## Discussion
### A Generic Two-Slope Scheme SIFc-GPP Relationship
\tag{2}\]  
The two-slope scheme provides a very convenient and effective tool for converting SIFc to GPP and monitoring GPP dynamics in time and space as it is almost land cover-independent (only the distribution of ENF needs to be identified). The significantly higher slopes for the ENF biome in the two-slope scheme are intriguing and deserve further study. This phenomenon is in line with the observations reported by [PERSON] and [PERSON] [46] and [PERSON] and [PERSON] [29], who pointed out that the lower SIF (and therefore, higher GPP/SIFc slope) for ENF is mainly caused by a stronger canopy reabsorption and/or scattering of SIF for the needle leaf forest, and the core mechanism is the high dependency between SIF and APAR, chlorophyll content [47], and photosynthetic light-use efficiency [48]. However, it is still a great challenge to measure (in field), observe (from satellite), and model (mechanism-based) photosynthesis in boreal forests, especially for the ENF biome [48; 49]. More in-depth research is still needed to expand our understanding of  
Figure 9: Comparison of the cumulative Flux_GPP and SIFc_GPP across 12 biomes. Median-based two-slope scheme (**A**) and mean-based two-slope scheme (**B**). The two two-slope schemes are listed in Table 1. The dash line is the 1:1 line. Blue and red lines are the fitted linear regression lines of cumulative CSIF_GPP and GOSIF_GPP with Flux_GPP, respectively, and their associated PB values are provided as well. To avoid the impacts of large unbalance in the number of GPP values across biomes on accumulative GPP, 10,000 daily Flux_GPP and SIFc_GPP value pairs were sampled with replacement for each biome for this comparison. the effects of needle leaf clumping index [50]), leaf chlorophyll content [51], and chlorophyll/carotonoid index [46] on plant photosynthesis (especially for SIF as an agents) from canopy to global scale [9]. The coefficients for the two SIF datasets are different from each other (Equations (1) and (2)). It can be explained by the fact that GOSIF is generally higher in value than CSIF across biomes and years (Figures S4 and S5), which can be traced back to their reconstruction methods and the uncertainty of the SIFc products. GOSIF, generated from discrete OCO-2 SIF soundings, EVI, and land cover type data from MODIS and meteorological reanalysis data, had an RMSE of only 0.07 W m\({}^{-2}\) um\({}^{-1}\) sr\({}^{-1}\)[31]. In contrast, CSIF, generated using a machine learning approach (trained by discrete OCO-2 SIF soundings and MODIS surface reflectance), had an RMSE of 0.18 W m\({}^{-2}\) um\({}^{-1}\) sr\({}^{-1}\)[29], more than double that of GOSIF. Previous studies have highlighted that the linear SIF-GPP relationship is either bioindependent [22; 26] or ecosystem-specific (e.g., Sun and [PERSON] [2]). To our knowledge, none of them has really examined the discriminatory power of their datasets on the observed differences of SIF-GPP relationship across ecosystems or biomes. In other words, previous studies often studied the uniqueness of the SIF-GPP relationships, and none addressed their commonality or the discriminatory power of their datasets across biomes. Our two-slope scheme represents a major step forward in this direction. In addition, it provides a practicable method for estimating GPP from SIFc with a greatly reduced need on land cover specificity, which should benefit the reduction of GPP uncertainty from land cover classification. This general scheme may have reconciled the differences among previous studies that were either restricted to small regions [52], had too few flux towers and/or biomes [21], or had low spatiotemporal resolutions [28].