Displays

Knowledge about the snow cover distribution is of high importance for climate studies, weather forecast, hydrological investigations, irrigation or tourism, respectively. The Hindu Kush Himalayan (HKH) region covers almost 3.5 million km² and extends over eight different countries. The region is known as ‘water tower’ as it contains the largest volume of ice and snow outside of the polar ice sheets and it is the source of Asia’s largest rivers. These rivers provide ecosystem services, the basis for livelihoods and most importantly living water for drinking, irrigation, energy production and industry for two billion people, a fourth of the world’s population, living in the mountains and downstream.

The spatio-temporal variability of snow cover in the HKH is high and studies reported average snow-covered area percentage of 10–18%, with greater variability in winter (21–42%) than in summer (2–4%). However, no study systematically investigated snow cover metrics, such as snow cover area percentage (SCA), snow cover duration (SCD) or snow cover onset (SCOD) and melt-out day (SCMD), for the entire region so far. Here, we thus present unique in-sights of regional and sub-regional snow cover dynamics for the HKH based on almost four decades, an exceptionally long and in view of the climate modelling community valuable timeseries, of satellite data obtained within the ESA CCI+ Snow project.

Our results are based on Advanced Very High Resolution Radiometer (AVHRR) data, collected onboard the polar orbiting satellites NOAA-7 to -19, providing daily, global imagery at a spatial resolution of 5 km. Calibrated and geocoded reflectance data and a consistent cloud mask pre-processed and provided by the ESA Cloud_cci project as global 0.05° composites are used. The retrieval of snow extent considers the high reflectance of snow in the visible spectra and the low reflectance values in the short-wave infrared expressed in the Normalized Difference Snow Index (NDSI). Additional thresholds related to topography and land cover are included to derive the fractional snow cover of every pixel. A temporal gap-filling was applied to mitigate the influence of clouds. Reference snow maps from high-resolution optical satellite data as well as in-situ station data were used to validate the time series.

How to cite: Naegeli, K., Marin, C., Premier, V., Schwaizer, G., Stengel, M., Wu, X., and Wunderle, S.: Revealing snow cover dynamics in the Hindu Kush Himalaya over the past decades, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11363, https://doi.org/10.5194/egusphere-egu2020-11363, 2020.

Offset tracking is one of the most widely applied methods for measuring glacier flow velocities using remote sensing data. It uses the pair-wise cross-correlation of images acquired at two different times to detect offsets between image templates of a certain size. Despite the simplicity and reliability of the method, accurate estimations of glacier velocities are limited by the accountability of features and the noise, e.g. radar speckles in synthetic aperture radar (SAR) images. One way of gaining robust estimations is to increase the size of image templates, but the resolution of obtained velocity field is inevitably depreciate. Furthermore, for templates that only contain extremely weak features with respect to the noise, increasing the size of templates is not helpful as the noise is boosted more than the features.

To overcome these issues, we propose a temporal stacking algorithm that first averages a time series of local cross-correlation functions calculated from a series of consecutive image pairs, and then estimates the averaged velocity from the stacked cross-correlation functions. Assuming the flow velocity of a glacier is constant during a certain time span (e.g. a season), the offsets between consecutive image pairs in the time series ought to be equal. Therefore, the cross-correlation functions can be considered as a time series of signals that record the identical offsets and thus are temporally coherent. Hence, we can temporally stack the signals to enhance the signal-to-noise ratio (SNR) of cross-correlation functions and better estimate offsets from the stacked cross-correlation functions. 

The proposed algorithm is assessed by mapping the flow velocity of the Aletsch Glacier using a time series of about 10 SAR images acquired by TanDEM-X in 2017 with constant revisit time of 11 days. The results show that temporal stacking of cross-correlation functions significantly enhances the spatial coverage and resolution of the obtained velocity fields compared to standard offset tracking using only pair-wise cross-correlation functions. This algorithm promotes the ability of mapping glacier velocities to a new extent with larger spatial coverage and higher spatial resolution, and provides a new perspective of measuring glacier velocities through exploiting the emerging time series data from recent high resolution space-born imaging sensors.

How to cite: Li, S., Bernhard, P., Hajnsek, I., and Leinss, S.: Temporal Stacking of Cross-Correlation for Glacier Offset Tracking, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7348, https://doi.org/10.5194/egusphere-egu2020-7348, 2020.

Glacier surging provides a unique opportunity to examine rapid changes in glacier sliding that occur when some glaciers alternate between slower-than-normal (quiescence) and faster-than-normal (surge) velocities. On surging glaciers, mechanical instabilities within the glacier set off a regime of fast glacier flow, which causes these glaciers to accelerate and advance. The precise processes that cause a surging remain uncertain and likely vary between glaciers. However, the uptake of studies on glacier surging over the past decade continues to yield invaluable insights in glacier dynamics. In this study, we combine optical remote sensing and numerical modeling to examine the recent surge of Shishper glacier, in the Pakistani Karakorum. This glacier started surging in 2018, showed a dramatic terminus advance that reached rates of several meters per day. In the process, it dammed the adjacent valley, forming a lake which drained in June 2019 flooding the downstream valley, damaging the Karakorum Highway and threatening nearby communities. We leverage a high spatio-temporal resolution dataset of glacier velocities, using roughly 100 open-access images, across the Landsat-8 and Sentinel-2 record, thus encompassing the quiescence (2013-2018) and surge (2018-2019) phases. We created the dataset in an updated and nearly automated workflow by using the COSI-Corr software package to calculate displacements between images combined with a unique algorithm to filter data and remove artifacts. The result consists in high-resolution velocity maps with resolution with time intervals as short as five days. Such dataset provide a complete time-series of the spatio-temporal evolution of ice-surface velocities during a surge. One of the most notable finding is that the surge onset occurs progressively. In the two years leading up to the surge, spring speed-ups became increasingly larger in than the long-term median. We further identify three periods with  surge velocities far higher than the long-term median that likely coincide with hydrological events. Two periods occur in the spring (2018 and 2019) and the third corresponds with the lake formation in the winter of 2018-2019. Finally, we establish that the surge termination coincided with the lake drainage at the end of June 2019. The current availability of open-access imagery and  glacier topography allow us to  make an increased quantity of observations and thus better quantify glacier dynamics.

How to cite: Delaney, I., Aati, S., Beaud, F., Gremion, S., Adhikari, S., and Avouac, J.-P.: Gradual build up and episodic character of glacier velocity preceding and during the surge of a Karakorum glacier enabled by open-access image-processing , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12528, https://doi.org/10.5194/egusphere-egu2020-12528, 2020.

The world’s glaciers distinct from the Greenland and Antarctic ice sheets are shrinking rapidly, altering regional hydrology and raising global sea level. Yet, due to the scarcity of globally consistent observations, their recent evolution is only known as a heterogeneous temporal and geographic patchwork and future projections are thus not optimally constrained.

Here, we present the first globally complete, consistent and resolved estimate of glacier mass change derived from more than half a million digital elevation models (DEMs) generated or extracted from multiple satellite archives including ASTER, ArcticDEM and REMA. Combining state-of-the-art numerical photogrammetry and novel statistical approaches, we reconstruct two decades of glacier surface elevation change at an unprecedented spatial and temporal resolution. We validate our results by comparing them to independent, high-precision elevation measurements from the ICESat and IceBridge campaigns, as well as to very high resolution DEM differences from LiDAR, Pléiades, and SPOT-6. The elevation time series are integrated to volume changes for every single glacier on Earth and, by assuming an average density, aggregated to regional and global mass changes. We compare our revised glacier mass changes to earlier estimates derived from altimetry, gravimetry, geodetic and field data. As an illustration, our integrated geodetic mass loss over all Icelandic glaciers yields -8.3 +- 1.1 Gt yr-1 over the period 2002-2016 in agreement with a recent gravimetry estimate of -8.3 +- 1.8 Gt yr-1 (Wouters et al., 2019), known to perform well in this region. Both estimates are more negative than -5.7 +- 1.2 Gt yr-1, compiled from glaciological observations and geodetic data (Zemp et al., 2019).

Our global estimate of glacier mass change constitutes a new benchmark dataset that will help to: (i) assess present-day and future climate change impacts on glaciers; (ii) close the sea-level rise budget; (iii) assess the threat on water resources and (iv) facilitate research on natural hazards related to glaciers. Our results specifically provide a strong observational basis that holds a great potential to further our understanding of the multi-scale morphologic and climatic drivers of glacier mass change, essential to improve physically-based glaciological modelling and calibrate future projections.

How to cite: Hugonnet, R., McNabb, R., Berthier, E., Menounos, B., Nuth, C., Girod, L., Farinotti, D., Huss, M., Dussaillant, I., Brun, F., and Kääb, A.: A globally complete, spatially and temporally resolved estimate of glacier mass change: 2000 to 2019, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20908, https://doi.org/10.5194/egusphere-egu2020-20908, 2020.

