Description: In permafrost areas, seasonal freeze-thaw cycles of active layer result in upward and downward movements of the ground. Additionally, relatively uniform thawing of the ice-rich layer at the permafrost table can contribute to net long-term surface lowering. We use a simple method to quantify surface lowering (subsidence) and uplift in a yedoma area of the Lena River Delta, Siberian Arctic (Kurungnakh Island), using reference rods (metal pipes and fiberglass rods) installed deeply in permafrost. The metal pipes were 2 m long and 3 cm in diameter and were anchored at least 1 m below the typical active layer. The fiberglass rods were 2 m long and 1 cm in diameter and were anchored at least 70 m below the typical active layer. We assume, therefore, that the rods were motionless relative to the permafrost. The plexiglass plate with a size of 10 by 10 cm was fixed in its horizontal position by the rod but could move freely with the surface vertically along the rod. We repeatedly measured distance between the top of a rod and a plexiglass plate resting on the ground surface. Several distance measurements around each rod were taken at each visit and averaged. Altogether 12 metal pipes were installed at the study site in April 2013 and 19 fiberglass rods were installed in April 2014. Measurements were conducted during field campaigns from spring 2013 to summer 2017 with some gaps. We provide here the measured distances between the top of a rod and a plexiglass plate. To obtain the ground displacement, the user have to define the period of interest and calculate the displacement.

Description: In permafrost areas, seasonal freeze-thaw cycles of active layer result in upward and downward movements of the ground. Additionally, relatively uniform thawing of the ice-rich layer at the permafrost table can contribute to net long-term surface lowering. We use a simple method to quantify surface lowering (subsidence) and uplift in a yedoma area of the Lena River Delta, Siberian Arctic (Kurungnakh Island), using reference rods (metal pipes and fiberglass rods) installed deeply in permafrost. The metal pipes were 2 m long and 3 cm in diameter and were anchored at least 1 m below the typical active layer. The fiberglass rods were 2 m long and 1 cm in diameter and were anchored at least 70 m below the typical active layer. We assume, therefore, that the rods were motionless relative to the permafrost. The plexiglass plate with a size of 10 by 10 cm was fixed in its horizontal position by the rod but could move freely with the surface vertically along the rod. We repeatedly measured distance between the top of a rod and a plexiglass plate resting on the ground surface. Several distance measurements around each rod were taken at each visit and averaged. Altogether 12 metal pipes were installed at the study site in April 2013 and 19 fiberglass rods were installed in April 2014. Measurements were conducted during field campaigns from spring 2013 to summer 2017 with some gaps. We provide here the measured distances between the top of a rod and a plexiglass plate. To obtain the ground displacement, the user have to define the period of interest and calculate the displacement.

Description: Differential SAR interferometry (DInSAR) uses the phase difference between two SAR signals acquired on two dates over the same area to measure small-scale ground motion. During the last decade the method has been adapted for monitoring permafrost-related ground motion. Here we perform DInSAR on TerraSAR-X data to assess its viability for seasonal thaw subsidence detection in a yedoma landscape of the Lena River Delta. TerraSAR-X is a right-looking SAR satellite launched in 2007, operating in the X-band (wavelength 3.1 cm, frequency 9.6 GHz), with a revisit time of eleven days. All data that we used were acquired in StripMap mode with HH polarization from a descending orbit at 08:34 local acquisition time (22:34 UTC). The incidence angle of the track we use is approximately 31 degrees. The scene size covered an area of approximately 18 km x 56 km. The slant range and azimuth pixel spacing were approximately 0.9 m and 2.4 m, respectively. Based on the ground temperature data we roughly estimated the beginning and the end of thaw season in 2013. The corresponding TerraSAR-X time series used for this study includes nine Single-Look Slant Range Complex (SSC) images taken between 7 June and 14 September 2013. The time span between the acquisitions that we used for interferometry was 11 days, with one exception when the time span was 22 days due to a missing acquisition. The data were processed using the Gamma radar software. The SSC data were converted to Gamma Single Look Complex (SLC) format and the SLC data were then consecutively co-registered with subpixel accuracy (typically better than 0.2 pixels) in such a way that the co-registered slave image became the master for the next image. This way of co-registering also ensures subpixel co-registration accuracy for all interferometric combinations of the nine images. Multilooking was performed with the factor 4 in the range and factor 3 in the azimuth directions to reduce the noise and obtain roughly square ground range pixels. The ground size of the multilooked pixel is approximately 7 m. We removed the topographic phase term using ArcticDEM that is a freely available high-resolution (5 m) circum-Arctic DEM produced from optical stereographic WorldView imagery acquired from 2012–2016. Obtained differential interferograms were then filtered with an adaptive filter based on the local fringe spectrum with the filtering window size of 128 pixels and an alpha exponent of 0.4. Interferograms, featuring especially low coherence, were additionally filtered with a window size of 64 pixels. For the phase unwrapping we used a branch-cut algorithm with the seeding point located approximately in the middle of the study area with relatively high coherence. We did not attempt to unwrap the areas, separated from the main study area by the river channels. The influence of atmospheric phase delays was evident in the unwarpped interferograms. In order to enhance the displacement signal and reduce atmospheric noise, all eight unwrapped interferograms were summed up in a time-continuous stack. Phase rate per day was calculated from the stack. A strong linear ramp was present across the phase rate map. To remove the trend, a 2D linear function was fit to the data and then subtracted from the phase rate map. The phase rate was then converted to vertical displacement rate in meters, under the assumption that the ground movement is purely vertical. The resulting displacement rate map was geocoded using ArcticDEM to the Universal Transverse Mercator (UTM) projection, zone 52N WGS84 with a pixel size of 5 m. The map was finally converted to the displacement magnitude by multiplying the rate by 99 days (from 7 June to 14 September 2013) and converted to centimeters. As opposed to the results, published in the related paper, here we did not start the unwrapping from the known bedrock position, as it was partly affected by low coherence as well as rather remote from the main area of interest and only weakly connected to the rest of the map over a small and noisy area of valid pixels. It means that the displacement map published here, features only displacement values relative to each other, without a fixed reference point. The spatial pattern of the signal, however, did not change with this alteration in processing. The DInSAR map showed a distinct subsidence in most of the thermokarst basins relative to the upland. Moreover, the spatial pattern of DInSAR signal was in high agreement with the surface wetness in the basins, identified with the near infra-red band of a high-resolution optical image. Drier parts of the basins were clearly separated from wetter parts that showed a prominent subsidence. In general, low coherence in combination with atmospheric effects as well as remoteness of a reference ground point were severe obstacles for the retrieval of a wide-area seasonal thaw subsidence map with TerraSAR-X data.

