Wide Area Persistent Scatterer Interferometry: Algorithms and Examples

Transcription

1 Wide Area Persistent Scatterer Interferometry: Algorithms and Examples Nico Adam, Fernando Rodriguez Gonzalez, Alessandro Parizzi, Werner Liebhart with contributions of Ramon Brcic and Xiao Ying Cong

2 ca. 200 Km 100 Km Folie 2

3 Bremerhaven Folie 3

4 Cuxhaven & Elbe Estuary Folie 4

5 Jade Estuary Folie 5

6 Participation in Terrafirma DLR participates in the ESA funded GSE Terrafirma project The Terrafirma project delivers public services to public users concerned with geo-hazard risk assessment: Tectonics Hydrogeology: Abandoned Mines, Groundwater, Landslides Flood Defense / Costal lowland subsidence It also has a task dedicated to service evolution focused on the development of PSI Wide area mapping (including preparations for Sentinel-1 datasets) Folie 6

7 Coverage: Continent Wide Sentinel-1 ERS-1/2 250 Km 100 Km Folie 7

8 Challenge Processing Wide Areas city of Munich Difficult troposphere compensation Low PS density Spatially varying PS quality Spatial error propagation Compensation by new techniques PS detection and characterization Clever reference network L1 and L2 norm network inversion Troposheric effect mitigation Robust and operator free processing Bavaria 100 x100 Km Folie 8

9 WAP Processing Chain InSAR stacking Troposphere Effect Mitigation Processor SAR DEM PS detection Network construction Quality Control Estimation Network inversion APS estimation Weather Research and Forecasting (WRF) model NWP data Block adjustment Export + visualisation Folie 9

10 Troposphere Effect Mitigation Processor (TEMP) geocoded projected zenith wet effect slant range precise dry and wet range effect ECMWF/NOAA SAR D-InSAR GENESIS SBAS-GENESIS PSI-GENESIS Folie 10

11 WAP Processing Chain InSAR stacking PS detection Network construction Quality Control Estimation Network inversion APS estimation SAR Block adjustment Export + visualisation DEM Folie 11

12 WAP Processing Chain InSAR stacking PS detection Network construction Quality Control Estimation Network inversion APS estimation SAR Block adjustment Export + visualisation DEM Folie 12

13 WAP Processing Chain InSAR stacking PS detection DEM Network construction Quality Control Estimation Network inversion APS estimation Block adjustment Export + visualisation Radar coord UTM Coord Folie 13

14 WAP Processing Chain Quality Control InSAR stacking PS detection Network construction Estimation Network inversion APS estimation Max 3 arcs per PS Max distance 1 Km Error propagation Unestimated areas High standard deviation Block adjustment Export + visualisation Max 10 arcs per PS Max distance 1 Km Huge network matrix High memory consumption High computational load Network Deformation variance Folie 14

15 WAP Processing Chain InSAR stacking PS detection Network construction Quality Control Estimation Network inversion APS estimation Block adjustment Export + visualisation Folie 15

16 WAP Processing Chain InSAR stacking PS detection Solver: L1-norm L2-norm Quality Control Network construction Estimation Network inversion APS estimation arc defo PS defo Block adjustment Export + visualisation arc defo variance PS defo variance Folie 16

17 WAP Processing Chain InSAR stacking PS detection Network construction Quality Control Estimation Network inversion APS estimation Block adjustment atmosphere state geocoded projected zenith wet effect Export + visualisation slant range precise wet range effect slant range precise dry range effect [mm] [mm] Folie 17

18 WAP Processing Chain InSAR stacking PS detection Network construction Quality Control Estimation Network inversion APS estimation Block adjustment Export + visualisation arc defo PS defo Folie 18

19 WAP Processing Chain InSAR stacking PS detection Network construction Quality Control Estimation Network inversion APS estimation Block adjustment Export + visualisation Folie 19

20 WAP Processing Chain InSAR stacking PS detection Network construction Quality Control Estimation Network inversion APS estimation Block adjustment Export + visualisation PS candidate density: 85.7 [PSC / km^2] Folie 20

21 Error Propagation f(ps distance) N = 40 T = m likelihood N = 20 T = 4 Folie 21

22 Test Site: Crete Characterized by strong atmosphere and topography Greece range Crete Crete azimuth Folie 22

23 Dominant Effects longitude [deg] longitude [deg] Folie 23

24 Dominant Effects longitude [deg] longitude [deg] topography Folie 24

25 Troposphere Effects on SAR Acquisition Without Topography (WRF Topography only) Folie 25

26 Troposphere Effects on SAR Acquisition Without Topography (WRF Topography only) Folie 26

27 Troposphere Effects on SAR Acquisition With Topography Folie 27

28 Wet Troposphere Effect Comparison Winter & Summer 24 December 22 July 50mm 60mm Folie 28

29 Projected Zenith Wet Effect 10 [mm] 230 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Folie 29

30 Geocoded Projected Zenith Wet Effect Product model residual (APS) Orbit: [mm] [rad] Orbit: [mm] [rad] Folie 30

31 Structure Function Estimation model residual (APS) Orbit: [rad] Orbit: [rad] Folie 31

32 Master Orbit: / Slave Orbit: APS_012 InSAR APS InSAR NWP InSAR APS [rad] 4 InSAR NWP [mm] [rad] [mm] +20 Folie 32

33 Vertical Stratification Correction Precise dry range effect prediction Slave: topography [m] [mm] Master: 6560 dry range effect [mm] [mm] [mm] Folie 33

34 Vertical Stratification Correction Slave: Master: 6560 Topography & estimation area Correlation & correction Folie 34

35 Summary Wide Area PSI (WAP) developed Currently ERS data Standard product for Sentinel-1 Many algorithmic updates Support by NWP Geocoded projected zenith wet effect Slant range precise dry range effect Slant range precise wet range effect Folie 35

36 Summary Wide Area PSI (WAP) developed Currently ERS data Standard product for Sentinel-1 geocoded projected zenith wet effect slant range precise dry range effect prediction Many algorithmic updates Support by NWP Geocoded projected zenith wet effect Slant range precise dry range effect Slant range precise wet range effect [mm] slant range precise wet range effect prediction [mm] Folie 36

37 Acknowledgement ESA s Terrafirma Project WRF developed by National Center for Atmospheric Research (NCAR) et al. The CFSR data for driving regional model simulations are provided by Dr. Lifeng Luo at Michigan State University, and are based on the original datasets from NCEP CFSRR project. geocoded projected zenith wet effect slant range precise dry range effect prediction [mm] slant range precise wet range effect prediction CFSR WRF (NCAR) TEMP (DLR) [mm] Folie 37