王鹏新, 陈弛, 张悦, 张树誉, 刘峻明. 利用双变量同化与PCA-Copula法的冬小麦单产估测[J]. 武汉大学学报 ( 信息科学版), 2022, 47(8): 1201-1212. DOI: 10.13203/j.whugis20220038
引用本文: 王鹏新, 陈弛, 张悦, 张树誉, 刘峻明. 利用双变量同化与PCA-Copula法的冬小麦单产估测[J]. 武汉大学学报 ( 信息科学版), 2022, 47(8): 1201-1212. DOI: 10.13203/j.whugis20220038
WANG Pengxin, CHEN Chi, ZHANG Yue, ZHANG Shuyu, LIU Junming. Estimation of Winter Wheat Yield Using Assimilated Bi-variables and PCA-Copula Method[J]. Geomatics and Information Science of Wuhan University, 2022, 47(8): 1201-1212. DOI: 10.13203/j.whugis20220038
Citation: WANG Pengxin, CHEN Chi, ZHANG Yue, ZHANG Shuyu, LIU Junming. Estimation of Winter Wheat Yield Using Assimilated Bi-variables and PCA-Copula Method[J]. Geomatics and Information Science of Wuhan University, 2022, 47(8): 1201-1212. DOI: 10.13203/j.whugis20220038

利用双变量同化与PCA-Copula法的冬小麦单产估测

Estimation of Winter Wheat Yield Using Assimilated Bi-variables and PCA-Copula Method

  • 摘要: 为了进一步提高冬小麦产量估测的精度,基于集合卡尔曼滤波算法和粒子滤波(particle filter, PF)算法,对CERES–Wheat模型模拟的冬小麦主要生育期条件植被温度指数(vegetation temperature condition index,VTCI)、叶面积指数(leaf area index, LAI)和中分辨率成像光谱仪(moderate-resolution imaging spectroradiometer, MODIS)数据反演的VTCI、LAI进行同化,利用主成分分析与Copula函数结合的方法构建单变量和双变量的综合长势监测指标,建立冬小麦单产估测模型,并通过对比分析选择最优模型,对2017—2020年关中平原的冬小麦单产进行估测。结果表明,单点尺度的同化VTCI、同化LAI均能综合反映MODIS观测值和模型模拟值的变化特征,且PF算法具有更好的同化效果;区域尺度下利用PF算法得到的同化VTCI和LAI所构建的双变量估产模型精度最高,与未同化VTCI和LAI构建的估产模型精度相比,研究区各县(区)的冬小麦估测单产与实际单产的均方根误差降低了56.25 kg/hm2,平均相对误差降低了1.51%,表明该模型能有效提高产量估测的精度,应用该模型进行大范围的冬小麦产量估测具有较好的适用性。

     

    Abstract:
      Objectives  Accurate, timely and effective monitoring of the growth and yield of winter wheat over a large area can help optimize the wheat planting structure, adjust the regional layout and ensure the country's food security. Therefore, it is very important to further improve the estimation accuracy of winter wheat yield.
      Methods  Vegetation temperature condition index (VTCI) and leaf area index (LAI) at the main growth period of winter wheat, which were simulated by the CERES (crop environment resource synthesis)-Wheat model and retrieved from MODIS (moderate resolution imaging spectroradiometer) data, were assimilated by using ensemble Kalman filtering (EnKF) algorithm and particle filtering (PF) algorithm. In addition, the principal component analysis combined with the Copula function was used to develop univariate (VTCI or LAI) and bi-variate (VTCI and LAI) winter wheat yield estimation models, and the optimal model was selected to estimate winter wheat yields from 2017 to 2020.
      Results  The experimental results show that, at the sampling-sites scale, both VTCI and LAI after assimilated can comprehensively reflect the variation characteristics of MODIS observed and model simulated values, and the application of PF algorithm has a better assimilation effect. At the regional scale, the bivariate yield estimation model developed by using PF algorithm has the highest accuracy. Compared with the accuracy of the models constructed by VTCI and LAI without assimilation, the root mean square error of the optimal assimilation model is reduced by 56.25 kg/hm2, and the average relative error is reduced by 1.51%.
      Conclusions  The above results indicate that the model can effectively improve the accuracy of winter wheat yield estimation and has good applicability for large area yield estimation.

     

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