## 3 Results  
While SIF has a strong and highly linear relationship with GPP and has been previously used for its quantification without auxiliary data [22; 27], it is necessary to ensure that no information useful for GPP quantification purposes is lost. In our case, one caveat can potentially stem from the relatively coarse spatial resolution of the datasets. It is to be noted that while changes in soil moisture and precipitation are already reflected in corresponding SIF signal fluctuations, it is possible that these parameters might not be well delineated by SIF due to its comparatively lower spatial resolution. Therefore, in order to provide substantiation for usage of a few predictor variables in addition to SIF, we have turned to the covariogram analysis. The covariogram might be thought of as the covariance, i.e., similarity, of point values as a function of distance between the points, thus enabling an understanding of how important spatial variability is for each given variable. Over both the Great Plains (Figure 2a) and Texas (Figure 2b) soil moisture and GPP show a pattern of an almost linear reduction in covariance, i.e., the degree of similarity between the values of each variable at two given points becomes significantly lower as the distance between the points increases. Contrastingly, SIF covariogram is represented by a relatively straight line, which is characterized by a greater number of smaller scale fluctuations. As can be inferred from Figure 2, SIF generally demonstrates less variability over the same spatial extent compared to precipitation, soil moisture, and GPP itself, at least in part due to the coarseness of the satellite SIF data. One important aspect in GPP and SIF relationship, as seen from the covariogram analysis, lies in the fact that GPP and SIF covariance changes do not go hand in hand, and GPP covariance trends are similar to those of precipitation and soil moisture, not SIF. Since spatial variability is more significant in GPP, using SIF that is less prone to variations over the same spatial domain might lead to larger errors in GPP values if they are predicted based solely on SIF. Figure 2: Covariogram of SIF, precipitation, soil moisture, and GPP data over (**a**) the Great Plains and (**b**) Texas. Figure 3 demonstrates the lead-lag relationship between precipitation and SIF, soil moisture and precipitation, and SIF and soil moisture for US-Ne3 and US-KFS stations, respectively. It can be inferred that for US-Ne3 station with the dominant crop vegetation, the lag between precipitation and corresponding SIF is on the scale of 4 weeks (Figure 3a), while over US-KFS station, where grass is prevalent, this lag is about 2 weeks (Figure 3b). There is no significant lag between precipitation and soil moisture values for both US-Ne3 and US-KFS stations (Figure 3c,d), which is also expected. Similar temporal relationships between SIF, precipitation and soil moisture have also been found for the remaining FLUXNET stations used in this research (not shown). Figure 4 illustrates the temporal relationship between plant water, expressed as soil moisture index based on ESA soil moisture, and SIF data. The soil moisture index calculations are similar to those used in the parameterization scheme for soil moisture limitation to transpiration in land surface models, e.g., Noah-Multiparameterization Land Surface Model (Noah-MP LSM) [87]: when the index approaches 1, the plant water need reaches its maximum; when the index is equal to 0, the need is at its minimum. As can be inferred from Figure 4, water need has a peak towards mid June-end of July, which roughly coincides with the timing of pollination and kernel development in corn, and the beginning of pod development in soybean--the most important periods for corn and soy plants to have an adequate water supply. This peak in water need, as expressed by soil moisture index, is followed by a peak in SIF, which is indicative of high photosynthetic rates and potentially mass gain and GPP in crops by this later time in the growing season.