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  • This data set contains velocity and strain rate fields over the northeast Tibetan Plateau, which are derived from Sentinel-1A and -1B synthetic aperture radar satellite data (SAR) and stored in GeoTIFF (.tif) or NETCDF (.grd) formats. The velocities in the line-of-sights (LOS) of the satellites were processed at ~100 m resolution from time series in ~250km x 250km frames. The data set consists of velocities from 10 frames in ascending tracks and 13 frames in descending tracks of the satellites' orbits. The spatial extent of the velocities spans 96E-108E and 32N-43N, covering an area of 660,000 km^2. The temporal coverages of the data span from October 2014 to December 2019 across 65-110 acquisition epochs. The uncertainties of the velocities average to <1 mm/yr. The time series are inverted from fully-connected networks of short-temporal-baseline interferograms which are generated from interfering and unwrapping pairs of SAR imagery. The velocities represent the average velocity through the displacement time series. The LOS velocities were decomposed into east and vertical velocities which are also archived with associated uncertainties. These Cartesian fields cover the overlapping areas between ascending and descending tracks and total 440,000 km^2. By combining the horizontal gradients of the filtered east velocities and interpolated north velocities from Global Navigational Satellite System, we derive second invariant, maximum shear, and dilatation strain rate fields for the same area with 1 km sampling intervals. These strain rate fields highlight creeping sections and strain concentration on faults and fault junctions. The velocity fields reveal fault kinematics in terms of slip rates and partitioning. The vertical velocities also show non-tectonic signals such as subsidence related to permafrost melting, groundwater extraction, and reservoir loading, as well uplift from blocked drainages. The data are collected and processed by Qi Ou with the automatic processing tools developed by Milan Lazecky. Velocity and strain rate fields were interpreted by all authors. By default, interferograms were generated from each epoch to six consecutive epochs and between acquisition pairs with six-month and nine-month temporal baselines. Interferograms with the unwrapping error were removed from the network and all networks were continuous and fully connected.

  • Data provided are monthly surface water layers extracted from Sentinel1A SAR data for 3 districts in India (Shivamogga, Sindhudurg, Wayanad) for the year 2017 and 2018. Surface water body layers were mapped using an average monthly threshold value extracted from the image backscatter histogram. The average threshold value excluded the monsoon months due to the difference in water and not water area. The threshold value was slightly lesser than the mean threshold value. The end product was validated using field data which resulted in user and producer accuracies. Monthly surface water body layers were not produced for a few months due to the non-availability of Sentinel 1 data. The work was supported by MRC, AHRC, BBSRC, ESRC and NERC [grant number MR/P024335/1] and NERC - SUNRISE project [grant number NE/R000131/1] Full details about this dataset can be found at