## 1 Introduction
The main reasons for the uncertainty in the SIF-GPP relationship across sites and biomes are spatiotemporal mismatches and data uncertainties among the SIF and GPP products, which can be traced back into at least three major issues. First, the spatial mismatch of EC flux tower sites and OCO-2 orbit is the general limitations of satellite SIF application [5]. Second, the temporal inconsistent between the short lifetime of OCO-2 SIF (available from 6 September 2014 to present) and GPP estimated from EC flux towers (i.e., FLUXNET data are only updated to 2015 (FLUXNET2015)) is not relevant for the development or validation of the SIF-GPP relationship [2]. Third, thereare uncertainties in estimating GPP from EC towers [27] and SIF sampling instrument and retrieval methodologies [21]. Thus, the amount (spatial and temporal coverage) of data available from the satellite SIF at present are insufficient to support comprehensive analysis the SIF-GPP relationship [28]. Therefore, more studies tackling these issues are required to truly address the complexities and drivers of variability in the SIF-GPP relationships across biomes. Several global spatially contiguous SIF datasets (hereafter referred to as SIFc) developed recently can contribute to address the above issues. [PERSON] and [PERSON] [29] generated a global spatially contiguous SIF dataset (hereafter referred to as CSIF, i.e., clear-sky instantaneous and all-sky daily average) at moderate spatiotemporal resolutions (0.05\({}^{\circ}\) and 4-day) by training a neural network with surface reflectance from MODIS and OCO-2 SIF soundings. [PERSON] and [PERSON] [30] developed another spatially contiguous global SIF product (hereafter referred to as GCSIF) at 0.05\({}^{\circ}\) and 16-day resolutions using machine learning with a time- and biome-specific model. [PERSON] and [PERSON] [31] further developed a global OCO-2 SIF dataset (GOSIF) with a similar spatiotemporal resolution (0.05\({}^{\circ}\) and 8-day) based on discrete OCO-2 SIF soundings, EVI and land cover type data from MODIS, and meteorological reanalysis data from Modern-Era Retrospective analysis for Research and Applications (MERRA-2) [32]. Moreover, [PERSON] and [PERSON] [33] presented a new SIF dataset (hereafter referred to as GOMESIF) based on GOME-2 satellite observations with an enhanced spatial resolution covering the period 2007-2018. In general, differences exist among SIFc products due to different reconstruction methods in this study (see Supplementary Material), and there is an urgent need to recognize and, if possible, reconcile the differences of SIFc datasets and understand their potential impacts on GPP estimation. To improve the quantification of terrestrial photosynthesis at various spatial and temporal scales using the recently available remotely sensed spatially contiguous SIFc datasets, further efforts should focus on the application of expanded SIFc datasets to test the robustness of the SIF-GPP relationship across all vegetated biomes [2]. Here, we use two global spatially contiguous SIFc datasets (CSIF [29] and GOSIF [31]), coupled with GPP obtained by an EC flux tower from the worldwide network FLUXNET2015 [34], to address the following objectives: (1) to explore the commonality and differences of the SIFc-GPP relationship across 12 vegetated IGBP biomes; (2) to examine the variability of SIFc-GPP relationships over a range of spatial and temporal scales; (3) to elucidate the application prospects and limitations of existing spatially contiguous SIFc datasets.