为了进一步挖掘无人机载激光雷达(Light Detection and Ranging,Li DAR)在农作物长势监测方面的潜力,探究机载Li DAR与多光谱遥感数据融合反演冬小麦叶面积指数(Leaf Area Index,LAI)的效果,以无人机载Li DAR和可见光-近红外多光谱为研...为了进一步挖掘无人机载激光雷达(Light Detection and Ranging,Li DAR)在农作物长势监测方面的潜力,探究机载Li DAR与多光谱遥感数据融合反演冬小麦叶面积指数(Leaf Area Index,LAI)的效果,以无人机载Li DAR和可见光-近红外多光谱为研究手段,获取试验区冬小麦孕穗期的无人机载Li DAR点云和多光谱数据,从中提取并筛选合适的Li DAR点云结构参数和植被指数,借助多元线性回归法(Multivariable Linear Regression,MLR)和偏最小二乘回归法(Partial Least Squares Regression,PLSR),通过融合Li DAR点云结构参数与植被指数以及单独使用植被指数作为模型输入参数,分别与实测LAI构建了LAI反演模型。用决定系数(Coefficient of Determination,R^(2))和均方根误差(Root Mean Square Error,RMSE)来评价模型时,结果显示融合Li DAR点云与多光谱数据能够较好地反演冬小麦LAI。而且,无论是利用MLR还是PLSR法,融合Li DAR点云结构参数与植被指数的模型(MLR︰R^(2)=0.901,RMSE=0.480;PLSR︰R^(2)=0.909,RMSE=0.445(n=16))均优于仅使用植被指数的模型(MLR︰R^(2)=0.897,RMSE=0.492;PLSR︰R^(2)=0.892,RMSE=0.486(n=16))。因此,加入无人机载Li DAR数据可以一定程度上弥补光谱数据在作物垂直方向上信息提取不足的缺陷,提高冬小麦LAI的反演精度,为冬小麦LAI反演提供了更优的手段。展开更多
The microphysical structure of snow clouds and the growth process of snow crystals were observed by means of instrumented aircraft, weather radar, snow crystal observations etc. in Urumqi region during the winter of 1...The microphysical structure of snow clouds and the growth process of snow crystals were observed by means of instrumented aircraft, weather radar, snow crystal observations etc. in Urumqi region during the winter of 1982. The analysis of three cases show that about 70% of snow mass growth is produced in the lower layer below 2000 m under the cold front, and that the concentration of ice crystals is as high as 60 L^(-1) and the supercooled water is absent in lower clouds. We may infer that the deposition of ice crystals and the aggregation of snow crystals are important processes for the snow development. The microphysical structure of the snow band near the front aloft and its characteristics as a seeder cloud are also described in this paper.展开更多
E1 Nifio-Southem Oscillation （ENSO） events significantly affect the year-by-year variations of the East Asian winter monsoon （EAWM）. However, the effect of La Nifia events on the EAWM is not a mirror image of that...E1 Nifio-Southem Oscillation （ENSO） events significantly affect the year-by-year variations of the East Asian winter monsoon （EAWM）. However, the effect of La Nifia events on the EAWM is not a mirror image of that of E1 Nifio events. Although the EAWM becomes generally weaker during El Nifio events and stronger during La Nifia winters, the enhanced precipitation over the southeastern China and warmer surface air temperature along the East Asian coastline during E1 Nifio years are more significant. These asymmetric effects are caused by the asymmetric longitudinal positions of the western North Pacific （WNP） anticyclone during El Nifio events and the WNP cyclone during La Nifia events; specifically, the center of the WNP cyclone during La Nifia events is westward-shifted relat- ive to its El Nifio counterpart. This central-position shift results from the longitudinal shift of remote E1 Nifio and La Nifia anomalous heating, and asymmetry in the amplitude of local sea surface temperature anomalies over the WNP. However, such asymmetric effects of ENSO on the EAWM are barely reproduced by the atmospheric models of Phase 5 of the Coupled Model Intercomparison Project （CMIP5）, although the spatial patterns of anomalous circula- tions are reasonably reproduced. The major limitation of the CMIP5 models is an overestimation of the anomalous WNP anticyclone/cyclone, which leads to stronger EAWM rainfall responses. The overestimated latent heat flux an- omalies near the South China Sea and the northern WNP might be a key factor behind the overestimated anomalous circulations.展开更多
