## 4 Discussion
### In-Season Forecasting for Winter Wheat Grain Production  
The LUE-based GPP models provide a simple but efficient way to estimate biomass or yield [59]. Unlike empirical models, they are semi-empirical and can be applied at both site or regional scales with calibration and validation [59; 60]. Our study combined the GPP estimated from the VPM and CDL crop area and to predict winter wheat grain production. At the county and state scales, the predicted grain production estimates correlated well with the NASS grain production. This in-season forecasting pilot study was run at county and state scales because the NASS grain production data are readily available at those scales. Lack of access to the field-scale grain production data limits the validation of GPP\({}_{\text{VPM}}\)-based grain production data at the field scale. GPP data at a moderate spatial resolution (500 m) were used in this study. For the major production regions of winter wheat, which produced more than 40% of the total winter wheat grain, the field sizes are usually large and there are few mixed pixels in these counties and states. However, for other regions, the mixed pixels problem can cause significant discrepancies in calculating wheat GPP and grain production. GPP products with high spatial resolutions (e.g., 30 m Landsat, and 10 m Sentinel-2) and suitable temporal frequency (weekly) may overcome the above limitations for field-scale agricultural applications.  
This study highlighted the importance of accurate and in-season or annual maps of crop types for the prediction of crop production, including both planted and harvested areas. Annual or in-season maps of crop planted areas provided the basic information at the beginning of the growing season. Annual to-be-harvested area maps in the growing season would be helpful in predicting grain production as grain production is calculated as a function of \"to-be-harvested\" crop areas. For some major crops like maize and soybean, there could be no significant differences between planted area and harvested area in the CONUS [33]. Thus, early mapping of the planted area in the growing season could be used for grain production estimation. However, for winter wheat, our study showed that there were significant differences between planted and harvested areas over the CONUS, especially the Southern Great Plains, where the relative difference could be greater than 20%. Though numerous studies have tried to provide in-season crop type maps as earlyas possible based on remote sensing data [61; 62], there is not much attention paid to the research of planted and harvested area mapping. A separate classification of planted area and harvested area can help to get a more accurate prediction of grain production. Another issue is to have updated (in-season) and high spatial resolution (e.g., 30 m or 10 m) crop classification maps. The CDL dataset usually has a six-month lag time after harvest, before being released to the public, and a delay of six months after harvest would preclude in-season grain production prediction. Time series satellite observations at high spatial resolutions, for example, from Sentinel-2 (5-day to 10-day revisit) and Sentinel-1 (6-day or 12-day revisit), offer rich data to map different types of crops at a field scale in a timely fashion, especially in those regions with complicated crop landscapes and frequent cloud cover.