Surface topography within the marginal zone of the Greenland Ice Sheet continually evolves in response to varying weather, season, climate and ice dynamics. However, existing ice sheet Digital Elevation Models (DEMs) usually rely on multi-year data, obscuring these changes over time. We have here developed an annual series (2019-2023) of summer DEMs in 500m resolution for the Greenland ice sheet marginal zone, referred to as PRODEMs. Encompassing all outlet glaciers from the Greenland ice sheet, these PRODEMs result from fusing CryoSat-2 radar altimetry and ICESat-2 laser altimetry using a regionally-varying Kriging method. Validated through leave-one-out cross-validation, they demonstrate accurate representation of surface elevations within the spatially varying prediction uncertainties with a median value of 1.4m.

The PRODEMs capture the recent annual evolution in summer surface topography of all outlet glaciers from the Greenland ice sheet. We observe a general lowering of surface elevations compared to ArcticDEM, but the spatial pattern of change is highly complex and with annual changes superimposed. The PRODEMs offer detailed insights into marginal ice sheet elevation changes, temporally as well as spatially, making them valuable for researchers and users studying ice sheet dynamics under changing environmental conditions.

How to cite: Winstrup, M., Ranndal, H., Larsen, S. H., Simonsen, S. B., Mankoff, K. D., Fausto, R. S., and Sørensen, L. S.: PRODEM: An annual series of summer DEMs (2019-2023) for the marginal areas of the Greenland Ice Sheet, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5136, https://doi.org/10.5194/egusphere-egu24-5136, 2024.

Detecting supraglacial lakes is becoming increasingly crucial in the rising of global warming. Serving as vital indicators of glacier surface runoff storage and loss, these lakes provide significant insights to the understanding of glacier mass balance and global sea-level changes. Synthetic Aperture Radar (SAR) images have been used in numerous automatic detection algorithms due to their unique advantages of being independent from weather and illumination conditions. However, most SAR-based algorithms primarily use SAR backscattering intensities, which limits detection accuracy due to the high sensitivity of backscattering intensities to varying surface dielectric and geometric properties. To address this problem, it becomes essential to incorporate the polarization information to better distinguish different surface properties.

In this work, we introduced an innovative automated lake detection method that integrated a dual-polarization decomposition approach in a deep learning scheme. We enhanced the traditional decomposition method for HH and HV polarizations by segmenting the Stokes vector into fully and partially polarized sections to isolate volume and surface scattering components. The alpha angle, derived through eigenvalue decomposition of the covariance matrix, was further determined to quantify the degree of polarization. Subsequently, the decomposed SAR images were used to train an Attention U-Net deep learning model for lake segmentation. The Atrous Spatial Pyramid Pooling (ASPP) technique was introduced to the Attention U-Net to facilitate end-to-end training with limited datasets. 

The proposed method was applied to the Gaofen-3 dual-polarization SAR imagery over expansive study regions in Greenland. Results indicated that the proposed decomposition approach was effective in detecting lakes in areas of complex surface conditions and discriminating frozen lakes in winter months. The method significantly reduced misidentification and inaccuracy in distinguishing various surface features such as dark ice, blue ice, wet snow, and actual lakes. Compared to existing methods, the proposed method provided improved attention to the finer details, exhibiting higher accuracy in identifying small lakes. More importantly, comprehensive physical interpretations of the data were also provided based on the polarization decomposition, offered valuable insights into the future development of lake detection algorithms.

In summary, this work introduces an effective and innovative methodology combining SAR dual-polarization decomposition and deep learning for accurate supraglacial lake detection. The proposed method shows promising potential for an operational supraglacial lake mapping on a large scale and is expected to provide valuable insights into the understanding of ice sheet and glacier runoff dynamics.

How to cite: Jiang, D., Li, S., and Hajnsek, I.: Detection of Supraglacial Lakes using Gaofen-3 data and Advanced Attention U-Net, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6243, https://doi.org/10.5194/egusphere-egu24-6243, 2024.

