To accurately estimate winter wheat yields and analyze the uncertainty in crop model data assimilations, winter wheat yield estimates were obtained by assimilating measured or remotely sensed leaf area index （LAI） v...To accurately estimate winter wheat yields and analyze the uncertainty in crop model data assimilations, winter wheat yield estimates were obtained by assimilating measured or remotely sensed leaf area index （LAI） values. The performances of the calibrated crop environment resource synthesis for wheat （CERES-Wheat） model for two different assimilation scenarios were compared by employing ensemble Kalman filter （EnKF）-based strategies. The uncertainty factors of the crop model data assimilation was analyzed by considering the observation errors, assimilation stages and temporal-spatial scales. Overalll the results indicated a better yield estimate performance when the EnKF-based strategy was used to comprehen- sively consider several factors in the initial conditions and observations. When using this strategy, an adjusted coefficients of determination （R2） of 0.84, a root mean square error （RMSE） of 323 kg ha-1, and a relative errors （RE） of 4.15% were obtained at the field plot scale and an R2 of 0.81, an RMSE of 362 kg ha-1, and an RE of 4.52% were obtained at the pixel scale of 30 mx30 m. With increasing observation errors, the accuracy of the yield estimates obviously decreased, but an acceptable estimate was observed when the observation errors were within 20%. Winter wheat yield estimates could be improved significantly by assimilating observations from the middle to the end of the crop growing seasons. With decreasing assimilation frequency and pixel resolution, the accuracy of the crop yield estimates decreased; however, the computation time decreased. It is important to consider reasonable temporal-spatial scales and assimilation stages to obtain tradeoffs between accuracy and computation time, especially in operational systems used for regional crop yield estimates.展开更多
Data assimilation in agricultural remote sensing research is of great significance to integrate with remote sensing observations and model simulations for parameters estimation. The present investigation not only desi...Data assimilation in agricultural remote sensing research is of great significance to integrate with remote sensing observations and model simulations for parameters estimation. The present investigation not only designed and realized the Ensemble Kalman Filtering algorithm （EnKF） assimilation by combing the crop growth model （CERES-Wheat） with remote sensing data, but also optimized and updated the key parameters （LAI） of winter wheat by using remote sensing data. Results showed that the assimilation LAI and the observation ones agreed with each other, and the R2 reached 0.8315. So assimilation remote sensing and crop model could provide reference data for the agricultural production.展开更多
为了提高河北省中部平原夏玉米的估产精度和进一步验证粒子滤波同化算法对农业作物估产的适用性,采用粒子滤波算法同化CERES-Maize模型模拟和MODIS数据反演的叶面积指数(Leaf area index,LAI)、条件植被温度指数(Vegetation temperature...为了提高河北省中部平原夏玉米的估产精度和进一步验证粒子滤波同化算法对农业作物估产的适用性,采用粒子滤波算法同化CERES-Maize模型模拟和MODIS数据反演的叶面积指数(Leaf area index,LAI)、条件植被温度指数(Vegetation temperature condition index,VTCI),应用随机森林回归算法确定夏玉米不同生育时期LAI和VTCI的权重,构建单产估测模型。结果表明,无论是单点尺度还是区域尺度,同化的LAI和VTCI均能较好地响应外部观测数据,同化LAI可减缓CERES-Maize模型模拟LAI的剧烈变化;同化VTCI结合模型模拟和遥感观测,更能反映夏玉米对水分胁迫的敏感性。利用2015年河北省中部平原各县(区)夏玉米产量对较优估产模型进行精度验证,结果表明,同化前后夏玉米产量模拟结果与统计产量间的归一化均方根误差由12.71%下降到10.50%,平均相对误差由12.57%下降到8.43%,说明基于同化LAI和VTCI构建的双参数单产估产模型可用于区域夏玉米单产估测。展开更多
农作物物候信息对农作物长势监测和估产具有重要意义。该文以河北省中南部冬小麦为研究对象,以叶面积指数(LAI,leaf area index)为同化量,采用重采样粒子滤波算法同化WOFOST(world food studies)作物生长模型和遥感观测LAI,重构LAI时间...农作物物候信息对农作物长势监测和估产具有重要意义。该文以河北省中南部冬小麦为研究对象,以叶面积指数(LAI,leaf area index)为同化量,采用重采样粒子滤波算法同化WOFOST(world food studies)作物生长模型和遥感观测LAI,重构LAI时间序列数据,基于重构数据提取冬小麦返青期、抽穗期和成熟期等关键物候期。重构结果表明,重构的LAI具有良好的时间连续性和空间连续性,可减缓WOFOST作物模型LAI变化剧烈程度,峰值出现时间与遥感LAI曲线基本同步,且可一定程度上解决遥感观测LAI数值整体偏低和数据缺失的问题。物候期监测结果表明,在空间分布上与冬小麦实际生长状况基本相符,时间上也较为合理,但因在返青期存在LAI高初始值、成熟期存在LAI下限不确定性等问题致使在具体日期存在偏差。展开更多
