摘要
为进一步提高陕西省关中平原冬小麦产量估测的精度,利用集合卡尔曼滤波算法(EnKF)将CERES Wheat模型模拟的0~20 cm土壤含水率和叶面积指数(LAI)与遥感观测的条件植被温度指数(VTCI)和LAI进行同化,同时利用交叉小波变换分析冬小麦各生育时期同化VTCI和LAI与产量之间的共振周期,通过计算小波互相关度获得各生育时期同化VTCI和LAI的权重,进而构建基于加权VTCI和LAI的冬小麦单产估测模型。结果表明,在样点尺度,经过EnKF同化的VTCI和LAI能够综合表达模型模拟值和遥感观测值的变化趋势;在区域尺度,无论是否同化,经过交叉小波变换的各生育时期VTCI和LAI分别与产量之间存在特定的共振周期,同时发现,同化有助于对关键生育时期的特征提取;相较于未同化构建的估产模型,经过同化构建的估产模型的归一化均方根误差为13.23%,决定系数为0.50,平均相对误差为10.58%,精度略有提升,且估测产量的分布与统计产量的分布更为一致,因此认为将同化与交叉小波相结合构建的双变量单产估测模型精度更高,可为进一步实现高精度的区域产量估测提供研究基础。
To further improve the accuracy of winter wheat yield estimation in Guanzhong Plain of Shaanxi Province,the ensemble Kalman filter(EnKF)algorithm was used to assimilate the CERES Wheat model simulated soil moisture at the depth of 0~20 cm and leaf area index(LAI)with remote sensing observations of the vegetation temperature condition index(VTCI)and LAI,respectively.At the same time,the resonance periods between assimilated VTCI and LAI at each growth stage and yield were analysed by using the cross⁃wavelet transform,respectively,and the weights of assimilated VTCI and LAI at each stage were obtained by calculating the wavelet cross⁃correlation degrees,and then a regional yield estimation model for winter wheat based on weighted VTCI and LAI was constructed.The results showed that at the sample point scale,the assimilated VTCI and LAI can combine the effects of model simulations and remote sensing observations,and the trends were more consistent with the actual crop growth changes.At the regional scale,there were specific resonance periods between VTCI,LAI and yield for each growth stage after cross⁃wavelet transform,regardless of assimilation or not,respectively.It was also found that the assimilation promoted the feature extraction for key growth stages.Compared with the estimated yield model constructed without assimilation,the estimated yield model constructed with assimilation had normalized root mean square error of 13.23%,coefficient of determination of 0.50,and mean relative error of 10.58%,with a slight improvement in accuracy,and the distribution of yield estimation results from the assimilated model was closer to the official statistical yields.In summary,the regional yield estimation model combining assimilation and cross⁃wavelet transform can effectively improve the estimation accuracy and provide a relevant research basis for further precision agricultural management.
作者
张悦
王鹏新
陈弛
刘峻明
李红梅
ZHANG Yue;WANG Pengxin;CHEN Chi;LIU Junming;LI Hongmei(College of Information and Electrical Engineering,China Agricultural University,Beijing 100083,China;Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications,Ministry of Agriculture and Rural Affairs,Beijing 100083,China;College of Land Science and Technology,China Agricultural University,Beijing 100193,China;Shaanxi Provincial Meteorological Bureau,Xi'an 710014,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2023年第4期170-179,共10页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金项目(41871336、42171332)。
关键词
冬小麦
估产
数据同化
交叉小波变换
条件植被温度指数
叶面积指数
winter wheat
yield estimation
data assimilation
cross⁃wavelet transform
vegetation temperature condition index
leaf area index
