# Upscaling Gross Primary Production in Corn-Soybean Rotation Systems in the Midwest
## 4 Discussion
Another significant parameter to improve GPPMODIS is a crop-specific RUEmax, which determines the maximum amount of GPP per unit of absorbed IPAR (i.e., fPAR \(\times\) IPAR). Similar corn and soybean RUEmax of 3.17 and 1.91 g C MJ\({}^{-1}\) had previously been reported using EC data and MODIS fPAR in corn and soybean fields in the Midwestern US [12]. For the GPPMODIS product the RUEmax is determined according to land cover categories (from MODIS MCD12Q1 land cover types), and a cropland RUEmax of 1.044 g C MJ\({}^{-1}\) was proposed [6]. This is lower than the corn and soybean RUEmax in this study and does not distinguish between both crops. This consequently leads to the substantial underestimation of GPPMODIS, especially for corn as a C4 crop (Figures A2 and A3, Video S2, Video S3). The MODIS fPAR product within the EC footprint corresponded well with NDVI measurements on the ground for both crops, which is interesting considering the difference in resolution (i.e., 500 \(\times\) 500 m satellite image vs. 1.5 m \(\times\) 1.5 m pixel size of field measurement). It also indicates that MODIS fPAR displays the NDVI of both crops. It was also noted that Equation (3) overestimates GPPcalc at the beginning and the end of the growing season, where the EC-measured GPP was zero (Figure A2). This problem was somewhat resolved by setting GPPcalc to zero, when fPAR \(<\) 0.4 (i.e., early and late in plant development). A field-measured NDVI value of 0.15 was found for bare soil in corn and soybean, similar to this study [28], and GPP is zero. It is likely that NDVI and MODIS fPAR do not represent GPPcalc well in the early and late stages of plant development, despite that NDVI was found to be a good indicator of intercepted PAR [35]. A field-based fPAR as well as other MODIS imagery-derived VIs further improved GPPcalc in corn and soybeans [14; 15]. The importance of RUEmax, fPAR, and land use maps should not diminish the importance of accurate meteorological data, as it determines the amount of energy intercepted by the crop (IPAR) and to what extent RUE is limited by temperature and VPD constraints (i.e., Tmins and VPDs scalars). This eventually determines the amount of GPPcalc. For example, on DOY 201, the median-count GPPcalc was low, because IPAR was low at all eight EC stations (Figures 2A and 5B). Although the variation of IPAR, Ta, and VPD was low among EC stations (Figure 2), these variables impacted among-county daily GPPcalc variation. For example, corn GPPcalc on DOY 193 was higher in Hardin county owing to slightly higher VPDs and IPAR (Figure 6, Video S3). Political boundary delimitations (i.e., counties) are not connected to these climatic changes, and further methods are needed to model the spatial variation of IPAR, Ta, and VPD with higher resolution. Within-county variation of corn and soybean GPPcalc is therefore only driven by the MODIS fPAR products in this study. In this study, IPAR, Ta, and VPD from ground measurements were used because of the relatively high resolution among stations. However, this may not always be the case. For example, the global GPPMODs product is calculated with meteorological data provided from the NCEP-DOE reanalysis II datasets, which have a coarser resolution than the density of EC stations used in this study [49]. The seasonal sum of county-median GPPcalc was used to estimate YieldGPP, which was compared to actual county yield data. While corn YieldGPP corresponded well with county corn yield in 2016, soybean YieldGPP was overestimated (Table 2). There has been good agreement between YieldGPP and actual yield values in corn and soybean [13], and wheat [7], however, both studies found limitations of the approach. YieldGPP as shown in Equation (6) depends on more factors (HI, M, OC, and RS may differ among fields) than GPPcalc and this estimation only represents a general trend. The overestimation of soybean YieldGPP could be connected to the mineralization of the 2015 corn residues in soybean-years in these corn-soybean rotation systems.