## 4 Discussion  
Values of LAI and fAPAR derived from the JRC-TIP algorithm applied to the GlobAlbedo data were compared with MODIS-derived LAI and fAPAR estimates. As noted previously, these two sets of products are based on different assumptions: the GlobAlbedo-derived fAPAR and LAI are generated using a 1D RT model inversion scheme designed to be consistent with the observations used to drive it; the MODIS products are generated using a 3D RT model scheme. As a result the LAI values are \"real\" in the sense that they represent the LAI within this 3D scheme, assuming some biome-specific cover, clumping and understory/soil properties. However, the MODIS LAI values are still not real in as much as they depend on these biome-specific assumptions, are derived from large-scale (250 m to 1 km) observations assuming cover is homogeneous within each pixel, and assume the underlying biome map is accurate at this scale. All of these issues combine to make the resulting LAI (and corresponding fAPAR) values essentially impossible to verify in any real sense. The JRC-TIP GlobAlbedo approach results in values of LAI (and derived fAPAR) that are consistent with the 1D approximation to a 3D RT problem, where clumping is not accounted for, but not intended to correspond to the real LAI and fAPAR of the surface. This approach is useful for the following reasons: (i) consistency between RT model assumptions and observations is ensured; (ii) meaningful uncertainty core to the GlobAlbedo approach and is provided with the derived LAI and fAPAR values; (iii) the inversion scheme can be applied globally, without any requirement for any biome-specific structural (cover, clumping, understory) assumptions required for 3D RT model approaches; and (iv) the RT scheme is very similar to schemes used by many Earth System Models (ESMs) [(22)]. However [(43)] note that even 1D RT schemes used in ESMs are often implemented differently, leading to differences in both fAPAR (and hence photosynthesis) and surface albedo. A key point to note in the comparison between GlobAlbedo and MODIS products above is the temporal consistency across each year. At a given site, and even averaged over several pixels, the temporal variation of the MODIS values can be up to a factor of 2 or more at times between subsequent estimates (see Figures 3-8). We note that this variation has reduced significantly between collections 5 and 6 of MODIS LAI/fAPAR: previous sample-to-sample variation could be 7\(\times\) or more (see [44] for summary of changes from collection 5 to 6). This temporal variation is largely due to the influence of residual cloud and snow contamination that causes poor quality RT model retrievals, even though they may not be flagged as such in the underlying reflectance data. Additional improvements made in collection 5 to collection 6 of the MODIS LAI/fAPAR retrievals include an updated underlying land cover map. This will potentially affect retrieved parameter values by altering the biome type used in RT model retrieval. This is likely to be of most importance in areas with relatively low (and mixed) vegetation cover, such as semi-arid regions, savannas and scrub grassland [45; 46]. These regions may also have rapidly changing phenology, which can introduce further uncertainty in RT model retrievals based on land cover classifications that may be static or updated relatively infrequently (annually or less) [47]. The authors of [48] showed that fAPAR from MODIS and SPOT-VGT are particularly variable in woody savanna regions, due to the combination of low vegetation cover, mixed woody cover and dynamic phenology. The authors of [47] looked explicitly at the issue of mixed cover and biome misclassification on uncertainty in retrieved MODIS LAI and showed that this was most important in savannas, resulting in uncertainty in retrieved LAI of up to 0.51. The GlobAlbedo albedo values are based on a moving window approach which generates a model-based prediction of albedo with a prior expectation [32].