1. F Rocca Dipartimento di Elettronica e Informazione Politecnico di Milano SAR interferometry for sub millimeter land motion studies

2. ERS -1 - 1991 35 days revisit cycle

3. Terrain Deformation Monitoring: The ERS – Envisat era

4. Decennial Etna motion: vertical

5. Decennial Etna motion: E-W

6. Piton de la Fournaise (Isle de la Reunion) Piton de la Fournaise Madagascar LA REUNION

7. Differential Interferograms: examples (1) S2 Ascending T84 π - π 15 -74 radians Master : 20031130; Slave: 20030921 Bt=70 [days] Bn=29 [m] wrapped unwrapped wrapped unwrapped wrapped unwrapped

8. S2 Ascending T84 Velocity Field

9. S4 Ascending T127 Velocity Field

10. S2 Ascending T84 Time Series: examples (1) 1 2 3 1 2 3 master master master

11. S4 Ascending T127 Time Series: examples (1) 1 2 3 1 2 3 master master master

12. Vulcano, Eolie Islands (Italy) Descending track 494 30 scenes Envisat S2 Ascending track 129 37 scenes Envisat S2

13. Velocity field, along LOS

14. Velocity field, along LOS

15. Decomposition in East and Vertical velocities Vertical velocity field Easting velocity field east west up down Ascending and descending results both cover the crater area and other parts of the island: wherever the two data are simultaneously available, a decomposition from ascending and descending displacement to easting and vertical components is possible, on a grid of 100x100 meters resolution

16. Displacement Time Series, examples

17. Displacement Time Series, examples

18. Landslides detection and monitoring Identifying landslides: Piedmont Piedmont landslides

23. Sentinel 1 A/B 22 years later 12 days revisit cycle

25. Densifying the set of reference targets (Persistent Scatterers)

26. Covariance matrices of multipass SAR Rows and columns correspond to progressive times Examples for: Persistent Scatterers (that terminate) Progressive decorrelation Seasonal effects

28. SqueeSAR uses nearly all interferograms, weighted with their coherence (see above). Phase linking is then carried out, estimating the sequence of the N-1 phases using the N(N-1)/2 interferograms. seasonal Markov

30. PS

31. Squeezing all the information: Squeesar

35. Inversion results UD component Measures Model

36. Inversion results EW component Measures Model

39. The reject across the fault

40. Cosmo Skymed data: Total change: 1.8mm 28kt/mm

41. We can map surface deformations into permeability changes; the InSalah story is condensed in 1 cm surface motion 1 mm sensitivity is achieved today with reduced resolution, but can improve with numerical atmospheric models. The revisit time is paramount

44. We still have to solve efficiently the problem of the Atmospheric Phase Screen that biases the outcomes

45. APS estimation procedure Model Parameters DEM

46. Mathematical modeling of the APS Characterized by a variogram K,A,B,C LMS from the data Z from a DEM 4-parameter model

47. Typical example of atmospheric data

48. APS from Ground Based RADAR GBSAR measures APS fluctuations from ms to months (range = 4 km). The t a power law (Kolmogorov) has been verified on variograms from t > 1 s APS has considerably less power during night time APS in short time APS in long time σ φ =1 rad 2hours ~ 40km mm 2 mm 2

49. K values: synoptic view Ascending Descending

50. 6am

51. 6pm

52. What we can do today with good control of the Atmospheric Phase Screen

53. The CESI experiment Estimated displacement (Radarsat 1 data) Vertical displacement (ground truth) Vertical displacement (estimated) Horizontal displacement (ground truth) Horizontal displacement (estimated) Rmse = 0.58mm (h!); 0.75mm (v)

54. A Ground Based SAR at the XX dam The future: UAV, geosynchronous, both? IBIS-L

55. Corner 2 Corner 1 Corner 4 Corner 3

56. Permanent Scatterers analysis : displacement series

57. Permanent Scatterers analysis : displacement series

58. Conclusions for the Ground Based SAR (15 h. observation) The data show coherence > 0.8 The atmospheric effect is very low for the good meteorological conditions The rms noise is of the order of 0.1mm

60. InSAR vs NWP T351, desce, 10UTC 30 images 10 std IWV maps T172, asce, 21UTC 41 images 20 std IWV maps The Rome dataset

61. InSAR vs NWP Spectral decorrelation Long spatial wavelengths have a few hours correlation, short spatial wavelengths decorrelate in less than 1 hour

62. InSAR vs NWP Analysis and separation of the stationary (layered) delay Layered delay Differential delay

63. MM5 vs InSAR, Rome T172 9pm asce Prediction of the change with height

64. InSAR vs NWP MM5: Positive in retrieving the change with height Very low correlation of turbulent terms Strong dependence on the starting time WRF performs better than MM5

