## 4 Discussion
### To What Degree Do Landsat Images Improve GPP Estimation  
The improvement in GPP estimation that was achieved by using fine-resolution Landsat data, compared to relatively coarse-resolution MODIS data, varied between sites, vegetation types, and for different surface heterogeneities. At the site level, model accuracy improved by 1-29% over the 78 sites when Landsat-footprint NDVI data were used to drive the model rather than MODIS-footprint NDVI data (Figure 5). For vegetation types,using Landsat-footprint NDVI data improved GPP estimates by 18% for savannas, 14% for croplands, and 9% for wetlands; Landsat-footprint NDVI was comparable to or slightly stronger than MODIS-footprint NDVI in estimation GPP at grasslands, shrublands, and forest ecosystems (Figure 6). Additionally, the accuracy of GPP estimates improved dramatically at sites with high surface heterogeneity when Landsat-footprint NDVI data were used. For example, R2 increased by 0.29 at the US-Ne1 cropland site with (Figure 8), by 0.14 at the US-Myb wetland site (Figure 9), and by 27% at the CZ-BK1 mountain (Table 2). This showed that Landsat-footprint-NDVI can be used to describe the spatial surface heterogeneity of mixed planting in cropland, grass and water in wetlands, and complex terrain, providing more detailed phenological information to better describe GPP distribution.  
Surface heterogeneity fell into two general categories in this study. The first was the mountainous regions. Characteristics of these regions included vegetation heterogeneity and surface topography with steep slopes and elevation variance, which were important factors for the GPP estimation. Vegetation heterogeneity directly determines photosynthetic capacity and topography influences the redistribution of precipitation and incoming solar radiation [41]. Moreover, vegetation is often highly fragmented in these regions due to the vegetation structures with climatic and topographic gradients [42]. Therefore, the high spatial heterogeneity of mountain vegetation could influence the quality of MODIS NDVI at coarse resolution. Additionally, data from the higher spatial resolution Landsat sensor could provide more detailed information for mountainous regions, despite topographic illumination condition correction being of no consideration [43]. Moreover, the authors of [44] suggested that considering the footprint of flux towers in mountainous regions could also improve GPP estimates. Our results revealed that remote sensing data with high spatial resolution, combined with introducing the footprint into model parametrization, improved GPP estimates in mountainous areas with strong heterogeneity (Table 2). This was because high resolution remote sensing data provided sufficient spatial information to capture surface heterogeneity [38].  
The second category of surface heterogeneity in this study was landscape fragmentation in croplands and wetlands, which have high surface heterogeneity and relatively small footprints. Using MODIS NDVI as the forcing data for model GPP simulation may not have accurately captured the phenology of the plant with a strong spring green-up signal at the US-Ne1 [45] and US-Myb sites. Similar to the problems associated with using a coarse data source for mountainous regions, MODIS NDVI with coarse resolution contained other plant information due to fragmentation planting within the footprint area. Furthermore, the mismatch of spatial resolution of MODIS NDVI and EC footprint resulted in mixed pixel information, which led to the MODIS NDVI being unable to capture the phenology information of plants within the footprint area (Figures 8 and 9). However, Landsat images, with their high spatial resolution, had an absolute advantage in capturing the pixels representing the flux footprint [24]. The large difference between the spatial resolutions of Landsat and MODIS led to differences in their ability to capture the footprints at heterogeneous sites, which led to GPPMC-30m outperforming GPPMC-500m for ecosystems with a fragmented plant structure (Figures 8 and 9).