Data assimilation, a state-of-the-art method that merges remote sensing data with a dynamic model to improve model performance, has been widely used in land surface process modeling. Application of data assimilation under various water conditions can provide insight of crop response to different water supply rates, which is useful for agricultural water management in arid and semi-arid regions. For this purpose, we developed a generic data assimilation methodology by integrating both the Shuffled Complex Evolution (SCE) and the Ensemble Kalman Filter (EnKF) algorithms into the Simple Algorithm For Yield and Evapotranspiration (SAFYE) model to provide improved simulation of winter wheat biomass and yield, and simulation of evapotranspiration (ET) under different water-supply scenarios. An experiment with nine irrigation scenarios was conducted during the 2013—2015 growing cycles. Field spectral data were employed to retrieve the leaf area index (LAI), which was then used as a single state variable to determine other parameters in the SAFYE model using a global optimization algorithm. Time-series LAI was eventually assimilated in the SAFYE model based on the EnKF algorithm to improve overall model simulation. The results showed that the simulated crop growth dynamics followed the measurements well in most cases when the estimated LAI was assimilated. The accuracy of simulated biomass at the daily step was improved, with the maximum RMSE decreased from 199.4 to 123.8 g m⁻² and from 466.6 to 393.4 g m⁻² for the 2013–2014 and 2014–2015 growing seasons respectively. A good agreement was also achieved between the estimated and field measured grain yield (R² = 0.901, RMSE= 31.9 g m⁻², RRMSE=6.55%) for both growing seasons. The simulation of soil water content in the top 0—20 cm soil layer was better (RMSE: 3.3—5.0 mm) than that of 0—100 cm layer (RMSE: 11.7—29.6 mm). Accuracy of the simulated ET under early-stage water deficit scenarios was lower than that under other scenarios, with a positive mean relative error of 14% (3.4—24.3%) during two growing seasons. This study demonstrates the great potential of coupling remote sensing data to improve the performance of SAFYE model in modeling winter wheat growth.

数据同化是一种将遥感数据与动态模型相结合以提高模型性能的最新方法，已广泛应用于地表过程建模。在各种水条件下应用数据同化可以深入了解作物对不同供水速率的反应，这对于干旱和半干旱地区的农业水资源管理非常有用。为此，我们通过将 Shuffled Complex Evolution (SCE) 和 Ensemble Kalman Filter (EnKF) 算法集成到简单的产量和蒸散算法 (SAFYE) 模型中，开发了一种通用的数据同化方法，以提供对冬小麦生物量的改进模拟和产量，以及不同供水情景下蒸散量 (ET) 的模拟。在 2013 年至 2015 年的生长周期内进行了九种灌溉方案的实验。田间光谱数据用于检索叶面积指数 (LAI)，然后将其用作单个状态变量，以使用全局优化算法确定 SAFYE 模型中的其他参数。时间序列LAI最终被同化在基于EnKF算法的SAFYE模型中，以提高整体模型模拟。结果表明，当估计的 LAI 被同化时，模拟的作物生长动态在大多数情况下都能很好地遵循测量结果。每日步长模拟生物量的准确性得到提高，最大RMSE从199.4降至123.8 g m 然后将其用作单个状态变量，以使用全局优化算法确定 SAFYE 模型中的其他参数。时间序列LAI最终被同化在基于EnKF算法的SAFYE模型中，以提高整体模型模拟。结果表明，当估计的 LAI 被同化时，模拟的作物生长动态在大多数情况下都能很好地遵循测量结果。每日步长模拟生物量的准确性得到提高，最大RMSE从199.4降至123.8 g m 然后将其用作单个状态变量，以使用全局优化算法确定 SAFYE 模型中的其他参数。时间序列LAI最终被同化在基于EnKF算法的SAFYE模型中，以提高整体模型模拟。结果表明，当估计的 LAI 被同化时，模拟的作物生长动态在大多数情况下都能很好地遵循测量结果。每日步长模拟生物量的准确性得到提高，最大RMSE从199.4降至123.8 g m 结果表明，当估计的 LAI 被同化时，模拟的作物生长动态在大多数情况下都能很好地遵循测量结果。每日步长模拟生物量的准确性得到提高，最大RMSE从199.4降至123.8 g m 结果表明，当估计的 LAI 被同化时，模拟的作物生长动态在大多数情况下都能很好地遵循测量结果。每日步长模拟生物量的准确性得到提高，最大RMSE从199.4降至123.8 g m^-2和 466.6 至 393.4 g m ^{-2 分别}为 2013-2014 和 2014-2015 生长季节。对于两个生长季节，估计的和现场测量的谷物产量（R ² = 0.901，RMSE= 31.9 g m ^-2，RRMSE=6.55%）之间也取得了很好的一致性。表层0—20 cm土层土壤含水量模拟（RMSE：3.3—5.0 mm）优于0—100 cm层（RMSE：11.7—29.6 mm）。早期缺水情景下模拟ET的精度低于其他情景，两个生长季节的正平均相对误差为14%（3.4-24.3%）。本研究证明了耦合遥感数据在改善 SAFYE 模型在模拟冬小麦生长方面的巨大潜力。