Application of China's First Generation Global Atmospheric Reanalysis Data in InSAR Atmospheric Correction: A Case Study of Southern California
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摘要: 大气延迟是合成孔径雷达干涉测量(interferometric synthetic aperture radar,InSAR)地表形变监测的主要误差源之一。提出采用中国第一代全球大气再分析业务系统(CRA40)数据改正InSAR对流层延迟,并以南加州SBAS-InSAR地表变形监测为例,进行了详细分析与评估。首先提出了利用该产品计算对流层延迟的方法,通过顾及大气参数垂直分层及水平变化的物理特性,分别对该产品原始气象参数进行垂直向和水平向的插值,沿卫星视线方向积分计算大气延迟,通过与原始干涉图差分,得到大气改正结果;其次将结果从干涉图标准差、空间相关性和相位-高程相关系数三个方面进行误差分析;最后利用Sentinel-1数据进行南加州形变测量实验,将本文方法与经典SBAS-InSAR结果对比,使用GPS数据进行验证。结果表明:(1)校正后的干涉图平均标准差减小了34.7%,65%的干涉图平均标准差减小了20%以上; (2)干涉图空间结构函数的期望平方差显著性下降表明该产品能有效抑制长波大气; (3)相关性的拟合系数变化表明该产品能够有效降低高程影响带来的大气垂直分层分量; (4)说明利用分辨率较低的全球大气模型数据进行大气改正时考虑对流层空间变化的有效性。所得结果说明了CRA产品在InSAR大气校正上的可行性。
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关键词:
- CRA /
- SBAS-InSAR /
- 大气改正 /
- 变形监测 /
- 统计指标
Abstract: Objectives: Atmospheric delay is one of the main error sources ininterferometric synthetic aperture radar ( InSAR) surface deformation monitoring. Using the global meteorological model to correct the tropospheric delay has been successfully applied in recent years. In this paper, an InSAR atmospheric correction method is presented, which introduces China's first Generation Global Atmospheric reanalysis data (CRA). Taking into account the physical characteristics of tropospheric meteorological parameters, the method was tested in Los Angeles, Southern California, and evaluated quantitatively. The results show the feasibility of CRA products in InSAR atmospheric correction. Methods: The meteorological parameters such as temperature, water vapor and air pressure are obtained by CRA, taking into account the spatial variation characteristics of atmospheric parameters, according to the vertical stratification characteristics of meteorological data, the piecewise function is used to interpolate in the vertical direction. By considering the spatial variability of the atmosphere, the interpolation is carried out in the horizontal plane. According to the results, the atmospheric delay is calculated along the satellite line of sight. The atmospheric correction result is obtained by difference with the original interferogram. Verify and analyze the results, first, the error analysis is carried out from three aspects: Interferogram standard deviation, spatial correlation and phaseelevation correlation coefficient; second, it is compared with the results of GACOS and PyAPS methods; Finally, 78 interferograms obtained from 40 scenes Sentinel-1 data of Southern California based on short baseline principle are used to carry out sequential deformation measurement experiments, the proposed method is compared with classical SBAS-InSAR results, and verified by GPS data. Results: The results show that: (1) The average standard deviation of the corrected interferogram is reduced by 34.7%, The average standard deviation of 65% interferograms has been reduced by more than 20%., especially for the interferograms which are seriously affected by the atmosphere. It is better than GACOS and PYAPS with an average improvement of 30%. (2) The significant decrease of the expected square variance of the spatial structure function of the interferogram shows that the product can effectively suppress the long-wave atmosphere. (3) The change of fitting coefficient of correlation shows that the product can effectively reduce the atmospheric vertical stratification component caused by the influence of elevation. Depending on the degree of atmospheric influence, CRA can improve the vertical stratification component of the atmosphere, ranging from 20% to 60% or more. (4) It is effective to take the spatial variation of convection into account when establishing a high-resolution InSAR tropospheric delay map using low-resolution global atmospheric model data. Conclusions: This paper confirms that CRA can correct and improve the overall accuracy of deformation monitoring. Through quantitative evaluation, researchers can fully understand the correction effect and performance of this product, which is helpful to promote the application and development of this product. However, due to the differences of external data, follow-up research can combine GNSS data with CRA solution to further improve the accuracy of InSAR atmospheric correction.-
Keywords:
- CRA /
- SBAS-InSAR /
- Atmospheric correction /
- Deformation monitoring /
- Statistical index
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[1] Massonnet D, Rossi M, Carmona C, et al. The displacement field of the Landers earthquake mapped by radar interferometry[J]. Nature, 1993, 364(6433):138-142.
