CR2.3

The cryosphere plays a vital role in the climate system by changing the energy and mass transfer between the atmosphere and the surface, and it is the most important land cover type in the Himalayan and Polar Regions, which act as an important source of freshwater for rivers. This study examines Snow-Covered Area (SCA) and Snowline variations in the Uttarakhand Himalaya using the Normalized Difference Snow Index (NDSI). Three Landsat series imagery for 1990, 2000, 2010, and Sentinel Data from 2015 to 2021 were processed and analyzed through open-source software. In order to estimate the average elevation of the snowline, a digital elevation model was used and an area estimation study using multispectral imagery. The research focuses on the snowline and snow cover variations over the Uttarakhand Himalaya from 1990 to 2021 and the average snow line-height, respectively. This study shows that in the years 1990, 2000, 2010, and 2015 to 2021, snow line variations and area estimation differences in Uttarakhand, Central Himalaya, and snow line at above sea level (a.s.l.) in the western and eastern part of the study area. This study deals with the analysis of snow line shifting and cover. It suggests that the snow cover area in the Uttarakhand, Central Himalaya, is depleting steadily, which will have adverse impacts, especially upon water resources causing various economic and social disruptions in the near future.

How to cite: Pandey, A., Parashar, D., Palni, S., and Singh, A. P.: Application of Remote Sensing and GIS to Assess Snow Cover and its Dynamics: A Case Study of Uttarakhand Himalaya, India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1867, https://doi.org/10.5194/egusphere-egu22-1867, 2022.

How to cite: Tanis, C. M., Luojus, K., Kosmale, M., Gascoin, S., Schwaizer, G., Hetzenecker, M., Zschenderlein, L., Ablain, M., and Dorandeu, J.: Gap-filled snow cover fraction from Sentinel-1 and Sentinel-2 constellations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12419, https://doi.org/10.5194/egusphere-egu22-12419, 2022.

The Andes Mountains span a length of 7000 km and are important for sustaining regional water supplies in South America. Rivers flow from the west side of the Andes to the Pacific Ocean and are the main source of water supply for energy generation, irrigation, and drinking water. Snow variability across this region has not been studied in detail due to sparse and unevenly distributed instrumental climate data. The optical remote sensing approach has been developed as a great tool to avoid this limitation. However, cloud cover reduces the ability to use it in the northernmost and southernmost portions of the Andes and the winter and spring in the central part of the Andes. We tested the performance of temporal, spatial algorithms in consecutive and simultaneous steps over daily MODIS snow cover products (Aqua and Terra). We evaluated the cloud reduction (effectiveness) and accuracy using simulated experiments from MODIS data by selecting low cloud cover images (“truth”) and cover with artificial clouds, then we ran all the algorithms and tested them based on the “truth” dataset. On clear sky days, we include higher spatial remote sensing data (Landsat and Sentinel) and in-field data from UAV. The combination of Aqua and Terra reduced the cloud cover by 10-15% on a yearly scale. The temporal combination with previous and following days yielded a substantial improvement in cloud removal but is usually less effective for large-area cloud cover. Developing a new dataset with cloud reduction can help to increase the performance of the snowmelt runoff model and extend a large latitude range across the Andes Mountains to use optical remote sensing data for seasonal snow studies. The use of machine learning, fusion with other snow products (e.g. radar), and more intense use of UAVs point to the next research.

How to cite: Saavedra, F., Hernandez, A., Gonzalez, D., Aguirre, Y., Contreras, V., Caro, A., and Romero, C.: Validation of Cloud Reduction Algorithms Over MODIS Snow Products on Andes Mountain, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12911, https://doi.org/10.5194/egusphere-egu22-12911, 2022.

Melting ice sheets are a major contributor to the rising sea level. At the Liège University, the Regional Climate Model MAR (Modèle Atmosphérique Régional) has been developed to monitor and study the current and future evolution of various properties of ice sheets. However, uncertainties remain on the surface melt extent upon Antarctic ice sheets as models are subject to error propagation and need some external data to model the climate.

In Antarctica, unlike Greenland, the produced surface meltwater does not leave the ice sheet through visible rivers in which the quantity of meltwater can be estimated. Remote sensing is then the only product able to provide an estimation of the surface melt extent with a satisfying spatial and temporal coverage. The assimilation of melt spatial extent estimated by remote sensing allows the mitigation of the uncertainties linked to the models as well as a better quantification of the melt quantity.

