## 3 Ground Dataset
### Analysis of the Spatial Representativeness of Tower Measurements  
The reference albedo measured from towers covers a circular footprint that varies with the tower height. It is unlikely that the footprint of the ground measurements exactly matches the satellite pixel sizes. Then, the spatial representativeness of the tower-based measurements should be evaluated to minimize the issues associated with spatial representativeness in the point-to-pixel comparison [31; 32]. The semivariogram [58; 59] is one of the most efficient tools for describing spatial representativeness. Semivariograms were computed for the stations under evaluation using Sentinel-2 surface reflectances at a spatial resolution of 10 m near-nadir. Two different periods (leaf-off and leaf-on) periods during the year were considered to evaluate the spatial representativeness of the region around the ground tower. The spatial attributes (e.g., the range, sill and nugget) were computed by fitting the variogram estimator to an isotropic spherical model [60], and they can reveal the spatial variability of land surfaces and the scaling effects associated with remotely sensed data [31; 32; 33; 34; 61; 62; 63]. The methodology adopted in this study for the estimation of the geostatistical indexes is based on the comparison of the variogram model parameters retrieved at different spatial resolutions (i.e., from 1.0 km\({}^{2}\) to 1.5 kmsquared subsets). Four different geostatistical attributes were used [16; 32]: The relative coefficient of variation (\(R_{CV}\)), the scale requirement index (\(R_{SE}\)), the relative strength of the spatial correlation (\(R_{ST}\)) and the relative proportion of the structural variation (\(R_{SV}\)). The four geostatistical attributes can be combined in a compact metric (\(ST_{score}\)[31; 32], see Equation (1)), which represents a spatial representativeness score using R\({}_{\text{SE}}\) as the primary marker and the others like secondary weights. When the spherical variogram model does not provide a good fit to the variogram estimator, another indicator (\(RAW_{score}\)[31; 32], see Equation (2) could be used to provide a spatial representativeness score, which is only based on the \(R_{CV}\). \[ST_{score}=\left(\frac{|R_{CV}|+|R_{ST}|+|R_{SV}|}{3}+R_{SE}\right)^{-1} \tag{1}\]  
\[RAW_{score}=|2\;R_{CV}|^{-1} \tag{2}\]  
Both scores are directly proportional to the site representativeness. We consider that sites are heterogeneous or not spatially representative when both scores are lower than 2.0 (see Table 5) because large differences, due to spatial heterogeneity, are expected for scores below this threshold [16]. Based on it, the ground measurements coming from _Boulder_ (USA_BAO), _Renon_ (ITA_REN), _Rock Springs_ (USA_PSU) and _Table Mountain_ (USA_TBL) sites are not used for the accuracy assessment during the leaf-off seasonal period. In addition, the _Cabauw_ (NET_CAB), _Goodwin Creek_ (USA_GCM) and _Gobabeb_ (NAM_GOB) sites were discarded during leaf-on seasonal period. Figure 2 shows two examples of sites of stations spatially representative (top side), and two examples of not spatially representative (bottom side).