## 4 Discussion
### Impact of Spatial Scale Mismatch on Parameterization  
Mismatches between the spatial scale of model parameters and input data can introduce significant uncertainties in GPP estimation [37]. In this study, we characterize how the footprint mismatching influenced the parameterization and performance of the revised EC-LUE model. We found that matching Landsat NDVI and flux footprints resulted in model parameters that were more representative at finer spatial resolutions, and considerably improved the estimated GPP, particularly for heterogeneous landscapes.  
We considered that the footprint directly influenced estimated model parameters. This can be explained by the scale mismatch that existed between flux tower footprints and remote sensing data [14]. The tower-based observed GPP represented a flux integrated over the tower footprints, which had a typical area about \(10^{3}\)-\(10^{7}\) m\({}^{2}\). MODIS data has a resolution of \(10^{6}\) m\({}^{2}\), and so a MODIS pixel is often several orders of magnitude larger than a tower footprint. The Landsat image with 30 m satellite-based grid cells was able to provide enough spatial information to capture the EC footprint and thus better reflect the boundary of the flux measurement [38]. This explained the difference that we found between the Landsat-footprint NDVI and MODIS-footprint NDVI (Figure 4d), which led to a difference in the estimated model parameters at 30 m and 500 m spatial resolution values (Table 1) and in the resulting GPP (Figures 5 and 6).  
To quantify the effects of the spatial resolution of the remote sensing data on the results of GPP estimation, we evaluated whether the footprint matching could still be effective for MODIS at 500 m spatial resolution rather than 30 m. We found that MODIS-footprint NDVI resulted in poor estimates of GPP for grasslands, wetlands, savannas, shrulands, and croplands due to the low proportion of footprint area within a MODIS pixel of a high heterogeneous surface. Landsat-footprint-NDVI consistently had a robust and reliable performance in estimation of GPP at ten vegetation types. This highlighted the advantage of high spatial resolution remote sensing data when considering the footprint for GPP simulation; these data could not be replaced by coarse spatial resolution remote sensing data.  
The differences between the optimized parameters and the performance of the resulting models varied only slightly at forest ecosystem sites, but were large for other ecosystem types, which was related to vegetation heterogeneity and footprint size. For homogeneous vegetation, such as a forest ecosystem with a relatively large footprint, the fixed areas of 3 km \(\times\) 3 km surrounding the towers for MODIS-footprint-NDVI could adequately represent the footprint of flux measurement. However, this was not true for other ecosystems with more heterogeneous vegetation, such as savanna and cropland, where the flux measurement footprints were smaller. Therefore, the impact of spatial mismatch between satellite imagery and EC footprints was more acute in heterogeneous landscapes or complex terrains [39; 40], which led to the large variance of parameters and GPP estimation, especially in grasslands, wetlands, savannas, shrulands, and croplands (Figure 6).