In this research, active (Sentinel-1) and passive (AMSR2 & SSMIS) microwave satellite data are assimilated into MAR model over the Antarctic Peninsula, where surface melt has caused hydrofracturing and destabilization of ice shelves in the past. The assimilation of the different satellite products is also conducted to study the effect of spatial resolution on melt detection, Sentinel-1 having a pixel size of a few meters while passive satellites are at the 10km scale. This difference can be crucial upon the Peninsula as Foehn effects are occurring locally and can generate local surface melt, not detectable while using a coarser resolution.

How to cite: Dethinne, T., Kittel, C., Glaude, Q., Fettweis, X., and Orban, A.: Interest of the Assimilation of Surface Melt Extent Derived From Passive and Active Microwave Satellites Into the Regional Climate Model MAR Over the Antarctic Peninsula, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5913, https://doi.org/10.5194/egusphere-egu22-5913, 2022.

The Landsat-1 satellite provides sporadic coverage of coastal Antarctica between 1972 and 1975.  This dataset is a highly valuable scientific resource but has yet to be utilized to its full potential. The imagery are of reasonable quality and have a spatial resolution of 60 m, but are often difficult to process owing to their poor geolocation accuracy, where most images are displaced by >10 km. This requires a time-consuming manual correction which can be especially tricky over the featureless sections of the ice sheet (e.g. Ross Ice Shelf). Here we report on progress towards creating a geolocated mosaic over coastal Antarctica that preliminary analysis indicates will have near-complete coverage with only limited cloud cover. Potential glaciological uses for the mosaic include coastline change both in terms of fast flowing outlet glaciers and the slower flowing regions of the coastline, ice shelf damage, basal channel evolution and migration, changes in ice rises and also any changes in bedrock exposure. We highlight these potential uses with a few small-scale examples.

How to cite: Miles, B. and Bingham, R.: Landsat-1 mosaic of Antarctica from the 1970s, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7452, https://doi.org/10.5194/egusphere-egu22-7452, 2022.

About a third of Greenland’s total ice losses come from the Northwest sector, a sector that includes a large number of marine-terminating outlet glaciers, which have all experienced widespread retreat triggered by ocean-induced melting. Here, we measure changes in surface elevation in the Northwest sector of the Greenland Ice Sheet from CryoSat-2 between July 2010 and July 2021 and find that the surface has lowered at a rate of 21.9 ± 1.1 cm/yr on average over this period, with rapid thinning occurring at the ice sheet margins at a rate of 46.9 ± 5.9 cm/yr. We further compute mass change by combining our CryoSat-2 surface elevation change dataset with firn densities from a regional climate model, and we show that the Northwest sector lost 456 ± 5.7 Gt of ice between July 2010 and July 2021.

To evaluate our altimetry-based mass balance solution, we compare our solution to independent estimates derived from satellite gravimetry and the mass budget method. We show that our altimetry estimate is the least negative for the Northwest sector as a whole, in contrast, the mass budget method leads to the largest ice losses. However, when partitioning these three estimates into sub-regions of the Northwest sector, we show that the spatial pattern of differences between mass balance estimates is complex, suggesting that discrepancies between techniques do not solely originate from one single region or technique.

Thanks to the higher spatial resolution afforded by satellite altimetry retrievals and the mass budget method, we examine the mass balance of the Northwest sector within its 74 glacier basins and find that differences between the two techniques greater than 0.5 Gt/yr  are recorded in 19 basins, with the largest disagreement recorded at Steenstrup-Dietrichson and Kjer Gletscher.

Comparing altimetry, gravimetry and the mass budget estimates at different spatial scales is critical to isolate the differences between geodetic techniques as well as the drivers of these differences. Previous studies, such as the Ice Sheet Mass Balance Inter-comparison Exercise (IMBIE), have demonstrated that combining independent estimates of ice sheet mass balance can lead to greater certainty. Here, aggregating the altimetry, gravimetry and mass budget method estimates results in a rate of mass loss of 55.6 ± 1.5 Gt/yr for the Northwest sector between June 2010 and June 2019.

