## 3 Results and Discussion
### Evergreen Needleleaf Forest
#### 3.2.2 Regional Comparisons  
Comparisons are presented across complete 1200 \(\times\) 1200 km tiles, covering some of the regions outlined above in the site-level comparisons. MODIS and GlobAlbedo-derived fAPAR and LAI values are compared for the following tiles specified by the horizontal and vertical MODIS tile IDs from the MODIS tile map [32]:  
* h09v05: Central USA
* h18v03: Western Europe
* h18v07: Central West Africa
* h29v12: South Australia  
Tiles are compared from a given 8-day period in 2002 at mid-summer for each tile _i.e._ day of year (DOY) 185 for northern hemisphere tiles h09v05, h18v03, h18v07, and DOY 001 for southern hemisphere tile h29v12. In each case, scatter plots of the GlobAlbedo-derived and MODIS fAPAR and LAI values for all pixels in each tile, for all equivalent DOY values over the period 2002-2011 are also shown. These scatter plots are shown as 2D histograms (or \"heatmaps\") _i.e._, the (x, y) location of each point represents the value of GlobAlbedo-derived and MODIS parameter in 20 bins along \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Biome**} & \multicolumn{2}{c}{**Slope (Lower, Upper)**} & \multicolumn{2}{c}{**Intercept (Lower, Upper)**} \\ \cline{2-5}  & **fAPAR** & **LAI** & **fAPAR** & **LAI** \\ \hline Grassland & 0.9, 1.1 & 1.2, 2.1 & \(-\)0.1, 0.07 & \(-\)0.3, 1.3 \\ deciduous broadleaf forest & 1.3, 1.4 & 2, 2.3 & \(-\)0.1 & \(-\)0.5, \(-\)0.3 \\ Evergreen needleleaf forest & 1.4, 2.3 & 3.1, 8.3 & \(-\)0.2, 0.1 & \(-\)1.7, 0 \\ Mixed forest & 1.1, 1.7 & 1.5, 3.5 & \(-\)0.1, 0.1 & \(-\)0.7, 0.2 \\ Crop/natural & 1, 1.4 & 1.1, 1.6 & \(-\)0.2, 0.1 & \(-\)0.4, 0.2 \\ Miscellaneous & 0.2, 1.4 & 0.1, 2.5 & \(-\)0.1, 0.5 & \(-\)0.1, 3.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of bias (slope) and intercept of comparisons for all biome types. Figure 8: Top row: GlobAlbedo-derived and MODIS fAPAR values for 2002–2011; second row: scatter plot of fAPAR values; third row: GlobAlbedo-derived and MODIS LAI values for 2002–2011; fourth row: scatter plot of LAI values. Error bars for GlobAlbedo values represent the uncertainty of parameter retrieval, and for the MODIS data, the variance of the 3 \(\times\) 3 pixel window centered on the comparison site. each axis respectively, and the color represents the frequency of samples in each bin. Comparisons are shown first for fAPAR and then LAI for each of the tiles. Agreement across tiles ought to be expected to be lower than for individual sites, due to the mixed nature of the cover types covered in a given tile. This is particularly true for very heterogeneous tiles where there is significant agriculture _i.e._, tile h18v03 for W. Europe. These comparisons are included to illustrate large-scale regional trends between the two datasets. Central USA: Tile h09v05  
Figure 9 shows the comparison between GlobAlbedo and MODIS fAPAR and LAI values for 2002 DOY 185 over central Southern USA. In this case, significant cloud cover over the region means that both coverage and retrieval quality are variable. The vegetation is primarily sparse grassland, scrub, savanna and low-cover woodland, with correspondingly low retrieved LAI values over much of the tile except in the SE corner. This results in low fAPAR values over much of the tile. The correlation between the GlobAlbedo-derived and MODIS fAPAR values shows r\({}^{2}\) = 0.3, and for LAI r\({}^{2}\) = 0.08 _i.e._, there is very little spatial correspondence between the GA and MO values, particularly for LAI. There is clearly a strong seasonal variation in LAI, with much higher values (and range) observed over DOY 185 than for DOY 361 over both years. Areas of low vegetation cover such as this (and/or particularly heterogeneous areas) are likely to prove more problematic for RT parameter retrieval at these scales than mid-range LAI, heterogeneous regions. Correct identification of the underlying land cover class (or biome) is simply more difficult/uncertain, resulting in potentially higher uncertainty in retrieved RT model parameters.