 Melt ponds are a dominant feature on the Arctic sea ice surface in summer, occupying up to about 50 – 60% of the sea ice surface during advanced melt. Melt ponds normally begin to form around mid-May in the marginal ice zone and expand northwards as the summer melt season progresses. Once melt ponds emerge, the scattering characteristics of the ice surface changes, dramatically lowering the sea ice albedo. Since 96% of the total annual solar heat into the ocean through sea ice occurs between May and August, the presence of melt ponds plays a significant role in this transfer of solar heat, influencing not only the sea ice energy balance, but also the amount of light available under the sea ice and ocean primary productivity. Given the importance melt ponds play in the coupled Arctic climate-ecosystem, mapping and quantification of melt pond variability on a Pan-Arctic basin scale are needed. Satellite-based observations are the only way to map melt ponds and albedo changes on a pan-Arctic scale. Rösel et al. (2012) utilized a MODIS 8-day average product to map melt ponds on a pan-Arctic scale and over several years. In another approach, melt pond fraction and surface albedo were retrieved based on the physical and optical characteristics of sea ice and melt ponds without a priori information using MERIS.Here, we propose a novel machine learning-based methodology to map Arctic melt ponds from MODIS 500m resolution data. We provide a merging procedure to create the first pan-Arctic melt pond product spanning a 20-year period at a weekly temporal resolution. Specifically, we use MODIS data together with machine learning, including multi-layer neural network and logistic regression to test our ability to map melt ponds from the start to the end of the melt season. Since sea ice reflectance is strongly dependent on the viewing and solar geometry (i.e. sensor and solar zenith and azimuth angles), we attempt to minimize this dependence by using normalized band ratios in the machine learning algorithms. Each melt pond retrieval algorithm is different and validation ways are different as well producing somewhat dissimilar melt pond results. In this study, we inter-compare melt ponds products from different institutes, including university of Hamburg, university of Bremen, and university college London. The melt pond maps are compared with melt onset and freeze-up dates data and sea ice concentration. The melt pond maps are evaluated by melt pond fraction statistics from high resolution satellite (MEDEA) images that have not been used for the evaluation in melt pond products. 

How to cite: Lee, S., Stroeve, J., and Tsamados, M.: Inter-comparison of melt pond products with melt/freeze-up dates and sea ice concentration data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20874, https://doi.org/10.5194/egusphere-egu2020-20874, 2020.

The co-existence of satellite missions provides a unique opportunity for making novel observations not possible with a single satellite. Here we process data from CryoSat-2, Sentinel-3A and Sentinel-3B satellites for the 2018-19 and 2019-20 winters. Basin-average radar freeboards from Sentinel-3A/B are shown to agree with CryoSat-2 to within 3mm. A merged product is developed combining data from the CryoSat-2 and Sentinel 3A/B missions, permitting basin-wide observations of Arctic sea-level anomaly and radar freeboard at synoptic time-scales. A comparison of 9-day radar freeboard variability with snowfall data from ERA5 reanalysis reveals a strong positive correlation over first-year ice, a result which appears to contradict traditional assumptions of Ku-band radar penetration of snow. A detailed spatial analysis including a comparison of freeboard before and after the passage of storms reveals for the first time the ability to detect synoptic scale weather events in the satellite radar freeboard record.

How to cite: Lawrence, I., Armitage, T., Shepherd, A., and Tsamados, M.: A merged CryoSat-2 Sentinel-3 freeboard product, its sensitivity to weather events, and what it can tell us about Ku-band radar penetration, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20143, https://doi.org/10.5194/egusphere-egu2020-20143, 2020.

NASA’s Operation IceBridge (OIB) was a multi-year, multi-platform, airborne mission which took place between 2009-2019. OIB was designed and implemented to continue monitoring the changing sea ice and ice sheets in both the Arctic and Antarctic by ‘bridging the gap’ between NASA’s ICESat (2003–2009) and ICESat-2 (launched September 2018) satellite missions. OIB’s instrument suite most often consisted of laser altimeters, radar sounders, gravimeters and multi-spectral imagers. These instruments were selected to study polar sea ice thickness, ice sheet elevation, snow and ice thickness, surface temperature and bathymetry. With the launch of ICESat-2, the final year of OIB consisted of three campaigns designed to under fly the satellite: 1) the end of the Arctic growth season (spring), 2) during the Arctic summer to capture many different types of melting surfaces, and 3) the Antarctic spring to cover an entirely new area of East Antarctica. Over this ten-year period a coherent picture of Arctic and Antarctic sea ice and snow thickness and other properties have been produced and monitored. Specifically, OIB has changed the community’s perspective of snow on sea ice in the Arctic. Over the decade, OIB has also been used to validate other satellite altimeter missions like ESA’s CryoSat-2. Since the launch of ICESat-2, coincident OIB under flights with the satellite were crucial for measuring sea ice properties. With sea ice constantly in motion, and the differences in OIB aircraft and ICESat-2 ground speed, there can substantial drift in the sea ice pack over the same ground track distance being measured.Therefore, we had to design and implement sea ice drift trajectories based on low level winds measured from the aircraft in flight, adjusting our plane’s path accordingly so we could measure the same sea ice as ICESat-2. This was implemented in both the Antarctic 2018 and Arctic 2019 campaigns successfully. Specifically, the Spring Arctic 2019 campaign allowed for validation of ICESat-2 freeboards with OIB ATM freeboards proving invaluable to the success of ICESat-2 and the future of sea ice research to come from these missions.

How to cite: Boisvert, L., MacGregor, J., Medley, B., Kurtz, N., Kwok, R., Blanchard-Wrigglesworth, E., Petty, A., and Harbeck, J.: Farewell to IceBridge: 10 years of polar sea ice remote sensing, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12019, https://doi.org/10.5194/egusphere-egu2020-12019, 2020.

The Ice, Cloud, and Land Elevation Satellite-2 has entered it’s second year on orbit, and continues to collect high-quality measurements of the changing cryosphere. The Advanced Topographic Laser Altimeter System (ATLAS) has now emitted more than 500 billion laser shots which provide elevation measurements of sea ice and the polar oceans, glaciers and ice sheets, the world’s forests, oceans, lakes and rivers in addition to vertical profiles of clouds and aerosols. ATLAS is an innovated lidar technology that utilizes low power and higher repetition rates to collect measurements every 70 cm along-track. These measurements have been shown to have high precision and accuracy comparable to or better than past and present cryospheric missions. The ICESat-2 data has also shown great promise with its ability to act as both complementary observations to many other missions as well as allow for us to extend the timeseries associated with our understanding of elevation and mass change in the polar regions. In this presentation, we will provide an update on the operations and health of the observatory, review the many available data products served through the National Snow and Ice Data Center in the US, and highlight the early science results from the mission. As of this writing, more than 2.5 million data granules have been downloaded by 1500 unique data users. Initial science papers have documented the ongoing loss of mass from the Antarctic and Greenland ice sheets, the ability of ICESat-2 to measure the seasonal changes in sea ice freeboard and thickness throughout the year, and the potential for world-wide measurements of coastal bathymetry. 

How to cite: Kurtz, N., Neumann, T., and Magruder, L.: The Ice, Cloud, and Land Elevation Satellite – 2 (ICESat-2): Mission Status, Science Results, and Outlook, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22119, https://doi.org/10.5194/egusphere-egu2020-22119, 2020.

Current InSAR satellite missions have proven to be a valuable tool to monitor land ice worldwide. Applications are monitoring of glacier motion, ice/snow characteristics, but also glacier and snow type extend. Because these satellites work under all weather and lighting conditions, these missions are especially valuable in polar regions.

However, almost all current systems are restricted to repeat-pass interferometry and a single viewing geometry, limiting their use for land ice applications.

Harmony, an Earth Explorer 10 candidate mission, will strongly improve the capabilities of InSAR data for monitoring of land ice worldwide. This constellation comprises of two satellites that will fly as companions of one of the Sentinel-1 satellites. Harmony will make single-pass interferometry possible, which will be used to create improved, high-resolution digital elevation models to monitor ice mass loss. Additionally, single-pass interferometry will also provide us more details on ice and snow characteristics. Finally, every scene will be viewed from different look angles, which can enhance current ice flow estimates and allows the generation of precise three-dimensional ice motion products over the ice sheets.

In this study we show the future capabilities of the Harmony for land ice monitoring using performance models. Where possible, these models are calibrated and compared with already available Sentinel-1 data. Capabilities of the Sentinel-1 satellites for the monitoring of year-round ice movements and ice/snow characteristics are illustrated by a number of case studies.

How to cite: Mulder, G., Kleinherenbrink, M., Theodosiou, A., and Lopez-Dekker, P.: Future impact of the Harmony InSAR satellite mission on land ice monitoring, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16944, https://doi.org/10.5194/egusphere-egu2020-16944, 2020.

In this presentation the focus is laid on cryospheric applications served by the single-pass interferometer TanDEM-X. The German Radar mission TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurement) is already successfully in operation since 2010 and is delivering continuously data over the Earth surfaces.

The main mission objective was the generation of a global and consistent digital elevation model (DEM) with a spatial resolution of 12m and a relative vertical height accuracy of 2 m. For this at least two global acquisitions where needed and innovative algorithms where developed to process the data into a global high resolution DEM. In addition to the high resolution DEM also a 90-m DEM was generated to facilitate the comparability with the former SRTM DEM. Beyond the generation of DEMs super-test sites have been establish to collect continuously data over a limited area of interest and to demonstrate and develop new algorithms to support application development. In addition TanDEM-X supports the demonstration and application of new SAR techniques, with focus on multi-static SAR, polarimetric SAR interferometry, digital beam forming and super resolution.

Today it is known through observations, delivered by satellites and conventional observing systems that the Cryosphere reacts very sensitively to climate change. However, the feedbacks to the global climate system are not well understood, impairing predictions of the impact of future climate change. Improved observational data have been provided to better quantify the main cryospheric processes and improve the representation of the Cryosphere in climate models. TanDEM-X data (product but also interferometric data) have been used from an international science team for a diversity of cryosphere applications. The presentation will provide an overview of the operation status of TanDEM-X and will focus on the applied cryopheric applications so far applied. Examples of the detection of permafrost features, the estimation of the firn-line zone, derivation of vertical ice structure, the mass loses of  over ice sheets and sea ice height estimation will be presented.

How to cite: Hajnsek, I., Fischer, G., Parrella, G., Bernhard, P., and Leinss, S.: TanDEM-X for Cryosphere Applications, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8458, https://doi.org/10.5194/egusphere-egu2020-8458, 2020.

