## 3 Results
### GPP Estimation under the Different Schemes of Land Cover  
The NIRv-based GPP can be estimated after clarifying the land cover types and land cover changes in the YRB. Figure 4a shows the GPP estimated by the MODIS product in the year of 2001 (StaMOD). After that, we estimated the difference in GPP caused by dynamic changes in land cover, i.e., the difference between the MODIS-based GPP in 2001 and 2018 (Figure 4b). Then, we estimated the difference in GPP caused by heterogeneous land cover data, i.e., the difference between the MODIS-based GPP and the CCI-based GPP (Figure 4c,d). The average value of GPP in the YRB was 334.16 gCm\({}^{-2}\)yr\({}^{-1}\). In addition, the basins with higher GPP were ALG (673.23 gCm\({}^{-2}\)yr\({}^{-1}\)) and LGL (606.35 gCm\({}^{-2}\)yr\({}^{-1}\)), whereas those with lower GPP were LHT (45.25 gCm\({}^{-2}\)yr\({}^{-1}\)) and IZ (5.90 gCm\({}^{-2}\)yr\({}^{-1}\)). Overall, both LCC and DMP would have significant influences on the estimation of GPP by NIRv. The dynamic changes in land cover over the 18 years resulted in an average increase by 1.40 gCm\({}^{-2}\)yr\({}^{-1}\) of GPP. The sub-basins with severe land cover changes were LHT, LSG, and SGH, located in the Loess Plateau in the middle reaches of the YRB (Figure 4b). The results showed that the areas with obvious differences in GPP corresponded to the areas where land cover types changed during 2001-2018 (Figure 2c,d and Figure 4b). The impact of discrepancies in multisource products on the estimation of GPP was significant (Figure 4c), with an average difference of 46.45 gCm\({}^{-2}\)yr\({}^{-1}\), which was much greater than that in the GPP estimation caused by the land cover dynamic changes. Figure 4: Differences in the multi-year average values of NIRv-based GPP using the different land cover (LC) schemes over the YRB from 2001 to 2018. The panel (**a**) shows the geographical distribution of the GPP using MODIS LC data in 2001 (staMOD). The panel (**b**) shows the geographical distribution of the difference between the GPP using annual MODIS LC data from 2001 to 2018 (dynMOD) and that using the MODIS LC data in 2001 (staMOD). The panel (**c**) shows the geographical distribution of the difference between the GPP using MODIS LC data in 2001 (staMOD) and that using CCI LC data in 2001 (staCCI). The panel (**d**) shows the geographical distribution of the difference between the GPP using annual MODIS LC data from 2001 to 2018 (dynMOD) and that using annual CCI LC data from 2001 to 2018 (dynCCI). Frequency histograms displaying the areal proportions (%) of corresponding differences are inset. The statistics for the multi-year average values of GPP using the different LC schemes and their differences by the sub-basins are shown in Figure 5. Each panel in Figure 5 corresponds to Figure 4. Figure 5a shows the sub-basin statistics of the average staMOD GPP. Figure 5b-d represents the distribution of the differences in GPP. Most of the dynamic GPP changes in sub-basins were positive, i.e., the dynamic changes in land cover over the 18-year period led to an increase in GPP. Figure 4c,d shows the difference in GPP caused by land cover heterogeneity, i.e., the difference between the MODIS-based GPP and the CCI-based GPP. The impact of differences in land cover products on the estimation of GPP was significant, with a high uncertainty. In the middle reaches of the YRB, including HTL and LSG, the average differences between the GPP estimations using the two land cover products were 48.40 gCm\({}^{-2}\)yr\({}^{-1}\) and 66.24 gCm\({}^{-2}\)yr\({}^{-1}\), respectively. The aforementioned regions are located in the Loess Plateau, with a high heterogeneity of land cover types that were significantly influenced by the mixed pixels. In the ALG and LGL of the Tibetan Plateau, the differences in GPP derived from the different land cover products were relatively low, with average values of 25.48 gCm\({}^{-2}\)yr\({}^{-1}\) and 2.31 gCm\({}^{-2}\)yr\({}^{-1}\), respectively.