How to cite: Otosaka, I., Shepherd, A., Groh, A., Mouginot, J., and Fettweis, X.: A Regional Mass Balance Assessment of the Northwest Sector of the Greenland Ice Sheet, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10065, https://doi.org/10.5194/egusphere-egu22-10065, 2022.

Our ability to monitor and quantify ice sheet runoff is essential for a better understanding of the hydrology of the Greenland Ice Sheet and its contribution to global sea-level rise in a future warming climate. The amount of liquid water at the surface of the Greenland Ice Sheet has particularly increased due to large regional warming that this region has experienced over the last decades.

Here, we present the initial results of monthly runoff estimates from the Watson drainage basin in West Greenland, developed within the 4DGreenland project. 4DGreenland is a 2-year project funded by  the European Space Agency (ESA), which is focused on quantifying and analyzing the dynamic variations in the hydrological components of the ice sheet, while maximizing the use of Earth Observation (EO) data. 

The basin scale runoff estimate is derived from adding each component of the hydrological cycle: We have used a Random Forest approach (Supervised Learning algorithm) to map supraglacial hydrology ice sheet wide from optical imagery, and used ICESat-2 derived lake bathymetry as calibration to derive storage volumes. To map meltwater we have developed algorithms for generating maps of surface melt extent from high-medium resolution C-band backscatter measurements, and Low resolution PMW data. The subglacial melt is inherently difficult to monitor. The melt produced by friction heat though is derived from a model run of an ice sheet model (Elmer/Ice) which is tuned to assimilate observed ice velocities. Lastly, we have further developed a firn model to quantify the amounf of the meltwater that is retained in the snow/firn column.

By adding these component we are able to present monthly estimated of runoff from the drainage basin, and specifically show how each of the hydrologically components change over time. 

How to cite: Sandberg Sørensen, L. and the 4DGreenland team: Maximizing the use of remote sensing to quantify Greenland ice sheet runoff, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12244, https://doi.org/10.5194/egusphere-egu22-12244, 2022.

The German Research Centre for Geosciences (GFZ), together with the Technische Universität Dresden and the Alfred-Wegener-Institute (AWI), maintains the ‘Gravity Information Service’ portal (GravIS, gravis.gfz-potsdam.de). GravIS facilitates the dissemination of user-friendly data of mass variations in the Earth system, based on observations of the GFZ and NASA/JPL satellite gravimetry mission GRACE (Gravity Recovery and Climate Experiment, 2002-2017) and its successor mission GRACE-FO (GRACE-Follow-On, since 2018).

The portal provides mass changes of the Greenland and Antarctic ice sheets on a regular 50 km by 50 km Polar stereographic grid and as basin averages accompanied by empirical uncertainties. Both ice mass balance products rely on the same input data of GRACE/GRACE-FO spherical harmonic coefficients, generated and post-processed by GFZ. Corrections applied to the data include the insertion of estimates of the geocentre motion, replacement of the C20 and C30 coefficients, and the correction for glacial isostatic adjustment with the ICE-6G model.

The gridded data product is processed with sensitivity kernels, tailored explicitly to resolving mass changes of the ice sheets. A regional integration applies these sensitivity kernels to the unfiltered GRACE and GRACE-FO spherical harmonic coefficients. The sensitivity kernels optimise a trade-off between leakage errors and propagated GRACE solution errors.

The basin-average product consists of continent-wide estimates of ice sheet mass changes, and basin averages for seven basins in Greenland and 25 basins in Antarctica. The regional time series are calculated using a forward-modelling  inversion approach, which considers the typical spatial anomalies of the surface-mass balance and dynamic ice discharge.

In addition to the ice mass change data, GravIS provides terrestrial water storage (TWS) variations over the continents and ocean bottom pressure (OBP) variations from which global mean barystatic sea-level rise can be estimated. These data sets are provided either on 1° grids or as regional averages.

The data sets of all Earth system domains can be interactively displayed with the portal and are freely available for download. This contribution aims to show the features and possibilities of the GravIS portal to cryosphere researchers.

How to cite: Sasgen, I., Boergens, E., Dahle, C., Döhne, T., Groh, A., Dobslaw, H., Reißland, S., and Flechtner, F.: GravIS Portal: User-friendly Ice Mass Variations in Greenland and Antarctica from GRACE and GRACE-FO, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6961, https://doi.org/10.5194/egusphere-egu22-6961, 2022.

