解毅, 王鹏新, 刘峻明, 李俐. 基于四维变分和集合卡尔曼滤波同化方法的冬小麦单产估测[J]. 农业工程学报, 2015, 31(1): 187-195. DOI: doi:10.3969/j.issn.1002-6819.2015.01.026
    引用本文: 解毅, 王鹏新, 刘峻明, 李俐. 基于四维变分和集合卡尔曼滤波同化方法的冬小麦单产估测[J]. 农业工程学报, 2015, 31(1): 187-195. DOI: doi:10.3969/j.issn.1002-6819.2015.01.026
    Xie Yi, Wang Pengxin, Liu Junming, Li Li. Winter wheat yield estimation based on assimilation method combined with 4DVAR and EnKF[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2015, 31(1): 187-195. DOI: doi:10.3969/j.issn.1002-6819.2015.01.026
    Citation: Xie Yi, Wang Pengxin, Liu Junming, Li Li. Winter wheat yield estimation based on assimilation method combined with 4DVAR and EnKF[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2015, 31(1): 187-195. DOI: doi:10.3969/j.issn.1002-6819.2015.01.026

    基于四维变分和集合卡尔曼滤波同化方法的冬小麦单产估测

    Winter wheat yield estimation based on assimilation method combined with 4DVAR and EnKF

    • 摘要: 为了通过数据同化方法提高冬小麦的估产精度,以陕西省关中平原为研究区域,采用标定的CERES-Wheat模型模拟8个典型样点冬小麦整个生育期的叶面积指数(LAI),通过四维变分(4DVAR)和集合卡尔曼滤波(EnKF)2种同化算法同化CERES-Wheat模型模拟的LAI和遥感数据反演的LAI,获得单点尺度的LAI同化数据,将单点尺度的LAI同化值扩展到区域尺度,对两种同化方法的单点尺度和区域尺度的同化结果进行对比与分析。结果表明,两种同化方法均能综合遥感反演LAI和模型模拟LAI的优势,使LAI同化值更符合冬小麦LAI的实际变化规律;在单点尺度和区域尺度上,EnKF-LAI均更能反映关中平原冬小麦的实际生长状况。采用EnKF-LAI构建关中平原冬小麦估产模型估测2008年和2014年的冬小麦单产,通过实测单产对估产模型进行验证,结果表明,2008年样点估测单产与实测单产的相对误差均小于15%,部分县估测单产与实测单产的相对误差均小于10%;与2014年模拟单产与实测单产间的相对误差相比,估测单产与实测单产间的相对误差降低0.57%~9.30%,RMSE降低217 kg/hm2,其中,8个样点的估产精度达到94%以上,表明组合估产模型的估产精度较高。

       

      Abstract: Abstract: The CERES-Wheat model is used to simulate leaf area index (LAI) of winter wheat for reflecting accurately the growth of winter wheat and estimating the yield. But it is difficult to simulate wheat yield at a large area with CERES-Wheat model because of the lack of regional input parameters. Data assimilation algorithm is an efficient method to combine crop growth model and remote sensing data and it solves the shortcoming of CERES-Wheat model by taking advantage of macro of remote sensing data. NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) data is normally used in assimilation with crop growth model for yield estimation; but the spatial resolution of MODIS is low, and the estimation accuracy of crop will be reduced by the mixed pixel problem, as the farmland is small. Landsat remotely sensed data with a spatial resolution of 30 m would be helpful to make crop yield estimation at field scale in China, and TM and ETM+ remotely sensed data were both used in assimilation for solving the disadvantage of low temporal resolution of Landsat. The CERES-Wheat model was used to simulate LAI of the whole growth period of winter wheat in Guanzhong Plain of Shaanxi province. The assimilation of simulated LAI with LAI retrieved from TM and ETM+ data was carried out in eight typical sampling sites by using two data assimilation approaches, the four-dimensional variational (4DVAR) and ensemble Kalman filter (EnKF). The assimilated LAI image of the whole study area was achieved by employing the linear correlation model between remotely sensed LAI and assimilated LAI of the eight sampling sites. After establishing the assimilation system, the remotely sensed LAI and the simulated ones of the eight sampling sites were used to test the two assimilation approaches. The result showed that the assimilated LAI values of the sites were more accurate and closer to the real ones after combining the advantages of both remotely sensed LAI and simulated LAI. In order to utilize a more accurate assimilation algorithm for estimating yield of winter wheat in Guanzhong Plain, the 4DVAR and EnKF approaches were compared and analyzed for both assimilated LAI values of the sites and assimilated LAI images of the two assimilation approaches. The root mean square error (RMSE) between EnKF-LAI values and measured LAI values of the sites was 0.41, while the RMSE of 4DVAR-LAI values was 0.55, so EnKF-LAI values of the eight sites were closer to the measured ones than those of the 4DVAR-LAI. After comparing assimilated LAI images from 4DVAR and EnKF approaches, it concluded that the EnKF-LAI images were more in line with spatial distribution characteristics of winter wheat's LAI in Guanzhong Plain, and the EnKF algorithm was an appropriated approach for assimilating LAI. The EnKF-LAI images of 4 main growth stages of winter wheat including the reviving stage, jointing stage, heading-filling stage and dough stage were used to estimate winter wheat yield of the whole plain by constructing winter wheat yield estimation model. By comparing with the measured yields of winter wheat in the crop year of 2007-2008, the relative errors (RE) of the estimated yields of the eight sites were from 2.05% to 14.57% with an average of 9.15%, and the RMSE between the estimated and measured yields was 596.7 kg/hm2. While the RE between the simulated and measured yields was from 8.80% to 36.61% with an average of 22.13%, and the RMSE was 1699.0 kg/hm2. In addition, wheat yields in the crop year of 2013-2014 were used for further validation of the winter wheat yield estimation model. Compared with the RE between the simulated and measured yields, the RE between the estimated and measured yields was decreased by 0.57%-9.30%, with an average relative error reduction of 3.89% in the 15 sampling sites; and the estimated accuracies in the 8 sampling sites are larger than 94%. These show that the estimated yields of the sampling sites are close to the measured ones, and the accuracy of the yield estimation model is obviously improved.

       

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