Advertisement

Journal of Geodesy

, Volume 92, Issue 2, pp 185–204 | Cite as

Stochastic modeling for time series InSAR: with emphasis on atmospheric effects

  • Yunmeng Cao
  • Zhiwei Li
  • Jianchao Wei
  • Jun Hu
  • Meng Duan
  • Guangcai Feng
Original Article

Abstract

Despite the many applications of time series interferometric synthetic aperture radar (TS-InSAR) techniques in geophysical problems, error analysis and assessment have been largely overlooked. Tropospheric propagation error is still the dominant error source of InSAR observations. However, the spatiotemporal variation of atmospheric effects is seldom considered in the present standard TS-InSAR techniques, such as persistent scatterer interferometry and small baseline subset interferometry. The failure to consider the stochastic properties of atmospheric effects not only affects the accuracy of the estimators, but also makes it difficult to assess the uncertainty of the final geophysical results. To address this issue, this paper proposes a network-based variance–covariance estimation method to model the spatiotemporal variation of tropospheric signals, and to estimate the temporal variance–covariance matrix of TS-InSAR observations. The constructed stochastic model is then incorporated into the TS-InSAR estimators both for parameters (e.g., deformation velocity, topography residual) estimation and uncertainty assessment. It is an incremental and positive improvement to the traditional weighted least squares methods to solve the multitemporal InSAR time series. The performance of the proposed method is validated by using both simulated and real datasets.

Keywords

Interferometric synthetic aperture radar (InSAR) Time series InSAR (TS-InSAR) Atmospheric delays Stochastic modeling Variance–covariance matrix (VCM) 

Notes

Acknowledgements

This study was supported by the National Natural Science Foundation of China (Nos. 41474007, 41404013) and the Doctoral Innovation Foundation of Central South University (2015zzts068), and the SAR images were provided by WInSAR and the European Space Agency (ESA) Cat-1 18234.