Complex processes within the ice govern and record the evolution of the Greenland Ice Sheet. Low frequency microwave measurements have been used to gain insight into what happens deep inside the ice for some time now. NASA’s SMAP mission offers a valuable additional set of observations. SMAP covers virtually the entire ice sheet twice a day with its L-band radiometer. The overpasses center on morning and evening hours as the satellite is on a 6 AM/6PM equator-crossing orbit, and the spatial resolution of the instrument is about 40 km.

In this study, we investigated the response of L-band (1.4 GHz) measurements to surface melting of the ice sheet from the 2015 through 2019 melt seasons. The changes in brightness temperature caused by surface melt differs in the ablation zone, the active melt areas, and the interior’s dry snow zone. The melt area can be tracked with SMAP when accounting for these differences. SMAP’s frequent revisit time enables tracking of the melt events with comparatively high temporal fidelity. The evolution of the seasonal melt area derived from SMAP is consistent with other methods used for tracking ice sheet melt area.

Most notably, Greenland experienced an unusually strong melt event at the end of July 2019, which extended the melt area to the dry snow zone of the ice sheet over a period of two days. In-situ temperatures measured at Greenland’s Summit station show above-freezing temperatures during this event, and subsequent in-situ ice analyses have revealed ice structure changes associated with melt on these dates and subsequent refreezing. SMAP was able to record the extent of this unusual melt event on both days, and to show the anomalous extent of the melt event compared to the past 4 years of operational measurements.

This presentation will discuss the SMAP signal sensitivity to ice structure changes, the seasonal melt extent evolution and its inter-annual variation, and the comparison of the results to other data sources.

How to cite: Colliander, A., Mousavi, M., Miller, J., Entekhabi, D., Johnson, J., Shuman, C., Courville, Z., and Kimball, J.: Detecting Greenland Melt with the SMAP L-band Radiometer, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11508, https://doi.org/10.5194/egusphere-egu2020-11508, 2020.

Long-term continuous monitoring of Antarctic Ice Sheet mass balance is imperative to better understand its multi-decadal response to changes in climate and ocean forcing. Additionally, more accurate knowledge of contemporaneous mass balance is key for improved parameterisations in ice sheet models. The Antarctic Peninsula has undergone rapid changes in mass balance and ice dynamics over the last two decades, with satellite observations showing the presence of grounding line retreat and increases in ice sheet velocity. This is particularly the case after the collapse of the Larsen A and B ice shelves in 1995 and 2002, and more recently the glaciers draining the southern Antarctic Peninsula. As a result, this region provides analogues for future ice sheet response to ice shelf collapse in other regions of Antarctica. 

Despite the region’s importance to understanding ice sheet dynamics, it is challenging to accurately assess mass balance due its geometry and mountainous topography. Conventional pulse-limited altimetry suffers from poor coverage and data loss over steep mountainous terrain, particularly before the launch of CryoSat-2 in 2010. In the case of gravimetry, the geometry of the region means the coarse spatial resolution of the GRACE mission (~300 km) cannot resolve small spatial scale glacier changes (particularly over northern Antarctic Peninsula) and suffers from signal leakage into the ocean. For the mass budget approach, the challenge of accurately modelling surface mass balance over the region’s mountainous topography coupled with the sparsity of ice thickness observations at the grounding line for many sectors can result in large uncertainties. As a result, it can be difficult to reconcile the results from different conventional approaches in this region. 

To resolve this, we have developed and optimised the BHM framework used previously over the Antarctic Ice Sheet to specifically investigate the Antarctic Peninsula. This enables each latent process driving ice sheet mass change to be resolved at a higher spatial resolution compared to previous implementations across Antarctica as a whole. The new regional solution also incorporates more recent and higher resolution observations including: CryoSat-2 swath altimetry, stereo-image DEM differencing and NASA Operation Ice Bridge laser altimetry elevation rates. This is the first time such a range of observations of varying spatio-temporal resolutions will be combined into one assessment for the region. We will present results from the regionally optimised model from 2003 until present, including basin-scale mass trends and changes in spatial latent processes at an annual resolution. Additionally, we will discuss future opportunities, such as extending the record from this approach into the next decade and further understanding of the GIA response in this region. 

How to cite: Chuter, S., Rougier, J., Dawson, G., and Bamber, J.: Antarctic Peninsula mass trends from 2003 - 2016 using a Bayesian hierarchical model approach, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9879, https://doi.org/10.5194/egusphere-egu2020-9879, 2020.

The Antarctic Ice sheet is a key component of the Earth system, impacting on global sea level, ocean circulation and atmospheric processes. Meltwater is generated at the ice sheet base primarily by geothermal heating and friction associated with ice flow, and this feeds a vast network of lakes and rivers creating a unique hydrological environment. Subglacial lakes play a fundamental role in the Antarctic ice sheet hydrological system because outbursts from ‘active’ lakes can trigger, (i) change in ice speed, (ii) a burst of freshwater input into the ocean which generates buoyant meltwater plumes, and (iii) evolution of glacial landforms and sub-glacial habitats. Despite the key role that sub-glacial hydrology plays on the ice sheet environment, there are limited observations of repeat sub-glacial lake activity resulting in poor knowledge of the timing and frequency of these events. Even rarer are examples of interconnected lake activity, where the draining of one lake triggers filling of another. Observations of this nature help us better characterise these events and the impact they may have on Antarctica’s hydrological budget, and will advance our knowledge of the physical mechanism responsible for triggering this activity. In this study we analyse 9-years of CryoSat-2 radar altimetry data, to investigate a newly identified sub-glacial network in the Amery basin, East Antarctica. CryoSat-2 data was processed in ‘swath mode’, increasing the density of elevation measurements across the study area. The plane fit method was employed in 500 m by 500 m grid cells, to measure surface elevation change at relatively high spatial resolution. We identified a network of 10 active subglacial lakes in the Amery basin. 7 of these lakes, located below Lambert Glacier, show interconnected hydrological behaviour, with filling and drainage events throughout the study period. We observed ice surface height change of up to 6 meters on multiple lakes, and these observations were validated by independently acquired TanDEM-X DEM differencing. This case study is an important decade long record of hydrological activity beneath the Antarctic Ice Sheet which demonstrates the importance of high resolution swath mode measurements. In the future the Lambert lake network will be used to better understand the filling and draining life cycle of sub-glacial hydrological activity under the Antarctic Ice Sheet.

How to cite: Hogg, A., Gourmelen, N., Rigby, R., and Slater, T.: Draining and Filling of an Interconnected Sub-glacial Lake Network in East Antarctica, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10448, https://doi.org/10.5194/egusphere-egu2020-10448, 2020.

The ESA Earth Explorer CryoSat-2 was launched on 8 April 2010 and from an altitude of just over 700 km and reaching latitudes of 88 degrees, monitors precise changes in the thickness of terrestrial ice sheets and marine ice. The aim of the CryoSat-2 mission is to determine variations in the thickness of the Earth's marine ice cover and understand the extent to which the Antarctic and Greenland ice sheets are contributing global sea level rise. In its 10 years of operations, CryoSat has achieved its mission objectives and has provided high-quality of data for a number of Earth science applications and opened up new research streams and triggered new scientific questions which have emerged from the previous phases. The purpose of this paper is to provide a general overview of the mission status and provide programmatic highlights in its new extended phase until 2021. It will also provide an overview of CryoSat data products covering both Ocean and Ice processing chains, presenting also the main evolutions and improvements that have implemented to the processors and anticipating evolutions for the future. 

How to cite: Meloni, M., Bouffard, J., Parrinello, T., Webb, E., Wright, B., Scagliola, M., and Fornari, M.: CryoSat Mission and data products status after 10 years of operation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19627, https://doi.org/10.5194/egusphere-egu2020-19627, 2020.

Glaciers are important sources of fresh water particularly in arid regions which have low summer precipitation. Moreover, retreating glaciers can cause serious hazards by destabilizing slopes or causing outbursts of glacial lakes. Therefore glacier monitoring is an essential task for water resources and risk management. Recently, efforts have been made to monitor glaciers using manual or semi-automated remote sensing techniques. However a particular challenge remains: as glaciers retreat they commonly develop a surface debris layer that optically is similar to zones that have not been glaciated or that are truly deglaciated: the debris cover on the glacier surface has a similar reflectance to surrounding moraines in the visible to near-infrared wavelength region. In other hand, where debris cover develops, it may insulate ice from solar radiation and diurnal temperature rises, and this will also reduce melt. Therefore, debris cover on glacier boundaries critically hinders the global inventory of glaciers. To overcome the challenges this study uses a multiple band ratio approach. The method was tested for delineating three glaciers in Afghanistan at different scales and locations to map both clean ice and debris-covered ice. We used Landsat Enhanced Thematic Mapper Plus, and a 5-meter resolution digital surface model DSM data to extract the morphological parameters. Since clean glacier ice has a high reflectivity in the visible to near-infrared wavelengths, at first we used NDIS to extract the clean ice area, but It was found that the NDSI method for glacier mapping is less sensitive to cast shadows and steep terrain. Similarly, a slope parameter has tested to map the debris cover ice area but it did not map areas with gentle slopes correctly.

Nonetheless, NIR and SWIR were identified as potential candidates for distinguishing between glaciers in shade and clean ice for the debris free case; and a combination of those bands in three different ratios and thresholds was applied successfully (Red/SWIR>= 1.5, Pan/SWIR>0.1, and NIR/SWIR>1). With regards to debris-covered ice the thermal infrared bands show potential in resolving such ambiguity, as considerable temperature differences are found to exist between debris covered ice and surrounding moraines. However, we found that thermal infrared bands have too coarse a resolution (60m) for valley glaciers. Hence, we developed a new band ratio image combining thermal infrared and panchromatic bands to better distinguish periglacial debris and supraglacial debris. This new band ratio image is given by (PAN-TIR)/(PAN+TIR), and is named as normalized supraglacial debris index (NSDI).