Description: In permafrost areas, seasonal freeze-thaw cycles result in upward and downward movements of the ground. For some permafrost areas, long-term downward movements were reported during the last decade. We measured seasonal and multi-year ground movements in a yedoma region of the Lena River Delta, Siberia, in 2013–2017, using reference rods installed deep in the permafrost. The seasonal subsidence was 1.7 ± 1.5 cm in the cold summer of 2013 and 4.8 ± 2 cm in the warm summer of 2014. Furthermore, we measured a pronounced multi-year net subsidence of 9.3 ± 5.7 cm from spring 2013 to the end of summer 2017. Importantly, we observed a high spatial variability of subsidence of up to 6 cm across a sub-meter horizontal scale. In summer 2013, we accompanied our field measurements with Differential Synthetic Aperture Radar Interferometry (DInSAR) on repeat-pass TerraSAR-X (TSX) data from the summer of 2013 to detect summer thaw subsidence over the same study area. Interferometry was strongly affected by a fast phase coherence loss, atmospheric artifacts, and possibly the choice of reference point. A cumulative ground movement map, built from a continuous interferogram stack, did not reveal a subsidence on the upland but showed a distinct subsidence of up to 2 cm in most of the thermokarst basins. There, the spatial pattern of DInSAR-measured subsidence corresponded well with relative surface wetness identified with the near infra-red band of a high-resolution optical image. Our study suggests that (i) although X-band SAR has serious limitations for ground movement monitoring in permafrost landscapes, it can provide valuable information for specific environments like thermokarst basins, and (ii) due to the high sub-pixel spatial variability of ground movements, a validation scheme needs to be developed and implemented for future DInSAR studies in permafrost environments.

Description: Thermokarst lakes and ponds are major elements of permafrost landscapes, occupying up to 40% of the land area in some Arctic regions. Shallow lakes freeze to the bed, thus preventing permafrost thaw underneath them and limiting the length of the period with greenhouse gas production in the unfrozen lake sediments. Radar remote sensing permits to distinguish lakes with bedfast ice due to the difference in backscatter intensities from bedfast and floating ice. This study investigates the potential of a unique time series of three-year repeat-pass TerraSAR-X (TSX) imagery with high temporal (11 days) and spatial (10 m) resolution for monitoring bedfast ice as well as ice phenology of lakes in the zone of continuous permafrost in the Lena River Delta, Siberia. TSX backscatter intensity is shown to be an excellent tool for monitoring floating versus bedfast lake ice as well as ice phenology. TSX-derived timing of ice grounding and the ice growth model CLIMo are used to retrieve the ice thicknesses of the bedfast ice at points where in situ ice thickness measurements were available. Comparison shows good agreement in the year of field measurements. Additionally, for the first time, an 11-day sequential interferometric coherence time series is analyzed as a supplementary approach for the bedfast ice monitoring. The coherence time series detects most of the ice grounding as well as spring snow/ice melt onset. Overall, the results show the great value of TSX time series for monitoring Arctic lake ice and provide a basis for various applications: for instance, derivation of shallow lakes bathymetry, evaluation of winter water resources and locating fish winter habitat as well as estimation of taliks extent in permafrost.

