PU （phase unwrapping） is the key step and important problem in DEM （digital elevation model） extraction and the measurement of surface deformation of InSAR （Interferometric synthetic aperture radar）. The CKFPUA ...PU （phase unwrapping） is the key step and important problem in DEM （digital elevation model） extraction and the measurement of surface deformation of InSAR （Interferometric synthetic aperture radar）. The CKFPUA （conventional Kalman filter phase unwrapping algorithm） can obtain reliable results in the flat terrain areas, but it caused error transmission not making the accurate inversion of surface deformation information in the steep terrain. Considering this situation, so it needs to introduce topographic information for guiding phase unwrapping. Here the 90 m resolution DEM data have been used and it is obtained by SRTM （shuttle radar topography mission） measured jointly by NASA （National Aeronautics and Space Administration） and NIMA （National Imaging Mapping Agency） of U.S. Department of Defense. This paper presents a SD-KFPUA （Kalman filter phase unwrapping algorithm） based on SRTM DEM. With SRTM DEM directing InSAR image to implement phase unwrapping, the speed and accuracy are improved. By analyzing with the conventional Kalman filter phase unwrapping algorithms, it is shown that the proposed method can achieve good results in particular to improve unwrapping accuracy in the low coherence region.展开更多
为更加精确地模拟复杂地形地区大气边界层中气象要素,将NASA发布的SRTM3(约90m分辨率)地形高度数据引入中尺度气象模式WRF(weather research and forecasting)中,结合四种边界层参数化方案(YSU、ACM2、MYN2.5level TKE(简称MYN)、Bougea...为更加精确地模拟复杂地形地区大气边界层中气象要素,将NASA发布的SRTM3(约90m分辨率)地形高度数据引入中尺度气象模式WRF(weather research and forecasting)中,结合四种边界层参数化方案(YSU、ACM2、MYN2.5level TKE(简称MYN)、Bougeault and Lacarrere TKE(简称BL))及模式自带地形数据GTOPO30(约1km分辨率),模拟了2008年4月24—25日安徽黄山及周边地区大气边界层气象要素场变化特征,并对模式输出的2m气温、2m露点温度、10m风速、湿度廓线与模拟区域内19个气象站及2个探空站数据进行比较。结果表明,无论采用哪种地形数据,四种边界层参数化方案中,YSU方案模拟的2m气温误差最小,ACM2方案模拟的2m露点温度和10m风速误差最小;采用SRTM3数据后,四种边界层参数化方案模拟的2m气温平均均方根误差(root mean squared error,RMSE)分别降低了3.79%(YSU方案)、2.48%(ACM2方案)、3.8%(MYN方案)、0.87%(BL方案);对2m露点温度模拟,除MYN方案模拟平均RMSE降低了0.59%外,其他三种方案模拟误差分别增加了1.39%(YSU方案)、0.49%(BL方案)、0.89%(ACM2方案);而对10m风速的模拟结果,除ACM2方案模拟平均RMSE降低了2.28%外,其他三种方案模拟误差分别增加了0.22%(YSU方案)、2.32%(MYN方案)、2.45%(BL方案);对2个探空站点湿度廓线的模拟显示,各边界层方案均能模拟出水汽的垂直变化趋势,但模拟效果总体表现为偏湿,采用SRTM3地形数据之后,ACM2方案模拟部分时刻的低层水汽廓线有所改善。展开更多
基金Acknowledgments The research is supported by the National Science Foundation of China （40874001） and National 863 plans projects of China （2009AA12Z147）. The authors would like to express thanks to ESA （European Space Agency） for providing ENVISAT satellite data.
文摘PU （phase unwrapping） is the key step and important problem in DEM （digital elevation model） extraction and the measurement of surface deformation of InSAR （Interferometric synthetic aperture radar）. The CKFPUA （conventional Kalman filter phase unwrapping algorithm） can obtain reliable results in the flat terrain areas, but it caused error transmission not making the accurate inversion of surface deformation information in the steep terrain. Considering this situation, so it needs to introduce topographic information for guiding phase unwrapping. Here the 90 m resolution DEM data have been used and it is obtained by SRTM （shuttle radar topography mission） measured jointly by NASA （National Aeronautics and Space Administration） and NIMA （National Imaging Mapping Agency） of U.S. Department of Defense. This paper presents a SD-KFPUA （Kalman filter phase unwrapping algorithm） based on SRTM DEM. With SRTM DEM directing InSAR image to implement phase unwrapping, the speed and accuracy are improved. By analyzing with the conventional Kalman filter phase unwrapping algorithms, it is shown that the proposed method can achieve good results in particular to improve unwrapping accuracy in the low coherence region.
文摘为更加精确地模拟复杂地形地区大气边界层中气象要素,将NASA发布的SRTM3(约90m分辨率)地形高度数据引入中尺度气象模式WRF(weather research and forecasting)中,结合四种边界层参数化方案(YSU、ACM2、MYN2.5level TKE(简称MYN)、Bougeault and Lacarrere TKE(简称BL))及模式自带地形数据GTOPO30(约1km分辨率),模拟了2008年4月24—25日安徽黄山及周边地区大气边界层气象要素场变化特征,并对模式输出的2m气温、2m露点温度、10m风速、湿度廓线与模拟区域内19个气象站及2个探空站数据进行比较。结果表明,无论采用哪种地形数据,四种边界层参数化方案中,YSU方案模拟的2m气温误差最小,ACM2方案模拟的2m露点温度和10m风速误差最小;采用SRTM3数据后,四种边界层参数化方案模拟的2m气温平均均方根误差(root mean squared error,RMSE)分别降低了3.79%(YSU方案)、2.48%(ACM2方案)、3.8%(MYN方案)、0.87%(BL方案);对2m露点温度模拟,除MYN方案模拟平均RMSE降低了0.59%外,其他三种方案模拟误差分别增加了1.39%(YSU方案)、0.49%(BL方案)、0.89%(ACM2方案);而对10m风速的模拟结果,除ACM2方案模拟平均RMSE降低了2.28%外,其他三种方案模拟误差分别增加了0.22%(YSU方案)、2.32%(MYN方案)、2.45%(BL方案);对2个探空站点湿度廓线的模拟显示,各边界层方案均能模拟出水汽的垂直变化趋势,但模拟效果总体表现为偏湿,采用SRTM3地形数据之后,ACM2方案模拟部分时刻的低层水汽廓线有所改善。
