**5. Conclusions**

In this study, a new method is introduced which allows the derivation of DHR and BHR values from tower albedometer measurements. This method was applied to derive DHRs and BHRs over 20 tower sites, including both homogeneous and heterogenous land surfaces from the FLUXNET, SURFRAD and BSRN networks between the years 2012 and 2016. The retrieved DHR and BHR values were directly compared with the satellite albedo values, including CGLS, MODIS and MISR retrievals. The MODIS 16-day albedo products show the best agreemen<sup>t</sup> with in situ retrievals, whilst the MISR near-simultaneous measurements show a similar good agreemen<sup>t</sup> with in situ retrievals for a smaller time-window. The CGLS 30-day products have larger biases than MODIS and MISR products. Overall, the direct intercomparison with tower albedometer derived values shows a better match over the homogeneous sites than the heterogenous sites. The agreemen<sup>t</sup> between tower and satellite retrieved DHR values are better than the BHR values. This is because DHRs are only measured at local solar noon, whereas BHRs are derived from measurements at all possible solar zenith angles.

A surface albedo upscaling method, for tower FoV albedos to coarse resolutions, is described. This method employs atmospherically corrected BRFs from high-resolution EO alongside coarseresolution albedos predicted from a MODIS BRDF climatology over a larger area as inputs. The high-resolution albedo values are retrieved from the MODIS BRDF derived albedo-to-nadirreflectance ratios. This method was applied to upscale tower measured DHR and BHR values to 1-km resolutions and compared with the CGLS products. These results imply that this surface albedo upscaling strategy can be applied to both homogeneous and heterogenous surfaces if the optimal sample size for optimising this upscaling is known. For example, one of the sites, where 3 × 3 km were compared, shows that the pixel to the north-west of where the tower is located yields a better correlation than the pixel containing the tower, which appears to be associated with the observation that the land cover changes in the south-east. This will be explored in more detail in future.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2072-4292/11/6/644/s1, Figure S1: CGLS, MODIS and MISR DHR products compared with tower derived DHRs. Figure S2: CGLS, MODIS and MISR BHR compared with tower derived BHRs. Figure S3: Scatter plot of DHR values retrieved from CGLS, MODIS and MISR between 2012 and 2016. Figure S4: Scatter plot of BHR values retrieved from CGLS, MODIS and MISR between 2012 and 2016. Figure S5: Density plot of DHR and BHR upscaled from tower FoV to CGLS resolution over a 20\*20 pixel region.

**Author Contributions:** Conceptualization, J.-P.M. (Jan-Peter Muller); methodology, R.S. (Rui Song), S.K. (Said Kharbouche); software, R.S., S.K.; validation, R.S.; formal analysis, R.S.; investigation, R.S.; resources, J.-P.M.; data curation, J.-P.M.; writing—original draft preparation, R.S.; writing—review and editing, J.-P.M., S.K., and W.W. (William Woodgate); visualization, R.S.; supervision, J.-P.M.; project administration, J.-P.M.; funding acquisition, J.-P.M.

**Funding:** This research was funded by the European Commission Joint Research Centre gran<sup>t</sup> number [FWC932059], part of the Global Component of the European Union's Copernicus Land Monitoring Service.

*Remote Sens.* **2019**, *11*, 644

**Acknowledgments:** This work used JASMIN, the UK's collaborative data analysis environment http://jasmin.ac. uk. This work has been undertaken using data from the Global Component of the European Union's Copernicus Land Monitoring Service, European Commission Joint Research Centre FWC [932059]. We would like to thank Nadine Gobron and Christian Lanconelli of JRC Ispra for fruitful discussions. We would like to thank NOAA for access to their datasets through SURFRAD (http://www.esrl.noaa.gov/gmd/grad/surfrad/) and the BSRN (http://bsrn.awi.de) [5] for access to their datasets. This work also used tower albedometer data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (U.S. Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04ER63917 and DE-FG02-04ER63911), CarboEuropeIP, CarboItaly, CarboMont, OzFlux, USCCC. This study has been undertaken using data from GBOV "Ground Based Observation for Validation" (https://land.copernicus.eu/global/gbov), part of the Global Component of the European Union's Copernicus Land Monitoring Service. GBOV product developments are managed by ACRI-ST from the research work of University College London, University of Leicester, University of Southampton, University of Valencia and Informus GmbH. We acknowledge the financial support to the tower albedometer data harmonization provided by CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, Max Planck Institute for Biogeochemistry, National Science Foundation, University of Tuscia, Université Laval and Environment Canada and US Department of Energy and the database development and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, University of California-Berkeley, University of Virginia.

**Conflicts of Interest:** The authors declare no conflict of interest.