References

  1. Agram PS, Jolivet R, Riel B, Lin YN, Simons M, Hetland E, Doin MP, Lassere C (2013) New radar interferometric time series analysis toolbox released. Eos Trans AGU 94(7):69–76CrossRefGoogle Scholar
  2. Bamler R, Hartl P (1998) Synthetic aperture radar interferometry. Inverse Probl 14:R1–R54CrossRefGoogle Scholar
  3. Bekaert DPS, Hooper A, Wright TJ (2015a) A spatially variable power law tropospheric correction technique for InSAR data. J Geophys Res 120(2):1345–1356. doi: 10.1002/2014JB011558 CrossRefGoogle Scholar
  4. Bekaert DPS, Walters RJ, Wright TJ, Hooper AJ, Parker DJ (2015) Statistical comparison of InSAR tropospheric correction techniques. Remote Sens Environ 170:40–47. doi: 10.1016/j.rse.2015.08.035 CrossRefGoogle Scholar
  5. Berardino P, Fornaro G, Lanari R, Sansosti E (2002) A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Trans Geosci Remote Sens 40(11):2375–2383. doi: 10.1109/TGRS.2002.803792 CrossRefGoogle Scholar
  6. Cao YM, Li ZW, Wei JC, Zhan WJ, Zhu JJ, Wang CC (2014) A novel method for determining the anisotropy of geophysical parameters: unit range variation increment (URVI). Appl Geophys 11(3):340–349. doi: 10.1007/s11770-014-0448-y CrossRefGoogle Scholar
  7. Chaussard E, Wdowinski S, Cabral-Cano E, Amelung F (2014) Land subsidence in central Mexico detected by ALOS InSAR time-series. Remote Sens Environ 140:94–106. doi: 10.1016/j.rse.2013.08.038 CrossRefGoogle Scholar
  8. Ding XL, Li ZW, Zhu JJ, Feng GC, Long JP (2008) Atmospheric effects on InSAR measurements and their mitigation. Sensors 8(9):5426–5448. doi: 10.3390/s8095426 CrossRefGoogle Scholar
  9. Doin MP, Lasserre C, Peltzer G, Cavalié O, Doubre C (2009) Corrections of stratified tropospheric delays in SAR interferometry: validation with global atmospheric models. J Appl Geophys 69(1):35–50CrossRefGoogle Scholar
  10. Emardson TR, Simons M, Webb FH (2003) Neutral atmospheric delay in interferometric synthetic aperture radar applications: statistical description and mitigation. J Geophys Res 108(B5):ETG4-1–ETG4-8. doi: 10.1029/2002JB001781 CrossRefGoogle Scholar
  11. Fattahi H, Amelung F (2015) InSAR bias and uncertainty due to the systematic and stochastic tropospheric delay. J Geophys Res 120(12):8758–8773. doi: 10.1002/2015JB012419 CrossRefGoogle Scholar
  12. Ferretti A, Prati C, Rocca F (2001) Permanent scatterers in SAR interferometry. IEEE Trans Geosci Remote Sens 39(1):8–20. doi: 10.1109/36.898661 CrossRefGoogle Scholar
  13. Ferretti A, Fumagalli A, Novali F, Prati C, Rocca F, Rucci A (2011) A new algorithm for processing interferometric data-stacks: SqueeSAR. IEEE Trans Geosci Remote Sens 49(9):3460–3470. doi: 10.1109/TGRS.2011.2124465 CrossRefGoogle Scholar
  14. Foster J, Kealy J, Cherubini T, Businger S, Lu Z, Murphy M (2013) The utility of atmospheric analyses for the mitigation of artifacts in InSAR. J Geophys Res 118(2):748–758. doi: 10.1002/jgrb.50093 CrossRefGoogle Scholar
  15. Gong WY, Meyer FJ, Liu SZ, Hanssen RF (2015) Temporal filtering of InSAR data using statistical parameters from NWP models. IEEE Trans Geosci Remote Sens 53(7):4033–4044. doi: 10.1109/TGRS.2015.2389143 CrossRefGoogle Scholar
  16. González PJ, Fernández J (2011) Error estimation in multitemporal InSAR deformation time series, with application to Lanzarote, Canary Islands. J Geophys Res 116:B10404–1–B10404–17. doi: 10.1029/2011JB008412 CrossRefGoogle Scholar
  17. Hanssen RF (2001) Radar interferometry: data interpretation and error analysis. Kluwer, Alphen aan den RijnCrossRefGoogle Scholar
  18. Hooper A (2008) A multi-temporal InSAR method incorporating both persistent scatterer and small baseline approaches. Geophys Res Lett 35(16):96–106. doi: 10.1029/2008GL034654 CrossRefGoogle Scholar
  19. Hooper A, Zebker HA, Segall P, Kampes B (2004) A new method for measuring deformation on volcanoes and other natural terrains using InSAR persistent scatterers. Geophys Res Lett 31(23):611–615. doi: 10.1029/2004GL021737 CrossRefGoogle Scholar
