## 2 Experimental Section
### MODIS LAI and fAPAR  
The MODIS LAI and fAPAR products are described in full in the Algorithm Theoretical Basis Document (ATBD) [8]. These data are perhaps the most widely-used global LAI and fAPAR products, in part due to being the only physically-based estimates of LAI and fAPAR derived using 3D RT model inversion [7; 8]. The data used are the MODIS collection 6 products (released 2015) _i.e._, fAPAR and LAI derived from MODIS observations of surface reflectance, aggregated over 4 days (MCD15A2) from 1 km composites of observations, using data aggregated from MODIS sensors aboard both the Terra and Aqua platforms (collection 6 MCD15A2 product). MODIS data were obtained from [38]. Aggregation over time maximizes the chances of obtaining sufficient good quality (cloud free primarily) observations to allow model inversion, and assumes that the surface is stable over the aggregation period. 3D RT model inversion is via a look-up-table (LUT) containing pre-computed values of RT-modelled reflectance across a range of model parameter and observation configurations. The LUT is divided into 8 biome types, representing structurally different 3D vegetation canopy types. The resulting LAI and fAPAR values are provided at 1 km, with an associated uncertainty derived from the inversion scheme, and quality assurance (QA) flags indicating the quality of the inversion. Here we only consider values flagged as highest quality in the QA data. It is important to reiterate that when comparing between GlobAlbedo-derived and MODIS values we do not imply that either product is \"right\"; as described above, the products rely on different RT model (and processing) assumptions, \begin{table}
\begin{tabular}{c c} \hline \hline
**FLUXNET Code, Full Name** & **Location: Lat, Lon** \\ \hline \multicolumn{2}{c}{_Grassland_} \\ \hline AU-Stp: Australia, Stuart Plains & \(-17.15\), \(133.35\) \\ CN-Du3: China, Doulum Degraded Meadow & \(42.06\), \(116.28\) \\ RU-Upo: Russia, Ust Pojeg & \(61.93\), \(50.23\) \\ US-AR2: ARM USDA UNL OSU Woodward Switchgrass 2 & \(36.64\), \(-99.60\) \\ \hline \multicolumn{2}{c}{_Deciduous broadleaf forest_} \\ \hline US-Oho: Ohio Oak Openings & \(41.55\), \(-83.84\) \\ US-UMB: Univ. of Michigan Biological Station & \(45.56\), \(-84.71\) \\ US-WCr: Willow Creek & \(45.81\), \(-90.08\) \\ US-Wi3: Wisconsin Mature Hardwood & \(46.63\), \(-91.10\) \\ \hline \multicolumn{2}{c}{_Evergreen needleleaf forest_} \\ \hline DE-6N: Schechenfiz Nord, Germany & \(47.81\), \(11.33\) \\ FI-Ves: Vesijako, Finland & \(61.37\), \(25.11\) \\ PL-Tcz: Tuczo, Poland & \(53.19\), \(16.10\) \\ US-NR2: Niwot Ridge, Colorado, US & \(40.04\), \(-105.55\) \\ \hline \multicolumn{2}{c}{_Mixed forest_} \\ \hline AT-StM: Stubai Meadow, Austria & \(47.13\), \(11.31\) \\ CH-Dsc: Dischma, Switzerland & \(46.79\), \(9.86\) \\ EE-Hi2: Hiteso, Estonia & \(59.35\), \(27.10\) \\ US-Ha2: Harvard Forest Hemlock, US & \(42.54\), \(-72.18\) \\ \hline \multicolumn{2}{c}{_Crop/Natural_} \\ \hline CA-MA2: Manitoba Agricultural Site 2, Canada & \(50.17\), \(-97.88\) \\ EE-Aar: Aardlapalu, Estonia & \(58.31\), \(26.74\) \\ ML-Kem: Kelma, Mali & \(15.22\), \(-1.57\) \\ US-Wi6: Wisconsin Pine Barrens, US & \(46.62\), \(-91.30\) \\ \hline \multicolumn{2}{c}{_Miscellaneous_} \\ \hline DE-Hai: Hainich, Germany (mixed forest) & \(51.08\), \(10.45\) \\ DE-Tha: Tharandt, Germany (evergreen needleaf) & \(50.96\), \(13.57\) \\ BR-Cax: Caxiuana Forest, Brazil (evergreen broadleaf) & \(-1.72\), \(-51.46\) \\ GF-Guy: Guyaflux, French Guiana (evergreen broadleaf) & \(5.28\), \(-52.92\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Site name, full name and location of the various FLUXNET sites used for comparison. and these are different from those used in field measurements using optical devices, which are in turn not the same as direct, destructive measurements (the only real answer). Separate site-level comparisons are presented, for varying cover types across the globe.