Description: Satellite-based monitoring strategies for permafrost remain under development and are not yet operational. Remote sensing allows indirect observation of permafrost, a subsurface phenomenon, by mapping surface features or measuring physical parameters that can be used for permafrost modeling. We have explored high temporal resolution time series of TerraSAR-X backscatter intensity and interferometric coherence for the period between August 2012 and September 2013 to assess their potential for detecting major seasonal changes to the land surface in a variety of tundra environments within the Lena River Delta, Siberia. The TerraSAR-X signal is believed to be strongly affected by the vegetation layer, and its viability for the retrieval of soil moisture, for example, is therefore limited. In our study individual events, such as rain and snow showers, that occurred at the time of TerraSAR-X acquisition, or a refrozen crust on the snowpack during the spring melt were detected based on backscatter intensity signatures. The interferometric coherence showed marked variability; the snow cover onset and snow melt periods were identified by significant reduction in coherence. Principal component analysis provided a good spatial overview of the essential information contained in backscatter and coherence time series and revealed latent relationships between both time series and the surface temperature. The results of these investigations suggest that although X-band SAR has limitations with respect to monitoring seasonal land surface changes in permafrost areas, high-resolution time series of TerraSAR-X backscatter and coherence can provide new insights into environmental conditions.

Description: We have generated a digital elevation model (DEM) of an area between the town of Inuvik and Eskimo Lakes near the Mackenzie Delta, Northwest Territories, Canada. We used seven TanDEM-X CoSSC pairs, acquired in summer 2015 during the TanDEM-X Science Phase, and provide here the mean elevation of the seven produced DEMs (GeoTIFF raster). The processing was based on differential SAR interferometry with the use of ArcticDEM as reference. We also provide the standard deviation map of the seven DEMs (GeoTIFF raster) as a quality indicator. The final mean DEM was validated against DGPS measurements. Height values are given in meters in reference to ellipsoid (WGS84). The pixel size of the products is 10 m. The coordinate reference system is UTM Zone 8N WGS84. Detailed description of the TanDEM-X data, interferometric processing, and the validation is given in the attached metadata file.

Description: The goal of this study was to examine the potential of a unique X-band SAR dataset to monitor ice phenology and bedfast ice on a number of thermokarst lakes in the Siberian Arctic. Three-year repeat-pass TSX time series with high temporal (11 days) and spatial (10 m) resolution were used. Two different parameters derived from SAR imagery were employed in the analysis: backscatter intensity and 11-day interferometric coherence. In situ ice thickness measurements were collected at 14 locations from a sample of 10 lakes in April 2015. A region of interest (ROI) was created around each in situ ice thickness measurement location as a circle with a diameter of approximately 10 pixels. We provide extracted backscatter and coherence for all the ROIs as georeferenced .tif files for the entire time series.

Description: Principal Component Analysis (PCA) is a well-established technique in remote sensing for the visualization of multidimensional data. It reduces redundancy in multiband or multitemporal imagery, increases the signal-to-noise ratio and provides an opportunity to use multitemporal datasets for change detection. PCA transforms the axes of multidimensional data in such way that the new axes (the principal components) account for variances within the data, with the first PC accounting for the largest variance and the last PC accounting for the smallest variance. In our study PCA of TerraSAR-X time stacks of backscatter intensity and interferometric coherence provided a good spatial overview of the essential information contained within the multiple time slices. The PC1 for both stacks showed the most common features of the contributing images and represented the means of the temporal stacks. The PC1 of the coherence stack accounted for 29% of the variance (or unique information) and mapped (i) water bodies (lakes and river), (ii) rocky outcrops, and (iii) the remaining land surfaces. The PC1 of the backscatter stack accounted for 35% of the variance and was contaminated by such effects as the presence or absence of lake ice and shadow/layover in the rocky outcrops region. Anomalies in seasonal patterns were demonstrated by the higher PCs. The PC2 of the backscatter stack accounted for 22% of the variance and delineated water bodies. The PC3 of backscatter stack accounted for only 4% of the variance in the dataset and represented the spatial variance in river ice conditions during spring. The PC2 of coherence, which accounted for 9.5% of the variance in the coherence stack, represented the spatially variable snow conditions in spring (snowmelt to the south and stable snow cover to the north).