Accuracy assessment was carried out through comparisons of the classified maps with a manual delineation done using 1-meter high resolution RGB image with same temporal resolution. The accuracy assessment shows that the results from the proposed method are in good agreement with the manual delineation. The proposed synergistic approach therefore appears useful in the accurate mapping of debris-covered glaciers in Afghanistan.

How to cite: Shokory, J. A. N. and Lane, S.: Comparison of different remote sensing methods for glacier mapping in Afghanistan, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2751, https://doi.org/10.5194/egusphere-egu2020-2751, 2020.

How to cite: Thielke, L., Spreen, G., and Huntemann, M.: Thermal Infrared Imaging of Sea Ice During the MOSAiC Expedition, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4871, https://doi.org/10.5194/egusphere-egu2020-4871, 2020.

The grounding line is the point where the grounded ice sheet detaches from the bed and begins to float. Knowledge of its position and dynamics are critical in mass budget assessments, ice sheet instability monitoring and ice sheet numerical modelling. The grounding line is typically mapped from the landward limit of tidal flexural using different satellite techniques, such as differential synthetic aperture radar interferometry (DInSAR) and ICESat-1 laser altimetry repeat track analysis. However, these methods have, to date, been limited by either spatial or temporal coverage. Launched on 15 September 2018, ICESat-2 satellite offers the potential to address both spatial and temporal coverage issues. Its six-beam pattern as well as the small footprint (~17 m in diameter) and high pulse repetition frequency (10 KHz) of laser altimeter instrument, can achieve a higher accuracy and an order of magnitude denser spatial coverage than ICESat-1. Here we present the results of mapping the grounding line position in Antarctica by detecting the landward limit of tidal flexure from a combination of ICESat-2 repeat track data with a crossover analysis of ascending and descending tracks. Grounding line positions mapped from this method are compared with previous estimates from DInSAR, ICESat-1 altimetry and the break-in-slope mapped from optical imagery. The results show an overall good agreement and highlight the improvements with the new satellite to provide high accuracy and density observations of grounding line in both space and time. 

How to cite: Li, T., Dawson, G., Chuter, S., and Bamber, J.: Mapping Antarctic Grounding Lines from ICESat-2 Laser Altimetry, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5928, https://doi.org/10.5194/egusphere-egu2020-5928, 2020.

The avalanche hazard is a critical task for the regional services in the Alpine region. For this reason, the characteristics of surface snow are continuously monitored in terms of micro-physics and metamorphism. The spatial distribution of the different types of snow covers (fresh snow, drift snow, melted snow, surface hoar, rain crusts, wet snow, dry snow) are used in the models aimed to forecast the avalanche hazard.

Satellite data are very important for routinely monitoring the snow cover and data provided by the Moderate Resolution Imaging Spectroradiometer (MODIS), onboard on the Terra and Aqua platforms, are an useful source of information for a modern avalanche assessment service.

More than one hundred MODIS images were processed, in the 2013-2020 period, for 2 areas located in the Dolomites, between Marmolada and Pale di San Martino groups (Veneto Region, Italy). The two training sites were used for the definition of a workflow useful for discriminating different types of snow surface. The defined workflow, based on the average radiometric values of bands 4, 5 and 6, were applied on the reflectances derived by the daily product MOD02HKM, with a spatial resolution of 500m. While band 4 and 5 (respectively visible radiation at 550nm and short-wave infrared at 1240nm) support the discrimination of different snow surfaces, the band 6 (short-wave infrared at 1630nm) is linked mainly to the presence of dry or wet snow on the surface.

The proposed workflow provided classification maps that were validated using observations recorded at the meteorological stations located in the test areas and by field surveys carried out by snow scientists. These results support the availability of a reliable tool based on remotely-sensed data, evidenced by the good agreement with field observations, which can be an optimal input for avalanche forecasting.

How to cite: Valt, M., Salvatori, R., and Salzano, R.: MODIS images anda avalanche: operational use of satellite images in forecasting avalanche Hazard ., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9411, https://doi.org/10.5194/egusphere-egu2020-9411, 2020.

The main payload of CryoSat is a Ku-band pulsewidth limited radar altimeter, called SIRAL (Synthetic interferometric radar altimeter), that is equipped with two antennas for single-pass interferometric capability.

Due to the unique characteristics of SIRAL, a proper calibration approach was developed. In fact, not only corrections for transfer function, gain and instrument path delay have to be computed (as in previous altimeters), but also corrections for phase (SAR/SARIn) and phase difference between the two receiving chains (SARIN only). To summarize, SIRAL performs regularly four types of internal calibrations:

-           CAL1 in order to calibrate the internal path delay and long-term power drift.

-           CAL2 in order to compensate for the instrument IF transfer function.

-           CAL4 to calibrate the interferometer.

-           AutoCal, a specific sequence used to calibrate the gain and phase difference for each AGC setting.

After about 10 years of operational activity of the CryoSat satellite, the performance of the SIRAL instrument are revealed to be in line or better than the expected one.

In fact the calibration products, that have been designed to model a wide range of imperfections of the instrument, can be analyzed to highlight whether and how the instrument is changing over the time also as function of its thermal status. It is worth underlining here that each variation of the instrument measured by the calibration data is compensated in the Level1 processing. Inspecting the temporal evolution of the calibration data, SIRAL has been verified to be stable during its life. The performance of the SIRAL will be presented together with the outcomes of the stability analysis on the calibration data, in order to verify that the instrument has reached the requirements and that it is maintaining the performance over its life.

In order to monitor the performance of the CryoSat interferometer along the mission, in orbit calibration campaigns have been periodically performed about once a year. The end-to-end calibration strategy for the CryoSat interferometer uses the ocean surface as the known external target. In fact, the interferometer can be used to determine the across-track slope of the overflown surface and the slope of the ocean surface can be considered as known starting from the geoid. Denoting by β the across-track slope of the ocean and assuming that the knowledge error of the geoid slope is negligibly small, β can be compared with the across-track slope derived from CryoSat SARin Level1b products which results in β'=η(θ-χ) where η is a geometric factor, θ is the angle of earliest arrival measured by the CryoSat interferometer and χ is the baseline roll angle. By comparison of the expected across-track slope β and the measured across-track slope β', the accuracy and the precision of the angle of arrival θ measured by the CryoSat interferometer can be assessed.

In our analysis, the long-term accuracy (i.e. the closeness of the measurement to the true value) and the long-term precision (i.e. the closeness of agreement among a set of measurements) of the CryoSat interferometer have been assessed.

How to cite: Scagliola, M., Fornari, M., Meloni, M., Bouffard, J., and Parrinello, T.: CryoSat SIRAL: calibration and achievable performance after ten years of operations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9655, https://doi.org/10.5194/egusphere-egu2020-9655, 2020.

Over the eleven-year lifetime of NASA’s Operation IceBridge, the Project Science Office has released an along-track sea ice freeboard, snow depth and thickness product in varying forms. Multiple versions of archival products are available for a number of the project’s early years and more recently quicklook versions, rapid-turnaround products primarily produced for summer sea ice forecasting, have been available for Arctic campaigns. During 2020, the mission’s close-out year, we are producing a final archival version of the product that will fill gaps in data availability and incorporate multiple improvements in the processing chain. These improvements include laser altimetry and snow radar pre-processing and ingestion upgrades, improved image analysis, updated tide and atmospheric models, updated gridding methodology and enhanced product outputs. The final result will constitute a state-of-the-art, internally self-consistent data product for all springtime Arctic and Antarctic Operation IceBridge campaigns.

How to cite: Harbeck, J., Kurtz, N., and Petty, A.: The NASA Operation IceBridge Sea Ice Freeboard, Snow Depth and Thickness Product, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11405, https://doi.org/10.5194/egusphere-egu2020-11405, 2020.

Glaciers in the southeastern part of the Tibet Plateau (TP) have experienced the most rapid mass loss over the High Mountain Asia. Hence, a multi-period investigation on the mass balance with focus on how glaciers evolve is imperative for better understanding of the glacier dynamics responding to climate change. Taking the Yanong glacier connected with a proglacial lake in the southeast TP as an example, we estimate the glacier mass budget at multiple-year and interannual timescales via reproducing a multiple-period DEM datasets, including KH-9 (1975), SRTM (2000), TanDEM-X (2011−2014) and SPOT-7 (2015) DEMs. We also estimate the penetration depths of both X- and C-band radar using Pléiades stereo images and TanDEM-X data , which are found to be 3.2 m and 4.5 m on average in this area. The results show that the Yanong glacier has been subject to an accelerated mass loss over the past four decades (1975−2015), and the tendency of surface thinning spread from low altitudes to high altitudes. Specifically, the mass balance of the Yanong glacier changes from −0.50 ± 0.13 m w.e./a (1974−2000) to −0.95 ± 0.13 m w.e./a (2000−2012) and to −1.02 ± 0.31 m w.e./a (2012−2015) at the multi-year timescale. A serious surface subsidence event is noted in areas that are about 2 km away from the glacier fronts after 2012, which are possibly caused by the internal/basal melting or collapsing. After further analyzing the evolution process of the proglacial lake, we found that the continuous disintegration of the glacier fronts may be the main reason for the accelerated mass deficit.

How to cite: Zhou, Y., Li, Z., Li, X., and Zheng, D.: Estimation of multi-period glacier mass balance in southeast Tibet using high-resolution remote sensing observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12890, https://doi.org/10.5194/egusphere-egu2020-12890, 2020.

