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Research on remote sensing information and WheatSM model-based winter wheat yield estimation

机译：遥感信息与麦小麦模型冬小麦产量估计研究

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摘要

The research was implemented in a planted area dominated by winter wheat in the Hebi city of Henan Province driving method and EnKF assimilation method were used to coupling the optimized and reconstructed MODIS LAI time series data and the rigorously calibrated wheat growth model WheatSM(Wheat Growth and Development Simulation Model) developed according to the planting varieties of the winter wheat region in North China respectively, the research on winter wheat yield estimation was conducted at field scale and regional scale from 2013 to 2016.The results show that: (1) The coupling simulation precision was inferior to the pre-coupled simulation precision at field scale simulation.(2)When the grid was used for regional scale simulation, the simulation accuracy of 1 km resolution grid point is higher than 5km resolution. (3)At two kinds of grid resolutions, EnKF assimilation simulation has the highest accuracy, followed by the driving method, and the simulation precision before coupling is the lowest. The RMSE of the total output of the EnKF assimilation simulation area and the actual total output at the 1 km resolution grid is 15.1. Assimilation of remote sensing information at fine grid resolution can improve the precision of regionalization application of the WheatSM model.
机译：该研究是在河南河北省河北省河北省冬小麦占冬小麦的种植区中实施，恩科斯同化方法用于耦合优化和重建的Modis Lai时间序列数据和严格校准的小麦生长模型小麦（小麦生长和发展仿真模型分别根据冬小麦地区的种植品种分别开发，冬小麦产量估计研究于2013年至2016年的现场规模和区域等级进行。结果表明：（1）耦合仿真精度在现场刻度模拟下的预耦合模拟精度差。（2）当网格用于区域尺度仿真时，1公里分辨率网格点的仿真精度高于5km分辨率。 （3）在两种网格分辨率下，ENKF同化仿真具有最高的精度，其次是驱动方法，以及耦合前的仿真精度是最低的。 ENKF同化仿真区域的总输出的RMSE和1公里分辨率网格的实际总输出为15.1。精细网格分辨率的遥感信息的同化可以提高惠麦模型的区域化应用精度。

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