## 4 Discussion  
### Uncertainty in GPP Modelling  
GLOPEM-CEVSA based GPP estimates were calculated with three different FPAR datasets (\(\mathrm{FPAR}_{\mathrm{NDVI3g}}\), \(\mathrm{FPAR}_{\mathrm{NDVI1g}}\) and \(\mathrm{FPAR}_{\mathrm{MOD15}}\)) as well as the same meteorological data (e.g., air temperature, dew point, and wind speed). Validations and comparisons showed that GIMMS NDVI3g is comparable in capabilities to MODIS data for spatial and temporal modelling of GPP. While using the same MODIS FPAR data, GLOPEM-CEVSA based \(\mathrm{GPP}_{\mathrm{MOD15}}\) showed better accuracy than that of \(\mathrm{GPP}_{\mathrm{MOD17}}\) based on the MODIS algorithm, indicating better potential for application of the GLOPEM-CEVSA model for the study region. An important factor contributing to uncertainty in \(\mathrm{GPP}_{\mathrm{NDVI3g}}\) could be that of the GIMMS NDVI3g data. For example, [PERSON] _et al._[81] found that correlations between GIMMS NDVI3g and MODIS NDVI datasets are highly significant for areas with a distinct phenological cycle (e.g., temperate forest), while the discrepancies between them are large in equatorial areas (e.g., tropical evergreen forest). The temporal and spatial resolutions could also affect the quality of satellite-based FPAR and GPP estimates. GIMMS NDVI3g and MODIS have temporal resolutions of half-month and eight-days, respectively, while their spatial resolutions are 8-km and 1-km, respectively. Consequently, GIMMS NDVI3g most likely neglects some fragmentary landscapes. Since the landscape of Southeast Asia has been seriously fragmented due to its mountainous terrain and rapid deforestation over the course of the study period [82], MODIS likely performs better in areas of high disturbance and/or land cover change. However, we aggregated \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Vegetation** & \(\mathrm{\mathbf{GPP}_{\mathrm{NDVI3g}}}\) & \(\mathrm{\mathbf{GPP}_{\mathrm{NDVI3g}}}\) & \(\mathrm{\mathbf{GPP}_{\mathrm{MOD15}}}\) & \(\mathrm{\mathbf{GPP}_{\mathrm{MOD17}}}\) & **Vegetation** & \(\mathrm{\mathbf{Total}\ \mathbf{GPP}_{\mathrm{NDVI3g}}}\) \\
**Type** & **(kgC \(\mathrm{m}^{-2}\) \(\mathrm{a}^{-1}\))** & **(kgC \(\mathrm{m}^{-2}\) \(\mathrm{a}^{-1}\))** & **(kgC \(\mathrm{m}^{-2}\) \(\mathrm{a}^{-1}\))** & **(kgC \(\mathrm{m}^{-2}\) \(\mathrm{a}^{-1}\))** & **(kgC \(\mathrm{m}^{-2}\) \(\mathrm{a}^{-1}\))** \\ \hline Forest & 2.12 \(\pm\)0.06 & 1.79 \(\pm\)0.03 & 2.08 \(\pm\)0.09 & 2.20 \(\pm\)0.06 & 29.8 & 8.78 \\ _ENF_ & 1.14 \(\pm\)0.04 & 0.84 \(\pm\)0.03 & 0.91 \(\pm\)0.06 & 1.03 \(\pm\)0.05 & 4.2 \(\mathrm{\SIUnitSymbolDegree}\) & 0.20 \\ _DNF_ & 1.41 \(\pm\)0.04 & 1.01 \(\pm\)0.01 & 1.18 \(\pm\)0.07 & 1.49 \(\pm\)0.05 & 0.1 \(\mathrm{\SIUnitSymbolDegree}\) & 0.00 \\ _MF_ & 1.69 \(\pm\)0.05 & 1.31 \(\pm\)0.02 & 1.55 \(\pm\)0.06 & 1.43 \(\pm\)0.04 & 21.9 \(\mathrm{\SIUnitSymbolDegree}\) & 1.53 \\ _EBF_ & 2.33 \(\pm\)0.07 & 2.02 \(\pm\)0.04 & 2.35 \(\pm\)0.10 & 2.57 \(\pm\)0.07 & 68.6 \(\mathrm{\SIUnitSymbolDegree}\) & 6.63 \\ _DBF_ & 1.84 \(\pm\)0.04 & 1.53 \(\pm\)0.01 & 1.73 \(\pm\)0.06 & 1.53 \(\pm\)0.04 & 5.3 \(\mathrm{\SIUnitSymbolDegree}\) & 0.41 \\ Crop & 1.51 \(\pm\)0.02 & 1.28 \(\pm\)0.01 & 1.21 \(\pm\)0.04 & 1.10 \(\pm\)0.03 & 30.8 & 6.46 \\ Grass & 1.28 \(\pm\)0.03 & 1.00 \(\pm\)0.01 & 1.00 \(\pm\)0.04 & 1.01 \(\pm\)0.03 & 23.8 & 4.26 \\ Shrub & 0.64 \(\pm\)0.02 & 0.54 \(\pm\)0.02 & 0.46 \(\pm\)0.03 & 0.46 \(\pm\)0.03 & 7.4 & 0.66 \\ Barren & 0.08 \(\pm\)0.01 & 0.12 \(\pm\)0.01 & 0.06 \(\pm\)0.00 & 0.13 \(\pm\)0.01 & 8.2 & 0.09 \\ Total & 1.45 \(\pm\)0.03 & 1.21 \(\pm\)0.02 & 1.27 \(\pm\)0.05 & 1.28 \(\pm\)0.04 & 100 & 20.24 \\ \hline Forest types: ENF: evergreen needle-leaf forest; MF: mixed forest; EBF: evergreen broad-leaf forest; DBF: deciduous broad-leaf forest. * percentage area of forest subtypes in the whole forest area. & & & & \\ \hline \hline \end{tabular}
\end{table}