Satellite radar altimetry is one of the most important tools for monitoring changes in the mass balance of the world's ice sheets. Acquiring long time series of elevation changes is crucial, and the long lifetime of the CryoSat-2 mission has contributed wonderfully to this effort. However, once the CryoSat-2 mission ends, it will be important to bridge the gap between CryoSat-2 and future radar altimetry missions. IceSat2 data can help aid this effort, assuming that the appropriate processing techniques are used to allow the comparison of radar and laser altimetry. Furthermore, different altimetry techniques come with their own pitfalls, in radar altimetry signal penetration into the snowpack introduces ambiguity in the origin of reflected echo, a major issue not present in laser altimetry. It is therefore important to minimize this ambiguity by developing processing algorithms for the radar altimetry form CryoSat-2 mission, with a special attention on relating it to the IceSat2 mission.  

Focusing on Greenland Ice Sheet (GIS), we have developed a processing chain for the estimation of surface elevations and elevation changes from the ESA level-1 product (L1b) Baseline D. As a first step, we investigated the importance of Digital Elevation Model (DEM) in the slope correction algorithm and how it affects the estimated surface elevation.

The waveform retracker algorithm was developed following the method by Nilsson (2015) with a range of thresholds in the threshold retracker applied to the waveform. Knowing the estimated range and the altitude of the satellite at the time of the measurement, we calculated the corresponding surface elevation at the point of the wavelet reflection.

We apply a slope correction method by Hurkmans (2012), where displacement from the nadir location in x- and y- directions is calculated using the slope angle and aspect retrieved from a DEM, giving a new set of coordinates that represents the location of the estimated elevation. We use two sets of slope angle and aspect calculated from two DEMs, ArcticDEM Release 7 (Porter et al., 2018) and Greenland Ice Mapping Project (GIMP) DEM (Howat et al., 2017). Both DEMs are similar in terms of optical imagery data source, processing and resolution, however, they have been referenced to different laser altimetry data. We investigate this effect in the slope correction of radar altimetry from CryoSat2 mission.

We checked the two sets of slope correction data using IceSat-2 data (Smith et al., 2019) corresponding to the same time period, and selected by nearest point calculation. We analyze and discuss the differences between IceSat-2 data and CryoSat-2 data with slope correction using GIMP DEM or ArcticDEM.

How to cite: Sejan, K., Wouters, B., and van den Broeke, M.: The importance of slope correction for studying Greenland ice change using radar altimetry (CryoSat-2), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15264, https://doi.org/10.5194/egusphere-egu2020-15264, 2020.

Recent years have seen major advancements in satellite Earth observation of polar land ice. Among the most notable are the developments enabled by the Copernicus Sentinel program, including the Sentinel-1 SAR mission. The Sentinel-1 constellation, with its dedicated polar acquisition scheme, has provided the opportunity to derive ice flow velocity of the Greenland and Antarctic ice sheets at an unprecedented scale and temporal sampling. A continuous observational record of the ice sheet margins since October 2014, augmented by dedicated ice sheet wide mapping campaigns, enabled the operational monitoring of key climate variables like ice velocity and glacier discharge. In 2019 additional tracks have been added to the regular acquisition scheme, covering the slow-moving interior of the Greenland Ice Sheet, opening up new opportunities for interferometric applications and permitting to derive monthly ice sheet wide velocity maps. 

Based on repeat pass Sentinel-1 SAR data, acquired in Interferometric Wide (IW) swath mode, we have generated a dense archive of ice velocity maps covering the polar regions and encompassing the entire mission duration, now spanning well over 5 years. Including the latest observational data, we present ice velocity maps of Greenland, Antarctica and other major ice caps, focusing on time series of ice flow fluctuations of major outlet glaciers. The ice velocity maps, complemented by high resolution DEMs and ice thickness data, form the basis for studying ice dynamics and discharge fluctuations and trends at sub-monthly to multi-annual time scales. Our results underscore the value of long-term comprehensive monitoring of the polar ice masses, which is vital for to gain insight for predicting their response to ongoing climate warming.

This poster highlights some of the main achievements and latest developments of 5 years of Sentinel-1 ice flow mapping in the Polar regions facilitated by the ESA Climate Change Initiative (CCI), EU Copernicus Climate Change Service (C3S) and Austrian Space Applications Programme (ASAP). 

How to cite: Wuite, J., Nagler, T., Hetzenecker, M., Keuris, L., Libert, L., and Rott, H.: 5 Years of Polar Land Ice Velocity and Discharge observed by Sentinel-1, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14036, https://doi.org/10.5194/egusphere-egu2020-14036, 2020.

Ice flow velocity is an important parameter for evaluating the stability of Antarctic ice shelves and analyzing the mass balance of the ice sheet. Large scale ice flow maps can be produced from satellite images with ground control and validation. Among various ground targets, corner reflectors show distinct intensity characteristics on SAR images due to its highly reflective surface shape and have been used for calibration and validation. This paper focuses on design and implementation of a set of corner reflectors to obtain the first-hand data of in-situ ice flow velocity for SAR image based ice velocity maps. The results should further help evaluate mass balance changes in East Antarctica using the input-output method.

Generally, the remote sensing method uses airborne or satellite optical and radar images from multiple periods to map ice flow velocity fields. The ground truth data are often sparse due to the harsh environment in the polar region. The annual Chinese Antarctic Research Expedition (CHINARE) makes it possible to obtain period field data of ice velocity within its campaign regions. The ~1200 km CHINARE-Route runs from Zhongshan Station to Kunlun Station along which the ice flow velocity varies from a few meters per year to 100s meters per year. 5 corner reflectors have been designed and installed along the 31st CHINARE-Route in 2015 and the 35th CHINARE-Route in 2019 (M1, M2 and M3 in the 31st CHINARE, A1and A2 in the 35th CHINARE). The ice flow velocities at the installation locations are of different orders of magnitude, about 44 m per year at the locations of M1 and A1, 93 m per year at M2 and M3 and 73 m per year at A2. The satellite orbit inclination, incident angle and the installation location were used to calculate the azimuth and elevation angles of the corner reflectors for installation. At all reflector locations GPS positions were collected at the time of installation. After that, the second time GPS coordinates of M3 in the 34th CHINARE in 2018, the third time GPS coordinates of M3, the second time GPS coordinates of A1 and A2 in the 36th CHINARE at the end of 2019 were measured respectively. TerraSAR-X was used to image the reflectors.

The results show that the mean in-situ ice flow velocity of M3 is 96.83 m per year between Feb. 2015 and Dec. 2019, with 97.51 m per year between Feb. 2015 and Jan. 2018 and 95.81m per year between Jan. 2018 and Dec. 2019. The in-situ ice flow velocity is 54.9 m per year at A1 between Jan. 2019 and Dec. 2019 and 86.92 m per year at A2 between Feb. 2019 and Dec. 2019. More TerraSAR-X and COSMO-SkyMed data will be used to extract the ice velocity corresponding to GPS measurements. The detailed information will be presented at the meeting.

How to cite: Qiao, G., Li, R., Hao, T., Tong, X., Li, Y., Li, H., Liu, S., Liu, S., Li, Y., and Dou, Y.: Corner Reflectors for Validation of Ice Flow Velocity Derived from SAR Images along the CHINARE-Route in Antarctica, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18221, https://doi.org/10.5194/egusphere-egu2020-18221, 2020.

Spire Global operates the world’s largest and rapidly growing constellation of CubeSats performing GNSS based science and Earth observation. Currently, the Spire constellation, with many satellites in polar orbits, performs a variety of GNSS science, including radio occultation (GNSS-RO), ionosphere and space weather measurements, and precise orbit determination. These satellites have been primarily tasked to perform GNSS-RO to produce accurate profiles of atmospheric temperature, pressure, and water vapor and to collect millions of daily ionospheric total electron content measurements. Previous work showed that grazing angle reflections of GNSS signals off of ocean and sea ice surfaces serendipitously collected during radio occultation measurements had the potential to perform precision altimetry (< 10 cm) over sea ice surfaces.

In 2019, Spire reprogrammed its STRATOS GNSS science receiver to collect grazing angle reflection observations on Spire's large constellation of orbiting GNSS-RO satellites. To accomplish this, the open-loop tracking used in GNSS-RO collection was modified to perform open-loop prediction and tracking of grazing angle reflections between 5-30 deg elevation. Initial results confirm coherency of reflections over most sea ice surfaces and some open ocean surfaces. Full altimetric processing has been performed and is being productionized, confirming  sub-10 cm precision over sea ice where reflections were coherent, with some initial measurements showing altimetric height precision less than 2 cm RMS relative a mean sea surface (e.g., DTU18). Due to the large number of current and planned GNSS-RO satellites as Spire's constellation scales to over 100 operating GNSS-RO satellites, this technique has excellent potential to complement other sensors such as ICESat-2 and Cryosat-2.

A larger production period has now begun on multiple Spire satellites that will result in much larger quantities of diverse cryospheric measurements (sea ice as well as ice sheets will be sampled). We will present further results of this new and potentially revolutionary technique to use existing orbiting GNSS-RO satellite constellations to perform precision sea ice altimetry.

How to cite: Nguyen, V., Yuasa, T., Nogués-Correig, O., Masters, D., Tan, L., Duly, T., Sikarin, R., Esterhuizen, S., Jales, P., and Freemand, V.: Cryospheric Applications of Novel GNSS Grazing Angle Reflections Collected by Spire CubeSats, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12619, https://doi.org/10.5194/egusphere-egu2020-12619, 2020.

