###### Abstract
Satellite-based Earth observation has been a viable technology for identifying and mapping planted and harvested crop areas since the early 1970s [9]. Numerous studies have used multiple temporal remote sensing data to characterize and map individual crop types [10; 11; 12; 13; 14]. The Cropland Data Layer (CDL) datasets, released by the NASS/USDA [15], have reported annual planted areas for all major crops in the contiguous United States (CONUS) since 2008. Annual maps of pixel-based crop planted areas from the CDL provide supplementary acreage estimates to NASS acreage survey data, but it was reported that CDL-based crop planted area estimates were slightly lower than the NASS statistical estimates [15]. To date, there has no systematic comparative study to examine the spatiotemporal consistency of winter wheat planted area estimates between NASS and CDL datasets from 2008-2018 in the CONUS. Additionally, the spatiotemporal consistency and dynamics between winter wheat planted areas (CDL, NASS) and harvested area (NASS) from 2008-2018 in the CONUS has not been investigated. Satellite remote sensing is also widely applied in estimating crop yield or production [16; 17; 18; 19]. The process-based mathematical models and statistical regression models use climate inputs and satellite images to predict crop yield [20; 21; 22; 23]. Climate variables, such as downward short-wave radiation, air temperature, and precipitation, are common inputs in those models [20; 24]. Though climate data describe the environmental conditions that affect crop growth, it cannot directly measure the impacts of abiotic and biotic factors on crop growth. Climate data are usually used with other datasets to predict crop yield [21]. Vegetation indices, such as the enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI), are widely utilized to monitor crop growth status and estimate crop yield [22; 25; 26].. Gross and net primary production (GPP, NPP, respectively), aboveground biomass (AGB), and grain production and yield (GP, GY, respectively) of crop fields are interlinked by the concept of the harvest index (HI), which can be defined in three ways: (1) HI\({}_{\text{AGB}}\) = GY/AGB, (2) HI\({}_{\text{NPP}}\) = GY/NPP, and (3) HI\({}_{\text{GPP}}\) = GY/GPP. Most of the agricultural literature uses HI\({}_{\text{AGB}}\) or HI\({}_{\text{NPP}}\) to estimate crop yield and/or grain production [27; 28; 29]. GPP estimates from light use efficiency (LUE) models driven by climate and satellite data are available to the public [30; 31; 32]. Crop yield is estimated from GPP and GPP-based HI (HI\({}_{\text{GPP}}\)). [PERSON] et al. [26] modeled HI\({}_{\text{GPP}}\) from county-level MODIS GPP and grain production statistics for croplands in Montana from 2008-2015 and applied the HI\({}_{\text{GPP}}\) for yield prediction in Montana. For maize and soybean croplands in the CONUS, [PERSON] et al. [33] reported strong relationships between GPP simulated by the vegetation photosynthesis model (VPM) and grain production from the NASS, and there are small interannual changes of HI\({}_{\text{GPP}}\). However, knowledge is still limited on the spatiotemporal relationship between GPP and grain production in the CONUS and on the performance of GPP for in-season grain production forecasting for winter wheat. In this study, as one of a series of papers to explore the relationship between \(\text{GPP}_{\text{VPM}}\) and grain production, we followed the general framework of the previous paper [33]. However, this paper focused on winter wheat croplands. First, we investigated the spatiotemporal dynamics of winter wheat cropping areas in the CONUS at national and county scales from 2008 to 2018. We quantified the spatiotemporal consistencies between NASS and CDL datasets for planted areas and for planted/harvested areas. Differences between winter wheat planted area and harvested area were expected because winter wheat fields can be used as dual-purpose fields: beef cattle grazing and/or grain production.