## 4 Discussion
### GPP Accuracy and Spatial Distribution Pattern  
Comparing the GPP\({}_{\text{SIF}}\), GPP\({}_{\text{GLASS}}\), and GPP\({}_{\text{LUE}}\) products with the flux data of the same period, the RMSE of the three products during non-drought and drought periods are 191.68 gC\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\), 325.82 gC\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\), 422.90 gC\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\) and 230.14 gC\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\), 368.07 gC\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\), 373.58 gC\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\), respectively. The RMSE of GPP\({}_{\text{SIF}}\) is the smallest and very stable. In addition, when the accuracy of the three products was verified with flux data, GPP\({}_{\text{SIF}}\) had the highest R\({}^{2}\), reaching 0.81, indicating the superiority and reliability of SIF-based GPP for analyzing the effects of drought.  
The GPP\({}_{\text{SIF}}\) is generally close to the other two products in terms of spatial distribution, but the Pearson correlation coefficients with the DEM show that the GPP\({}_{\text{SIF}}\) has the highest spatial agreement with the DEM, reflecting the change in vegetation with topographic gradients. The GLASS product shows high estimates and serious lags in drought years. It is also important to note that the RMSE of the GLASS and LUE-CI product is elevated during drought periods compared to normal periods, mainly due to the fact that the estimated values of the products are similar and generally low both in drought and non-drought years. Therefore, the RMSE is smaller in drought years.  
According to Figure 7, the lowest value of GPP is located in Northwestern Yunnan, which is an alpine area on the southeastern edge of the Qinghai-Tibet Plateau with a high altitude. The main vegetation types on the underlying surface are alpine vegetation such as meadows, fir forests, artemisinin shrub meadows, etc. Following Northwestern Yunnan are parts of Central and Eastern Yunnan, which are mostly grassland, scrub, and other vegetation with low vegetation cover. The high value areas of GPP are mainly distributed in Xishuangbanna, Southwest Yunnan, which has gentle terrain, low elevation, and is bordered by the Indian Ocean in the south, with abundant precipitation and lush vegetation mostly tropical rainforest. Overall, the differences in water and heat due to the effects of latitude, topography, and altitude, as well as the differences in vegetation types, are important reasons affecting the spatial distribution of vegetation GPP in Yunnan Province.  
The estimated GPP\({}_{\text{SIF}}\) results in this paper are consistent with the basic characteristics of the spatial distribution of GPP in Yunnan Province in the Chinese region simulated by [PERSON] et al. [34] and [PERSON] et al. [35]. Of course, there will be some variation in any results, and [PERSON] et al. also concluded that GPP derived from light use efficiency models, machine learning models, and process-based biophysical models all exhibit substantial variation in magnitude and interannual variability on a global scale [34]. Model uncertainty remains a challenge for carbon cycle research, and further improvements in CPP models and methods are needed to improve the accuracy of terrestrial ecosystem GPP estimates in the future.