Precisely quantifying the Antarctic Ice Sheet (AIS) mass balance remains a challenge as several processes compete at differing degrees in the basin-scale with regional variations. Understanding of changes in AIS has been largely based on observations from various altimetry missions and Gravity Recovery And Climate Experiment (GRACE) missions due to its scale and coverage. Analysis of linear trends in surface height variations of AIS since the early 1990s showed multiple variabilities in the rate of changes over the period of time. These observations are a reflection of various underlying ice sheet processes. Therefore understanding the processes that interact on the ice sheet is important to precisely determine the response of the ice sheet to a rapidly changing climate.

Changing climate constitutes variations in major short term processes including snow accumulation and surface melting. Variations in accumulation rate and temperature at the ice sheet surface cause changes in the firn compaction (FC) rate. Variations in the FC rate change the AIS thickness, that should be detected from altimetry, but do not change its mass, as observed by the GRACE mission. We focus our study on the seasonal and interannual changes in the elevation and mass of the AIS. We use surface elevation changes from Envisat data and gravity changes derived from the latest GRACE solutions between 10/2002 and 10/2010. As mass changes observed using the GRACE mission is strongly impacted by long term isostasy, as it involves mantle mass redistribution, we remove from all dataset an 8-year trend. We use weather variable historical data solutions including surface mass balance, temperature and wind velocities from the regional climate model RACMO2.3p2 as input to an FC model to estimate AIS elevation changes. We obtain a very good correlation between height change estimates from GRACE, Envisat and RACMO2.3p2 at several places such as along the coast of Dronning Maud Land, Wilkes land and Amundsen sea sector. Considerable differences in Oates and Mac Robertson regions, with a strong seasonal signal in Envisat estimates, reflect spatial variability in physical parameters of the surface of the AIS due to climate parameter changes such as winds.

How to cite: Kaitheri, A., Mémin, A., and Rémy, F.: Climate parameters influencing satellite-based volume and elevation changes of the Antarctic ice sheet, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18542, https://doi.org/10.5194/egusphere-egu2020-18542, 2020.

Since 1992, satellite-borne radar altimetry has been used to record surface elevation change over the Greenland ice sheet (GrIS). Until the launch of CryoSat-2 in 2010, conventional radar altimeters performed poorly over high sloping terrain with heterogenous topography. The novel synthetic aperture radar interferometric (SARIn) mode of CryoSat-2 has improved capability in these regions over the margins of the GrIS, which have been experiencing the largest mass loss. ESA’s Sentinel-3 mission is the latest radar-altimeter to be launched. The first satellite, Sentinel-3A, was launched in February 2016 followed by Sentinel-3B April 2018. The Sentinel-3 satellites are the first to use synthetic aperture radar (SAR) across the interior of the GrIS. This has improved the along-track resolution to approximately 300m compared to CryoSat-2’s Low Resolution Mode (LRM) footprint which has a diameter of ~1.65km.

Here we assess the performance of Sentinel’s SAR mode compared to the LRM mode of CryoSat-2 over the interior of the ice sheet and the SARIn mode over the margins of the GrIS, through crossover analysis and a point-to-point comparison. We then assess the implications of this comparison for monitoring elevation changes over the ice sheet and we present rates of elevation change for June 2016 - June 2019 for both radar altimeter missions. To calculate rates of volume change from elevation change we use a statistical interpolation method, universal kriging, and present volume changes per basin over Greenland before comparing volume change estimates between CryoSat-2 and Sentinel-3.

How to cite: Maddalena, J., Dawson, G., Chuter, S., Landy, J., and Bamber, J.: Monitoring the Greenland Ice Sheet: A comparison between Sentinel-3 and CryoSat-2 radar altimeters, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18612, https://doi.org/10.5194/egusphere-egu2020-18612, 2020.

The Antarctic Peninsula (AP) is one of the most dynamic Polar regions and is experiencing fast mass loss. In order to quantify the mass changes of the AP and the associated sea level rise, an accurate estimate of its contemporary mass change is essential. The calving front location (CFL) is one important parameter to measure the geodetic mass balance or the dynamic mass loss of outlet glaciers. In order to quantify the mass change of Antarctic Peninsula glaciers on regional or individual glacier scales, the CFL with high spatial resolution is required. Because the Antarctic Peninsula has long, narrow coastlines, it is extremely time-consuming to delineate the detailed CFL from optical or SAR remote sensing images manually. Furthermore, it is also challenging for automatic algorithms to detect the whole glacier calving front line of AP considering the similarity of spectral and backscattering response of sea ice, grounded ice and mélange. Currently the most up-to-date coastal product covering the entire AP, which is provided by the Antarctic Digital Database (ADD), is manually delineated with all of the available remote sensing imagery acquired in various years. A frequently updated CFL product for the entire AP coastline is not available.

Therefore, we propose an efficient method to extract the current coastline of AP from bi-static TanDEM-X DEM products, including the 12 m TanDEM-X global DEM and newly processed RawDEMs with a precise time stamp.  The CFL between grounded ice or ice shelves and the ice mélange or open water is characterized by strong elevation gradients. Besides, the grounded ice and the ice shelf show smoother and more continuous elevation values in the TanDEM-X DEM while the ice mélange and open water are noisier. Hence the ice mélange at the CFL may look similar to grounded ice or ice shelves in optical and SAR images but can be distinguished in the TanDEM-X interferometric DEM. In our work, we combine elevation and elevation gradient information to separate grounded ice/ice shelves and ice mélange. Afterwards, terrain analysis and morphological operations are applied to remove the residual ice mélange pixels in the segmented image.

The TanDEM-X global DEM covering AP is a consistent, timely and high-precision DEM, which was generated from the bistatic InSAR data acquired by the TanDEM-X mission during austral winters 2013 and 2014. Thus our coastline of AP extracted from the 12 m TanDEM-X global DEM will correspond to the CFL of outlet glaciers of years 2013/2014. Furthermore, the CFL extracted from TanDEM-X RawDEMs with a particular time stamp can be used for geodetic mass balance calculation during different time periods. The extracted glacier calving front line reveals the potential of the high resolution height information in assisting the separation of grounded ice/ice shelf and ice mélange. The resulting glacier calving front line product of AP will be validated with the geocoded TanDEM-X backscattering amplitude images acquired at the date closest to the time stamp of the DEM tile and with the Antarctic coastline provided by the ADD.

How to cite: Dong, Y., Krieger, L., Floricioiu, D., and Zhao, J.: Glacier calving front extraction from TanDEM-X DEM products of the Antarctic Peninsula, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20137, https://doi.org/10.5194/egusphere-egu2020-20137, 2020.

Launched in 2010, the European Space Agency’s (ESA) polar-orbiting CryoSat satellite was specifically designed to measure changes in the thickness of polar sea ice and the elevation of the ice sheets and mountain glaciers. Beyond the primary mission objectives, CryoSat is also valuable source of data for the oceanographic community and CryoSat’s sophisticated SAR Interferometric Radar Altimeter (SIRAL) can measure high-resolution geophysical parameters from the open ocean to the coast.

CryoSat data is processed operationally using two independent processing chains: Ice and Ocean. To ensure that the CryoSat products meet the highest data quality and performance standards, the CryoSat Instrument Processing Facilities (IPFs) are periodically updated. Processing algorithms are improved based on feedback and recommendations from Quality Control (QC) activities, Calibration and Validation campaigns, the CryoSat Expert Support Laboratory (ESL), and the Scientific Community. 

Since May 2019, the CryoSat ice products are generated with Baseline-D, which represented a major processor upgrade and implemented several improvements, including the optimisation of freeboard computation in SARIn mode, improvements to sea ice and land ice retracking and the migration from Earth Explorer Format (EEF) to Network Common Data Form (NetCDF). A reprocessing campaign is currently underway to reprocess the full mission dataset (July 2010 – May 2019) to Baseline-D.

The CryoSat ocean products are also generated in NetCDF, following a processor upgrade in November 2017 (Baseline-C). Improvements implemented in this new Baseline include the generation of ocean products for all data acquisition modes, therefore providing complete data coverage for ocean users. This upgrade also implemented innovative algorithms, refined existing ones and added new parameters and corrections to the products. Following the completion of a successful reprocessing campaign, Baseline-C ocean products are now available for the full mission dataset (July 2010 – present).

Since launch, the CryoSat ice and ocean products have been routinely monitored as part of QC activities by the ESA/ESRIN Sensor Performance, Products and Algorithms (SPPA) office with the support of the Quality Assurance for Earth Observation (QA4EO) service (formerly IDEAS+) led by Telespazio VEGA UK. The latest processor updates have brought significant improvements to the quality of CryoSat ice and ocean products, which in turn are expected to have a positive impact on the scientific exploitation of CryoSat measurements over all surface types.

This poster provides an overview of the CryoSat data quality status and the QC activities performed by the QA4EO consortium, including both operational and reprocessing QC. Also presented are the main evolutions and improvements that have implemented to the processors, and anticipated evolutions for the future.

How to cite: Webb, E., Wright, B., Meloni, M., Bouffard, J., Parrinello, T., Baker, S., Brockley, D., Geminale, T., Scagliola, M., and Fornari, M.: Quality Status of the CryoSat Data Products, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21748, https://doi.org/10.5194/egusphere-egu2020-21748, 2020.

