SIF, unlike VIs, is mechanistically linked to photosynthesis; thus, it has the potential to capture photosynthetic functioning missed by traditional VIs [24; 25; 3]. The links between SIF and GPP demonstrate that SIF estimates have the potential to more accurately track production, especially variation in production, but gaps remain in understanding how these two proxies are related [26; 27; 28; 29]. First, SIF estimates, particularly from satellites that deliver frequent and long-term monitoring capabilities, promise to improve the ability to monitor regional-level production from vegetated areas, but more studies are needed to clarify how SIF tracks with other proxies of production [30; 31; 32; 33; 34; 35; 30; 31; 32; 35]. Second, interpreting SIF signals, particularly disentangling geometric effects and physical versus physiological information in the signal and linking it to GPP, is complex. Uncertainties remain about the differences in SIF information and traditional VIs, especially on regional scales [36; 37; 38; 39; 40; 41]. One complexity of interpreting SIF signals is that they contain information about the light absorption and scattering regime (radiation regime) of vegetation and the physiological response (photosynthesis) of vegetation. More physiological information may be gleaned from the SIF signal by normalizing SIF using measurements of photosynthetically active radiation (PAR), the fraction of PAR (fPAR), or the absorbed PAR (APAR) for vegetation canopies. In previous studies, SIF normalized with PAR or APAR has shown stronger relationships with GPP and has been used as an estimate of SIF yield (SIF\({}_{\rm y}\)) [40; 42; 43; 44; 45; 46]. One challenge in calculating SIF\({}_{\rm y}\) is the need for a measurement or estimate of PAR, fPAR, or APAR, which are not widely available. If satellite-based fPAR from MODIS was demonstrated to be a suitable component for estimating APAR and used to normalize satellite SIF, a time series of SIF\({}_{\rm y}\) would contribute insight into the variability of photosynthesis, i.e., physiological responses, in vegetation. There are strong ties between production and environmental factors, such as temperature and vapor pressure deficit (VPD). Despite the widespread availability of these data, the relative influence of environmental factors on satellite-based production measures remains poorly understood. Several studies have shown the effects of temperature changes and precipitation on the timing and magnitude of production estimated by NDVI in multiple ecosystems and then linked them to GPP [2; 3; 5; 47; 48; 49]. Furthermore, land surface temperature (LST) available from MODIS has been used to infer that canopy temperature has key control over photosynthetic activity and GPP [50; 51; 52]. [PERSON] et al. [50] showed that LST was strongly correlated with PAR and VPD, which are important for quantifying GPP seasonality, and that GPP had different responses to temperature changes during the spring and fall. The importance of environmental factors, such as temperature and VPD, is well established in the literature, but mainly at plant and canopy levels, not so much at satellite level [50; 53; 54]. Despite these strong links and the estimates of these environmental factors becoming more widely available, few studies have examined the relationships among satellite-based SIF, VIs, and environmental factors. Studies using satellite time series have shown that SIF tracks interannual changes in GPP better than VIs, and this relationship is sometimes improved by normalizing SIF with PAR or APAR [8; 17; 46]. Studies have also evaluated satellite and ground observations of SIF, GPP, VIs, and environmental factors for relationships and empirical links using correlations and linear regression analysis (e.g., [55; 56; 57; 58]). But an understanding of intra-annual patterns can develop insight into the function of an ecosystem and improve the detection of changes over time, such as those under climate change [1].