关键物候期遥感数据缺失条件下的数据同化研究

Data Assimilation Experiment of Poor Quality of Remote Sensing Image in Critical Phenology

随着数据同化方法的不断发展,数据同化已被广泛应用于遥感数据与作物生长模型的结合之中,但在关键物候期遥感数据缺失条件下的同化方法还有待加强研究。以黑龙江省红星农场为研究区,以玉米为研究对象,利用遥感数据与WOFOST模型开展同化方法研究。结果表明:经改进后的集合卡尔曼滤波算法同化,明显改善了误差较大的遥感影像对叶面积指数时序曲线的影响,同时减弱了曲线的锯齿状波动;在田块尺度上,和原始算法同化产量结果相比,R^2提高到0.67,RMSE减少到92.23kg/hm^2;在农场尺度上,R^2提高至0.61,RMSE减少至122.44kg/hm^2。

In the process of data assimilation,influenced by the water vapor and cloud cover in the study area,the qualities of remote sensing images are poor in the key crop phenological phases.This will cause not to get the perfect remote sensing images for a long time.So we try to solve this problem by using an improved EnKF method to assimilate the WOFOST crop growth model and the terrible quality of remote sensing images to forecast the maize＇s yield in the Red Star Farm in Heilongjiang province.In order to improve the accuracy of simulated time series curve of the LAI and yield production results,the consideration on quality evaluation of the remote sensing images is introduced by using expansion coefficient and adjustable factor.The results shows based on the improved EnKF method,time series curve of the LAI keeps a normal tendency of LAI rather than negative fluctuations,and it also avoids the serrated fluctuation to a certain extent.In addition,compared with the original EnKF method,in the field level R^2 can increased to 0.67 from 0.59,RMSE is reduced to 92.23kg/hm^2 from 240.57kg/hm^2 and in the farm level R^2 can increased to 0.61 from 0.52,RMSE is reduced to 122.44kg/hm^2 from 310.94kg/hm^2 between simulated yield and measured yield.