Inspired by a recent paper on the lowest observed air temperature in the
northern hemisphere (AWS observation near Summit Station; Weidner et al., 2021),
we will composite Aqua MODIS Land Surface Temperatures (LST, data set
MYD11_I2 ver061) over the Greenland Ice Sheet spanning 2003-2023. Our preliminary
analysis shows LST below 200°K with just part of the data processed. or colder
than -100°F. Using the record-setting AWS station data, we estimate the
temperature inversion between the ~2 meter air temperature and the snow surface
to adjust the LST satellite measurements.  Greenland shows a similar gradient in
temperature between LST ‘skin’ temperature and air temperatures as seen in
Antarctica from our earlier research (Scambos et al., 2018).  Lowest
temperatures occur on clear-sky polar nights under calm or nearly calm winds,
in general just to the west and northwest of the ice divide. Maps of LST show
small scale geographic variations that are closely associated with local lows in
topography. We infer that this is a result of cold air pooling and further
chilling of the surface under conditions that maximize radiative heat loss.

How to cite: Campbell, G. and Scambos, T.: Satellite thermal mapping of the lowest surface temperatures in Greenland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7048, https://doi.org/10.5194/egusphere-egu24-7048, 2024.

Proglacial lakes often form due to the availability of meltwater at a glacier margin. The greatest increase in proglacial lake area and volume is currently occurring in the Arctic. This research quantified the annual change and seasonal variations in proglacial lake area and colour of Múlajökull outlet glacier southeast Hofsjökull. The Normalised Difference Water Index is used to calculate the annual and seasonal area of proglacial lakes between 1987 and 2021 in Google Earth Engine. As the terminus of Múlajökull has retreated, the number and area of proglacial lakes has increased. This has been most noticeable after the year 2000 following which, the glacier terminus retreated up to 400m. Results from this study have shown a retreat of Múlajökull terminus caused increase in area of proglacial lakes. Between 1987 and 2021 an increase in proglacial lake area from 0.16 km² to 1.27 km² was observed and the glacier terminus retreated by 700m. In addition to this, spatial and temporal variation of proglacial colour was observed between 1987 and 2021. The results of this study will provide greater insight into the annual and seasonal changes in the proglacial lake area and colour of Múlajökull outlet glacier.

How to cite: Lee, N., Shepherd, A., Hill, E., and Carr, R.: A Temporal Study of the Proglacial Lakes Surrounding Múlajökull Outlet Glacier, Iceland Between 1987 and 2021., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2200, https://doi.org/10.5194/egusphere-egu24-2200, 2024.

Retrogressive thaw slumps (RTS) are one of the most rapid abrupt thaw events that have a positive feedback on climate warming. RTS are not yet well understood because of the lack of geospatial products describing abrupt thaw distribution and changes over time in the Arctic. Although many standalone RTS digitisation data sets have been archived, it is challenging to find, access and pool the existing data sets into a comprehensive and unified one due to the lack of common data curation standards. Therefore we collected the existing RTS digitisation data sets known to date and compiled them into a scalable and uniform data set - Arctic Retrogressive Thaw Slumps (ARTS). Besides, we developed an RTS data curation framework, which provides guidelines for RTS remote sensing data digitisation, metadata formatting, RTS indexing, storage format, contribution guidelines and more. So far the ARTS data set contains around 24,000 RTS digitisations and 3,300 non-RTS background labels. This data set will empower a wide range of Arctic studies, especially beneficial for deep learning studies that are highly data-intensive.

How to cite: Yang, Y., Rodenhizer, H., Rogers, B. M., Dean, J., Singh, R., Windholz, T., Poston, A., Potter, S., Zolkos, S., Fiske, G., Watts, J., Huang, L., Witharana, C., Nitze, I., Nesterova, N., Barth, S., Grosse, G., Lantz, T., Runge, A., and Lombardo, L. and the coauthors: ARTS: a scalable data set for Arctic Retrogressive Thaw Slumps, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1365, https://doi.org/10.5194/egusphere-egu24-1365, 2024.

Glacial lake outburst floods (GLOFs) from ice-dammed lakes are frequent in Svalbard, impacting local ice dynamics, and subglacial hydrological systems, causing geomorphological changes, and posing flooding hazards. Additionally, GLOFs can influence nutrient dynamics in the fjord of tidewater glaciers, affecting the local ecosystem.

