Hi, I'm an error. x

Time Series of InSAR-Measured Seasonal Surface Deformation Induced by the West African Monsoon in Sudanian West Africa

Resource type: Composite Resource
Storage: The size of this resource is 51.6 MB
Created: Mar 05, 2019 at 11:31 p.m.
Last updated: Mar 06, 2019 at 12:15 a.m.
DOI: 10.4211/hs.d03e53c0d3eb4c72ad43edc0b309755a
Citation: See how to cite this resource
Sharing Status: Published
Views: 715
Downloads: 73
+1 Votes: Be the first one to  +1 this.  (You need to be logged in to rate this.)
Comments: No comments (yet)


This dataset consists of InSAR-measured line-of-sight surface deformation over the Ara watershed (located in northern Benin) for two dry seasons (November 2015-June 2016 and November 2016-June 2017). Thirty-six single look complex (SLC) images acquired by the Sentinel 1 mission were obtained from the Alaska Satellite Facility (ASF) Distributed Active Archive Centers (DAAC; http://www.asf.alaska.edu/). 12-, 24-, and 36-day interferograms were generated using the open source (GNU General Public License) Generic Mapping Tools 5 Synthetic Aperture Radar (GMT5SAR) processing system (Sandwell et al 2016, Massonnet and Feigl 1998). GMT5SAR geometrically aligns Sentinel TOPSAR images to a single master image with centimeter accuracy, maps topography into phase, and forms a stack of complex interferograms (Sandwell et al 2016). The Generic Mapping Tools- (GMT-) (Wessel et al 2013) based GMT5SAR postprocesser filters the interferogram and generates phase, coherence, and phase gradient products. GMT5SAR unwraps the interferograms using the well-known snaphu algorithm (Chen and Zebker 2000). Filter and decimation parameters for the inSAR processing were chosen to produce relatively high resolution interferograms, considering the computational cost of phase unwrapping. Lighter filtering and decimation improves interferogram resolution, but increases the computational time for phase unwrapping. Pixels were decimated by a factor of 8 in the range and 2 in the azimuth directions, generating interferograms with a pixel size of approximately 18.4 x 28.2 meters (range x azimuth). A 100 meter Gaussian filter was selected for the Ara study area. Enhances spectral diversity was used to reduce phase mismatch at the burst boundary (Sandwell et al 2016). The new small baseline subset(NSBAS) technique (Doin et al 2011) was used was used to generate a time series analysis of deformation across the study area. The NSBAS algorithm was applied using the Generic InSAR Analysis Toolbox (GIAnT; Agram et al 2012, 2013). The GIAnT tool box stacked the geometrically-aligned phase-unwrapped interferograms, estimated and applied corrections for residual long‐wavelength errors due to imprecise orbits, and estimated line-of-sight displacements using the NSBAS technique.

Agram P S, Jolivet R, Riel B, Lin Y N, Simons M, Hetland E, Doin M-P and Lasserre C 2013 New Radar Interferometric Time Series Analysis Toolbox Released Eos Trans. Am. Geophys. Union 94 69–70
Chen C W and Zebker H A 2000 Network approaches to two-dimensional phase unwrapping: intractability and two new algorithms J Opt Soc Am A 17 401–414
Doin M-P, Guillaso S, Jolivet R, Lasserre C, Lodge F, Ducret G and Grandin R 2011 Presentation of the small baseline NSBAS processing chain on a case example: the Etna deformation monitoring from 2003 to 2010 using Envisat data Proceedings of the Fringe Symposium (ES) pp 3434–3437
Massonnet D and Feigl K L 1998 Radar interferometry and its application to changes in the Earth’s surface Rev. Geophys. 36 441–500
Sandwell D, Mellors R, Tong X, Wei M and Wessel P 2016 Gmtsar: An insar processing system based on generic mapping tools (second edition)
Wessel P, Smith W H, Scharroo R, Luis J and Wobbe F 2013 Generic mapping tools: improved version released Eos Trans. Am. Geophys. Union 94 409–410

Subject Keywords

  • No subject keywords have been added.
  • ${ k }

Duplicate. Keyword not added.

Error: ${ error }
Deleting all keywords will set the resource sharing status to private.

Resource Level Coverage


Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
North Latitude
East Longitude
South Latitude
West Longitude


Start Date:
End Date:




Derived From: Torres R, Snoeij P, Geudtner D, Bibby D, Davidson M, Attema E, Potin P, Rommen B, Floury N, Brown M, Traver I N, Deghaye P, Duesmann B, Rosich B, Miranda N, Bruno C, L’Abbate M, Croci R, Pietropaolo A, Huchler M and Rostan F 2012 GMES Sentinel-1 mission Remote Sens. Environ. 120 9–24

Related Resources

The content of this resource serves as the data for: Slinski, K., T. Pellarin, B. Hector, J.M. Cohard, J.M. Vouillamoz, M. Descloitres, T. Hogue, and J. McCray. “InSAR-Measured Seasonal Surface Deformation Induced by the West African Monsoon in Sudanian West Africa.” Applied Sciences [Special Issue: Advances in Geohydrology: Methods and Applications] (in preparation).


Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
National Science Foundation Graduate Research Fellowship DGE-1057607


People that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.

Name Organization Address Phone Author Identifiers
Dr. Thierry Pellarin
Dr. Jean-Martial Cohard
Dr. Basile Hector
Dr. Terri Hogue
Dr. John McCray

How to Cite

Slinski, K. (2019). Time Series of InSAR-Measured Seasonal Surface Deformation Induced by the West African Monsoon in Sudanian West Africa, HydroShare, https://doi.org/10.4211/hs.d03e53c0d3eb4c72ad43edc0b309755a

This resource is shared under the Creative Commons Attribution CC BY.



There are currently no comments

New Comment