## 3 Results
### Model Accuracy Comparison
We chose ten tower sites, representing ten vegetation types, to examine the seasonal cycles of simulated GPP and tower GPP. In general, compared with GPPMC-500m, GPPMC-300m was better able to capture the seasonal cycles of GPP at croplands site of US-Ne1 (Figure 7i), US-Ne3 (Figure 7j), and at the wetland site of US-Myb (Figure 7g). For example, we noted high surface spatial heterogeneity at cropland and wetland sites, such as the US-Ne1 and US-Myb sites (Figures 8c and 9c). The NDVI derived from the Landsat and MODIS for the footprints at these two sites contained different phenological information, due to the different spatial scales at which the footprints were extracted (Figure 8a,c, Figure 9a,c). This meant that GPPMC-30m was closer to the phenology of the site than GPPMC-500m (Figures 8b and 9b). Although the performance of both GPPMC-30m and GPPMC-500m exhibited overestimation in simulating GPP, the extent of the overestimates of the GPPMC-30m was lower than that of GPPMC-500m at the US-Ne1, US-Ne3, and US-Myb sites. GPPMC-30m explained 87% and 86% of the variances of tower GPP at the US-Ne1 site and US-Myb site, respectively, while GPPMC-500m explained only 58% and 72% of the variance at these sites, respectively (Figures 8d and 9d). This indicated that the Landsat-footprint NDVI was more appropriate for simulating GPP for highly heterogeneous land surfaces than the MODIS footprint NDVI. In 2005, there was a drought at AU-Dap, a grassland site in Australia, with high VPD and decreasing tower GPP. This was captured in GPPMC-30m, which followed the tower data and decreased. However, there was no decrease in GPPMC-500m (Figure 7f). This indicated that GPPMC-30m could also capture variance in GPP introduced by drought at an individual site. For forest ecosystems (ENF, DBF, and MF), the performance of GPPMC-30m in simulating GPP was comparable to GPPMC-500m (Figure 7a-c). Figure 6: Boxplots of the values of the R\({}^{2}\) (**a**) and RMSE (**b**) for GPPMC-30m, GPPMC-500m, and site GPP (GPPEC) at the 78 sites with 10 vegetation types. The calculation of GPPMC-30m and GPPMC-500m used the same meteorological data (temperature, VPD, air atmospheric pressure, and CO\({}_{2}\) concentration) as driving data for fair comparison; the only differences between GPPMC-30m and GPPMC-500m are the optimized parameters and NDVI, with footprint sources at different spatial resolutions. Figure 7: The seasonal cycles of estimated GPP using the optimized parameters (GPPMC-30m and GPPMC-500m) at different spatial scales against tower GPP (GPPEC) at 10 sites over 16-day time steps. Figure 8: The seasonal cycles of NDVI and simulating GPP, and a scatter plot of cropland at US-Ne1 site. (**a**): The seasonal cycles of NDVI. The NDVI Landsat and NDVI_MODIS correspond to the footprint from Landsat and MODIS, respectively. (**b**): The diagram of the simulated GPP (GPPMC-30m and GPPMC-500m) and tower GPP (GPPEC). (**c**): The red line represents Landsat footprint of the US-Ne1 site extracted by the 30 m spatial resolution pixel (Landsat). The green rectangle is the footprint of the US-Ne1 site extracted by the 500 m spatial resolution pixel (MODIS). (**d**): The scatter plot of GPPMC-30m, GPPMC-500m, and tower GPP (GPPEC). To further assess the superiority of Landsat-footprint NDVI for simulating GPP for heterogeneous mountainous regions, we chose another five mountain sites, with steep surface gradients of over 10\({}^{\circ}\), to evaluate the performance of \(\text{GPP}_{\text{MC-30m}}\) and \(\text{GPP}_{\text{MC-500m}}\) (Table 2). The comparison of model performances showed that \(\text{GPP}_{\text{MC-30m}}\) generally outperformed \(\text{GPP}_{\text{MC-500m}}\), especially at JP-MBF and CZ-BK1 (Table 2), with a significant improvement for GPP estimation using Landsat-footprint NDVI, with R\({}^{2}\) increasing from 0.50 to 0.77, and RMSE decreasing from 2.41 to 1.72 g C/m\({}^{2}\)/16 day. This indicated that the smaller footprint of Landsat could greatly improve GPP estimation in areas with high heterogeneity.