## 4 Discussion
### Comparison of Three Approaches
#### 4.1.2 Comparison between Parameterization Approaches and Regression Tree Approach  
Figure 10 shows the global annual GPP estimated by the three approaches in each type of vegetation. It is easy to find that EBF absorbed the most carbon among all types of vegetation, with a \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Site** & **Site Name** & **Latitude** & **Longitude** & **Elevation** & **Landcover** & **Mean Annual** & **Mean Annual** \\
**ID** & & (°) & (°) & (m) & **Landcover** & **Temperature** & **Precipitation** \\ \hline IT-Isp & Ispra ABC-IS & 45.813 & 8.634 & 210 & DBF & 12.2 & 1300 \\ AU-Whr & Whroo & –36.673 & 145.029 & 152 & EBF & 16.7 & 625 \\ CH-Cha & Chamau & 47.210 & 8.410 & 393 & GRA & 9.5 & 1136 \\ SE-St1 & Stordalen & & & & & & \\ grassland & 68.354 & 19.050 & 351 & WET & –0.7 & 303.3 \\ US-WCr & Willow Creek & 45.806 & \(-\)90.080 & 520 & DBF & 4.02 & 787 \\  & Metolius & & & & & & \\ US-Me2 & mature & 44.452 & \(-\)121.557 & 1253 & ENF & 6.28 & 523 \\  & ponderosa & & & & & & \\ AU-Gin & Gingin & –31.376 & 115.714 & 105 & WSA & 18.76 & 346 \\ DE-Kli & Klingenberg & 50.893 & 13.522 & 478 & CRO & 7.6 & 842 \\ CH-Oe2 & Oensing & 47.286 & 7.734 & 452 & CRO & 9.8 & 1155 \\ US-Los & Lost Creek & 46.083 & \(-\)89.979 & 480 & WET & 4.08 & 828 \\ US-Tw4 & Twitchell East & 38.103 & \(-\)121.641 & \(-\)5 & WET & 15.6 & 421 \\ CH-Lae & Laegern & 47.478 & 8.364 & 689 & MF & 8.3 & 1100 \\ US-SRG & Santa Rita & 31.789 & \(-\)110.828 & 1291 & GRA & 17 & 420 \\  & Grassland & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Detailed information for a few sites. Figure 9: LUE and GPP curves in IT-Isp, AU-Whr, CH-Cha and SE-St1. GPP greater than 30 Pg/yr, followed by SAV, WSA, GRA and CRO, with GPP values of approximately 15 Pg/yr. The lowest GPP values were produced by DNF and CSH because of the extremely small areas for DNF and the weak carbon sequestration capacity for CSH. Although the GPP values estimated by the three approaches were similar, there were still some differences for some vegetation types, such as EBF, WSA and GRA. Most of the EBFs were located in equatorial regions with suitable temperature and abundant rain. More rain implied more diffuse radiation; therefore, the clearness index obviously increased the LUE and GPP. Our WSA calibration sites were located in low-latitude areas. The Cubist regression approach was likely to lead to the overestimation when extrapolating this relationship to high-latitude areas (southern Canada, northern USA, Europe and Russia). In contrast, the Cubist regression tree approach underestimated GPP and NPP in GRA because of these inconsistent locations between the calibration sites and land areas. To analyze the contributions of different latitudes to global vegetation carbon uptake and to compare the performances of the three approaches, we averaged the LUE and summed the annual GPP every 5 km along latitude (Figure 11). In general, LUE demonstrated a tendency of having higher values at lower latitudes and lower values at higher latitudes. In the first season, LUE had its highest value of approximately 1.5 gC/MJ in equatorial regions corresponding to South America, middle Africa and Southeast Asia. There was another important peak near 1.0 gC/MJ at 40\({}^{\circ}\)S latitude because of the southern coastal vegetation in Australia. Areas from 50\({}^{\circ}\) to 55\({}^{\circ}\)S saw higher LUE values, some higher than 1.0 gC/MJ. The curves in the Southern Hemisphere illustrated more fluctuations. The total area of vegetation was much smaller; consequently, the mean LUE was more sensitive to spatial changes. Then, looking at the Northern Hemisphere, it was obvious that vegetation showed a relatively lower LUE, i.e., less than 0.5 gC/MJ, because of the lower temperature and less radiation. The LUE curves calculated by V1 and V2 were more stable than that calculated by V3.