[2] Zebker H, Rosen P, Goldstein R, et al. On the derivation of coseismic displacement fields using differential radar interferometry:The Landers earthquake[J]. Journal of Geophysical Research B, 2002, 99(B10):19617-19634.
[3] Hu J, Li Z, Ding X, et al. 3D coseismic Displacement of 2010 Darfield, New Zealand earthquake estimated from multi-aperture InSAR and D-InSAR measurements[J]. Journal of Geodesy, 2012, 86(11):1029-1041.
[4] Xu Wenbin, Luo Xingjun, Zhu Jianjun, et al. Review of Volcano Deformation Monitoring and Modeling with InSAR[J]. Geomatics and Information Science of Wuhan University, 2023, 48(10):1632-1642.(许文斌,罗兴军,朱建军,等.InSAR火山形变监测与参数反演研究进展[J].武汉大学学报(信息科学版), 2023, 48(10):1632-1642.) [5] Xu B, Li Z, Feng G, Zhang Z, Wang Q, Hu J, et al. Continent-Wide 2-D Co-Seismic Deformation of the 2015 Mw 8.3 Illapel, Chile Earthquake Derived from Sentinel-1A Data:Correction of Azimuth Co-Registration Error[J]. Remote Sensing, 2016, 8(5):1-12.
[6] Li Da, Deng Kazhong, Gao Xiaoxiong, Niu Haipeng. Monitoring and Analysis of Surface Subsidence in Mining Area Based on SBAS-InSAR[J]. Geomatics and Information Science of Wuhan University, 2018, 43(10):1531-1537.(李达,邓喀中,高晓雄,等. 基于SBAS-InSAR的矿区地表沉降监测与分析[J].武汉大学学报(信息科学版), 2018, 43(10):1531-1537.) [7] Zhu Jianjun, Li Zhiwei, Hu Jun. Research Progress and Methods of InSAR for Deformation Monitoring[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10):1717-1733.(朱建军,李志伟,胡俊.InSAR变形监测方法与研究进展[J].测绘学报, 2017, 46(10):1717-1733.) [8] Yang Z, Li Z, Zhu J, et al. Deriving time-series three-dimensional displacements of mining areas from a single-geometry InSAR dataset[J]. Journal of Geodesy, 2017, 11):1-16.
[9] Li Z, Cao Y, Wei J, et al. Time-series InSAR ground deformation monitoring:Atmospheric delay modeling and estimating[J]. Earth-Science Reviews, 2019, 192:258-284.
[10] Zebker H, Rosen P, Hensley S. Atmospheric effects in interferometric synthetic aperture radar surface deformation and topographic maps[J]. Journal of Geophysical Research Solid Earth, 1997, 102(B4):7547-7563.
[11] Hanssen R. Radar Interferometry Data Interpretation and Error Analysis[M]. IEEE, 2001.
[12] Li Z, Ding X, Huang C, et al. Modeling of atmospheric effects on InSAR measurements by incorporating terrain elevation information[J]. Journal of Atmospheric and SolarTerrestrial Physics, 2006, 68(11):1189-1194.
[13] Xu W, Li Z, Ding X, et al. Interpolating atmospheric water vapor delay by incorporating terrain elevation information[J]. Journal of Geodesy, 2011, 85(9):555-564.
