## 4 Discussion
### Limits of the Method in This Study  
PRYM-Maize is proved to perform reasonably well in reproducing regional crop yield, having comparable or even better performances than models in recent studies in terms of RMSD [2; 69]. PRYM-Maize was then used to develop a new method in this study to quantify Yp, Yp\({}_{0}\), Yg, and Yg\({}_{0}\). This new method produced a Yp magnitude similar to that produced by the calibrated CGM (Section 3.2), and the spatial pattern in Yp over the NCP simulated using our model was also closed to the results of [PERSON] [68], who reproduced regional Yp across the NCP using CGM simulations at multiple meteorological sites. This new method can also be used in other regions, where farmers' potential yields are far below the potential levels. However, one should be careful with the value of PLAI\({}_{\text{opt}}\). We used PLAI\({}_{\text{opt}}\) = 7.5 in this study, and this value was a conservative estimate for PLAI\({}_{\text{opt}}\) and was obtained by analyzing historical field trials over China. However, the value of PLAI\({}_{\text{opt}}\) may be reduced in higher latitude or altitude regions, where low temperatures and heat dominate the growth of maize.  
The accuracy of an RS-based approach to estimate crop yield highly depends on the input RS data. The accuracy of Ya estimates over the NCP in this study was degraded by mixed pixels. There was an overall underestimation of the Ya over Shandong Province, where many pixels were mixed with plastic greenhouses. The plastic greenhouse is widely used across Shandong, and it weakens the vegetation information [70], reducing the yield estimated from mixed pixels. Future studies are required to resolve such issues. Using higher-resolution images may be feasible, but the temporal resolution of such data such as Landsat, Sentinel-2, and SPOT may become a new limitation. Alternative approaches, such as pixel downscaling, can also be useful for addressing the above issue. For example, we can merge RS data with coarse spatial resolution and high temporal resolution (e.g., MODIS), high-spatial-resolution panchromatic product [71], or other bands [72] to obtain high-spatial- and high-temporal-resolution data to drive the RS-based crop yield model, thereby reducing the effect of non-vegetation information. In addition, the pixel change detection method [73] is available for further removing the bad pixels or outliers in yield or Ygs produced by the RS-based model. We should consider these approaches in our future work to improve the quantification of regional crop yield and Ygs.  
The WBM is a critical part of PRYM-Maize for crop photosynthesis modeling in the context of climate change in the future. Extreme climate events (e.g., drought and heatwave) have great impacts on crop water status and thus crop yield [74; 75]. Thus, with the elevating intensification of these extreme climate events [76; 77], water availability estimated using the WBM of PRYM-Maize will play a more important role in quantifying crop yield. The WBM consists of evapotranspiration (loss of water of croplands) and soil water balance processes; hence, water availability can, in turn, affect the water balance process through its impact on evapotranspiration [38; 40]. Evapotranspiration modeling in the current PRYM-Maize does not explicitly include the effect of extreme climate events. Therefore, in future work, the improvement of PRYM-Maize with regard to better characterizing the water status of crops during extreme climate events will be required.