# An Open Data Approach for Estimating Vegetation Gross Primary Production at Fine Spatial Resolution
## 3 Results
### Comparison of GPP Estimates at Three Spatial Resolutions  
In order to derive the intercomparison results from the three resolution GPP products, we compared 193 sites' GPP\({}_{\rm FLUX}\) with three resolutions of GPP product in constant dates. Compared to the other coarse-resolution GPP products (Figure 2), the GPP\({}_{\rm L}\) had higher R\({}^{2}\) and lower RMSE than the GPP\({}_{\rm M}\) (R\({}^{2}\) = 0.56, RMSE = 2.58 g C m\({}^{-2}\) d\({}^{-1}\)) driven by the 500-meter-spatial-resolution MODIS data. The GPP\({}_{\rm A}\) driven by the 0.05\({}^{\rm o}\)AVHRR data had the lowest accuracy, with an R\({}^{2}\) equal to 0.45 and an RMSE equal to 2.88 g C m\({}^{-2}\) d\({}^{-1}\). Moreover, the GPP\({}_{\rm L}\) showed the least bias (\(-\)0.09 g C m\({}^{-2}\) d\({}^{-1}\)) compared to the GPP\({}_{\rm M}\) (0.21 g C m\({}^{-2}\) d\({}^{-1}\)) and the GPP\({}_{\rm A}\) (0.60 g C m\({}^{-2}\) d\({}^{-1}\)). Most of the observed and modeled GPP were close to the 1:1 line. The GPP\({}_{\rm L}\) showed higher R\({}^{2}\) than the GPP\({}_{\rm M}\) and GPP\({}_{\rm A}\) in five of the nine individual vegetation types (Table 4). The GPP\({}_{\rm L}\) significantly improved the GPP modeling, especially for cropland, savanna, and wetland, explaining more than 16%, 6%, and 16% of the variance in the coarse-spatial-resolution GPP, respectively. The GPP\({}_{\rm L}\) also corrected the high bias of the GPP (>1 g C m\({}^{-2}\) d\({}^{-1}\)) for cropland and shrub in the GPP\({}_{\rm M}\) and GPP\({}_{\rm A}\). The coarse-spatial-resolution GPP GPP (GPP\({}_{\rm M}\), GPP\({}_{\rm A}\)) also followed the seasonal change in the GPP but could not reproduce the low GPP of January (Figure 4C). For the dryland vegetation types (SHR, SAV), however, the GPP\({}_{\rm M}\) and GPP\({}_{\rm A}\) were not able to reproduce the seasonal cycle of the GPP\({}_{\rm FLUX}\). The GPP\({}_{\rm M}\) and GPP\({}_{\rm A}\) showed overestimations of GPP in the Mediterranean dryland (Figure 4G)  
The GPP\({}_{\rm L}\) captured the spatial and temporal change in GPP better than the coarse-resolution GPP in agroecosystems (Figure 5). The coarse-resolution GPP (GPP\({}_{\rm M}\) and GPP\({}_{\rm A}\)) showed less spatial variance than the fine-resolution GPP. The highest standard deviation of the GPP (GPP\({}_{\rm STD}\)) in the GPP\({}_{\rm L}\) occurred in August 2017 (4.561 g C m\({}^{-2}\) d\({}^{-1}\)). At the same time, the GPP\({}_{\rm M}\) and GPP\({}_{\rm A}\) had a GPP\({}_{\rm STD}\) of 0.381 and 0.045 g C m\({}^{-2}\) d\({}^{-1}\), respectively (Figure 5B). Moreover, the GPP\({}_{\rm L}\) during the peak of the growing season (\(\sim\)17 g C m\({}^{-2}\) d\({}^{-1}\)) was much higher than the GPP\({}_{\rm M}\) (\(\sim\)9 g C m\({}^{-2}\) d\({}^{-1}\)) and GPP\({}_{\rm A}\) (\(\sim\)9 g C m\({}^{-2}\) d\({}^{-1}\)). The GPP\({}_{\rm L}\) also showed that the annual mean GPP was higher in the irrigated regions (blue circles in Figure 5A,C,E) compared to the GPP\({}_{\rm M}\). Overall, the GPP\({}_{\rm L}\) reproduced the GPP (0 g C m\({}^{-2}\) d\({}^{-1}\) at some northern pixels) of roads and non-planted areas (Figure 5C), but the GPP\({}_{\rm M}\) did not indicate these conditions (Figure 5E). The fine-spatial-resolution GPP also  
Figure 4: Time series of GPP (flux), GPP\({}_{\rm L}\) (Landsat), GPP\({}_{\rm M}\) (MODIS), and GPP\({}_{\rm A}\) (AVHRR) at eight typical vegetation sites. The symbols \(\bullet\), \(\bigcirc\), and \(\bigtriangledown\) in GPP\({}_{\rm L}\) represent the GPP driven by observed short-term gap-filled reconstructed NDVI and long-term gap-filled reconstructed NDVI, respectively. The symbols \(\bullet\) and \(\bigcirc\) in the flux-tower-based GPP (red curve) are the observed GPP and gap-filled GPP, respectively. characterized the spatial heterogeneity of the seasonal change in GPP. The GPP\({}_{\text{L}}\) showed that the overall CV of the GPP ranged from 0.75 to 1.23 in this region, but the GPP\({}_{\text{M}}\) showed that the CV of the GPP ranged from 0.81 to 1.16.