基金supported by the National Natural Science Foundation of China (41401491,41371396,41301457,41471364)the Introduction of International Advanced Agricultural Science and Technology,Ministry of Agriculture,China (948 Program,2016-X38)+1 种基金the Agricultural Scientific Research Fund of Outstanding Talentsthe Open Fund for the Key Laboratory of Agri-informatics,Ministry of Agriculture,China (2013009)
文摘To accurately estimate winter wheat yields and analyze the uncertainty in crop model data assimilations, winter wheat yield estimates were obtained by assimilating measured or remotely sensed leaf area index （LAI） values. The performances of the calibrated crop environment resource synthesis for wheat （CERES-Wheat） model for two different assimilation scenarios were compared by employing ensemble Kalman filter （EnKF）-based strategies. The uncertainty factors of the crop model data assimilation was analyzed by considering the observation errors, assimilation stages and temporal-spatial scales. Overalll the results indicated a better yield estimate performance when the EnKF-based strategy was used to comprehen- sively consider several factors in the initial conditions and observations. When using this strategy, an adjusted coefficients of determination （R2） of 0.84, a root mean square error （RMSE） of 323 kg ha-1, and a relative errors （RE） of 4.15% were obtained at the field plot scale and an R2 of 0.81, an RMSE of 362 kg ha-1, and an RE of 4.52% were obtained at the pixel scale of 30 mx30 m. With increasing observation errors, the accuracy of the yield estimates obviously decreased, but an acceptable estimate was observed when the observation errors were within 20%. Winter wheat yield estimates could be improved significantly by assimilating observations from the middle to the end of the crop growing seasons. With decreasing assimilation frequency and pixel resolution, the accuracy of the crop yield estimates decreased; however, the computation time decreased. It is important to consider reasonable temporal-spatial scales and assimilation stages to obtain tradeoffs between accuracy and computation time, especially in operational systems used for regional crop yield estimates.
基金supported by the National Natural Science Foundation of China (40701120)the Beijing Natural Science Foundation, China (4092016)the Beijing Nova, China (2008B33)
文摘Data assimilation in agricultural remote sensing research is of great significance to integrate with remote sensing observations and model simulations for parameters estimation. The present investigation not only designed and realized the Ensemble Kalman Filtering algorithm （EnKF） assimilation by combing the crop growth model （CERES-Wheat） with remote sensing data, but also optimized and updated the key parameters （LAI） of winter wheat by using remote sensing data. Results showed that the assimilation LAI and the observation ones agreed with each other, and the R2 reached 0.8315. So assimilation remote sensing and crop model could provide reference data for the agricultural production.
文摘为了提高河北省中部平原夏玉米的估产精度和进一步验证粒子滤波同化算法对农业作物估产的适用性,采用粒子滤波算法同化CERES-Maize模型模拟和MODIS数据反演的叶面积指数(Leaf area index,LAI)、条件植被温度指数(Vegetation temperature condition index,VTCI),应用随机森林回归算法确定夏玉米不同生育时期LAI和VTCI的权重,构建单产估测模型。结果表明,无论是单点尺度还是区域尺度,同化的LAI和VTCI均能较好地响应外部观测数据,同化LAI可减缓CERES-Maize模型模拟LAI的剧烈变化;同化VTCI结合模型模拟和遥感观测,更能反映夏玉米对水分胁迫的敏感性。利用2015年河北省中部平原各县(区)夏玉米产量对较优估产模型进行精度验证,结果表明,同化前后夏玉米产量模拟结果与统计产量间的归一化均方根误差由12.71%下降到10.50%,平均相对误差由12.57%下降到8.43%,说明基于同化LAI和VTCI构建的双参数单产估产模型可用于区域夏玉米单产估测。
文摘农作物物候信息对农作物长势监测和估产具有重要意义。该文以河北省中南部冬小麦为研究对象,以叶面积指数(LAI,leaf area index)为同化量,采用重采样粒子滤波算法同化WOFOST(world food studies)作物生长模型和遥感观测LAI,重构LAI时间序列数据,基于重构数据提取冬小麦返青期、抽穗期和成熟期等关键物候期。重构结果表明,重构的LAI具有良好的时间连续性和空间连续性,可减缓WOFOST作物模型LAI变化剧烈程度,峰值出现时间与遥感LAI曲线基本同步,且可一定程度上解决遥感观测LAI数值整体偏低和数据缺失的问题。物候期监测结果表明,在空间分布上与冬小麦实际生长状况基本相符,时间上也较为合理,但因在返青期存在LAI高初始值、成熟期存在LAI下限不确定性等问题致使在具体日期存在偏差。
