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Mitigation of Atmospheric Delay in InSAR: The ESA METAWAVE Project

This study explores methods to mitigate atmospheric delay in InSAR data through the ESA METAWAVE project. The research focuses on decomposing the atmospheric signal, connecting it with Integrated Water Vapor (IWV), and evaluating experiments and performances using GPS, Meris, and NWP data. The study emphasizes the importance of removing spatially linear trends and compares differential and pseudo-absolute correction strategies for APS. Results show the potential of different data sources in improving InSAR precision and offer insights into water vapor extraction methodologies. The study also examines turbulent terms, standard deviations, and correlation analyses between different datasets. Conclusions highlight the strengths and limitations of using Meris, NWP, GPS, and PS data in atmospheric delay mitigation processes.

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Mitigation of Atmospheric Delay in InSAR: The ESA METAWAVE Project

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  1. Vancouver, 28th July 2011 Mitigation of atmospheric delay in InSAR: the ESA METAWAVE project Daniele Perissin (1), Fabio Rocca（2）, Mauro Pierdicca（3）, Emanuela Pichelli（4）, Domenico Cimini（4）, Giovanna Venuti（5）, Bjorn Rommen（6） (1) ISEIS, Chinese University of Hong Kong, NT, Shatin, Hong Kong (2) DEI, Politecnico di Milano, Milan, Italy (3) DIEI, Università la Sapienza, Rome, Italy (4) CETEMPS, University of L’Aquila, Italy (5) DIIAR, Politecnico di Milano, Milan, Italy (6) ESA, ESTECNoordwijk, The Netherlands

  2. Mitigation of atmospheric delay in InSAR: the ESA METAWAVE project Table of Contents 1. decomposition of atmospheric signal 2. connection between APS and IWV 3. experiments and performances (GPS, Meris, NWP) 4. PS precision assessment 5. Conclusions

  3. …which can be divided into - stationary part - variational part Points to keep in mind - The APS contains only the variational part of the atmosphere Decomposition of atmospheric signal (from InSAR point of view) Atmospheric components… - stratification (correlated with topography) - turbulence - spatially linear component - The APS gathers spatially correlated noise components (also orbital artifacts)  spatially linear trends must be removed!

  4. Connection between APS and IWV 2 different strategies for comparison/correction of APS - in APS domain: differential way (multi-master) - in APS domain: pseudo-absolute way (single Master)  the Master delay is estimated and removed, so the atmospheric delay can be compared day by day To be able to extract Water Vapor from the APS - in IWV domain: stationary term + spatial linear terms must be provided by external data

  5. Experiments and performances Table of Contents NWP data in Rome vs APS Meris data in Rome vs APS GPS data in Como vs APS

  6. InSAR vs NWP Rome Envisat datasets T172, asce, 21UTC 41 images 20 std IWV maps T351, desce, 10UTC 30 images 10 std IWV maps

  7. InSAR vs NWP NWP domain and topography

  8. InSAR vs NWP “differential” comparison IWV APS APS-IWV Scatter plot APS vs IWV APS-IWV APS-IWV vs SRTM

  9. InSAR vs NWP “differential” comparison IWV std APS std APS-IWV std IWV stratif. APS stratif. APS-IWV stratif.

  10. InSAR vs NWP “pseudo-absolute” comparison Estimated Master APS Average IWV

  11. InSAR vs NWP “pseudo-absolute” comparison IWV APS APS-IWV Scatter plot APS vs IWV APS-IWV APS-IWV vs SRTM

  12. InSAR vs NWP Variational stratification Scatter plot disp: 0.7 mm/km vs InSAR disp: 1.3 mm/km MM5 can help reducing the stratification component

  13. InSAR vs NWP Comparison of turbulent terms IWV APS APS-IWV Scatter plot APS vs IWV APS-IWV APS-IWV vs SRTM

  14. InSAR vs NWP Comparison of turbulent terms Spatial cross-correlation Standard deviations [mm]

  15. InSAR vs NWP Comparison of turbulent terms Kolmogorov-Smirnov test MM5 turbulent term has very low correlation with the APS one Cumulative distribution functions Test statistics

  16. InSAR vs NWP IWV evolution in time

  17. InSAR vs NWP NWP-APS synchronization Standard deviation vs delay NO significant improvement

  18. 00 06 11 06 11 InSAR vs NWP Impact of starting time Strong random component in MM5 simulations!! 3 October 08, residuals after subtraction of stationary term The average map has been subtracted

  19. InSAR vs MERIS The Rome dataset Meris can be used only for day time passes T172, asce, 21UTC 41 images T351, desce, 10UTC 30 images 26 Meris image No use for night passes

  20. InSAR vs MERIS Examples in the Rome dataset Meris needs clear sky conditions 40% of loss Rome T351, morning passes

  21. InSAR vs MERIS Spectral analysis Meris has spectral content closer to the APS one

  22. InSAR vs MERIS Scatter plots and correlation In our experiment the Meris success rate is quite low Meris IWV [mm] InSAR IWV [mm]

  23. InSAR vs GPS The Como test-site 5 GPS stations, 5 overlapping days 480 descending, 10am (28 images) 487 ascending, 9pm(38 images)

  24. InSAR vs GPS Different ways for estimating the GPS stationary term descending track ascending track

  25. InSAR vs GPS Correlation and deviation reduction GPS has a 50% success rate

  26. InSAR alone APS interpolation in presence of PS’s 1mm path delay error if we interpolate PS’s distributed along a circle with 10km diameter By F. Rocca D =1000 4.6mm*sqrt(0.036)=4.6*0.19=0.9mm.

  27. Experiments and performances Conclusions at this time Meris: spectral content closer to APS however usable only in clear sky stratification not very robust NWP: powerful tools in space and time strong random component useful for long spatial wavelengths and stratification GPS: highest accuracy reliability depends on density of ground stations PS:where PS’s are present, no better way to estimate the APS

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