An accurate detection and mapping of glacial lakes in the Alpine regions such as the Himalayas, the Alps and the Andes are challenged by many factors. These factors include 1) a small size of glacial lakes, 2) cloud cover in optical satellite images, 3) cast shadows from mountains and clouds, 4) seasonal snow in satellite images, 5) varying degree of turbidity amongst glacial lakes, and 6) frozen glacial lake surface. In our study, we propose a fully automated approach, that overcomes most of the above mentioned challenges, to detect and map glacial lakes accurately using multi-source data and machine learning techniques such as the random forest classifier algorithm. The multi-source data are from the Sentinel-1 Synthetic Aperture Radar data (radar backscatter), the Sentinel-2 multispectral instrument data (NDWI), and the SRTM digital elevation model (slope). We use these data as inputs for the rule-based segmentation of potential glacial lakes, where decision rules are implemented from the expert system. The potential glacial lake polygons are then classified either as glacial lakes or non-glacial lakes by the trained and tested random forest classifier algorithm. The performance of the method was assessed in eight test sites located across the Alpine regions (e.g. the Boshula mountain range and Koshi basin in the Himalayas, the Tajiks Pamirs, the Swiss Alps and the Peruvian Andes) of the word. We show that the proposed method performs efficiently irrespective of geographic, geologic, climatic, and glacial lake conditions.

How to cite: Wangchuk, S. and Bolch, T.: Enhancing alpine glacial lakes detection and mapping using multi-source data and machine learning techniques, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21811, https://doi.org/10.5194/egusphere-egu2020-21811, 2020.

Remote sensing offers the possibility to efficiently monitor glacier changes on large scales and in remote regions. Glacier surface elevation changes and surface velocities can be derived automatically from satellite acquisitions and provide information on the evaluation of glacier dynamics and mass balance. However, the obtained data sets are often affected by voids due to various issues depending on the imaging technique (SAR, optical). Those missing data on the one hand lead to uncertainties in the quantification of glacier changes, on the other hand can limit the assimilation of the data sets in glacier models.

Inpainting techniques were developed to remove distortions from photographs or for retouch purposes. In this study, suitable Inpainting techniques are applied on glaciological remote sensing products and evaluated in comparison with previous attempts.

For Glacier Bay Alaska, a nearly complete coverage of a glacier area of ~6000 km² by surface elevation change information exists. Artificial voids were generated and filled by using different Inpainting techniques and parameter. The inpainted data sets are evaluated in comparison to the original data set.

How to cite: Seehaus, T., Eberhard, B., Robert, M., and Matthias, B.: Image Inpainting techniques for void filling in glaciological remote sensing products, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2896, https://doi.org/10.5194/egusphere-egu2020-2896, 2020.

Antarctica stores ~91 % of the global ice mass making it the biggest potential contributor to global sea-level-rise. With increased surface air temperatures during austral summer as well as in consequence of global climate change, the ice sheet is subject to surface melting resulting in the formation of supraglacial lakes in local surface depressions. Supraglacial meltwater features may impact Antarctic ice dynamics and mass balance through three main processes. First of all, it may cause enhanced ice thinning thus a potentially negative Antarctic Surface Mass Balance (SMB). Second, the temporary injection of meltwater to the glacier bed may cause transient ice speed accelerations and increased ice discharge. The last mechanism involves a process called hydrofracturing i.e. meltwater-induced ice shelf collapse caused by the downward propagation of surface meltwater into crevasses or fractures, as observed along large coastal sections of the northern Antarctic Peninsula. Despite the known impact of supraglacial meltwater features on ice dynamics and mass balance, the Antarctic surface hydrological network remains largely understudied with an automated method for supraglacial lake and stream detection still missing. Spaceborne remote sensing and data of the Sentinel missions in particular provide an excellent basis for the monitoring of the Antarctic surface hydrological network at unprecedented spatial and temporal coverage.

In this study, we employ state-of-the-art machine learning for automated supraglacial lake and stream mapping on basis of optical Sentinel-2 satellite data. With more detail, we use a total of 72 Sentinel-2 acquisitions distributed across the Antarctic Ice Sheet together with topographic information to train and test the selected machine learning algorithm. In general, our machine learning workflow is designed to discriminate between surface water, ice/snow, rock and shadow being further supported by several automated post-processing steps. In order to ensure the algorithm’s transferability in space and time, the acquisitions used for training the machine learning model are chosen to cover the full circle of the 2019 melt season and the data selected for testing the algorithm span the 2017 and 2018 melt seasons. Supraglacial lake predictions are presented for several regions of interest on the East and West Antarctic Ice Sheet as well as along the Antarctic Peninsula and are validated against randomly sampled points in the underlying Sentinel-2 RGB images. To highlight the performance of our model, we specifically focus on the example of the Amery Ice Shelf in East Antarctica, where we applied our algorithm on Sentinel-2 data in order to present the temporal evolution of maximum lake extent during three consecutive melt seasons (2017, 2018 and 2019).

How to cite: Dirscherl, M., Dietz, A., Baumhoer, C., Kneisel, C., and Kuenzer, C.: Automated mapping of Antarctic supraglacial lakes and streams using machine learning, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3280, https://doi.org/10.5194/egusphere-egu2020-3280, 2020.

The velocity of glaciers is commonly derived by offset tracking using pairwise cross correlation or feature matching of either optical or synthetic aperture radar (SAR) images.  SAR images, however, are inherently affected by noise-like radar speckle and require therefore much larger images patches for successful tracking compared to the patch size used with optical data. As a consequence, glacier velocity maps based on SAR offset tracking have a relatively low resolution compared to the nominal resolution of SAR sensors. Moreover, tracking may fail because small features on the glacier surface cannot be detected due to radar speckle. Although radar speckle can be reduced by applying spatial low-pass filters (e.g. 5x5 boxcar), the spatial smoothing reduces the image resolution roughly by an order of magnitude which strongly reduces the tracking precision. Furthermore, it blurs out small features on the glacier surface, and therefore tracking can also fail unless clear features like large crevasses are visible.

In order to create high resolution velocity maps from SAR images and to generate speckle-free radar images of glaciers, we present a new method that derives the glacier surface velocity field by correlating temporally averaged sub-stacks of a series of SAR images. The key feature of the method is to warp every pixel in each SAR image according to its temporally increasing offset with respect to a reference date. The offset is determined by the glacier velocity which is obtained by maximizing the cross-correlation between the averages of two sub-stacks. Currently, we need to assume that the surface velocity is constant during the acquisition period of the image series but this assumption can be relaxed to a certain extend.

As the method combines the information of multiple images, radar speckle are highly suppressed by temporal multi-looking, therefore the signal-to-noise ratio of the cross-correlation is significantly improved. We found that the method outperforms the pair-wise cross-correlation method for velocity estimation in terms of both the coverage and the resolution of the velocity field. At the same time, very high resolution radar images are obtained and reveal features that are otherwise hidden in radar speckle.

As the reference date, to which the sub-stacks are averaged, can be arbitrarily chosen a smooth flow animation of the glacier surface can be generated based on a limited number of SAR images. The presented method could build a basis for a new generation of tracking methods as the method is excellently suited to exploit the large number of emerging free and globally available high resolution SAR image time series.

How to cite: Leinss, S., Li, S., Bernhard, P., and Frey, O.: Temporal Multi-Looking of SAR Image Series for Glacier Velocity Determination and Speckle Reduction, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3643, https://doi.org/10.5194/egusphere-egu2020-3643, 2020.

The Greenland Ice Sheet (GIS) was the largest contributor to global sea level rise in the 2005 to 2016 period (Meredith et al. in press). Therefore, it is one of the biggest players influencing our climate and monitoring and understanding of its mechanisms and development are of highest relevance.

Means to observe and measure such large areas are remote sensing. The Tandem-X mission of DLR and Airbus consists of two satellites (TerraSAR-X and TanDEM-X) that are flying in single pass formation, mapping the Earth in interferometric SAR X-band with a resolution of 12m (Zink et al. 2014). The mission has been flying in this constellation since 2010. Due to the satellite constellation and the SAR system, digital elevation models (DEMs) can be created in high resolution, unaffected by the availability of daylight and the presence of clouds.

All data acquired between 2010 to 2014 (Rizzoli et al. 2017) were compled to a global elevation model. Besides this global product, several time slices were created for the GIS (Wohlfart et al. 2018). In this project, we created a DSM mosaic from winter 2015/16 acquisitions, more precisely using more than 2000 DEM scenes (Fritz at al. 2011) from end of October 2015 to beginning of February 2016.

One issue of a SAR system is the penetration of the signal into snow. Additionally, water surfaces appear dark in the images due to low backscatter towards the sensor. Therefore, we used winter scenes to minimize the height error.

We created an almost seamless DSM out of these scenes for 2015/16. Second, we used SAR features to delineate different snow zones. For this purpose, we used the amplitude, the height error map, and additionally ICESat and ICE Bridge data.

References
Fritz, T.; Rossi, C.; Yague-Martinez, N.; Rodriguez Gonzalez, F.; Lachaise, M.; Breit H. Interferometric processing of TanDEM-X data, IGARSS 2011, Vancouver, July 2011

Meredith, M.; Sommerkorn M.; Cassotta S.; Derksen C.; Ekaykin A.; Hollowed A.; Kofinas G.; Mackintosh A.; Melbourne-Thomas J.; Muelbert M.M.C.; Ottersen G.; Pritchard H.; and Schuur E.A.G.; 2019: Polar Regions. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.

Rizzoli, P.; Martone, M.; Gonzalez, C.; Wecklich, C.; Tridon, D.B.; Bräutigam, B.; Bachmann, M.; Schulze, D.; Fritz, T.; Huber, M.; et al. Generation and performance assessment of the global TanDEM-X digital elevation model. ISPRS J. Photogramm. Remote Sens. 2017, 132, 119–139.

Wohlfart, C.; Wessel, B.; Huber, M.; Leichtle, T.; Abdullahi, S.; Kerkhoff, S.; Roth, A. TanDEM-X DEM derived elevation changes of the Greenland Ice Sheet. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain, 22–27 July 2018.

