## 4 Discussion
### Impact of FPAR Product on MODIS GPP  
To assess the influences of FPAR on the accuracy of GPP estimation, we introduced an improved FPAR dataset (GLASS FPAR) and designed the GMAO_GLASS GPP algorithm. Our results showed that the greatest improvements were gained in estimating the eight-day GPP values when the MODIS FPAR was replaced by the GLASS FPAR (Figure 1a,c). Similarly, the precision in estimating annual GPP was also improved substantially after substituting GLASS FPAR for MODIS FPAR (Figure 3a,c). By comparing two groups of FPAR data, we found that the values of MODIS FPAR were generally smaller than that of GLASS FPAR (Table 4). Indeed, the MOD17A2H GPP (MOD_GMAO GPP algorithm) generally underestimated the flux derived GPP values (Tables 2 and 3). The improved GLASS FPAR data thus balanced the underestimation resulting from the use of MODIS FPAR to some extent.  
Admittedly, GLASS FPAR adopted in the GMAO_GLASS GPP algorithm was improved from the previous MOD15A2 LAI/FPAR product, which had a spatial resolution of 1 km. The MOD_GMAO GPP algorithm adopted the latest MOD15A2H FPAR product, whose spatial resolution increased to 500 m. However, our results showed that the GMAO_GLASS GPP algorithm estimated the site-based GPP much more effectively than the MOD_GMAO GPP algorithm. This indicated that what had been done in MODIS GPP products to reduce the error derived from FPAR during the version upgrading process was still far from sufficient.  
The qualities of the meteorological data and the FPAR product were simultaneously improved in the Tower_GLASS GPP algorithm. We simply substituted the site-based meteorological observations for the GMAO meteorology reanalysis dataset and replaced the MODIS FPAR with GLASS FPAR. The results showed that the effectiveness of eight-day GPP estimation had the greatest improvements (Figure 1a,d). However, this replacement strategy did not produce the most effective algorithm in terms of annual GPP estimates (Figure 3a,c,d). This may indicate that the accuracy of GPP estimates could be enhanced by simultaneously improving the qualities of multiple inputs of the MOD17A2H algorithm; but it did not imply that improvements could be gained simply by replacing the input data because the overall gain in algorithm performance was not simply the sum of the individual gains. More detailed analysis needs to be conducted to evaluate the interaction effects of the joint variation of the model inputs in future research [1].  
Compared with MOD17A2, MOD17A2H improved in terms of meteorological data and FPAR. Therefore, four groups of experiments (i.e., MOD_GMAO GPP, MOD_Tower GPP, GMAO_GLASS GPP and Tower_GLASS GPP) were designed to verify the contributions of the upgraded meteorology dataset and FPAR product to the estimation accuracy of GPP. It was discovered that the GMAO_GLASS GPP and Tower_GLASS GPP algorithms achieved enormous performance gains over the MOD_GMAO GPP algorithm in terms of flux-derived GPP estimates and that the performance of the GMAO_GLASS GPP algorithm was almost the same as that of the Tower_GLASS GPP algorithm. This may indicate that the error from FPAR was larger than that from the meteorological data in the current version of the MODIS GPP product.