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
为了解山西省植被物候特征,基于MODIS NDVI数据,采用Double Logistic拟合法重建2000—2015年的MODIS NDVI时间序列,利用动态阈值法提取研究区植被物候,并分析植被物候的时空变化和地形差异对物候的影响。结果表明:山西省2000—2015年物...为了解山西省植被物候特征,基于MODIS NDVI数据,采用Double Logistic拟合法重建2000—2015年的MODIS NDVI时间序列,利用动态阈值法提取研究区植被物候,并分析植被物候的时空变化和地形差异对物候的影响。结果表明:山西省2000—2015年物候变化显著,植被生长季开始日期(the start of the growing season,SOS)每年提前约0.7 d(R^(2)=0.665,P<0.01),生长季结束日期(the end of the growing season,EOS)每年推迟约1.5 d(R^(2)=0.601,P<0.001),生长季长度(the length of the growing season,LOS)每年延长约2.2 d(R^(2)=0.772,P<0.001);不同植被物候变化速率不同,植被SOS按提前速率依次为草地>农作物>林地;EOS按推迟速率依次为林地>农作物>草地;LOS按延长速率依次为林地>草地>农作物;区内植被物候空间差异显著,植被SOS、EOS、LOS由低纬度到高纬度,从西经到东经依次表现为推迟、提前、缩短;研究区植被物候在海拔、坡向、坡度3种地形因子上差异显著;海拔1200 m以下,植被物候随海拔变化显著,海拔升高植被SOS显著推迟、EOS显著提前、LOS显著缩短;阳坡SOS比阴坡提前约1 d,EOS推迟约0.8 d,LOS延长约1.8 d;坡度小于16°的区域,坡度增大,SOS显著提前,EOS显著推迟,LOS显著延长;植被物候在长时间尺度上变化显著,而在同一个时间段内,地形是物候存在空间差异的重要影响因子。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.
文摘为了解山西省植被物候特征,基于MODIS NDVI数据,采用Double Logistic拟合法重建2000—2015年的MODIS NDVI时间序列,利用动态阈值法提取研究区植被物候,并分析植被物候的时空变化和地形差异对物候的影响。结果表明:山西省2000—2015年物候变化显著,植被生长季开始日期(the start of the growing season,SOS)每年提前约0.7 d(R^(2)=0.665,P<0.01),生长季结束日期(the end of the growing season,EOS)每年推迟约1.5 d(R^(2)=0.601,P<0.001),生长季长度(the length of the growing season,LOS)每年延长约2.2 d(R^(2)=0.772,P<0.001);不同植被物候变化速率不同,植被SOS按提前速率依次为草地>农作物>林地;EOS按推迟速率依次为林地>农作物>草地;LOS按延长速率依次为林地>草地>农作物;区内植被物候空间差异显著,植被SOS、EOS、LOS由低纬度到高纬度,从西经到东经依次表现为推迟、提前、缩短;研究区植被物候在海拔、坡向、坡度3种地形因子上差异显著;海拔1200 m以下,植被物候随海拔变化显著,海拔升高植被SOS显著推迟、EOS显著提前、LOS显著缩短;阳坡SOS比阴坡提前约1 d,EOS推迟约0.8 d,LOS延长约1.8 d;坡度小于16°的区域,坡度增大,SOS显著提前,EOS显著推迟,LOS显著延长;植被物候在长时间尺度上变化显著,而在同一个时间段内,地形是物候存在空间差异的重要影响因子。