Zink, M.; Bachmann, M.; Bräutigam, B.; Fritz, T.; Hajnsek, I.; Krieger, G.; Moreira, A.; Wessel, B. TanDEM-X: The New Global DEM Takes Shape. IEEE GRSM 2014, 2, 8–23.

How to cite: Baumann, S., Wessel, B., Huber, M., Kerkhoff, S., and Roth, A.: Assessment of a TanDEM-X Digital Elevation Model of the Greenland Ice Sheet and its Zonation for Winter 2015/16, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6739, https://doi.org/10.5194/egusphere-egu2020-6739, 2020.

Abstract: The iceberg freeboard is an important geometric parameter for measuring the thickness of the iceberg and then estimating its volume. Based on the fact that the iceberg can cast elongated shadow on the surface of sea ice in winter, this paper proposes a method to measure the iceberg freeboard using shadow length and the predefined or estimated solar elevation angle. Three Landsat-8 panchromatic images are selected to test our method, with center solar elevation angle of respectively 5.43°, 7.49°and 11.01° on August 29, September 7, and 16 September in 2016. Shadow lengths of five isolated tabular icebergs are automatically extracted to calculate the freeboard height. For the accuracy assessment, we use the matching points at the different time as cross validation. The results show that the measurement error of shadow length is less than one pixel. When the sun elevation angle is lower than 11.01°, the Root Mean Square Error (RMSE) of the iceberg freeboard from the panchromatic 15 m image is less than 2.0 m, and the Mean Absolute Error (MAE) is less than 1.5 m. Such experiment shows that: under the angle of low solar elevation in winter, the landsat-8 panchromatic 15 m image can be used for high-precision measurement of the iceberg freeboard, and has the potential to measure the Antarctic iceberg freeboard in large scale.

Key words: Antarctic, icebergs, freeboard, shadow altimetry, Landsat-8

How to cite: Guan, Z. and Liu, Y.: Extracting icebergs freeboard from the shadows in Landsat-8 panchromatic images , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12810, https://doi.org/10.5194/egusphere-egu2020-12810, 2020.

Observations of ice dynamical change in the interior of the Greenland Ice Sheet, at distances >~100 km from the ice-margin, are sparse, exhibiting very low spatial and temporal resolution (e.g. Sole et al., 2013; Doyle et al., 2014; Van de Wal et al., 2015). As such, the behaviour of interior Greenland ice represents a significant unknown in our understanding of the likely response of the ice sheet to oceanic and atmospheric forcing. The observation of a 2.2 % increase in ice velocity over a three-year period at a location 140 km from the ice margin in South West Greenland (Doyle et a., 2014) has been inferred to suggest that the ice sheet interior has undergone persistent flow acceleration. It remains unclear, however, whether this observation is representative of wider trends across the ice sheet.

Here, we investigate changes in ice motion within Greenland’s interior by utilising recent satellite-derived ice velocities covering the period 2013-2018 (Gardner et al., 2019) in conjunction with in-situ velocities collected at 30 km intervals along the 2000 m elevation contour during the mid-1990s (Thomas et al., 2000). Previous observations from the late-1990s/early-2000s through to late-2000s/early-2010s have revealed significant speed-up at many of Greenland’s tidewater glaciers (e.g. Bevan et al., 2012; Murray et al., 2015), in contrast to widespread deceleration within the ablation zone of the South West land-terminating margin (e.g. Tedstone et al., 2015; Van de Wal et al., 2015; Stevens et al., 2016). The recent availability of satellite data enables us to compare annual ice velocities from the period 2013-2018 to those collected at GPS stations in the mid-1990s, thereby enabling us to detect any long-term changes in ice-sheet wide inland ice motion during a period of considerable climatic and potentially significant dynamic change.

We observe multi-decadal interior ice acceleration of >15 % at Jakobshavn Isbrae, with similar inland accelerations at Kangerlugssuaq, Sermiligarssuk Brae and Narsap Sermia, and smaller velocity increases upstream of other marine-terminating outlets; these accelerations suggest that dynamic change at the margins has propagated considerable distances into the ice sheet interior. By contrast, ice velocities have slowed inland of some tidewater outlets such as Helheim Glacier, Umiamako Isbrae and Hagen Brae, confirming complex spatial variability in interior response to oceanic and atmospheric forcing. Furthermore, whilst prior work suggested that South West Greenland’s land-terminating sector experienced persistent interior speed-up between 2009 and 2012 (Doyle et al., 2014), our results reveal a >10% multi-decadal slowdown within the same sector, suggesting this region is resilient to recent increases in surface melt forcing.

How to cite: Williams, J., Gourmelen, N., and Nienow, P.: Complex multi-decadal ice dynamical change within the interior of the Greenland Ice Sheet, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16492, https://doi.org/10.5194/egusphere-egu2020-16492, 2020.

How to cite: Johnson, T., Tsamados, M., Muller, J.-P., and Stroeve, J.: Mapping Arctic Sea Ice Surface Roughness with Multi-angle Imaging SpectroRadiometry, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17311, https://doi.org/10.5194/egusphere-egu2020-17311, 2020.

In [1] a new method is described for fusing spectral BRF and derived albedo at 1.1km within the 7 minutes that MISR acquires images of a surface point with coincident MODIS nadir spectral data processed into a 1km sea ice mask. NetCDF products were produced in polar stereographic projection and produced on daily, weekly, fortnightly and monthly from November to February each year from 2000-2016. Arctic sea ice albedo has been previously presented and in this presentation, Antarctic time series, will be presented covering the same time period. This area has less complete coverage than the Arctic due to data outages due to telecommunications issues. [2] has recently pointed out that sea ice coverage  has  reduced dramatically since 2014, mainly one quadrant centred on the Wendell sea and the spectral albedo for this area will be highlighted.

Acknowledgements: Support was provided by EU-FP7 QA4ECV (Quality Assurance for Essential Climate Variables) under Project Number 607405 for the development of the processing system.

References:
[1] Kharbouche, S.; Muller, J.-P. Sea Ice Albedo from MISR and MODIS: Production, Validation, and Trend Analysis. Remote Sens. 2019, 11, 9. doi: https://doi.org/10.3390/rs11010009

[2] Parkinson, C. A 40-y record reveals gradual Antarctic sea ice increases followed by decreases at rates far exceeding the rates seen in the Arctic. Proceedings of the National Academy of Sciences. 2019, 116 (29) 14414-14423; DOI: 10.1073/pnas.1906556116

How to cite: Muller, J.-P. and Kharbouche, S.: Mapping Antarctic sea ice albedo properties from MISR fused with MODIS, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18866, https://doi.org/10.5194/egusphere-egu2020-18866, 2020.

How to cite: Evers, L. G., Smets, P. S. M., Shani-Kadmiel, S., and Assink, J. D.: Listening to glaciers in Greenland, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20124, https://doi.org/10.5194/egusphere-egu2020-20124, 2020.

Ice surface temperature (IST) is of utmost importance to the ice sheet radiation budget and mass balance, which has been documented by many scientific researches.

This research firstly proposes an effective approach to retrieve IST in the Antarctic area by presenting a modified split-window algorithm (SWA) and introducing a polynomial fitting for atmospheric transmittance simulation. The effectiveness was quantitatively validated by a comparative study with a Moderate Resolution Imaging Spectroradiometer (MODIS) IST product (MOD29) and automatic weather station (AWS) data from Zhongshan Station and the Ross Ice Shelf from 2004 to 2013. From the algorithm validation and data comparison, it was found that: 1) The polynomial fitting can better describe the relationship between water vapor and atmospheric transmittance, with higher determination coefficients (0.99887 for band 31 and 0.99895 for band 32, respectively) and lower residual sum of squares (0.000373 for band 31 and 0.000234 for band 32, respectively). 2) Using the Zhongshan Station data set, the retrieved ISTs by the proposed method were more accurate than the MOD29 product, with a bias of −0.61 K and a root-mean-square error (RMSE) of 1.32 K; comparatively, the bias for MOD29 was −1.33 K and the RMSE for MOD29 was 1.81 K. 3) The proposed method also obtained the highest accuracy in the other experiment using the Ross Ice Shelf data set, in which the bias and RMSE for the retrieved ISTs were −1.62 K and 2.34 K, respectively; the corresponding accuracies for MOD29 were −2.54 K and 3.04 K, respectively. Overall, it was found that the proposed method shows a robust performance in Antarctic IST retrieval for MODIS data.

Besides, an improved single-channel (SC) algorithm is proposed for retrieving the ice surface temperature of polar regions from Landsat8 band10 in this study. The improved algorithm avoids using Taylor's theorem and eliminates Taylor approximation error. In addition, the atmospheric parameters suitable for polar regions are simulated and the effective mean atmospheric temperature is added to the fitting process. In order to maintain the advantage of the SC algorithm minimum input data requirements, the effective mean atmospheric temperature is obtained by using the existing parameters and iterative calculation. The results of sensitivity analysis show that the improved algorithm is not sensitive to atmospheric water vapor content but sensitive to the calibration precision of thermal infrared sensor. Theoretical verification results show that the RMSEs of the SC algorithm and the improved SC algorithm are 0.72 K and 0.33 K, respectively. Compared with MODIS land surface temperature product, the RMSE of the improved SC algorithm is 0.31K. Compared with the temperature of automatic weather stations, in the Antarctic, the RMSE of SC algorithm is of 1.48 K and the improved SC algorithm is 1.22 K. In conclusion, the improved SC algorithm performs better than SC algorithm in polar ice surface temperature retrieval.

How to cite: Liu, T., Li, Y., Wang, Z., Hao, W., Ai, S., and Zhou, C.: Antarctic ice and snow surface temperature retrieval from MODIS and Landsat8, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20949, https://doi.org/10.5194/egusphere-egu2020-20949, 2020.