## Materials and Methods
### Explanatory Variable Selection and Data Acquisition  
We first identified explanatory variables that can explain SIF. SIF can be expressed as follows [53] like the LUE approach [54; 55] for estimating GPP:  
\[\text{SIF}=fPAR\times PAR\times SIF_{yield} \tag{3}\]  
where \(f\)PAR is the fraction of photosynthetically active radiation (PAR) absorbed by vegetation canopies (APAR) and \(\text{SIF}_{\text{yield}}\) is the emitted SIF per photon absorbed. Previous studies have shown that SIF was largely determined by APAR, and it also contained environmental conditions related to LUE [2; 39]. Therefore, we considered three types of explanatory variables that may be indispensable factors to predict the SIF, including vegetation conditions, meteorological conditions, and land cover information. The enhanced vegetation index (EVI) was selected to characterize vegetation conditions in this study. The EVI is one of the most widely used vegetation indices for monitoring vegetation conditions [56], and is more sensitive to dense vegetation conditions than the normalized difference vegetation index (NDVI). The EVI is generally considered as a good proxy of LAI and \(f\)PAR [57]. We used PAR, vapor pressure deficit (VPD), and air temperature for characterizing the meteorological conditions. These three variables can account for the environmental conditions (e.g., solar radiation, water availability, temperature stress) that may influence the emission of plant fluorescence. We chose EVI, PAR, VPD, and air temperature also because the model based on these four variables had a slightly higher performance than the model based on surface reflectance of MODIS bands 1-7 and had an identical performance to the model based on MODIS bands 1-7 and three meteorological variables (Table S1). Surface reflectance was therefore not used in our predictive model. Land cover type was included as a categorical variable to develop the model, aiming to test whether a biome-specific model would improve the accuracy of SIF prediction. We obtained OCO-2 SIF (V8r) data from the OCO-2 data archive maintained at the NASA Goddard Earth Science Data and Information Services Center [17]. OCO-2 provides mid-day SIF retrievals at both 757 and 771 nm. Since 771 nm falls farther away from the peak emission of the SIF spectrum and SIF at 757 nm showed stronger correlation with GPP from EC flux towers than SIF at 771 nm [39], we only used SIF at 757 nm in this study. The mid-day SIF retrievals at 757 nm were then converted to the daily timescale by applying the daily correction factor included in the SIF Lite product. The SIF was acquired in three observation modes: Nadir, Glint, and Target. The instrument views the ground directly below the spacecraft in the Nadir mode, with viewing zenith angle (VZA) near zero, while the Glint mode has largely higher VZAs and the Target mode has varying VZAs [17]. Since the variation of VZA will influence the magnitude of observed SIF [39], we only used the observations from the Nadir mode to avoid the possible effects from changing viewing geometries. We aggregated all the OCO-2 SIF soundings (1.3 \(\times\) 2.25 km) in the Nadir mode during the period 2015-2017 to 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) grid cells for each 8-day interval for training and testing purposes. On average, these grid cells account for 0.3% of the total land area for a given 8-day interval. For each grid cell, the SIF was calculated as the average of all the soundings if there were more than five soundings within the grid cell. For each grid cell with SIF data, the corresponding EVI, land cover type, air temperature, VPD, and PAR were obtained from the gridded MODIS and MERRA-2 products. The EVI was derived and calculated from the daily MODIS bidirectional reflectance distribution function (BRDF)-corrected reflectance product MCD43C4 (Collection 6, 0.05\({}^{\circ}\)), and the land cover type of each cell was identified by the MODIS Land Cover Type product (MCD12C1, 0.05\({}^{\circ}\)).