Parameter calibration is normally required prior to crop model simulation, which can be a time-consuming and data-intensive task. Meanwhile, the growth stages of different hybrids/cultivars of the same crop often show some similarities, which implies that phenological parameters calibrated for one hybrid/cultivar may be useful for the simulation of another. In this study, a data assimilation framework is proposed to reduce the requirement for parameter calibration for maize simulation using AquaCrop. The phenological parameters were uniformly scaled from previous research performed in a different location for a different maize hybrid, and other parameters were taken from default settings in the model documentation. To constrain simulation uncertainties, soil moisture and canopy cover observations were assimilated both separately and jointly in order to update model states. The methodology was tested across a rain-fed field in Nebraska for 6 growing seasons. The results suggested that the under-calibrated model with uniformly scaled phenological parameters captured the temporal dynamics of crop growth, but may lead to large estimation bias. Data assimilation effectively improved model performance, and the joint assimilation outperformed single-variable assimilation. When soil moisture and canopy cover were jointly assimilated, the overall yield estimates (RMSE = 1.24 t/ha, nRMSE = 11.48%, R² = 0.695) were improved over the no-assimilation case (RMSE = 2.01 t/ha, nRMSE = 18.61%, R² = 0.338). Sensitivity analyses suggested that the improvement was still evident with temporally sparse soil moisture observations and a small ensemble size. Further testing using observations within 90 days after planting demonstrated that the method was able to predict yield around 3 months before harvest (RMSE = 1.7 t/ha, nRMSE = 15.74%). This study indicated that maize yield can be estimated and predicted accurately by monitoring the soil moisture and canopy status, which has potential for regional applications using remote sensing data.

通常在进行作物模型模拟之前需要进行参数校准，这可能是一项耗时且数据密集的任务。同时，同一作物的不同杂种/品种的生长阶段通常表现出一些相似性，这意味着为一种杂种/品种校准的物候参数可能对另一种杂种/品种的模拟有用。在这项研究中，提出了一个数据同化框架，以减少使用AquaCrop进行玉米模拟的参数校准的要求。物候参数是根据先前在不同位置针对不同玉米杂交种进行的研究统一缩放的，其他参数则取自模型文档中的默认设置。为了限制仿真不确定性，为了更新模型状态，将土壤水分和树冠覆盖观测值分别或联合吸收。该方法在内布拉斯加州的一个雨养田进行了6个生长季节的测试。结果表明，具有统一缩放的物候参数的未校准模型可以捕获作物生长的时间动态，但可能会导致较大的估计偏差。数据同化有效地改善了模型性能，联合同化的性能优于单变量同化。当土壤水分和冠层覆盖被共同吸收时，总产量估算值（RMSE = 1.24 t / ha，nRMSE = 11.48％，R 结果表明，具有统一缩放的物候参数的未校准模型可以捕获作物生长的时间动态，但可能会导致较大的估计偏差。数据同化有效地改善了模型性能，联合同化的性能优于单变量同化。当土壤水分和冠层覆盖被共同吸收时，总产量估算值（RMSE = 1.24 t / ha，nRMSE = 11.48％，R 结果表明，具有统一缩放的物候参数的未校准模型可以捕获作物生长的时间动态，但可能会导致较大的估计偏差。数据同化有效地改善了模型性能，联合同化的性能优于单变量同化。当土壤水分和冠层覆盖被共同吸收时，总产量估算值（RMSE = 1.24 t / ha，nRMSE = 11.48％，R² = 0.695）优于无同化情况（RMSE = 2.01 t / ha，nRMSE = 18.61％，R ² = 0.338）。敏感性分析表明，在时间稀疏的土壤湿度观测和较小的集合体中，这种改善仍然很明显。播种后90天内使用观测值进行的进一步测试表明，该方法能够预测收割前3个月左右的产量（RMSE = 1.7 t / ha，nRMSE = 15.74％）。这项研究表明，通过监测土壤水分和冠层状况，可以准确估算和预测玉米单产，这对利用遥感数据进行区域应用具有潜力。