In this study, we use high-resolution topographic data to monitor the formation of an ice-dammed lake and identify the drainage mechanisms of a GLOF that occurred in the summer of 2021 on the Kongsvegen glacier, a surge-type tidewater glacier located in Kongsfjorden (Svalbard). Additionally, seismometers were deployed to monitor the subglacial dynamics at the kilometre scale.

Over the 2.5-month-long process starting in early June, terrestrial laser scanning (TLS) data and drone images were acquired at nearly daily intervals to monitor the ice-dammed lake formation and drainage. A time-lapse camera and pressure logger installed at the border of the ice-dammed lake allowed us to estimate the drainage timing, occurring from July 23 to July 26, resulting in a total drainage duration of 77 hours. To reconstruct the lake volume, the lake extension was manually digitized from the TLS data and drone orthophotos. Elevation information of the corresponding lake outlines was extracted from a 1 m resolution Digital Elevation Model (DEM) generated from Pléiades stereo satellite images acquired on 20 September 2020, at the end of the thaw season. This DEM serves as bathymetric data, representing the lake bottom. The extracted water level was used to calculate the stage-volume curve. The lake's maximum volume reached approximately 7.17 million m³ with an average discharge rate of 26 m³/s. Analyzing seismic data allowed for monitoring of the development of the subglacial drainage, assessing the transition from an inefficient to an efficient system.

This study highlights the importance of very high spatial and temporal resolution data for accurate lake volume quantification and a better understanding of the link between GLOF and subglacial system.

How to cite: Piermattei, L., Alexander, A., Filhol, S., Nanni, U., Lefeuvre, P.-M., Kohler, J., Treichler, D., Earlie, C. S., Schmidt, L. S., and Schuler, T. V.: Monitoring an ice-dammed lake outburst using topographic data at Kongsvegen, Svalbard , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10069, https://doi.org/10.5194/egusphere-egu24-10069, 2024.

The speed at which the Antarctic Ice Sheet (AIS) flows from the continental interior to the ocean is a key indicator of its stability, and satellite-derived ice velocity measurements play a major role in our assessment of changes in ice dynamics. The Sentinel-1 constellation of synthetic aperture radar (SAR) satellites, part of the European Commission’s Copernicus program, has acquired repeat images of the AIS margins at a combination of 6 and 12-day intervals since 2014, leading to a dramatic improvement in the spatio-temporal resolution and coverage of key velocity measurements at the edge of the AIS.

Such modern Earth observation satellites provide ever increasing volumes of data which can be used to study changes over time; however, this growing archive poses two key issues when performing timeseries analysis at continental scale. Firstly, generation of dense, pixelwise time series from thousands of successive observations, each stored in separate files corresponding to observation date, can require extremely large numbers of file reads, limiting computation speeds and increasing the memory required for data handling. Secondly, with ever increasing volumes of data, analysis must be able to scale effectively and easily handle out-of-core computation where datasets are larger than the available memory.

In this study, we present results from an analysis pipeline built on the Xarray and Dask python packages, and deployed on a HPC service, which allows both large scale interactive analysis in Jupyter notebooks as well as traditional batch processing. We first use an ice velocity processing chain to generate Antarctic-wide mosaics of ice speed on a 100 m grid for each combination of 6 and 12-day Sentinel-1 repeat observation dates, using the GAMMA Remote Sensing software to derive ice displacements from offset tracking of the SAR image pairs. The resulting stack of 2-dimensional mosaics is then restructured into a 3-dimensional data cube with dimensions x, y, time to facilitate time series analysis, overcoming the issue of excessive file reads and memory requirements by storing chunks of time series data together using the Zarr storage format.

Using this pipeline we investigate trends in ice speed across the AIS, performing time series outlier removal on 11 billion time series and subsequently calculating linear rates of change across both grounded and floating ice during the study period. We present the resulting map of ice speed trends and highlight time series of notable individual outlet glaciers and ice shelves.

How to cite: Slater, R. A. W., Hogg, A. E., Dutrieux, P., Davison, B. J., and Rigby, R.: Trends in Antarctic Ice Speed 2014-2023 From Big Data Processing of Satellite Observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15497, https://doi.org/10.5194/egusphere-egu24-15497, 2024.

