This dataset represents measurements of vertical ground surface displacement in more than 200 of the high-use and populated groundwater basins across the State of California between January of 2015 and April of 2022. Vertical displacement estimates are derived from Interferometric Synthetic Aperture Radar (InSAR) data that are collected by the European Space Agency (ESA) Sentinel-1A satellite and processed by TRE ALTAMIRA Inc. (TRE), under contract with the California Department of Water Resources (DWR) as part of DWR’s SGMA technical assistance to provide important SGMA-relevant data to GSAs for GSP development and implementation. Sentinel-1A InSAR data coverage began in late 2014 for parts of California, and coverage for the entire study area began in June 13, 2015. Included in this dataset are point data that represent average vertical displacement values for 100 meter by 100 meter areas, as well as GIS rasters that were interpolated from the point data; rasters for total vertical displacement relative to June 13, 2015, and rasters for annual vertical displacement rates with earlier coverage for some areas, both in monthly time steps. Towill Inc. (Towill), also under contract with DWR as part of DWR’s SGMA technical assistance, conducted an independent study comparing the InSAR-based vertical displacement point time series data to data from Continuous Global Positioning System (CGPS) stations. The goal of this study was to ground-truth the InSAR results to best available independent data.

Data update frequency: Quarterly Report update frequency: Annual

Line-of-sight Displacement across Los Angeles as shown in Fig 14b of Lee and Shirzaei (2023) Remote Sensing of Environment. Volume 286, 113447 The SAR data sets spanning from 26 February 2016 to 29 August 2022 include 247 images in descending orbits of the Sentinel-1 C-band satellite. The data set is processed using WabInSAR algorithm. See citations below for details. Associated Resource Citation: Lee, Jui-Chi, and Shirzaei. Manoochehr (2023), Novel Algorithms for Pair and Pixel Selection and Atmospheric Error Correction in Multitemporal InSAR, Remote Sensing of Environment. Volume 286, 113447, doi:10.1126/j.rse.2022.113447 References: [1] M. Shirzaei, J. Freymueller, T. E. Törnqvist, D. L. Galloway, T. Dura, and P. S. J. Minderhoud, "Measuring, modelling and projecting coastal land subsidence," Nature Reviews Earth & Environment, 2020/12/10 2021, doi: 10.1038/s43017-020-00115-x. [2] M. M. Miller and M. Shirzaei, "Assessment of future flood hazards for southeastern Texas: Synthesizing subsidence, sea‐level rise, and storm surge scenarios," Geophysical Research Letters, vol. 48, no. 8, p. e2021GL092544, 2021. [3] E. Blackwell, M. Shirzaei, C. Ojha, and S. Werth, "Tracking California’s sinking coast from space: Implications for relative sea-level rise," Science Advances, vol. 6, no. 31, p. eaba4551, 2020, doi: 10.1126/sciadv.aba4551. [4] C. Ojha, M. Shirzaei, S. Werth, D. F. Argus, and T. G. Farr, "Sustained Groundwater loss in California's Central Valley exacerbated by intense drought periods," Water resources research, vol. 54, no. 7, pp. 4449-4460, 2018. [5] M. Shirzaei and R. Bürgmann, "Time-dependent model of creep on Hayward fault inferred from joint inversion of 18 years InSAR time series and surface creep data," J. Geophys. Res. Solid Earth, vol. 118, no. 1733–1746, p. doi:10.1002/jgrb.50149, 2013. [6] M. Shirzaei, "A Wavelet-Based Multitemporal DInSAR Algorithm for Monitoring Ground Surface Motion," (in English), Ieee Geoscience and Remote Sensing Letters, vol. 10, no. 3, pp. 456-460, May 2013, doi: Doi 10.1109/Lgrs.2012.2208935. [7] M. Shirzaei and R. Bürgmann, "Topography correlated atmospheric delay correction in radar interferometry using wavelet transforms," Geophysical Research Letters, vol. 39, no. 1, p. doi: 10.1029/2011GL049971, Jan 6 2012. [Online]. Available: ://WOS:000298930900002 [8] M. Shirzaei, R. Bürgmann, and E. J. Fielding, "Applicability of Sentinel‐1 Terrain Observation by Progressive Scans multitemporal interferometry for monitoring slow ground motions in the San Francisco Bay Area," Geophysical Research Letters, vol. 44, no. 6, pp. 2733-2742, 2017, doi: 10.1002/2017GL072663.

