## 4 Discussion  
We used a predictive SIF model based on four explanatory variables to develop the global, 0.05\({}^{\circ}\) SIF product. We selected the explanatory variables based on the findings of previous studies that the SIF was mainly determined by APAR [26; 27] and also contained environmental information which influenced the LUE [2; 39; 43; 62]. For example, APAR alone could explain a large portion of the variance in SIF, and APAR, along with temperature and water stresses, had a stronger correlation with  
Figure 11: Spatial distribution of the trends (2001-2017) in annual global mean SIF (**a**) and the inter-annual variations and trends in global mean SIF, enhanced vegetation index (EVI), and MODIS GPP (**b**). The trends in SIF (unit: W m\({}^{-2}\) μm\({}^{-1}\) sr\({}^{-1}\)) and EVI were derived over both periods: 2001–2017 and 2001–2015, while the trend in MODIS GPP was based on the period 2001–2015 (unit: Pg C yr\({}^{-1}\)). Gray color indicates grid cells with an insignificant trend detected by the Mann–Kendall (MK) test (\(p\) > 0.05). In (**b**), the SIF, EVI, and GPP were normalized by their maximum values for better display purposes because they have different units and magnitudes. The trends were derived based on the original values of these variables. SIF [39]. Therefore, our selected variables (i.e., EVI, PAR, VPD, and air temperature) could largely account for the variations in SIF. This approach is actually similar to the concept of LUE models, with EVI \(\times\) PAR equivalent to APAR and with VPD and air temperature as two environmental scalars. Considering more explanatory variables in the model might improve the SIF estimation. However, for the global-scale predictions at higher spatial and temporal resolution (0.05\({}^{\circ}\), 8-day) with a long period (2000-2017), we should consider not only model performance but also other factors such as computational efficiency, availability of data, and data uncertainty. We also examined the performance of other similar vegetation variables, such as LAI, fPAR, NDVI, and the normalized difference water index (NDWI), but found that they did not significantly improve the prediction for SIF. Having only four explanatory variables had almost identical predictive capability could also substantially reduce the computational complexity. Our study further found that the land cover map had a very limited role in improving the SIF predictions. Therefore, considering the large uncertainty of current land cover classifications [63] and the potential land cover changes during the study period, we predicted SIF only based on EVI, PAR, VPD, and air temperature. Our analysis also indicated that the model based on EVI, PAR, VPD, and air temperature had a slightly higher performance than the model based on surface reflectance of the seven MODIS bands. Two recent studies also used machine learning approaches to develop 0.05\({}^{\circ}\) SIF data from OCO-2 soundings [46; 27], and their models were based solely on MODIS surface reflectance. In contrast, we used MODIS EVI to characterize vegetation conditions, and PAR, VPD, and air temperature to account for meteorological conditions and water availability. These meteorological factors can better account for the environmental conditions (e.g., solar radiation, water availability, temperature stress) that influence the emission of plant fluorescence (Table S1). Our predictive model performed slightly better (R\({}^{2}\) = 0.79, RMSE = 0.07 W m\({}^{-2}\) um\({}^{-1}\) sr\({}^{-1}\)) than that of [PERSON] et al. [27], and had comparable performance to the combined time and biome stratification of [PERSON] et al. [46] in terms of R\({}^{2}\) and RMSE. These prediction statistics reflect the performance of the predictive models but not necessarily the performance of these three global products, due to the use of different global datasets with different uncertainties in the prediction. Our results showed that the training sample size influenced the SIF predictions.