[14] Hooper A, Bekaert, et al. Statistical comparison of InSAR tropospheric correction techniques[J]. Remote Sensing of Environment:An Interdisciplinary Journal, 2015. 170:40- 47.
[15] Cao Y, Li Z, Wei J, et al. Stochastic modeling for time series InSAR:with emphasis on atmospheric effects[J]. Journal of Geodesy, 2018, 92(2):185-204.
[16] Wei J, Li Z, Hu J, et al. Anisotropy of atmospheric delay in InSAR and its effect on InSAR atmospheric correction[J]. Journal of geodesy, 2019(2):93.
[17] Berardino P, Fornaro G, Lanari R, et al. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms[J]. Geoscience & Remote Sensing IEEE Transactions on, 2002, 40(11):2375-2383.
[18] Fukushima Y. Atmospheric Phase Delay Estimation From Multiple SAR Interferometry Measurements[C]. Fringe. 2012.
[19] Li Z, Ding X, Liu G. Modeling atmospheric effects on InSAR with meteorological and continuous GPS observations:algorithms and some test results[J]. Journal of Atmospheric and Solar-Terrestrial Physics, 2004, 66(11):907-917.
[20] Li Z, Fielding E, Cross P, et al. InSAR atmospheric correction GPS Topography-dependent Turbulence Model (GTTM)[J]. Journal of Geophysical Research Atmospheres, 2006, 111:B02404.
[21] Yu C, Penna N, Li Z. Generation of real-time mode high-resolution water vapor fields from GPS observations[J]. Journal of Geophysical Research, D. Atmospheres:JGR, 2017.
[22] Li Z. Correction of atmospheric water vapour effects on repeat-pass SAR interferometry using GPS, MODIS and MERIS data[J]. University College London (University of London), 2005.
[23] Li Z, Xu W, Feng G, et al. Correcting atmospheric effects on InSAR with MERIS water vapour data and elevation dependent interpolation model[J]. Geophysical Journal International, 2012, 189(2):898-910.
[24] Jung J, Kim D, Park S. Correction of Atmospheric Phase Screen in Time Series InSAR Using WRF Model for Monitoring Volcanic Activities[J]. IEEE Transactions on Geoscience & Remote Sensing, 2014, 52(5):2678-2689.
[25] Jolivet R, Agram P, Lin N, Simons M, Doin M, Peltzer G, et al. Improving InSAR geodesy using Global Atmospheric Models, Journal of Geophysical Research:Solid Earth, 2014,119:18.
[26] Hu Z, Jordi J. An Accurate Method to Correct Atmospheric Phase Delay for InSAR with the ERA5 Global Atmospheric Model[J]. Remote Sensing,2019,11(17):1969.
[27] Cao Y, Jonsson S, Li Z. Advanced InSAR Tropospheric Corrections from Global Atmospheric Models that Incorporate Spatial Stochastic Properties of the Troposphere[J]. Journal of Geophysical Research Solid Earth,2021.
[28] Wang Minyan, Yao Shuang, Jiang Lipeng, et al. Collection and preprocessing of Global Atmospheric reanalysis (CRA-40) Satellite remote Sensing data in China[J]. Advances in Meteorological Science and Technology, 2018,8(1):158-163(王旻燕,姚爽,姜立鹏,等.我国全球大气再分析(CRA-40)卫星遥感资料的收集和预处理[J].气象科技进展,2018,8(1):158-163.) [29] Shi Chunxiang. Multi-source fusion grid live data and the development and application progress of the first generation global atmospheric reanalysis products in China.[R] Beijing:Intelligent Earth Lecture Hall,2021.(师春香.多源融合网格实况数据与我国第一代全球大气再分析产品研发与应用进展[R].北京:智慧地球大讲堂,2021.) [30] Yu X, Zhang L, Zhou T, et al. The Asian subtropical westerly jet stream in CRA-40, ERA5, and CFSR reanalysis data:comparative assessment[J]. Journal of Meteorological Research, 2021,35(1):46-63
[31] Li Z, Cao Y, Wei J, et al. Time-series InSAR ground deformation monitoring:Atmospheric delay modeling and estimating[J]. Earth-Science Reviews, 2019, 192:258-284.
