## 2 Materials and Methods  
### Data  
In this study, we rely on two ground-truth maize datasets, which we term FLUX and LAIGROUND. The data sources are summarized in Table 2.  
The FLUX dataset consists of CO\({}_{2}\) flux tower measurements of gross primary productivity (GPP) and incoming solar radiation (SRAD) time series in maize fields. The eddy-covariance technique determines the CO\({}_{2}\) flux, which is termed the net ecosystem exchange (NEE), from the covariance of the vertical wind velocity and CO\({}_{2}\) flux, sampled by the tower at 10-20 Hz and averaged to 30-60 minute periods [69]. The height of the flux tower is selected to have an appropriate footprint covering the field being studied by the tower. The ecosystem respiration is removed from the NEE to obtain the amount of carbon captured by the producers in the field (GPP) by a partitioning algorithm. In this study, the GPP is either obtained from the nighttime-partitioned product provided by FLUXNET2015 [70] or the site principal investigators (PIs), or calculated from NEE using the nighttime-based partitioning algorithm of [71] implemented in [72]. In addition, ground-truth LAI that was measured at sites on some days of the season and the planting and harvest dates were obtained. The LAIGROUND dataset consist of ground-truth LAI measurements of maize obtained during various campaigns with different measurement technique (Destructive, LAI2000, AccuPAR, Hemispheric Photography) compiled by [27]. Destructive measurements of LAI rely on physically sampling leaves in predefined areas in the field and measuring them in a laboratory to estimate the LAI in the field. In contrast, the LAI2000, AccuPAR, and Hemispheric Photography techniques use ground-based optical measurements made by researchers in the field on sampling campaign days, along with physics and image-processing based techniques, to estimate the LAI. Further details on all the different measurement techniques can be found in [73]. Each site in this dataset represents a different measurement campaign and some consist of LAI measurements on a single day in neighboring plots, some consist of LAI measurements in different fields (sometimes many kilometers apart), and some consist of multitemporal measurements in the same field/plot. Two of the sites are taken at CO\({}_{2}\) eddy-covariance tower sites in the FLUX dataset (Italy and Mead) and the analysis conducted in this study takes care to ensure these are treated as the same sites across datasets when any site-based cross-validation-type analysis is conducted. Following [27], LAI measurments greater than 6 and less than 0.1 are excluded from the LAIGROUND dataset as they are beyond the prediction power of vegetation indicies. In addition to the ground data in Table 2, we also use solar-reflective satellite data collocated with the ground data. Data from the Thematic Mapper (TM) sensor was used from LANDSAT 5, while data from the Enhanced Thematic Mapper Plus (ETM+) sensor was used from LANDSAT 7. The LANDSAT satellites used for each site depend upon which LANDSAT satellites were active when the site's data was collected; LANDSAT 5 was active from March 1984-January 2013, while LANDSAT 7 was active from April 1999 to present (ca. August 2019). Data from both satellites was used at sites where data was collected when both satellites were active. For the LAIGROUND dataset, the plots tend to be small and we consequently use 30-m atmospherically-corrected LEDAPS surface reflectance data from LANDSAT 5 and 7 obtained from Google Earth Engine via the GEEXTRACT python tool within 5 m of the plot coordinates. For the FLUX dataset, the plots tend to be production-sized fields and we obtain the average LANDSAT LEDAPS [74] surface reflectance within a 100-m radius of the plot coordinates.