## 2 Study Area and Data
### Data  
The National Aeronautics and Space Administration (NASA) provides Moderate Resolution Imaging Spectroradiometer (MODIS) data for Earth environmental monitoring. We used multiple vegetation products from the MODIS database as indicators of vegetation vitality, biomass, and photosynthesis [15]. The enhanced vegetation index (EVI) can mitigate the signal saturation issue of the normalized difference vegetation index (NDVI). The EVI is widely used for cropland studies because it is more appropriate for the expression of vegetation vitality in highly vegetated areas [16; 17]. The leaf area index (LAI), which is calculated using a crop-specific growth coefficient and maximum primary production, can be used as an alternative for leaf biomass [18]. The gross primary production (GPP) represents the chemical energy (kg C m\({}^{-2}\)) for photosynthetic activity [19]. We used the EVI products with 16-day temporal and 250 m spatial resolutions and the LAI and GPP products with 8-day temporal and 500 m spatial resolutions.  
The Parameter-elevation Regressions on Independent Slopes Model (PRISM) Climate Group provides daily and monthly reanalysis data for seven climate elements for the US [20]. We used monthly data for precipitation (PPT) and maximum temperature (TMAX) at a spatial resolution of 4 km, considering extreme weather conditions such as drought and heatwave. From the Global Land Data Assimilation System (GLDAS) [21; 22], we used monthly soil moisture (SM) data [23] in a 25 km grid for corn yield modeling.  
The US Department of Agriculture (USDA) provides Cropland Data Layer (CDL) maps for crop type classification at 56 m and 30 m resolutions for 2006-2009 and for 2010-2015, respectively [24]. The CDL maps are updated yearly using the data from the cropland census, Landsat satellite images,  
Figure 1: Study area.  
and Advanced Wide Field Sensor (AWiFS) [25; 26]. In this study, the pixels recorded as the corn-sown area were extracted from the CDL maps. The National Agricultural Statistics Service (NASS) operated by the USDA provides annual corn yield statistics at the county level [27]. For convenience, we converted data in the unit of a bushel per acre to a ton per hectare.  
All the data for the period of July and August 2006-2015 are summarized in Table 1. We used cropland data layer (CDL), enhanced vegetation index (EVI), leaf area index (LAI), gross primary production (GPP), precipitation (PPT), maximum temperature (TMAX), and soil moisture (SM) for corn yield prediction. We constructed a spatiotemporal match-up database for corn-sown areas of the Midwestern US to combine the input variables and the yield statistics according to year and county (Figure 2). The temporal aggregation for July-August was conducted for EVI, LAI, and GPP in terms of the day-of-year (DOY), and for PPT, TMAX, and SM in terms of the month (Figure 3a). For spatial aggregation, we first extracted the corn-sown area from the CDL maps at a 56 m resolution for 2006-2009 and at a 30 m resolution for 2010-2015 on the World Geodetic System 1984 (WGS84) coordinate reference system. Then, we overlaid the EVI at 250 m resolution, LAI and GPP at 500 m resolution, PPT and TMAX at 4 km resolution, and SM at a 25 km resolution on the CDL maps. We derived the intersections between the CDL layer and each of the variable layers, which produced the data layers at 56 m or 30 m resolution according to year. The data layers were aggregated by the boundary of the counties in the study area using a zonal mean operation (Figure 3b).  
Figure 2: Process for constructing the match-up database for corn yield for the counties of the study area.