基于MODIS与WOFOST模型同化的区域冬小麦成熟期预测

Regional Winter Wheat Maturity Date Prediction Based on MODIS and WOFOST Model Data Assimilation

摘要:

针对遥感技术只能获取作物的表征信息、对作物内在机理过程变化描述较为困难的问题，引入作物生长模型与遥感数据同化进行作物成熟期预测研究。以叶面积指数（LAI）作为耦合变量，以MODIS LAI（MCD15A3H产品）作为遥感数据源，结合2017—2018年实时气象数据以及气象预报数据，以2018年5月1日为预报时间节点，构建LAI归一化代价函数，采用复合形混合演化算法（Shuffled complex evolution-University of Arizona, SCE-UA）最小化代价函数，优化WOFOST作物模型的输入参数，用优化后的参数重新驱动WOFOST模型逐像元模拟冬小麦生长过程，得到研究区冬小麦成熟期的预测结果，并使用研究区内农业气象站点的观测数据进行验证。结果表明，冬小麦预测开花期、成熟期的均方根误差(RMSE)分别为2.10、2.48d，预测精度较高。该方法能够为农作物的大区域成熟期预测提供重要理论基础。

Abstract:

Crop harvest time has an important impact on crop yield and quality. The development and wide application of remote sensing technology provides an effective method for large-area and real-time monitoring of crop growth. However, remote sensing cannot capture changes in its intrinsic mechanism characteristics. Therefore, a framework that assimilated leaf area index (LAI) derived from remote sensing data into crop growth mode was presented to predict the maturity of crops. LAI was used as the coupling variable, moderate resolution imaging spectroradiometer (MODIS) LAI was used as the remote sensing data source, meteorological data and meteorological forecast data of 2017—2018 were used as weather input of world food studies (WOFOST) crop growth model, May 1st as the predicting date. By means of shuffled complex evolution method developed by the University of Arizona (SCE-UA) algorithm, it was simulated in each pixel in the study area and retrieved the optimal parameters set of this pixel. Then the WOFOST was run by the optimal parameter set to simulate the growth and development of winter wheat and retrieve the maturity-prediction. Verified by the observation data of the agrometeorological sites in the study area, it was demonstrated that the method had substantial accuracy in predicting regional anthesis and maturity date with the root mean square error (RMSE) as 2.10d and 2.48d. The method provided a reference for the maturity prediction of other crops at a regional scale.