文摘为了进一步挖掘无人机载激光雷达(Light Detection and Ranging,Li DAR)在农作物长势监测方面的潜力,探究机载Li DAR与多光谱遥感数据融合反演冬小麦叶面积指数(Leaf Area Index,LAI)的效果,以无人机载Li DAR和可见光-近红外多光谱为研究手段,获取试验区冬小麦孕穗期的无人机载Li DAR点云和多光谱数据,从中提取并筛选合适的Li DAR点云结构参数和植被指数,借助多元线性回归法(Multivariable Linear Regression,MLR)和偏最小二乘回归法(Partial Least Squares Regression,PLSR),通过融合Li DAR点云结构参数与植被指数以及单独使用植被指数作为模型输入参数,分别与实测LAI构建了LAI反演模型。用决定系数(Coefficient of Determination,R^(2))和均方根误差(Root Mean Square Error,RMSE)来评价模型时,结果显示融合Li DAR点云与多光谱数据能够较好地反演冬小麦LAI。而且,无论是利用MLR还是PLSR法,融合Li DAR点云结构参数与植被指数的模型(MLR︰R^(2)=0.901,RMSE=0.480;PLSR︰R^(2)=0.909,RMSE=0.445(n=16))均优于仅使用植被指数的模型(MLR︰R^(2)=0.897,RMSE=0.492;PLSR︰R^(2)=0.892,RMSE=0.486(n=16))。因此,加入无人机载Li DAR数据可以一定程度上弥补光谱数据在作物垂直方向上信息提取不足的缺陷,提高冬小麦LAI的反演精度,为冬小麦LAI反演提供了更优的手段。
文摘The microphysical structure of snow clouds and the growth process of snow crystals were observed by means of instrumented aircraft, weather radar, snow crystal observations etc. in Urumqi region during the winter of 1982. The analysis of three cases show that about 70% of snow mass growth is produced in the lower layer below 2000 m under the cold front, and that the concentration of ice crystals is as high as 60 L^(-1) and the supercooled water is absent in lower clouds. We may infer that the deposition of ice crystals and the aggregation of snow crystals are important processes for the snow development. The microphysical structure of the snow band near the front aloft and its characteristics as a seeder cloud are also described in this paper.
基金Supported by the National Natural Science Foundation of China(41405103 and 41125017)China Meteorological Administration Special Public Welfare Research Fund(GYHY201506012)Joint Center for Global Change Studies(105019)
文摘E1 Nifio-Southem Oscillation （ENSO） events significantly affect the year-by-year variations of the East Asian winter monsoon （EAWM）. However, the effect of La Nifia events on the EAWM is not a mirror image of that of E1 Nifio events. Although the EAWM becomes generally weaker during El Nifio events and stronger during La Nifia winters, the enhanced precipitation over the southeastern China and warmer surface air temperature along the East Asian coastline during E1 Nifio years are more significant. These asymmetric effects are caused by the asymmetric longitudinal positions of the western North Pacific （WNP） anticyclone during El Nifio events and the WNP cyclone during La Nifia events; specifically, the center of the WNP cyclone during La Nifia events is westward-shifted relat- ive to its El Nifio counterpart. This central-position shift results from the longitudinal shift of remote E1 Nifio and La Nifia anomalous heating, and asymmetry in the amplitude of local sea surface temperature anomalies over the WNP. However, such asymmetric effects of ENSO on the EAWM are barely reproduced by the atmospheric models of Phase 5 of the Coupled Model Intercomparison Project （CMIP5）, although the spatial patterns of anomalous circula- tions are reasonably reproduced. The major limitation of the CMIP5 models is an overestimation of the anomalous WNP anticyclone/cyclone, which leads to stronger EAWM rainfall responses. The overestimated latent heat flux an- omalies near the South China Sea and the northern WNP might be a key factor behind the overestimated anomalous circulations.
