## Materials and Methods
### Global-Scale Prediction and Analysis of SIF  
For the purpose of producing global SIF maps, the daily MODIS EVI and the MERRA-2 meteorological data were temporally aggregated to 8-day intervals. We used the selected predictive model to estimate SIF for each 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) grid cell across the globe for each 8-day interval over the period 2000-2017, using wall-to-wall MODIS and MERRA-2 data. Our global, \"OCO-2 based\" SIF product is hereafter referred to as GOSIF. The 8-day SIF estimates were also temporally aggregated to monthly, seasonal, and annual time steps for different purposes. We made a pixel-wise (each 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) grid cell) comparison between predicted SIF and observed SIF in 2016 when OCO-2 passed by, and calculated the probability distribution of the SIFestimation errors. We then compared the seasonal variations between our new 0.05\({}^{\circ}\), 8-day SIF product and the coarse-resolution (1\({}^{\circ}\)) OCO-2 SIF directly aggregated from discrete soundings over 2014 to 2017 at four selected EC flux tower sites with different biomes: Daly River Cleared (AU-Das; savannas), Sturt Plains (AU-Stp; grassland), Gebesee (DE-Geb; cropland), and Park Falls (US-PFA; mixed forest). The coarse-resolution OCO-2 SIF map was directly aggregated from the discrete OCO-2 soundings under the Nadir mode at 8-day time steps, and temporally dense SIF retrievals could be obtained from the 1\({}^{\circ}\), 8-day maps during the period 2014 to 2017 at these four sites. In addition, these four sites provided flux tower data in 2014, which are available in the FLUXNET 2015 Tier 1 dataset. Therefore, we also examined how the new 0.05\({}^{\circ}\), 8-day SIF captured the trajectory of tower-based GPP estimates (henceforth tower GPP). To further evaluate our finer-scale SIF estimates, we used tower GPP from 91 EC flux towers across the globe based on the FLUXNET 2015 Tier 1 dataset. We collected a total of 116 flux sites having more than three years of GPP data. Since the size of our finer-resolution (0.05\({}^{\circ}\)) SIF does not exactly match the footprint of conventional EC flux towers, we only retained the flux sites that were relatively homogeneous. A site was considered homogeneous in this study when the dominant land cover type was similar to that of the site. The dominant land cover type for each 0.05\({}^{\circ}\) grid cell was determined based on the 500 m-resolution MCD12Q1 land cover product. As a result, 25 out of the 116 sites were excluded for the evaluation of the SIF. The FLUXNET dataset provides GPP estimates based on both night-time (GPP_NT_VUT_REF) and day-time (GPP_DT_VUT_REF) partitioning methods. We used the average of the two estimates to evaluate the new SIF, and removed the points with differences of the GPP estimates based on the two methods over 3 g C m\({}^{-2}\) d\({}^{-1}\). We also removed the daily averaged GPPs with more than 40% from gap-filled data. Finally, we obtained 91 sites, with a total of 27434 observations. We compared the 8-day SIF and tower GPP, and examined whether the finer-resolution SIF was closely related to the tower GPP across biomes. The spatial distributions of these flux tower sites and more detailed descriptions including site code, site name, location, biome type, measurement duration, and references are provided in Figure S1 and Table S2 from the online Supplementary Material. We examined the spatial patterns of the annual average and maximum SIF between our new SIF product and the 1\({}^{\circ}\) SIF data across the globe. For our new SIF product, the annual averages and maximum SIF for each grid cell were derived from all the 8-day values within each year. In contrast, the 1\({}^{\circ}\) SIF data had large data gaps, and therefore the annual averages and maximum SIF were calculated for each grid cell if there were more than eight valid monthly values.