sbas_parallel

Synopsis

sbas_parallel intf.tab scene.tab N S xdim ydim [-atm ni] [-smooth sf] [-wavelength wl] [-incidence inc] [-range -rng] [-rms] [-dem]

Description

sbas_parallel Coherence-based Small Baseline Subset time series processing (see references for theory) in parallel

FOR PARALLEL PROCESSING: Before run: set the environment variable OMP_NUM_THREADS to a proper number that fits your machine (you need to know how many cores and threads you have on your machine, as this process can easily overload the memory and processing capability of a machine).

This command sometimes needs to be compiled separately; see https://github.com/gmtsar/gmtsar/wiki/parallel-sbas-code for compiling information

The SBAS program outputs a cumulative displacement grid for every scene timestamp, with the filename disp_YYYY.DDD.grd where YYYY = year and DDD = Day of year. These are the final cumulative time series grids in units of millimeters.

In addition, the SBAS program outputs a final velocity field estimate, called vel.grd, which is the mean velocity in units of millimeters per year.

If you choose to include the optional outputs -dem or -rms these will output additional products, i.e. the dem error estimate and the velocity rms estimate respectively (see below).

Required Arguments

intf.tab

List of unwrapped (filtered) interferograms (can be created with prep_sbas.csh)

Format: unwrap.grd corr.grd ref_id rep_id B_perp

scene.tab

List of the SAR scenes in chronological order (can be created with prep_sbas.csh)

Format: scene_id number_of_days

Note: the number_of_days is relative to a reference date

N

Number of the interferograms

S

Number of the SAR scenes

xdim and ydim

Dimension of the interferograms (Tip: use GMT’s grdinfo tool to print the grid information (e.g. gmt grdinfo name.grd)

-smooth sf

Smoothing factors, default=0

-atm ni

Number of iterations for atmospheric correction, default=0(skip atm correction) Tip: ni=3 is a good place to start

-wavelength wl

Wavelength of the radar wave (m) default=0.236

-incidence theta

Incidence angle of the radar wave (degree) default=37

-range rng

Range distance from the radar to the center of the interferogram (m) default=866000

-rms

Include this flag to output RMS of the data misfit grids (mm): rms.grd

-dem

Include this flag to output the DEM error grid (m): dem.grd.

WARNING: This is an identical filename to a processing dem.grd, so make sure you do not have a dem.grd stored inside your working directory.

Example

sbas_parallel intf.tab scene.tab 88 28 700 1000

References

Berardino P., G. Fornaro, R. Lanari, and E. Sansosti, “A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms,” IEEE Trans. Geosci. Remote Sensing, vol. 40, pp. 2375–2383, Nov. 2002.

Schmidt, D. A., and R. Bürgmann 2003, Time-dependent land uplift and subsidence in the Santa Clara valley, California, from a large interferometric synthetic aperture radar data set, J. Geophys. Res., 108, 2416, doi:10.1029/2002JB002267, B9.

Tong, X. and Schmidt, D., 2016. Active movement of the Cascade landslide complex in Washington from a coherence-based InSAR time series method. Remote Sensing of Environment, 186, pp.405-415.

Tymofyeyeva, E. and Fialko, Y., 2015. Mitigation of atmospheric phase delays in InSAR data, with application to the eastern California shear zone. Journal of Geophysical Research: Solid Earth, 120(8), pp.5952-5963.

Xu, X., Sandwell, D. T., Tymofyeyeva, E., González-Ortega, A., & Tong, X. (2017). Tectonic and anthropogenic deformation at the Cerro Prieto geothermal step-over revealed by Sentinel-1A InSAR. IEEE Transactions on Geoscience and Remote Sensing, 55(9), 5284-5292. https://doi.org/10.1109/TGRS.2017.2704593