Accurate estimates of iceberg populations, disintegration rates and iceberg movements are essential to fully understand ice sheet contributions to sea level rise and freshwater and heat balances. Understanding and prediction of iceberg distributions is also of paramount importance for the safety of commercial and research shipping operations in polar seas. Despite their manifold implications the operational monitoring of icebergs remains challenging, largely due to difficulties in automating their detection at scale.  

Synthetic Aperture Radar (SAR) data from satellites, by virtue of its ability to penetrate cloud cover and strong sensitivity to the dielectric properties of the reflecting surface, has long been recognised as providing great potential for the identification of icebergs. Many existing studies have developed algorithms to exploit this data source but the majority are designed for open water situations, require significant operator input, and are susceptible to the substantial spatial and temporal variability in backscatter characteristics within and between SAR scenes that result from meteorological, geometric and instrumental differences. Further ambiguity arises when detecting icebergs in dense fields close to the calving front and in the presence of sea ice. For detection to be fully automated, therefore, adaptive iceberg detection algorithms are required, of which few currently exist. 

Here we propose an unsupervised classification procedure based on a recursive implementation of a Dirichlet Process Mixture Model that is robust to inter-scene variability and is capable of identifying icebergs even within complex environments containing mixtures of open water, sea ice and icebergs of various sizess. The method exploits freely available dual-polarisation Sentinel 1 EW imagery, allowing for wide spatial coverage at a high temporal density and providing scope for near-real-time monitoring.  It overcomes many of the limitations of existing approaches in terms of environments to which it may be applied as well as requirements for labelled training datasets or determination of scene-specific thresholds. Thus it provides an excellent basis for operational monitoring and tracking of iceberg populations at a continental scale to inform both scientific and navigational priorities. 

How to cite: Evans, B., Fleming, A., Faul, A., Hosking, S., Lieser, J., and Fox, M.: Unsupervised detection and quantification of iceberg populations within sea ice from dual-polarisation SAR imagery, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8267, https://doi.org/10.5194/egusphere-egu22-8267, 2022.

Icebergs impact the physical and biological properties of the ocean along their drift trajectory by releasing cold fresh meltwater and nutrients. This facilitates sea ice formation, fosters biological production and influences the local ocean circulation. The intensity of the impact depends on the amount of meltwater. A68 was the sixth largest iceberg ever recorded in satellite observations, and hence had a significant potential to impact its environment. It calved from the Larsen-C Ice Shelf in July 2017, drifted through the Weddell and Scotia Sea and approached South Georgia at the end of 2020. Finally, it disintegrated near South Georgia in early 2021. Although this is a common trajectory for Antarctic icebergs, the sheer size of A68A elevates its potential to impact ecosystems around South Georgia through release of fresh water and nutrients, through blockage and through collision with the benthic habitat.

In this study we combine satellite imagery data from Sentinel 1, Sentinel 3 and MODIS and satellite altimetry from CryoSat-2 and ICESat-2 to chart changes in the A68A iceberg’s area, freeboard, thickness, volume and mass over its lifetime to assess its disintegration and melt rate in different environments. We find that A68A thinned from 235 ± 9 to 168 ± 10 m, on average, and lost 802 ± 35 Gt of ice in 3.5 years. While the majority of this loss is due to fragmentation into smaller icebergs, which do not melt instantly, 254 ± 17 billion tons are released through melting at the iceberg’s base - a lower bound estimate for the fresh water input into the ocean. Basal melting peaked at 7.2 ± 2.3 m/month in the Northern Scotia Sea. In the vicinity of South Georgia we estimate that 152 ± 61 Gt of freshwater were released over 96 days, potentially altering the local ocean properties, plankton occurrence and conditions for predators. The iceberg may also have scoured the sea floor briefly. Our detailed maps of the A68A iceberg thickness change will be useful to investigate the impact on the Larsen-C Ice Shelf, and for more detailed studies on the effects of meltwater and nutrients released off South Georgia. Our results could also help to model the disintegration of other large tabular icebergs that take a similar path and to include their impact in ocean models.

