## Data and Methodology
### Framework for Quantifying GPP, SIF, Precipitation and Soil Moisture Relationships
Therefore, we multiplied the GPP equations with the grass and crop PFT fractions, since grass and crop responses to SIF and precipitation are different and have been already accounted for by the different coefficients obtained in the equations based on 100% grass or crop stations, as stated above. It is necessary to emphasize that PFT classification from the CLM [84; 85], PFT percentage and spatial patterns have been used to estimate GPP over the continental US. It has long been recognized that water stress has a large effect on chlorophyll fluorescence parameters, which reflect plant structural and functional damage [86]. In this analysis, we focused on temporal dependence between SIF, precipitation and soil moisture. As stated earlier in the introduction, such relationships appear not to be simultaneous. Thus, production from crops received later in the season may not be directly linked to the precipitation and/or soil moisture availability during the same period. Hence, we further performed lead-lag correlation analysis using the above independent variables of SIF, precipitation and soil moisture to ascertain the temporal relationship between them. We then introduced a new method of MLR GPP quantification, which employs knowledge of the lead-lag between precipitation and SIF. In order to do so, we have used SIF data at a given point in time, and precipitation and soil moisture from two to four weeks ago, depending on PFT, and entered these into MLR equations as predictors to calculate the predictant (GPP). MLR equations quantifying GPP were formulated for crop and grass vegetation types separately and produced GPP estimates for June, July and August (JJA) in 2007, 2011 and 2012. Calculation of predicted GPP is only performed if and when SIF, as an indicator of the activity of plant photosynthetic machinery, is greater or equal to zero. Since all the equations linking GPP, SIF, precipitation and soil moisture were generated for C3 non-arctic grasses, C4 grasses and C3 crops only, we expect the estimates based on them to be the most robust in the areas with a high percentage of these vegetation types, such as the Great Plains and Texas (Figure 1). Figure 1: Percentage of C3 non-arctic grasses (PFT 13 in CLM) (**a**), crops (PFT 15) (**b**), and their combination (**c**) over the contiguous US. PFT maps were derived from the 1-km IGBP (International Geosphere-Biosphere Program) DISCover dataset and the 1-km University of Maryland tree cover dataset [85]. This PFT product is modified by re-labeling the IGBP classes of the MODIS Land Cover Type 1 product. Red dots indicate FLUXNET station locations. Over regions where crop and grass PFT percentage is low (such as the East Coast or Southwest), our GPP estimation approach may not be accurate since we have not formulated equations for other PFTs. Therefore, our primary objective is to investigate and evaluate the performance of MLR equations over the Great Plains and Texas. We principally focus on finding the relationships between SIF and GPP that are hypothesized to vary for different plant types and be pertinent to environmental conditions over a specific region.