This dataset represents measurements of vertical ground surface displacement in more than 200 of the high-use and populated groundwater basins across the State of California between January of 2015 and April of 2022. Vertical displacement estimates are derived from Interferometric Synthetic Aperture Radar (InSAR) data that are collected by the European Space Agency (ESA) Sentinel-1A satellite and processed by TRE ALTAMIRA Inc. (TRE), under contract with the California Department of Water Resources (DWR) as part of DWR’s SGMA technical assistance to provide important SGMA-relevant data to GSAs for GSP development and implementation. Sentinel-1A InSAR data coverage began in late 2014 for parts of California, and coverage for the entire study area began in June 13, 2015. Included in this dataset are point data that represent average vertical displacement values for 100 meter by 100 meter areas, as well as GIS rasters that were interpolated from the point data; rasters for total vertical displacement relative to June 13, 2015, and rasters for annual vertical displacement rates with earlier coverage for some areas, both in monthly time steps. Towill Inc. (Towill), also under contract with DWR as part of DWR’s SGMA technical assistance, conducted an independent study comparing the InSAR-based vertical displacement point time series data to data from Continuous Global Positioning System (CGPS) stations. The goal of this study was to ground-truth the InSAR results to best available independent data. Data update frequency: Quarterly Report update frequency: Annual

This dashboard was created in support of California's Groundwater Live.The dashboard includes several filtering options and data summaries for statewide InSAR subsidence.For inquiries, please email calgw@water.ca.gov.

A stack of unwrapped interferograms in the San Francisco Bay area, California, USA

Sensor: Sentinel-1 descending track 42

Processor: GMTSAR

This is an input dataset for the time series analysis with MintPy.

The tropospheric delay estimated from ERA-5 using PyAPS is attached.

Version 1.x (~2.3 GB)Time: 2014.12.31 - 2024.06.05 (333 acquisitions, 1297 interferograms)

Version 0.x (~290 MB; for fast testing of code development)Time: 2020.01.04 - 2021.07.15 (70 acquisitions, 184 interferograms)

A stack of unwrapped interferograms on Owens Valley, California for the 2019 Ridgecrest earthquake sequence.

Sensor: Sentinel-1 descending track 71

Time: 2019.06.10 - 2019.08.21, 7 acquisitions, 11 interferograms

Processor: ASF HyP3 (GAMMA)

Tropospheric delay estimated from ERA-5 using PyAPS is attached.

This is an input dataset for the time series analysis with MintPy.

to the best of our knowledge

deformation digital-elevation-model egs energy forge geodetics geospatial-data geothermal ground-deformation ground-deformation-monitoring imaging insar interferometric milford phase-2c processed-data radar remote-sensing roosevelt-hot-springs sar satellite synthetic-aperture synthetic-aperture-radar utah utah-forge utah-forge-phase-2c

InSAR vertical deformation time series during 2017-2022 from Sentinel-1 images over Santa Clara Valley aquifer system. Figures showing the comparison between InSAR vertical deformation and groundwater level time series for 71 wells located in the Santa Clara Valley aquifer system are also included.

Preliminary retrievals of deformation rates during 2017-2022 computed from Sentinel-1 data with the MSBASX system.

Part I: Ascending, Canada Part II: Descending, Canada Part III: Ascending, China Part IV: Descending, China Part V: Ascending, Russia Part VI: Descending, Russia Part VII: Glaciers (from speckle offsets), Canada Part VIII: Glaciers (from speckle offsets), Russia

For additional information contact Sergey Samsonov at sergey.samsonov@nrcan-rncan.gc.ca or samsonov@insar.ca.

Dewberry contracted TRE Altamira Inc (TREA) for a historical InSAR analysis of ground displacement over the East Coast of the United States of America (U.S.A.), for the National Oceanic and Atmospheric Administration (NOAA). The area of interest (AOI) includes the major coastal cities and coastal plains and covers over 500,000 km2 (193,000 mi2). TREAs proprietary SqueeSAR algorithm was used to process Sentinel-1 (SNT) satellite imagery acquired between January 2017 and July 2023 and the results were calibrated using the NOAA's CORS GNSS (Global Navigation Satellite System) network for the generation of vertical InSAR (SAR Interferometry) measurements.

Interferometric Synthetic Aperture Radar data from the TerraSAR-X and the TanDEM-X satellite missions operated by the German Space Agency (DLR). Interferometric pairs (interferograms) were created using generic mapping tool GMT-SAR processing software (see link in Resources). Data from January through June 2022.

A stack of unwrapped interferograms in San Francisco Bay, California, USA from Sentinel-1descending track 42

Processor: ARIA (processed using ISCE/topsStack and prepared using ARIA-tools as shown below)

Tropospheric delay estimated from ERA-5 using PyAPS is attached.

This is an input dataset for the time series analysis with MintPy.

