## 2 Materials and Methods
### Data Analysis  
Area-based monthly averages were calculated for SIF metrics and VIs: SIF\({}_{\rm i}\), SIF\({}_{\rm d}\), SIF\({}_{\rm y}\), GPP, NDVI, and EVI and environmental factors: LST, VPD, T\({}_{\rm can}\), and T\({}_{\rm air}\) for the time period September 2014 to August 2018 (48 months). Time series of monthly values and anomalies for the full 48 months of data were calculated to examine interannual variability. Within the region, the occurrences of moderate to severe drought in 2016 were anticipated to be detectable in the signals. Average annual cycles, i.e., the mean of all measurements made in each of the twelve months of the year for all years of data [3; 5; 8], were used to examine seasonal differences in SIF metrics, VIs, and environmental factors. Altogether, five different types of analyses were conducted to compare SIF metrics and VIs and examine environmental drivers: long-term time series, average annual cycles, correlations, multiple regression, relative importance of variables, and seasonality analysis. Specifically, the strength and direction of relationships among SIF\({}_{\rm i}\), SIF\({}_{\rm d}\), SIF\({}_{\rm y}\), GPP, NDVI, EVI, LST, VPD, T\({}_{\rm can}\), and T\({}_{\rm air}\) were investigated using Pearson correlation for the time period September 2014 to August 2018 (48 months) and bivariate plots with trend lines to examine the relationships of individual SIF metrics and VIs to environmental variables. Relative importance of the environmental variables on productivity was measured using multiple regression techniques, specifically the LMG method, also known as a dominance-type method, or Shapely value regression [88; 89] and bootstrapping (1000 iterations), used to calculate confidence intervals as a guide to the significance of the differences in relative importance. In addition, partial least squares regression was applied to calculate the relative importance, with no significant difference in the result compared to LMG. The TIMESAT algorithms were used to gap-fill and smooth the signals and determine seasonality parameters with SIF\({}_{\rm i}\), SIF\({}_{\rm d}\), GPP, NDVI, and EVI as inputs [51; 90; 91]. TIMESAT was designed to analyze time series data from satellite sensors and generate seasonality parameters. Seasonality parameters calculated from the analyses include start of season (SOS), end of season (EOS), peak of season, season length (LOS), base value, maximum value, amplitude value, rates of increase and decrease, large (total) integrated value, and small (pulse) integrated value. Double logistic function fitting was applied within the TIMESAT algorithms to time series of SIF\({}_{\rm i}\), SIF\({}_{\rm d}\), SIF\({}_{\rm y}\), GPP, NDVI, and EVI. The average of the seasonality metrics for the seasons detected over the four-year period in each time series was calculated to represent the seasonality parameters of each quantity. The algorithm determines the base as the average of the left and right minimum values and the amplitude as the difference between the base value and the maximum. The times for the beginning and end of season are determined using 0.5 of the amplitude from the left and right, respectively, following methods for determining seasonality parameters in the literature [49; 51; 63; 65]. The resulting normalized base-integrated production (the base determined from TIMESAT results) and the pulse-integrated production (the amplitude determined from TIMESAT results) for each quantity were compared. The length of season (LOS) in days and the beginning and end of season (BOS and EOS, respectively) in the day of year (DOY) were extracted, and the differences between BOS, EOS, LOS, base, and pulse-integrated production (as values and percentages) were examined. Code to read, process, and analyze OCO-2, MODIS, and ancillary data was developed using Interactive Data Language (IDL, Harris Geospatial, Boulder, CO, USA). TIMESAT algorithms were used, as previously mentioned, to calculate metrics of seasonality [51; 90; 91].