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Assimilation of the leaf area index and vegetation temperature condition index for winter wheat yield estimation using Landsat imagery and the CERES-Wheat model

机译:利用土地特征及CERES - 小麦模型同化冬小麦产量估计叶面积指数及植被温度条件指标

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摘要

To improve the accuracy of regional winter wheat yield estimation in the Guanzhong Plain, China, the field measured leaf area index (LAI) and soil moisture at the depth of 0-20 cm (theta) and both Landsat-retrieved LAI and theta were assimilated into the CERES-Wheat model with an ensemble Kalman filter (EnKF) algorithm. The correlation between the assimilated LAI and the measured yield at each main wheat growth stage, including the green-up, jointing, heading-filling and milk stages, was compared with that between assimilated theta and yield. Then, five types of assimilation schemes were investigated to test the effects of assimilating different state variables at each wheat growth stage on wheat yield estimation. The results showed that the correlations between LAI and wheat yield at the jointing and heading-filling stages were higher than those between theta and wheat yield; moreover, the correlations between theta and wheat yield were higher at the green-up and milk stages. Among the five assimilation schemes, the accuracy of the yield estimation obtained by assimilating LAI at the jointing and heading-filling stages as well as by assimilating theta at the green-up and milk stages was the highest (R-2 = 0.76, root mean square error (RMSE) = 548.97 kg ha(-1)), followed by the accuracy of the yield estimation obtained by assimilating LAI and theta simultaneously at each growth stage (R-2 = 0.67, RMSE = 610.67 kg ha(-1)). Conversely, the accuracy of the yield estimation obtained by assimilating LAI at the green-up and milk stages as well as by assimilating theta at the jointing and heading-filling stages was the lowest (R-2 = 0.41, RMSE = 928.95 kg ha(-1)). Thus, the assimilation of more yield-related state variables at each wheat growth stage in an agricultural data assimilation framework provides a reliable and promising method for improving wheat yield estimation.
机译:为了提高区域冬小麦产量估计的准确性,中国,田野测量叶面积指数(LAI)和土壤水分在0-20厘米(θ)和土地上检索的赖和θ都被同化进入CERES - 小麦模型,具有集合卡尔曼滤波器(ENKF)算法。将同化的LAI与测量产量在每个主要小麦生长阶段之间的相关性,包括绿色,连接,前线填充和牛奶阶段,与同化的θ和产率之间进行比较。然后,研究了五种各种同化方案,以测试在小麦产量估计下同化不同状态变量的影响。结果表明,莱丽和小麦产量之间的相关性在填充阶段之间的相关性高于θ和小麦产量之间的相关性;此外,在绿色和牛奶阶段,θ和小麦产量之间的相关性较高。在五种同化方案中,通过在接合和前线填充阶段同化赖赖的产量估计的准确度以及通过在绿色的绿色阶段和牛奶阶段同化θ最高(R-2 = 0.76,根部平均值方误差(RMSE)= 548.97 kg ha(-1)),然后通过同时在每个生长阶段同时同时同时获得产量估计(R-2 = 0.67,Rmse = 610.67kg ha(-1) )。相反,通过在接头和标题填充阶段在绿色和牛奶阶段同化含量的含量获得的产量估计的准确性以及在接头和前线填充阶段同化θ是最低的(R-2 = 0.41,RMSE = 928.95kg HA( -1))。因此,在农业数据同化框架中的每个小麦生长阶段的更多产量相关状态变量的同化提供了一种可靠和有希望的改善小麦产量估计的方法。

著录项

  • 来源
    《Journal of Thermal Biology》 |2017年第1期|共13页
  • 作者单位

    China Agr Univ Coll Informat &

    Elect Engn Minist Agr Key Lab Remote Sensing Agri Hazards East Campus Beijing 100083 Peoples R China;

    China Agr Univ Coll Informat &

    Elect Engn Minist Agr Key Lab Remote Sensing Agri Hazards East Campus Beijing 100083 Peoples R China;

    China Agr Univ Coll Informat &

    Elect Engn Minist Agr Key Lab Remote Sensing Agri Hazards East Campus Beijing 100083 Peoples R China;

    China Agr Univ Coll Informat &

    Elect Engn Minist Agr Key Lab Remote Sensing Agri Hazards East Campus Beijing 100083 Peoples R China;

    Remote Sensing Informat Ctr Agr Shaanxi Prov Xian 710015 Shaanxi Peoples R China;

    China Agr Univ Coll Informat &

    Elect Engn Minist Agr Key Lab Remote Sensing Agri Hazards East Campus Beijing 100083 Peoples R China;

    China Agr Univ Coll Informat &

    Elect Engn Minist Agr Key Lab Remote Sensing Agri Hazards East Campus Beijing 100083 Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 分子生物学;
  • 关键词

    Winter wheat; Leaf area index; Soil moisture; Data assimilation; Yield; Estimation;

    机译:冬小麦;叶面积指数;土壤水分;数据同化;产量;估计;

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