## 3 Results
### Spatial and Temporal Comparison among Four GPP Results
Figure 6: Scatter plots of annual GPP between the GIMMS NDVI3g based GPP (GPP\({}_{\text{NDV5g}}\)) and (**a**) MOD15A2 based GPP (GPP\({}_{\text{MOD15}}\)); (**b**) GIMMS NDVI1g based GPP (GPP\({}_{\text{NDVI1g}}\)); (**c**) the MODIS GPP product MOD17A2 (GPP\({}_{\text{MOD17}}\)). Due to the data availability, GPP\({}_{\text{NDVISg}}\), GPP\({}_{\text{NDVIIg}}\), GPP\({}_{\text{MOD15}}\), and GPP\({}_{\text{MOD17}}\) had different date ranges for Southeast Asia, but all of them had an overlap period from 2000-2006. We calculated the multi-year average of GPPs in 2000-2006 to compare their spatial patterns. Figure 7 shows that all the four GPP results had similar spatial patterns. The Tibetan Plateau had the lowest GPP values, while the highest GPP values occurred in the tropical forest areas in Malaysia and Indonesia. In India, however, the four datasets had inconsistent patterns, and especially GPP\({}_{\text{MOD17}}\) had a lower estimate than the other three (Figure 7). This discrepancy could be associated with the lower estimate of MOD17 model in the crop GPP simulation (Table 4), as there is a large area of cropland (mainly double paddy rice cropping) in India. Statistics of the average GPPs of different vegetation types are shown in Table 4. The average forest GPP in Southeast Asia ranged from \(1.79\,\pm\,0.03\) kilogram of carbon per square meter per year (kgC m\({}^{-2}\) a\({}^{-1}\)) (GPP\({}_{\text{NDVIIg}}\)) to \(2.20\,\pm\,0.06\) kgC m\({}^{-2}\) a\({}^{-1}\) (GPP\({}_{\text{MOD17}}\)), while GPP\({}_{\text{NDVISg}}\), GPP\({}_{\text{MOD15}}\) had GPP estimates in the order of \(2.12\,\pm\,0.06\) kgC m\({}^{-2}\) a\({}^{-1}\) and \(2.08\,\pm\,0.09\) kgC m\({}^{-2}\) a\({}^{-1}\). All of the four products had highest GPP values in evergreen broadleaf forest (\(2.02\)-\(2.57\) kgC m\({}^{-2}\) a\({}^{-1}\)) while the lowest GPP values were in evergreen needle-leaf forest (\(0.84\)-\(1.14\) kgC m\({}^{-2}\) a\({}^{-1}\)). The forest ecosystem had the largest carbon uptake with total GPP of \(8.8\) petagram of carbon per year (Pg C a\({}^{-1}=10^{15}\) gram carbon per year). This accounted for \(43\%\) of the total vegetation carbon uptake although its area accounts for \(30\%\) of the study area. Croplands accounted for \(32\%\) of the carbon and \(31\%\) of the area, and grassland \(21\%\) of the carbon uptake over \(24\%\) of the area. Of the forest types, evergreen broadleaf forest had the highest GPP of \(6.6\) Pg C a\({}^{-1}\) over \(69\%\) of the total forest area, followed by mixed forest with \(1.5\) Pg C a\({}^{-1}\) over \(29.1\%\) of the area (Table 4). In total,  
Figure 7: Spatial patterns of the annual average GPP in 2000–2006 in Southeast Asia: (**a**) GPP\({}_{\text{NDVISg}}\); (**b**) GPP\({}_{\text{NDVIIg}}\); (**c**) GPP\({}_{\text{MOD15}}\); and (**d**) GPP\({}_{\text{MOD17}}\). all the vegetation in our studied Southeast Asia had a total carbon uptake of 20 Pg C \(\mathrm{a}^{-1}\). [PERSON] _et al._[3] reported an observation-based global GPP estimate of about 123 \(\pm\) 8 Pg C \(\mathrm{a}^{-1}\).Therefore, from our estimates, Southeast Asia contributes about 14%\(\sim\)16% of the global carbon flux.