# Estimating Maize Yield from 2001 to 2019 in the North China Plain Using a Satellite-Based Method
## 4 Discussion  
According to previous studies, the EC-LUE model is capable of simulating the spatiotemporal variation in winter wheat yield at the national level and has the capacity to do the same for other crop yields [35]. However, few studies have focused on maize yield. MODIS remote sensing images remain an important source of information for monitoring crop yields over large areas. In this study, maize yield in the NCP region was successfully estimated using an EC-LUE model based on MODIS vegetation index data and ERA-5 reanalysis data, indicating that the yield of different crops can be estimated using the EC-LUE model combined with the calibrated HI. In this work, we treated the HI calibrated at the county scale as the HI for each planted cell of maize within the corresponding administrative region. In China, farmers are basic agricultural production units [59], which increases the spatial variability in the crop HI. It can be particularly difficult to directly obtain the HI per pixel on a large spatial scale, because of the lack of regional cultivar and management data. The validation results show that it is feasible to use a calibrated HI in the model. Some studies have used similar methods to calibrate the HI for yield estimation with good results [51; 60]. In many crop models, such as AQUACROP [61] and CROPSYST [62], the HI is a crucial variable. Some methods for estimating the HI based on remote sensing are considered feasible in large areas. Based  
Figure 8: Spatial distribution map of the meteorological and management conditions: (**a–d**) show the mean GDD (accumulated temperature greater than 10 \({}^{\circ}\)C), Pre, PAR, and IWU from 2001 to 2019.  
on fractional post-anthesis phase growth, [PERSON] et al. [33] devised a straightforward method to calculate the HI, proposing that the HI was correlated with the ratio of the above-ground biomass produced following blooming with that produced throughout the entire growing season. However, this method requires a high temporal resolution in the remote sensing data in order to accurately extract the flowering period. [PERSON] et al. [32] used the ratio between the variables (e.g., absorbed photosynthetically active radiation, crop transpiration coefficient, and crop transpiration) related with biomass production to estimate the HI. However, the difficulty in accurately estimating the crop transpiration will lead to huge errors [35]. Although the HI estimated by remote sensing methods has high accuracy (R\({}^{2}\) values ranging from 0.69 to 0.79), further research is needed [12]. In this study, the calibrated HI for maize mainly varies between 0.2-0.6 at the county level, which is comparable to the reported values. [PERSON] et al. [54] summarized the HI with values ranging from 0.25 to 0.67 in the main corn producing regions across China (2009-2016), and the values in the HI are between 0.32 and 0.64 in the NCP. In addition, some counties have a calibrated HI less than 0.2 or greater than 0.6, and these areas tend to have fewer maize-growing pixels. In addition, the uncertainty in the statistical yield causes errors in the calibration of the HI [63]. Furthermore, a higher cumulative GPP of maize during the growing season does not imply a higher grain yield, and there is a significant positive correlation between grain yield and the HI [64]. The regional distribution of the estimated maize yield in this study is similar to the HI. One study showed that the contribution rate of the HI (52.67%) was higher than that of dry matter (47.33%) in the NCP [54]. Although the use of the calibrated HI in the model performed well, it ignores the changes in maize varieties and cultivars. The HI differed significantly between different maize cultivars [55]. Future studies will benefit from more precise variety and yield data for yield estimation. In addition, the crop area and NDVI have a significant impact on how accurately the GPP and yield are estimated.