## 2 Data and Methods
### FLUXNET Dataset  
We used standardized Level 4 FLUXNET data provided by AmeriFlux and CarboEurope networks. The Level 4 FLUXNET data include meteorological and eddy covariance flux data and estimated GPP(note that we use \"GPP\" interchangeably with \"Gross Ecosystem Production\", though a small difference in photorespiration exists between the two). Level 4 data contain GPP derived from estimated Net Ecosystem Exchange (NEE) by different methods (two different storage term calculations and two different gap-filling methods). The storage term required for the estimation of NEE was either provided by each tower principal investigator (original) or estimated by a standard method assuming that CO\({}_{2}\) concentration measured at eddy flux measurement height is constant within the canopy (standard). The gaps were filled either by the Marginal Distribution Sampling method [41] or by the Artificial Neural Network method [42]. We used the original Marginal Distribution Sampling GPP because the original storage term is more reliable than the standard method at tall tower sites [43] and the Artificial Neural Network approach may fill gaps that are too long. The u* filtering was applied to NEE following [PERSON] _et al._[41] and the time-series were finally gap-filled at the daily, weekly, and monthly time scale. The partitioning of the measured NEE fluxes into GPP and Total Ecosystem Respiration (TER) was based on a temperature-dependent exponential model of TER estimated based on nighttime NEE data. GPP was finally obtained as the sum of NEE and TER according to [PERSON] _et al._[41]. Results of this partitioning method have been recently validated at a large number of FLUXNET sites against alternative estimates based on light response curves [44]. Scaling from point measurements at the flux tower to the 1 km MODIS resolution is not trivial because the heterogeneity of the flux tower footprint can lead to a time-variant measurement bias. Ideally, we should use flux towers whose eddy covariance footprints are homogeneous over the MODIS pixel, but this condition is not often met. In some studies (e.g., [PERSON] _et al._[45]), finer resolution satellite data have been used to bridge between flux tower data and coarse resolution data to account for the heterogeneity of the tower sites. Here, to avoid scaling issues, we selected flux tower sites where the land cover extended homogenously roughly over a 500 m radius circle around the tower (assessed by visual interpretation of Google Earth imagery). Thus, we only selected AmeriFlux and CarboEurope forest flux tower sites (Table 2 and Figure 1) that satisfied the following requirements:  
* The tower was surrounded by homogeneous land cover for a radius of at least 500 m;
* Temporally overlapping coverage with the MODIS subset data existed;
* The gap-filled ratio was less than 20% for all 12 months in a year. The only tropical forest AmeriFlux tower meeting the above requirements was in Tapajos, Brazil. To increase the sample size for tropical forests and improve the robustness of our analysis, we added four AsiaFlux tropical sites from Southeast Asia: Bukit Soehart, Palangkaraya, Mae Klong, and Sakaerat. Only annual GPP data were published for these sites in [PERSON] _et al._[46], and were derived from nighttime respiration estimated via exponential respiration model as in [PERSON] _et al._[41]. Following our full screening, we assembled a total of 21 FLUXNET sites with corresponding MODIS subset data for the years 2001 to 2008. For brevity, we refer to the sites with the codes defined in Table 2.