How to cite: Braakmann-Folgmann, A., Shepherd, A., Gerrish, L., Izzard, J., and Ridout, A.: Observing the Disintegration of the A68A Iceberg from Space, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2491, https://doi.org/10.5194/egusphere-egu22-2491, 2022.

The Copernicus Imaging Microwave Radiometer (CIMR) [Kilic et al. 2018] is a wide-swath conically-scanning multi-frequency microwave radiometer from 1.4 to 36 GHz. It will to provide a wide range of sea-ice information, including sea ice concentration, thin sea ice thickness and snow depth over sea ice. 
The Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) [Kern et al. 2020] will carry a multi-frequency radar altimeter and microwave radiometer to monitor sea ice thickness and overlying snow depth. Both missions are Copernicus high priority to respond to the Integrated European Union Policy for the Arctic. At the same time, MetOp-SG will carry the ASCAT instrument, that shows sensitivity to sea ice properties, especially the ice type.
Here, we propose to analyze the potential synergies of these instruments, using existing observations with similar characteristics (although less optimal). 

The combination of AMSR2 and SMAP can mimic CIMR, SARAL and Sentinel-3 are proxies for CRISTAL, and ASCAT is already available on MetOp-A and -B. A data set of coincident AMSR2, SMAP, SARAL, Sentinel-3 and ASCAT observations is constructed, over the Poles, over a year. It includes both the Level 1 and Level 2 products. We concentrate first on the study of the complementarity between the observations, at Level 1. It has been previously shown that the exploitation of the observation synergy at Level 1 is more efficient than a posteriori combinations of products, independently estimated from different instruments [Aires 2011]. Then, in order to analyze results of this database, the Snow Microwave Radiometric Transfer (SMRT) [Picard et al. 2018] model is used.  It is an up-to-date radiative transfer model that is tailored to handle snow and sea ice in a plane-parallel configuration, and it can simulate both passive and active microwave responses.

A first study [Soriot et al. 2021] has shown that the use of CIMR-like data with the SMRT model can explain temporal and spatial distribution of microwave signatures over the whole North Pole during all year long. From this interpretation, a realistic characterization of the sea ice and its snow cover has been provided. Correlation and causalities, between microwave signatures and geophysical properties (such as sea ice type, sea ice thickness, snow depth or snow microstructure), have been classified. 

Here, we extend this study to the Austral Ocean and to altimetric data, southern sea ice being more covered by current altimeters than northern sea ice.
Both height and radiometric signals are exploited from the altimeters, using a unique dataset altimetric points space-time colocated. 
Recent developments in SMRT have made it able to simulate altimetric signal [Larue et al. 2021, Sandells et al. 2021], and are used to interpret CRISTAL-like data. Synergies between CIMR-like and CRISTAL-like data are highlighted for an improved sea ice and snow characterization.

How to cite: Soriot, C., Prigent, C., Frappart, F., and Picard, G.: Sea ice characterization from combined passive microwave, scatterometers and altimeters observation and radiative transfer modelling., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7584, https://doi.org/10.5194/egusphere-egu22-7584, 2022.

With the accelerated melting of the ice sheets and the sea ice cover, Earth’s Polar Regions are major witnesses to global warming. Arctic Amplification already modifies lifestyles, economies, ecologies, industries, and transportation across the region. But as a result of teleconnections with the climate system, the Polar Regions also impact on a global scale, affecting sea level rise, ocean circulation and weather patterns, which, in turn, disrupt the natural environment and society. Because of their scale and inaccessibility, observation of the Polar Regions requires a collection of space-based techniques.  Satellite altimetry provides a unique capability to monitor changes in the thickness of land ice and sea ice, and in the Polar Oceans. This information is essential for charting the response of the Polar Regions to climate change, and for predicting their future interactions with, and impacts on, the global climate system.

Although at least 7 satellite altimeters are in orbit today, only two reach polar latitudes: CryoSat-2 and ICESat-2.  CryoSat-2 was launched in 2010 and although it is still operational, it is projected to reach end of life between 2024 and 2026 due to known fuel leakage and battery degradation. ICESat-2 was launched in 2018 with a design-life of 3 years. Other satellite altimeters in lower inclination orbits, including Sentinel-3, survey only minor fractions of the Arctic sea ice pack and the polar ice sheets, and are therefore unable to provide observations of their overall imbalance. The European Commission has initiated the CRISTAL polar altimeter as a high priority candidate mission in partnership with the European Space Agency, in view of their Arctic policy, and based on user requirements. The earliest launch date for CRISTAL is in the final quarter of 2027.

