# Estimating Maize Yield from 2001 to 2019 in the North China Plain Using a Satellite-Based Method
## 4 Discussion
The crop area dataset that was employed in this study was spatially explicit and temporally continuous, which considerably reduced inaccuracies brought on by changes in maize planting areas. A pixel with a 1 km resolution may contain several different forms of land cover or crops, which might cause uncertainty in the results [65; 66]. In this instance, regions with large, contiguous planting areas have smaller errors in the results, while regions with tiny, dispersed planting areas have more errors in the results. A further factor in the inaccuracy is the treatment of non-crop growing areas as crop growing areas, or crop growing areas as non-crop planting areas, due to the intricacy of the surface. In terms of the NDVI, pixels that are combined with trees will have higher values, whereas pixels that are mixed with impermeable surface or bare ground will have lower values, resulting in a higher or lower GPP for estimation. This introduces an inevitable error in the harvest index correction. This is also one of the reasons for the extremely low or extremely high values in the yield estimates. In contrast, AH and JS have smaller and more dispersed maize planting areas than HB, HN, and SD, which may be one of the causes of the inaccurate maize yield estimations in these two regions. Fortunately, we were able to preserve the original signal when reconstructing the NDVI time series using the S-G filter, allowing the NDVI series to accurately depict maize growth and development. Although Landsat and Sentinel-2 have higher spatial resolution, they are more affected by noise [67]. In order to facilitate crop production estimation using remote sensing data, more advanced processing techniques are urgently required. Moreover, uncertainty exists in the parameters characterizing crop biophysics. A fixed value for \(\varepsilon_{\text{max}}\), a crucial model parameter, may result in systematic uncertainty or mistakes in the GPP for dynamic simulations of a specific region [63; 68]. [PERSON] et al. [69] demonstrated that even in the same site, the \(\varepsilon_{\text{max}}\) changed from year to year. Although utilizing fixed efficiency values to estimate the biomass over crop growth cycles has advantages, meteorological and management conditions are nonetheless crucial determinants of their dynamic variations, according to a recent study [30]. Additionally, variable canopy structures, row spacings, and plant densities effect the \(\varepsilon_{\text{max}}\) of maize [70; 71]. Although the \(\varepsilon_{\text{max}}\) value we used (2.84 g C m\({}^{-2}\) MJ\({}^{-1}\) APAR) was in the right range, GPP errorswere inevitable. For the temperature, we set the suitable growing temperature for maize to 0-45 \({}^{\circ}\)C, which is within the reported growth temperature range. [PERSON] and [PERSON] [72] discovered that the ideal temperature for maize ranged from 28 \({}^{\circ}\)C to 37.5 \({}^{\circ}\)C, and that setting the ideal temperature at 23 \({}^{\circ}\)C can cause GPP mistakes. The three parameters (AR = 0.53, RS = 0.18, and MC = 0.11) that translate GPP into crop production still have considerable room for error. These three criteria may change based on crop cultivars and varieties, as well as environmental factors [65]. For instance, [PERSON] et al. [73] discovered that there was significant uncertainty in the allocation of GPP with a fixed root/shoot ratio since the maize cultivar and planting year had a significant impact on the root/shoot ratio. The AR varies with the temperature and crop development stage [74]. Future efforts to accurately predict GPP and enhance yield estimates will benefit from the rigorous calibration of these parameters for particular regions. Additionally, because there are variations in cultivation seasons, particularly in regions where farmers are the primary unit of production, using a set summer maize-growing period across the whole research area creates errors [75].