65. Meris vs InSAR Cloud coverage can be a problem Rome T351, morning passes

66. Meris vs InSAR T351, Meris vs APS power spectra Similar frequency content after removing the stationary component

67. Meris vs InSAR And the accuracy is not enough… Meris IWV [mm] InSAR IWV [mm]

69. GPS vs InSAR The Como test-site 480 descending, 10am (28 images) 487 ascending, 9pm (38 images)

70. GPS vs InSAR Delay, Como experiment, descending track Different ways for estimating the stationary term, for different stations (color)

71. GPS vs InSAR Best performances, ~ 50% success Ascending Descending GPS std InSAR std diff std corr coeff GPS std InSAR std diff std corr coeff 3.69 3.37 3.43 0.53 3.43 1.72 2.85 0.56 2.34 4.27 4.27 0.28 2.73 5.26 3.52 0.79 3.61 5.00 1.98 0.95 0.63 3.35 3.03 0.59 2.94 3.36 1.06 0.95 5.36 6.07 2.79 0.89 2.18 1.15 1.27 0.89 1.67 1.83 2.05 0.32

72. Closer to any PS, the estimate of the APS improves: in a circle of PS with diameter D (m), the mean square APS reduces q times, q is lower than D q max q

73. In the case of interferogram chaining, if there is a limited coherence γ from one take to the next, the measured phase is: L = number of looks (> 4); N = number of takes in the observation time σ atm = dispersion of the APS; w1, w2 noises with unit variance Signal and noise grow with the number of takes Even if γ =0.3, just 6 looks are equivalent to a PS, as the coherence is limited only by APS and not by decorrelation.

74. MM5 has a strong “random” component and a more stable stratification estimation. WRF performs better, further work is needed. Meris has shown positive results in flat deserted areas, but not in our case studies. GPS has a success rate of 50%. The connection between InSAR and the other methodologies lies in the stationary term estimation. Permanent Scatterers (or interferogram chaining) are still the best way to accurately estimate the APS.

75. The competition: Optical and GPS levelling: Approximate results

76. A recent survey comparison for an accelerator design in Japan

81. Photon-counting detector with an accuracy of 20 ps (3.3 mm two way) Max point rate ~ 1000 pts/sec. Low atmospheric effects Photon counting devices

85. GEO synchronous S AR for A tmosphere and T errain observation Prof. A . Broquetas – UPC Univesitat Politècnica de Catalunya, Electromagnetic and Photonic Eng. Group Dr. D. D’Aria - ARESYS Prof. N. Casagli, G. Righini – Università degli studi di Firenze, Dept. of Earth Sciences Prof. S . Hobbs – Cranfield University, Cranfield Space Research Centre Prof. A. Monti Guarnieri, Prof. F. Rocca – Politecnico di Milano, Dept. of Electronic and Inf. Science Prof.sa R. Ferretti – University of L’Aquila, CETEMPS Prof. M. Nazzareno Pierdicca – Sapienza University of Roma, Dept. of Electronic Engineering Prof. G. Wadge – University of Reading, Environmental Systems Science Centre Prof. Dr. H Rott – University of Innsbruck Dr. C. Svara, Dr. A. Torre – Thales Alenia Space Italia Earth Explorer EE8 proposal: COM3/EE8/32 GEOSAT

89. GEOSAT: The Wide Beam GEOSAT may be a guest payload on Italian Space Agency (ASI) SIGMA missions, placed appox 9 o longitude . Look direction would then be close to SN for Europe. Backgound mission wide beam over central Europe (2000 km). NESZ = -19 dB, 0.5x 0.5km resolution, 20’ revisit. Fine resolution 10 x 10 m (twice daily revisit). Applications: WV maps, glaciers, urban.

91. Any TV antenna becomes a good reflector 80 cm antenna  SNR = 21 dB (12 hours); SNR=2 dB (10 mins) 47 Millions of users parabolas in Europe (2002) Number of home satellite antennas 1999 millions 2002 millions 1999-2002 Increase % total population % millions % millions and Pacific 19.5 17.5 -11 -2.1 1 1 and US 13.7 20.1 47 6.4 4 6 EUROPE 33.8 43.6 26 8.7 4 5 Latin America and the 1.6 2.7 62 1.0 0 1 North Africa and the 8.9 11.9 29 2.6 3 4 0.0 0.0 177 0.0 0.0 0 Sub Saharan 0.4 1.2 83 0.3 0 0 World 77.9 96.8 22 16.8 1 2