Without successful mitigation, there will be a gap of between 2 and 5 years in our polar satellite altimetry capability. This gap will introduce a decisive break in the long-term records of ice sheet and sea ice thickness change and polar oceanography and this, in turn, will degrade our capacity to assess and improve climate model projections. These capabilities are of major societal importance. In order to ensure the continuity of polar altimetry, there is an urgent need to consider mitigation measures. This paper aims to stimulate a community discussion and position on possible solutions, including extending the lifetime of CryoSat-2 or ICESat-2, manoeuvring an alternative satellite into a high-inclination orbit, accelerating the launch of CRISTAL, and initiating a systematic airborne measurement programme as a bridging capability. 

How to cite: Shepherd, A., Farrell, S., and Fleury, S.: Bridging the gap in polar altimetry, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-888, https://doi.org/10.5194/egusphere-egu22-888, 2022.

Surface roughness is a crucial parameter in climate and oceanographic studies, constraining momentum transfer between the atmosphere and ocean, providing preconditioning for summer melt pond extent, while also closely related to ice age and thickness. At a local scale, roughness in the form of ridges, hummocks, rafted ice can slow down and hinder safe transport on the ice as well as be a hazard for ice strengthened vessels and structures. High resolution roughness estimates from airborne laser measurements are limited in spatial and temporal coverage while pan-Arctic satellite roughness have remained elusive and do not extend over multi-decadal time-scales. The MISR (Multi-angle Imaging SpectroRadiometer) instrument acquires optical imagery from nine near-simultaneous camera view zenith angles sampling specular anisotropy, since 1999. Extending on previous work to model sea ice surface roughness from MISR angular reflectance signatures, a training dataset of cloud-free pixels and coincident roughness from coincident operation IceBridge (OIB) airborne laser data is generated. Surface roughness, defined as the standard deviation of the within-pixel lidar elevations to a best-fit plane, is modelled using several techniques and Support Vector Regression with a Radial Basis Function kernel selected. Hyperparameters are tuned using grid optimisation, model performance is assessed using blocked k-fold cross-validation. We present a derived sea ice roughness product at 1.1km resolution over the period of operation (April 2000 – 2020) and a corresponding time series analysis. To demonstrate the validity of the derived product, we first evaluate our roughness product against independent LiDAR characterisations of surface roughness consistent with our training data. We also evaluate our derived roughness product with known proxies of surface roughness on a pan-Arctic basis (AWISMOS CS2-SMOS sea ice thickness.) Both our instantaneous swaths and pan-Arctic monthly mosaics show considerable capacity in detecting newly formed smooth ice from polynyas, and detailed surface features such as ridges and leads.

How to cite: Johnson, T., Tsamados, M., Muller, J.-P., and Stroeve, J.: Mapping Arctic sea ice surface roughness with Multi-angle Imaging Spectro-radiometer., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11829, https://doi.org/10.5194/egusphere-egu22-11829, 2022.

Due to large temperature variations between the summer and winter seasons the active layer of permafrost undergoes repetitive thawing and freezing, and the increase of global temperature has accelerated permafrost degradation related to surface deformation seasonally and annually. Repeated freezing and thawing causes frost heave and thaw settlement, which results in displacement in the activity layer of permafrost. This surface displacement is also associated with ground ice and soil moisture content, and these factors in permafrost region could be observed through timely-dense SAR data. In particular, since the revisit time of Sentinel-1 (C-band) is relatively dense, timeseries SAR interferometry could be useful tools for monitoring and mapping subsurface soil properties over such a wide area. In this study, since the degree of freezing and thawing is very different spatially and temporally, we propose the method to indirectly estimate the ground ice content of the freezing period and the moisture content of the thawing period by quantifying the displacement using timeseries InSAR measurements in the Lena-river floodplain, Siberia.

How to cite: Jung, Y. T., Lee, Y., and Park, S.-E.: Monitoring Frost Heave and Thaw Settlement of Permafrost Using Timeseries InSAR Measurements, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11674, https://doi.org/10.5194/egusphere-egu22-11674, 2022.