Assimilating Sentinel-2 images with the CERES-Wheat model can improve the precision of winter wheat yield estimates at a regional scale. To verify this method, we applied the ensemble Kalman filter(EnKF) to assimilate...Assimilating Sentinel-2 images with the CERES-Wheat model can improve the precision of winter wheat yield estimates at a regional scale. To verify this method, we applied the ensemble Kalman filter(EnKF) to assimilate the leaf area index(LAI) derived from Sentinel-2 data and simulated by the CERES-Wheat model. From this, we obtained the assimilated daily LAI during the growth stage of winter wheat across three counties located in the southeast of the Loess Plateau in China: Xiangfen, Xinjiang, and Wenxi. We assigned LAI weights at different growth stages by comparing the improved analytic hierarchy method, the entropy method, and the normalized combination weighting method, and constructed a yield estimation model with the measurements to accurately estimate the yield of winter wheat. We found that the changes of assimilated LAI during the growth stage of winter wheat strongly agreed with the simulated LAI. With the correction of the derived LAI from the Sentinel-2 images, the LAI from the green-up stage to the heading–filling stage was enhanced, while the LAI decrease from the milking stage was slowed down, which was more in line with the actual changes of LAI for winter wheat. We also compared the simulated and derived LAI and found the assimilated LAI had reduced the root mean square error(RMSE) by 0.43 and 0.29 m^(2) m^(–2), respectively, based on the measured LAI. The assimilation improved the estimation accuracy of the LAI time series. The highest determination coefficient(R2) was 0.8627 and the lowest RMSE was 472.92 kg ha^(–1) in the regression of the yields estimated by the normalized weighted assimilated LAI method and measurements. The relative error of the estimated yield of winter wheat in the study counties was less than 1%, suggesting that Sentinel-2 data with high spatial-temporal resolution can be assimilated with the CERES-Wheat model to obtain more accurate regional yield estimates.展开更多
基于CASA模型估算了2000—2015年汾河流域植被净初级生产力,采用趋势分析、相关分析等方法对汾河流域NPP的驱动因素进行研究,并对汾河流域不同梯度的NPP进行分析。结果表明:2000—2015年汾河流域植被NPP呈现波动上升趋势,年均增长6.62g ...基于CASA模型估算了2000—2015年汾河流域植被净初级生产力,采用趋势分析、相关分析等方法对汾河流域NPP的驱动因素进行研究,并对汾河流域不同梯度的NPP进行分析。结果表明:2000—2015年汾河流域植被NPP呈现波动上升趋势,年均增长6.62g C·m-2·a-1,均值为291.57 g C·m-2·a-1,整体呈现中部低两翼高的空间分布特征;不同地类的年均NPP为林地>草地>耕地>其他用地>建设用地>水域用地;NPP与气温、降水量之间均为正相关;棕壤区植被NPP最大,栗褐土区植被NPP较小;NPP受地形和人为因素的影响,高值出现在海拔高、坡度大、人类活动较少的山区,低值主要在海拔低、坡度小、人类活动密集的沿河盆地;在汾河流域的东西条带上,距离汾河越远的梯度带NPP越大,在南北条带上,煤炭开采严重、城市化率高的梯度带NPP相对较低。展开更多
基金supported by the National Key Research and Development Program of China (2018YFD020040103)the National Key Research and Development Program of Shanxi Province, China (201803D221005-2)。
文摘Assimilating Sentinel-2 images with the CERES-Wheat model can improve the precision of winter wheat yield estimates at a regional scale. To verify this method, we applied the ensemble Kalman filter(EnKF) to assimilate the leaf area index(LAI) derived from Sentinel-2 data and simulated by the CERES-Wheat model. From this, we obtained the assimilated daily LAI during the growth stage of winter wheat across three counties located in the southeast of the Loess Plateau in China: Xiangfen, Xinjiang, and Wenxi. We assigned LAI weights at different growth stages by comparing the improved analytic hierarchy method, the entropy method, and the normalized combination weighting method, and constructed a yield estimation model with the measurements to accurately estimate the yield of winter wheat. We found that the changes of assimilated LAI during the growth stage of winter wheat strongly agreed with the simulated LAI. With the correction of the derived LAI from the Sentinel-2 images, the LAI from the green-up stage to the heading–filling stage was enhanced, while the LAI decrease from the milking stage was slowed down, which was more in line with the actual changes of LAI for winter wheat. We also compared the simulated and derived LAI and found the assimilated LAI had reduced the root mean square error(RMSE) by 0.43 and 0.29 m^(2) m^(–2), respectively, based on the measured LAI. The assimilation improved the estimation accuracy of the LAI time series. The highest determination coefficient(R2) was 0.8627 and the lowest RMSE was 472.92 kg ha^(–1) in the regression of the yields estimated by the normalized weighted assimilated LAI method and measurements. The relative error of the estimated yield of winter wheat in the study counties was less than 1%, suggesting that Sentinel-2 data with high spatial-temporal resolution can be assimilated with the CERES-Wheat model to obtain more accurate regional yield estimates.
文摘基于CASA模型估算了2000—2015年汾河流域植被净初级生产力,采用趋势分析、相关分析等方法对汾河流域NPP的驱动因素进行研究,并对汾河流域不同梯度的NPP进行分析。结果表明:2000—2015年汾河流域植被NPP呈现波动上升趋势,年均增长6.62g C·m-2·a-1,均值为291.57 g C·m-2·a-1,整体呈现中部低两翼高的空间分布特征;不同地类的年均NPP为林地>草地>耕地>其他用地>建设用地>水域用地;NPP与气温、降水量之间均为正相关;棕壤区植被NPP最大,栗褐土区植被NPP较小;NPP受地形和人为因素的影响,高值出现在海拔高、坡度大、人类活动较少的山区,低值主要在海拔低、坡度小、人类活动密集的沿河盆地;在汾河流域的东西条带上,距离汾河越远的梯度带NPP越大,在南北条带上,煤炭开采严重、城市化率高的梯度带NPP相对较低。
