (accessed on 10 November 2022).

46. Schaepman-Strub, G.; Schaepman, M.E.; Painter, T.H.; Dangel, S.; Martonchik, J.V. Reflectance quantities in optical remote sensing-definitions and case studies. *Remote Sens. Environ.* **2006**, *103*, 27–42. [CrossRef]

47. Lucht, W.; Schaaf, C.B.; Strahler, A.H. An algorithm for the retrieval of albedo from space using semiempirical BRDF models. *IEEE Trans. Geosci. Remote Sens.* **2000**, *38*, 977–998. [CrossRef]

48. Shuai, Y.; Tuerhanjiang, L.; Shao, C.; Gao, F.; Zhou, Y.; Xie, D.; Liu, T.; Liang, J.; Chu, N. Re-understanding of land surface albedo and related terms in satellite-based retrievals. *Big Earth Data* **2020**, *4*, 45–67. [CrossRef]

49. Schaaf, C.; Wang, Z. MODIS/Terra+Aqua BRDF/Albedo Daily L3 Global—500m V061 [Data set]. NASA EOSDIS Land Processes DAAC. Available online: https://lpdaac.usgs.gov/products/mcd43a3v061/ (accessed on 20 December 2022).

50. Lucht, W.; Lewis, P. Theoretical noise sensitivity of BRDF and albedo retrieval from the EOS-MODIS and MISR sensors with respect to angular sampling. *Int. J. Remote Sens.* **2000**, *21*, 81–98. [CrossRef]

51. Sun, Q.; Wang, Z.; Li, Z.; Erb, A.; Schaaf, C.B. Evaluation of the global MODIS 30 arc-second spatially and temporally complete snow-free land surface albedo and reflectance anisotropy dataset. *Int. J. Appl. Earth Obs. Geoinf.* **2017**, *58*, 36–49. [CrossRef]

52. Liang, S.A.H.S.C.W. Retrieval of Land Surface Albedo from Satellite Observations: A Simulation Study. *J. Appl. Meteorol.* **1999**, *38*, 712–725. [CrossRef]

53. Wang, Z.; Schaaf, C.B.; Sun, Q.; Shuai, Y.; Román, M.O. Capturing rapid land surface dynamics with Collection V006 MODIS BRDF/NBAR/Albedo (MCD43) products. *Remote Sens. Environ.* **2018**, *207*, 50–64. [CrossRef]
28. Justice, C.; Belward, A.; Morisette, J.; Lewis, P.; Privette, J.; Baret, F. Developments in the 'validation' of satellite sensor products for the study of the land surface. *Int. J. Remote Sens.* **2000**, *21*, 3383-3390. [CrossRef]

29. Wang, Z.; Schaaf, C.; Lattanzio, A.; Carrer, D.; Grant, I.; Roman, M.; Camacho, F.; Yang, Y.; Sánchez-Zapero, J. Global Surface Albedo Product Validation Best Practices Protocol. Version 1.0. In *Good Practices for Satellite-Derived Land Product Validation* (p. 45): Land Product Validation Subgroup (WGCV/CEOS); Wang, Z., Nickeson, J., Román, M., Eds.; Available online: https://lpvs.gsfc.nasa.gov/PDF/CEOS_ALBEDO_Protocol_20190307_v1.pdf (accessed on 1 March 2022).

30. Nightingale, J.; Schaepman-Strub, G.; Nickeson, J.; Baret, F.; Herold, M. Assessing Satellite-Derived Land Product Quality for Earth System Science Applications: Overview of the CEOS LPV Sub-Group. In Proceedings of the 34th International Symposium on Remote Sensing of Environment, Sydney, NSW, Australia, 10–15 April 2011.

31. Mayr, S.; Kuenzer, C.; Gessner, U.; Klein, I.; Rutzinger, M. Validation of Earth Observation Time-Series: A Review for Large-Area and Temporally Dense Land Surface Products. *Remote Sens.* **2019**, *11*, 2616. [CrossRef]

32. Liang, S.; Fang, H.; Chen, M.; Shuey, C.J.; Walthall, C.; Daughtry, C.; Morisette, J.; Schaaf, C.; Strahler, A. Validating MODIS land surface reflectance and albedo products: Methods and preliminary results. *Remote Sens. Environ.* **2002**, *83*, 149–162. [CrossRef]

33. Cescatti, A.; Marcolla, B.; Santhana Vannan, S.K.; Pan, J.Y.; Román, M.O.; Yang, X.; Ciais, P.; Cook, R.B.; Law, B.E.; Matteucci, G.; et al. Intercomparison of MODIS albedo retrievals and in situ measurements across the global FLUXNET network. *Remote Sens. Environ.* **2012**, *121*, 323–334. [CrossRef]

34. EOLAB. Surface ALbedo VALidation (SALVAL) Tool. Available online: https://salval.eolab.es/ (accessed on 10 November 2022).

35. JCGM-GUM Joint Committee for Guides in Metrology (JCGM)—Guides to the Expression of Uncertainty in Measurement (GUM). [ISO/IEC Guide 98—Part 3, 2008]. Available online: https://www.iso.org/sites/JCGM/GUM-introduction.htm (accessed on 10 November 2022).

36. GCOS-154 Systematic Observation Requirements for Satellite-Based Data Products for Climate. *Supplemental Details to the Satellite-Based Component of the "Implementation Plan for the GCOS in Support of the UNFCCC"*. Available online: https://library.wmo.int/doc_num.php?explnum_id=3710 (accessed on 10 April 2022).

37. SALVAL Sampling—CalValPortal. Available online: https://calvalportal.ceos.org/sampling (accessed on 19 December 2022).

38. Camacho, F.; Cernicharo, J.; Lacaze, R.; Baret, F.; Weiss, M. GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 2: Validation and intercomparison with reference products. *Remote Sens. Environ.* **2013**, *137*, 310–329. [CrossRef]

39. Sánchez, J.; Camacho, F.; Lacaze, R.; Smets, B. Early validation of PROBA-V GEOV1 LAI, FAPAR and FCOVER products for the continuity of the copernicus global land service. *Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.*—ISPRS Arch. **2015**, *40*, 93–100. [CrossRef]

40. Lewis, P.; Barnsley, M. Influence of the sky radiance distribution on various formulations of the earth surface albedo. In Proceedings of the 6th International Symposium on Physical Measurements and Signatures in Remote Sensing, ISPRS, Val d'Isère, France, 17–22 January 1994; pp. 707–715.

41. Román, M.O.; Schaaf, C.B.; Woodcock, C.E.; Strahler, A.H.; Yang, X.; Braswell, R.H.; Curtis, P.S.; Davis, K.J.; Dragoni, D.; Goulden, M.L.; et al. The MODIS (Collection V005) BRDF/albedo product: Assessment of spatial representativeness over forested landscapes. *Remote Sens. Environ.* **2009**, *113*, 2476–2498. [CrossRef]

42. Román, M.O.; Schaaf, C.B.; Lewis, P.; Gao, F.; Anderson, G.P.; Privette, J.L.; Strahler, A.H.; Woodcock, C.E.; Barnsley, M. Assessing the coupling between surface albedo derived from MODIS and the fraction of diffuse skylight over spatially-characterized landscapes. *Remote Sens. Environ.* **2010**, *114*, 738–760. [CrossRef]

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**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.