Assessing the Surface Mass Balance (SMB) of the Antarctic Ice Sheet is crucial for understanding its response to climate change. Synthetic Aperture Radar (SAR) observations from Sentinel-1 provide a potential to monitor the variability of SMB processes through changes in the scattering response of near-surface layers and internal snow layers. However, the interplay between accumulation, wind erosion, deposition and melt is complex, thereby complicating the interpretation of the changes in scattering of the microwave signal. Additionally, the lack of reliable ground truth measurements of snow surface limits our capability to relate the SMB processes to the dominant scattering processes. In this study, we focus on understanding how the surface processes relate to the changes in the dominant scattering mechanism from Sentinel-1 in a drifting snow-dominated region of East Antarctica. We introduce a new parameter, alpha_scat, derived from scattering-type and scattering entropy descriptors from Sentinel-1 SAR observations. This parameter quantifies the continuous scattering response from near-surface layers (i.e., pure scattering) and from internal snow layers (i.e., volume scattering). The changes in alpha_scat are evaluated from the repeated in-situ surface measurements acquired during Mass2Ant field campaigns. These measurements include roughness and accumulation derived from a terrestrial laser scanner, and surface densities from SnowMicroPen. At the field-scale, our analysis shows a strong correlation between surface roughness and alpha_scat (R-squared value of 0.99), thereby indicating the role of roughness on the dominant scattering mechanism. During periods associated with erosion, the vertical component of roughness (Root Mean Squared Height) is found to be more important than the horizontal component (Autocorrelation length) in changing the scattering response. This is also marked by an increase in alpha_scat value, indicating a tendency towards pure scattering. In contrast, accumulation events lead to surface smoothening with dominant scattering from internal snow layers. Looking at the long-term changes in alpha_scat (i.e., period 2017 - 2023), high surface densities are found to be associated with an increase in pure scattering. However, increasing (decreasing) accumulation rates contribute to suppressing (enhancing) the effect of surface density on dominant scattering. The analyses provide new insights into the connection between SMB processes and dominant scattering in Sentinel-1 observations, but more field data is needed from multiple locations to quantify the combined effect of roughness, surface density, and accumulation rates on dominant scattering mechanisms. Such a framework could lead into a better separation between pure scattering and volume scattering, thereby furthering our knowledge on observing the variability of SMB processes from Sentinel-1. 

How to cite: Shukla, S., Wouters, B., Picard, G., Wever, N., Izeboud, M., de Roda Husman, S., Kausch, T., Veldhuijsen, S., Matzler, C., and Lhermitte, S.: Sentinel-1 reveals large variability of dominant scattering in a drifting snow-dominated environment of East Antarctica, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9710, https://doi.org/10.5194/egusphere-egu24-9710, 2024.

Unmanned Aerial Vehicles (UAVs) have emerged as a promising tool, providing exciting opportunities for Antarctic research. They constitute a non-invasive, repeatable, affordable, and time-efficient alternative to address the observational gap between satellite imagery and ground-based techniques. Additionally, they provide an unparalleled advantage for collecting data in remote and difficult-to-access regions, as is the case of a significant portion of the cryosphere. In the last few years, a rising number of studies have used a wide variety of multispectral sensors mounted on UAVs to describe vegetated areas, monitor penguin colonies, or detect changes on Antarctic terrestrial ecosystems. The recent development of new hyperspectral (HS) sensors adapted to UAV platforms has enhanced the characterization of such heterogeneous ecosystems, combining an unprecedented scale in spectral and spatial resolutions for better discrimination in smaller and sparser areas within the Antarctic ecosystem. In this work, we demonstrate the potential of the synergy between HS technology and UAV imagery to address important and diverse ecological issues on Antarctic environments, including the spectral characterization of penguin colony ecosystems and the detection of massive snow algae blooms on glacial formations. Furthermore, this methodology has been validated using in-situ spectroradiometry and has been applied in conjunction with other remote sensing techniques, such as UAV-based multispectral technology and satellite imagery, to cover broader regions in a climate change context.

How to cite: Roman, A., Tovar-Sánchez, A., Fernández-Marín, B., Navarro, G., and Barbero, L.: High-Resolution UAV Hyperspectral Imagery for Antarctic Research, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-154, https://doi.org/10.5194/egusphere-egu24-154, 2024.

