# Satellite-Based Estimation of Carbon Dioxide Budget in Tropical Peatland Ecosystems
## 4 Discussions  
### Spatial Distributions of GPP and Potential Uncertainties  
We observed small regional variations in the GPP (Figure 9). Similarly to \(R_{e}\) distributions, meteorological conditions across the peatlands in the study area were evenly distributed, since the input variables of the MODIS GPP algorithm relied on a minimum of daily temperature (TMIN), vapor pressure deficit (VPD), and fPAR [39, 40]. Underestimations of the MOD17A2H GPP compared with the flux tower-based GPP were related to uncertainties of input variables in the GPP algorithm. In particular, MODIS fPAR data were essential because they were used to estimate detailed spatial and temporal patterns in the vegetation status. However, the data are acquired once per day at 10:30 am (local time). This may have caused a discrepancy between the MODIS GPP and the flux tower-based GPP. The other input variables of the MODIS algorithm were obtained from reanalysis datasets (e.g., MERRA-2) through numerical calculations of, e.g., solar radiation and relative humidity. Since reanalysis datasets have a coarser spatial resolution (~12.5 km) than the GPP product, resampling methods could have caused additional uncertainties [39, 40]. The accurate estimation of near-surface relative humidity or VPD is also challenging [57, 58]. Discrepancies between fPAR and the real surface situation including land cover changes could have occurred, since the land cover maps are updated on an annual basis (MCD12Q1); however, flux tower observations provide daily or shorter-interval GPP.  
Some uncertainties could have been induced by the discrepancy between the spatial representativeness of the flux towers and the MOD17A2H GPP. As mentioned above, the footprints of flux towers vary (100-1000 m); however the MOD17A2H GPP shows a fixed spatial resolution (500 m). Moreover, changes in sub-pixel scales might not be detectable for the MOD17A2H GPP [59], although flux towers can capture them. In addition, the spatial heterogeneity in both biome types could have resulted in the discrepancy in the process of calibration for whole peatland areas. For future studies, with more flux towers representing peatlands and associated biomes, the GPP calibration could be improved by considering a light use efficiency (LUE) and plant functional types by using satellite-based land cover maps [60].  
We also compared our results with another study, the FLUXNET multi-tree ensemble (MTE) based GPP [24]. As a result of the comparison, averages and standard deviations were 33.07 \(\pm\) 39.11 and 1.21 \(\pm\) 1.10 (Tg C/month), respectively, for FLUXNET-MTE and this study. The RMSE and bias were 6.31 and 6.03 (Tg C/month), respectively. Though the spatial resolutions of both GPP datasets differed by  
Figure 12: Monthly time series of the GPP, \(R_{e}\) (_UIF_), FE, NBE, net ecosystem CO\({}_{2}\) exchange (NEE) (\(R_{e}-\) GPP), and the Niño.3 index from 2002 to 2018. GPP, \(R_{e}\), FE, and NBE refer to primary axis (left) with negative signs for the GPP and positive signs for the rest. The Niño.3 index is listed on the secondary axis (right). The net CO\({}_{2}\) balance is positive, indicating that the tropical peatlands are net sources of CO\({}_{2}\). The temporal fluctuation of FE seems to be the dominant factor to change the NBE. In addition, FE usually appeared in the on-set of the Niño.3 index.  
100 times, the amount of the GPP and the seasonal patterns between both datasets were demonstrably similar (Figure is not shown).