## 3 Results
### Gpp\({}_{\rm SIF}\) Has Better Accuracy Than Other GPP Products  
According to Section 3.1, it is known that the correlation between SIF and drought index is higher, so this study estimates GPP in Yunnan Province based on SIF data to study the effect of drought on GPP. We simulated GPP in Yunnan Province from 2003 to 2018 at 8-day, monthly and annual scales based on SIF data, vegetation type data, and GPP flux site data, etc. Although there are many SIF-based GPP products [29], most of these products are global in scope, and this article only studies a small area on the southwestern border of China, so the applicability and accuracy may be not good. In addition, most current GPP-SIF products do not eliminate the effects of vegetation canopy. We use the SIF data that eliminate the influence of the canopy to estimate the GPP.  
We first validated the accuracy of the three GPPs using the 2007-2010 Changling flux data (only the 2007-2010 flux data were found at this site so far) that were not involved in the calculation of the GPP-SIF fit coefficients (Figure 6), and found that GPP_SIF, GPP_GLASS and GPP_LUE all had better accuracy, but GPP_SIF had the highest R\({}^{2}\) of 0.83.  
Figure 5: Scatter chart of SIF, NDVI, and EVI with sc_PDSI at the Yunnan sites.  
To further verify the accuracy of the three GPPs, we also compared the variation in the three products (Figure 7). We found that the change trends of GPP\({}_{\text{SIF}}\) and GPP flux observations were extremely similar, and although the estimated values were slightly higher in drought years (2009 and 2010), the difference was small. The hysteresis effect of GPP\({}_{\text{GLASS}}\) is obvious, and the response to seasonal changes is sluggish; GPP\({}_{\text{LUE}}\) values are generally low and differ greatly from the measured values. The RMSE of the three products during the non-drought (2007-2008) and drought (2009-2010) periods were 4.97 gC\(\cdot\)m\({}^{-2}\cdot\)8 day\({}^{-1}\), 6.09 gC\(\cdot\)m\({}^{-2}\cdot\)8 day\({}^{-1}\), 6.54 gC\(\cdot\)m\({}^{-2}\cdot\)8 day\({}^{-1}\) and 5.28 gC\(\cdot\)m\({}^{-2}\cdot\)8 day\({}^{-1}\), 5.65 gC\(\cdot\)m\({}^{-2}\cdot\)8 day\({}^{-1}\), 5.92 gC\(\cdot\)m\({}^{-2}\cdot\)8 day\({}^{-1}\), the smallest and more stable RMSE values for GPP\({}_{\text{SIF}}\). In addition, we found that GPP\({}_{\text{GLASS}}\) and GPP\({}_{\text{LUE}}\) had smaller RMSE values in the drought period than in the non-drought period.  
Furthermore, the spatial distribution of the three GPPs (Figure 8) is basically the same, with the Yunling-Yuanjiang valley as the boundary in general. The GPP in the western region is significantly higher than that in the east, showing the blocking effect of the vertical ridge and valley area, with a decreasing trend from the southwest to northeast. There are also differences in the distribution of the three, one is that the mean values are 2042.78 gC\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\), 2287.33 gC\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\) and 1865.47 gC\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\), and the second is that the highest absolute value of the correlation coefficient between GPP\({}_{\text{SIF}}\) and DEM data of Yunnan Province is 0.62 by Pearson correlation analysis, which indicates that the spatial distribution of GPP\({}_{\text{SIF}}\) coincided with the topography to a high degree. The  
Figure 6: GPP accuracy verification. (**a**) accuracy verification of GPP\({}_{\text{SIF}}\) and flux observations; (**b**) accuracy verification of GPP\({}_{\text{GLASS}}\) and flux observations; (**c**) accuracy verification of GPP\({}_{\text{LUE}}\) and flux observations.  
histogram statistics also found that the GPPSIF data values changed more gently, the data distribution was concentrated, and the extreme values accounted for less.  
In conclusion, in terms of the correlation, RMSE, and spatial distribution, the GPPSIF simulated in this study is more accurate, which can provide an accurate database for the subsequent analysis of the results.