[32] Yao Yibin, Zhang Liang, Zhang Qi, et al. Tropospheric Delay Model and Real-Time Differencial Service for Large Height Difference RTK[J]. Geomatics and Information Science of Wuhan University, 2023, 48(7):1019-1028.(姚宜斌,张良,张琦, 等.面向大高差RTK的对流层延迟改正模型及实时差分服务构建[J].武汉大学学报(信息科学版), 2023, 48(7):1019-1028.) [33] Hu Y, Yao Y. An Accurate Height Reduction Model for Zenith Tropospheric Delay Correction Using ECMWF Data[C]//China Satellite Navigation Conference. Springer, Singapore, 2017.
[34] Onn F, Zebker H. Correction for interferometric synthetic aperture radar atmospheric phase artifacts using time series of zenith wet delay observations from a GPS network[J]. Journal of Geophysical Research Solid Earth, 2006, 111(B9).
[35] Zhou Wenbin, Xu Wenbin, Li Zhiwei, et al. Elevation-dependent MERIS Water Vapor Interpolation and Its Application to Atmospheric Correction on ASAR Interferogram[J]. Geomatics and Information Science of Wuhan University, 2012, 37(8):963-977.(周文斌,许文斌,李志伟,等.考虑高程信息的MERIS水汽插值及其在ASAR干涉图大气改正中的应用[J].武汉大学学报(信息科学版), 2012, 37(8):963-977.) [36] Xu B, Li Z, Wang Q, et al. A Refined Strategy for Removing Composite Errors of SAR Interferogram[J]. 2014.DOI: 10.1109/LGRS.2013.2250903.
[37] Li Z, Ding X, Huang C, et al. Modeling of atmospheric effects on InSAR measurements by incorporating terrain elevation information[J]. Journal of Atmospheric and SolarTerrestrial Physics, 2006, 68(11):1189-1194.
[38] Cao Y, Li Z, Wei J, et al. Stochastic modeling for time series InSAR:with emphasis on atmospheric effects[J]. Journal of Geodesy, 2018, 92(2):185-204.
[39] Gao Zhuang, He Xiufeng, Xiao Ruya, et al. An Improved LiCSBAS Method for Joint Estimation of Deformation and Atmospheric Errors[J]. Geomatics and Information Science of Wuhan University, 2023, 48(2):285-294.(高壮,何秀凤,肖儒雅, 等.一种联合估计形变和大气误差的改进 LiCSBAS方法[J]. 武汉大学学报(信息科学版),2023,48(2):285-294.) [40] Xiao R, Yu C, Li Z, et al. Statistical assessment metrics for InSAR atmospheric correction:Applications to generic atmospheric correction online service for InSAR (GACOS) in Eastern China[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 96:102289.
[41] Yu C, Penna N, Li Z. Generation of real-time mode high-resolution water vapor fields from GPS observations[J]. Journal of Geophysical Research, D. Atmospheres:JGR, 2017.
[42] Chen Y, Li Z, Penna N, et al. Generic Atmospheric Correction Model for Interferometric Synthetic Aperture Radar Observations[J]. Journal of Geophysical Research:Solid Earth, 2018, 123.
[43] Yu C, Li Z, Penna N. Interferometric synthetic aperture radar atmospheric correction using a GPS-based iterative tropospheric decomposition model[J]. Remote Sensing of Environment, 2017, 204:109-121.
[44] Agram P, Jolivet R, et al, New Radar Interferometric Time Series Analysis Toolbox Released[J]. Eos Transactions American Geophysical Union, 2013, 94.
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