Accurate estimation of wheat maturity date (MD) is helpful to make reasonable harvest planning and guarantee crop yield and quality. In this study, wheat phenology extracted from satellite images was assimilated into WheatGrow model to develop wheat maturity date estimation model. Theoretical uncertainty was introduced into assimilation system as the error covariance matrix of remote sensing observations, which improved the performance of maturity date estimation model. Compared with the simulated maturity date of crop growth model and assimilation system combined with the constant uncertainty (Assimilation1), the accuracy of assimilation system combined with the theoretical uncertainty (Assimilation2) was higher (r = 0.81, RMSE = 4.5 d). Assimilation2 has better performance and robustness in different years and different subregions. The mean relative errors between the estimated values of Assimilation2 and the observations were generally small and concentrated in the range of −5 % to 5 %. The estimated maturity date showed latitude variation in spatial distribution in the Huang-Huai-Hai Plain (HHHP). In addition, the trend of wheat maturity date from 2001 to 2020 in the central region of HHHP was significant (p < 0.05), and the mean change rate of maturity date reached 3–6 d/10a. However, the overall change trend of maturity date in the HHHP was not significant. Temperature was main driver affecting the spatiotemporal variation of wheat maturity date. The regional wheat maturity date estimation model can provide technical support for wheat maturity date estimation at regional scale.

中文翻译：

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WheatGrow与卫星图像耦合提高小麦成熟期估算精度


准确估算小麦成熟期（MD）有助于制定合理的收获计划，保证作物产量和品质。在本研究中，从卫星图像中提取的小麦物候被同化到 WheatGrow 模型中，以开发小麦成熟日期估计模型。将理论不确定性引入同化系统作为遥感观测误差协方差矩阵，提高了成熟度估算模型的性能。与作物生长模型和同化系统结合常数不确定度（Assimilation1）模拟成熟期相比，同化系统结合理论不确定度（Assimilation2）的精度更高（r = 0.81，RMSE = 4.5 d）。同化2在不同年份和不同次区域都有更好的性能和鲁棒性。同化2估计值与观测值之间的平均相对误差一般较小，集中在-5%至5%的范围内。黄淮海平原（HHHP）的预计成熟日期在空间分布上呈现出纬度变化。此外，2001-2020年HHHP中部地区小麦成熟期变化趋势显着（p<<0.05），成熟期平均变化率达到3-6d/10a。但HHHP到期日总体变化趋势并不显着。温度是影响小麦成熟期时空变化的主要驱动因素。区域小麦成熟期估算模型可为区域尺度小麦成熟期估算提供技术支撑。