  20. Hooper A, Bekaert D, Spaans K, Arıkan M (2012) Recent advances in SAR interferometry time series analysis for measuring crustal deformation. Tectonophysics 514:1–13. doi: 10.1016/j.tecto.2011.10.013 CrossRefGoogle Scholar
  21. Jolivet R, Grandin R, Lasserre C, Doin MP, Peltzer G (2011) Systematic InSAR tropospheric phase delay corrections from global meteorological reanalysis data. Geophys Res Lett 38:L17311. doi: 10.1029/2011GL048757 CrossRefGoogle Scholar
  22. Journel AG, Huijbregts CJ (1978) Mining geostatistics. Academic Press, LondonGoogle Scholar
  23. Kampes B, Hanssen RF (2004) Ambiguity resolution for permanent scatterer interferometry. IEEE Trans Geosci Remote Sens 42(11):2446–2453. doi: 10.1109/TGRS.2004.835222 CrossRefGoogle Scholar
  24. Kinoshita Y, Furuya M, Hobiger T, Ichikawa R (2013) Are numerical weather model outputs helpful to reduce tropospheric delay signals in InSAR data? J Geodesy 87(3):267–277. doi: 10.1007/s00190-012-0596-x CrossRefGoogle Scholar
  25. Knospe SHG, Jónsson S (2010) Covariance estimation for dInSAR surface deformation measurements in the presence of anisotropic atmospheric noise. IEEE Trans Geosci Remote Sens 48(4):2057–2065. doi: 10.1109/TGRS.2009.2033937 CrossRefGoogle Scholar
  26. Lanari R, Mora O, Manunta M, Mallorqui J, Berardino P, Sansosti E (2004) A small-baseline approach for investigating deformations on full-resolution differential SAR interferograms. IEEE Trans Geosci Remote Sens 42(7):1377–1386. doi: 10.1109/TGRS.2004.828196 CrossRefGoogle Scholar
  27. Li ZW (2005) Modeling atmospheric effects on repeat-pass InSAR measurements. Ph.D. thesis, Hong Kong, Hong Kong Polytechnic University. http://hdl.handle.net/10397/2248
  28. Li ZH, Muller JP, Cross P, Fielding EJ (2005) Interferometric synthetic aperture radar (InSAR) atmospheric correction: GPS, Moderate resolution imaging spectroradiometer (MODIS), and InSAR integration. J Geophys Res 110(B3):B03410–1. doi: 10.1029/2004JB003446 CrossRefGoogle Scholar
  29. Li ZW, Xu WB, Feng GC, Hu J, Wang CC, Ding XL, Zhu JJ (2012) Correcting atmospheric effects on InSAR with MERIS water vapour data and elevation-dependent interpolation model. Geophys J Int 189(2):898–910. doi: 10.1111/j.1365-246X.2012.05432.x CrossRefGoogle Scholar
  30. Li ZW, Zhao R, Hu J, Wen LX, Feng GC, Zhang ZY, Wang QJ (2015) InSAR analysis of surface deformation over permafrost to estimate active layer thickness based on one-dimensional heat transfer model of soils. Sci Rep 5:15542. doi: 10.1038/srep15542 CrossRefGoogle Scholar
  31. Massonnet D, Rabaute T (1993) Radar interferometry: limits and potential. IEEE Trans Geosci Remote Sens 32(2):455–464. doi: 10.1109/36.214922
  32. Ng AHM, Ge LL, Li XJ, Zhang K (2012) Monitoring ground deformation in Beijing, China with persistent scatterer SAR interferometry. J Geodesy 86(6):375–392. doi: 10.1007/s00190-011-0525-4 CrossRefGoogle Scholar
  33. Rocca F (2007) Modeling interferogram stacks. IEEE Trans Geosci Remote Sens 45(10):3289–3299. doi: 10.1109/TGRS.2007.902286 CrossRefGoogle Scholar
  34. Williams S, Bock Y, Fang P (1998) Integrated satellite interferometry: tropospheric noise, GPS estimates and implications for interferometric synthetic aperture radar products. J Geophys Res 103(B11):27051–27067CrossRefGoogle Scholar
  35. Xu WB, Li ZW, Ding XL, Zhu JJ (2011) Interpolating atmospheric water vapor delay by incorporating terrain elevation information. J Geodesy 85(9):555–564. doi: 10.1007/s00190-011-0456-0 CrossRefGoogle Scholar
  36. Yang ZF, Li ZW, Zhu JJ, Yi HW, Hu J (2017) Deriving dynamic subsidence of coal mining areas using InSAR and logistic model. Remote Sens 9(2):125. doi: 10.3390/rs9020125 CrossRefGoogle Scholar
  37. Zebker HA, Rosen PA, Hensley S (1997) Atmospheric effects in interferometric synthetic aperture radar surface deformation and topographic maps. J Geophys Res 102:7547–7563. doi: 10.1029/96JB03804 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Yunmeng Cao
    • 1
  • Zhiwei Li
    • 1
  • Jianchao Wei
    • 1
  • Jun Hu
    • 1
  • Meng Duan
    • 1
  • Guangcai Feng
    • 1
  1. 1.School of Geosciences and Info-physicsCentral South UniversityChangshaChina

Personalised recommendations