摘要
对流层延迟是合成孔径雷达干涉测量(InSAR)的一个重要误差源,如何正确估计并从干涉相位中分离出对流层延迟是获取高精度InSAR产品的重要步骤。针对上述问题,该文利用Sentinel-1数据,以北京地区为研究区域,采用相位高程比、GPS天顶延迟校正图、InSAR通用型大气改正在线服务(GACOS)和第五代欧洲中尺度天气预报再分析产品(ERA5)这4种模型或数据生成对流层延迟,对该地区2017年1月8日—2017年7月31日生成的干涉图进行校正,并分析了4种手段的校正效果。结果表明,对单个干涉图来说,在山区相位高程比的校正效果最优,在平原地区GACOS和ERA5最优且相差不大;但对于形变的时间序列来说,GACOS与GPS的形变结果最为符合,且略优于ERA5;相位高程比虽然能显著降低标准差,但不能明显改善时序结果;GPS-ZTD方法受制于空间分辨率,较其他方法改正效果略差。
Tropospheric delay is an important source of error in interferometric synthetic aperture radar(InSAR).How to correctly estimate and isolate the troposphere from the interferometric phase delay is an important step in obtaining high-precision InSAR products.For the above problem,in this paper,we used Sentinel-1 data,taking Beijing as the research area,four models or data generation,such as phase elevation ratio,GPS zenith delay correction(GPS-ZTD),generic atmospheric correction online service for InSAR(GACOS)and the fifth generation ECMWF atmospheric reanalysis of global climate(ERA5),were used to correct the interferograms generated from January 8,2017 to July 31,2017,and analyzed the correction effect of the four means.The results showed that for individual interferograms,the correction effect of phase elevation ratios was best in mountainous areas,and GACOS and ERA5 were optimal and not much different in plain areas.However,for the time series of deformation,GACOS was most consistent with GPS and slightly better than ERA5;although phase elevation ratio method significantly reduced the standard deviation,but it did not significantly improve the time series results;the GPS-ZTD method was limited by spatial resolution,which was slightly less corrective than other methods.
作者
张双成
宋明鑫
罗勇
雷坤超
刘万林
庞校光
ZHANG Shuangcheng;SONG Mingxin;LUO Yong;LEI Kunchao;LIU Wanlin;PANG Xiaoguang(College of Geology Engineering and Geomatics,Chang’an University,Xi’an 710054,China;Beijing Institute of Hydrogeology and Engineering Geology,Beijing 100195,China)
出处
《测绘科学》
CSCD
北大核心
2021年第10期108-117,共10页
Science of Surveying and Mapping
基金
国家重点研发计划项目(2019YFC1509802)
陕西省自然科学基础研究计划项目(2020JM-227)
国家自然科学基金项目(41104019)。