## 2 Materials and Methods  
### Datasets  
Two available spatially contiguous SIFc datasets (unit in W m\({}^{-2}\) um\({}^{-1}\) sr\({}^{-1}\)) based on OCO-2 SIF (V8r) at 757 nm were used in this study. First, the CSIF dataset, generated by [PERSON] and [PERSON] [29], has two global spatially contiguous SIFc data layers at moderate spatiotemporal resolutions (0.05\({}^{\circ}\) spatial resolution, and 4-day temporal resolution, obtained upon request from the author [PERSON]): one from instantaneous measurements obtained in clear-sky conditions (2000-2017) and the other from daily averages including all sky conditions (2000-2016) (referred to as CSIFall-daily). They are generated based on the SIF retrievals from OCO-2, interpolated by an artificial neural networks (ANN) to a grid using the surface reflectance from MODIS aboard the Terra and Aqua satellites [29]. The ANN with one layer and five neurons exhibited the highest model performance with a good performance in validation (R\({}^{2}\) = 0.79, RMSE = 0.18 W m\({}^{-2}\) um\({}^{-1}\) sr\({}^{-1}\)). The errors of CSIF in 9 of 14 biomes to OCO-2 SIF were less than 10%, and most of them were lower than 5% [29]. To better match with the GPP data, the all-sky daily average CSIF dataset (CSIFall-daily) was used (referred to as CSIF), which exhibited strong spatial, seasonal, and inter-annual dynamics that were consistent with daily SIF from OCO-2 and GOME-2 [29].  
Second, we employed the global 'OCO-2' SIF dataset (referred to as GOSIF) (0.05\({}^{\circ}\) spatial resolution, 8-day temporal resolution, freely available at [[http://globalecology.unh.edu](http://globalecology.unh.edu)]([http://globalecology.unh.edu](http://globalecology.unh.edu)), accessed on 1 April 2019) [31]. The dataset was based on a data-driven model developed based on discrete OCO-2 SIF data, EVI and land cover data from MODIS, and meteorological reanalysis data. Similar to CSIF, the GOSIF dataset has extended the start date of the data record of OCO-2 SIF to March 2000 and with a daily time scale. The dataset also performed fairly well in SIF validation (R\({}^{2}\) = 0.79, RMSE = 0.07 W m\({}^{-2}\)\(\upmu\)m\({}^{-1}\) sr\({}^{-1}\)). These two reconstructed SIF products (i.e., CSIF and GOSIF) offer opportunities to examine the synergy between satellite SIF and photosynthesis at consistent spatial scales globally [29; 31; 35].  
GPP data were extracted from the global network FLUXNET2015 ([[http://fluxnet.fluxdata.org/data/fluxnet2015-dataset/](http://fluxnet.fluxdata.org/data/fluxnet2015-dataset/)]([http://fluxnet.fluxdata.org/data/fluxnet2015-dataset/](http://fluxnet.fluxdata.org/data/fluxnet2015-dataset/)), accessed on 1 April 2019), which contains terrestrial ecosystem carbon flux data from 212 EC flux towers worldwide [34]. Considering small differences between different GPP partitioning methods [36] (Table S1 and Figures S1 and S2), the daily average GPP estimates (GPP_M, unit in g C m\({}^{-2}\) d\({}^{-1}\)) were calculated as the mean of GPP estimates from both daytime respiration (GPP_D) and nighttime respiration (GPP_N) [37] and used to analyze the SIFc-GPP relationship globally. Four sites (i.e., IT-SRo, NO-BIv, US-LWW, and US-Me4 sites) were removed due to the limited data and large landscape heterogeneity in the SIFc pixel, after visually examining the landscape composition of all flux tower footprints and associated SIFc pixels using Google Earth images. Consequently, 208 EC flux tower sites distributed across 12 vegetated biomes were used, which was different from some previous researches [21; 29] (Figure 1, Table S2). In addition, all the daily data used for analysis were extracted at an 8-day time interval (i.e., CSIF from days 4 to 8, GOSIF on day 8, and GPP from days 1 to 8).