Version 1+ (~2.7 GB): Time: 2015.05.12 - 2020.03.10, 114 acquisitions, 505 interferograms Used ARIA-tools commands (access date Nov 9th, 2020):

ariaDownload.py -b '37.25 38.1 -122.6 -121.75' --track 42 --end 20200315 ariaTSsetup.py -f 'products/*.nc' -b '37.25 38.1 -122.6 -121.75' --mask Download

Version 0.* (~410 MB; for fast testing of code development) Time: 2015.05.12 - 2017.05.10, 32 acquisitions, 121 interferograms Used ARIA-tools commands (access date Nov 9th, 2020):

ariaDownload.py -b '37.35 38.00 -122.45 -121.80' --track 42 --end 20170510 ariaTSsetup.py -f 'products/*.nc' -b '37.35 38.00 -122.45 -121.80' --mask Download

This dataset includes compute codes for computing deformation field using GNSS and InSAR data, and the input and output data files for its application to California and Western Nevada. Fortran code gic3d.f is for integrating GNSS and inSAR data to produce the 3-D velocity field, and fortran code visr_n.f is for interpolating the horizontal velocity field produced by gic3d to produce the strain rate field. Please read the file "readme.txt" for their input and output files and how to run the codes.

This dataset contains Interferometric Synthetic Aperture Radar (InSAR) data used for ground deformation monitoring during Phase 2C of the Utah FORGE project. The dataset includes measurements of the mean rate of range change and associated standard errors, provided in both CSV and NetCDF formats. Supporting materials include histograms, maps, and MATLAB scripts used for data processing, as well as unit vector information describing the satellite's sensor orientation. Compressed archives contain additional metadata and masked range change data for individual interferometric pairs. A README file is included to provide further details on the dataset's structure and contents.

The application of InSAR deformation measurements is feasible in a geometry sensitive to deformation. Our sensitivity index is a static analysis of the lower bound of the sensitivity of Sentinel-1 to the downslope deformation of a potential landslide.

The full process is explained in the linked paper (https://doi.org/10.1016/j.jag.2022.102829). Provided here is a global data set of the sensitivity index derived from the Copernicus digital elevation model (COP-DEM_GLO-30, 2019_1).

Individual sheets of 1°×1° are identified by their lower left corner in WGS84. Sheets are combined in archives of 10°×10°, numbered by the truncated sheet indices (e.g. S49_00_W053.tiff is in S40_W050.zip). A sheet index is provided in S_v1.gpkg and as an interactive map. No data is provided over Armenia and Azerbaijan, as these areas were not included in the public Copernicus data. The underlying Python script (20210208_CopernicusSpherical.py) is provided with the data.

This dataset contains the deformation data for 191 data sets and figures (LOS velocities, amplitude and time offset of the annual deformation, decomposed vertical and EW velocities, rice paddy fields, NDVI, optical images, topography, and SB network) mentioned in the paper “Nationwide urban ground deformation monitoring in Japan using Sentinel-1 LiCSAR products and LiCSBAS”

Morishita, Y. Nationwide urban ground deformation monitoring in Japan using Sentinel-1 LiCSAR products and LiCSBAS. Prog Earth Planet Sci 8, 6 (2021). https://doi.org/10.1186/s40645-020-00402-7

View on a web map:

https://yumorishita.github.io/gsimaps_S1_Japan_LiCSBAS/#9/35.766572/140.038605/&base=std&base_grayscale=1&ls=std%2C0.5%7Chillshademap%2C0.5%7CallUD%7Clanduse_veg&blend=100&disp=1110&vs=c1j0h0k0l0u0t0z0r0s0m0f2&d=m

This submission provides Interferometric Synthetic Aperture Radar (InSAR) data covering the Utah FORGE site via the TerraSAR-X and TanDEM-X satellite missions operated by the German Space Agency (DLR). Data was collected between 2019/01/01 and 2023/06/30. Interferometric pairs (interferograms) were created using generic mapping tool GMT-SAR processing software. The best 112 pairs were selected based on having short orbital separations (perpendicular baseline less than 5 meters in absolute value).

This is the InSAR dataset used in Utah FORGE Phase 2C to augment ground deformation monitoring. It is accompanied by a README.txt file which contains an explanation of the data in the .zip archive.

Vertical Land Motion across San Francisco Bay as shown in Fig 2 of Shirzaei and Bürgmann (2018) Science Advances.The SAR data sets spanning from 13 July 2007 to 17 October 2010 include 32, 24, and 19 images in descending (incidence angle = 23°, heading angle = 193°) and ascending (incidence angle = 23°, heading angle = 350°) orbits of Envisat C-band satellite and ascending (incidence angle = 34.5°, heading angle = 350°) orbit of ALOS L-band satellite, respectively. The data set is processed using WabinSAR algorithm. See citations below for details.