In this work, we develop a deep-learning generative model to offer a new visualization for the Alps and its glaciation over the last 120’000 years as if a satellite had passed over and taken high resolution images from above. This visualization utilizes a recently coupled climate-glacier evolution model, which uses the latest paleo-climate and ice thickness field reconstructions (Jouvet et al., 2023). The ultimate goal of this project is to use it in the “IceAgeCam”, a joint SNSF (Swiss National Science Foundaton) project developed by ZHDK, UZH and UNIL that aims to better inform the public about the cause of climate change in a long-term climatic context.

To obtain such a visualization, we use an image-2-image translation model called Pix2PixHD (Wang et al., 2018). Similar to how one can use an image-2-image translation model to map images of winter to summer, or zebras to horses, we will map relevant fields of multi-band climatic images into artificial satellite images. Each multi-band image is composed of physical predictors such as ice thickness, ice velocity, precipitation, surface temperature, etc. In the end, the model produces semantically meaningful results that allow one to visualize the last glacial cycle. Moreover, although the motivation is rooted in the aforementioned ''IceAgeCam'' and seeks to visualize the last 120'000 years, it is a well-generalizable model, and as such, can be applied to visualize future simulations as well as terrain outside the Alps, given that the user has access to the same predictors. Finally, we aim to include this into IGM (the Instructed Glacial Model), a community-led glacier modeling software, such that it is user-friendly and easily accessible. Overall, we hope to advance efforts in the domain of remote sensing in relation to the cryosphere by providing a new way to visualize scientific results and foster community outreach.

How to cite: Finley, B. and Jouvet, G.: GlacierGan: Visualizing the Alps during the Last Ice Age, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11133, https://doi.org/10.5194/egusphere-egu24-11133, 2024.

Close-range remote sensing often fills the gap between in situ measurements and space-based observations. While most platforms are equipped with RGB cameras, there is a growing availability of thermal infrared (TIR) cameras. Both Uncrewed Aerial Vehicles (UAV) surveys in general and TIR remote sensing pose their individual challenges, especially in complex topography. In particular, TIR datasets are far from being ready-to-use upon acquisition, and thorough post-processing is required. However, they offer a great potential to monitor cryospheric landforms and assess their surface energy budget and related dynamics.

In this contribution, we present two years of TIR and RGB UAV data in combination with multiple in situ measurements, both for calibration and validation, obtained for a creeping permafrost landform, rock glacier Murtèl in the Engadine, Switzerland. We highlight the challenges evoked by the complex topography in the alpine environment (e.g. irradiance distribution, wind) and shed light on varying correction possibilities (e.g. laboratory-, field-, camera-based) that allow for a more accurate retrieval of land surface temperature over middle-sized landforms, such as a rock glacier.

In light of future thermal infrared satellite missions, an appropriate use of close-range remote sensing techniques, including survey protocols for calibration and validation, is urgently needed. This application study contributes to a better across-scale methodological understanding of sensors and methods, as well as the role of close-range remote sensing in complementing in situ and space-based observations, but also illustrates the potential of TIR datasets for cryospheric process understanding and long-term monitoring.

How to cite: Naegeli, K., Adams, J. S., Bramati, G., Gärtner-Roer, I., and Rietze, N.: Close-range thermal remote sensing over a cryospheric landform in complex topography – challenges and lessons learnt, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9873, https://doi.org/10.5194/egusphere-egu24-9873, 2024.

In recent years, the number and capability of Synthetic Aperture Radar (SAR) sensors in low earth orbit has grown considerably, with multiple satellites now capable of capturing sub-metre resolution imagery. We present the first application of such very fine resolution SAR imagery to measure ice velocity of a high mountain glacier. To achieve this, we apply feature tracking to a pair of Capella images in spotlight mode (0.35 m resolution) acquired in July 2021 over Baltoro Glacier in the Karakoram, Pakistan, and compare the results to ice velocities derived from feature tracking using more commonly employed TerraSAR-X Stripmap (3 m) and Sentinel-1 Interferometric Wide (IW) (5 x 20 m) imagery. We show that Capella-derived velocities reveal subtle features that are not evident in velocities derived using coarser resolution imagery. In particular, slower moving ice at the glacier margin, variations in velocity between different flow units, and lateral fluctuations reflecting the local topography are all more clearly resolved. However, the small footprint of the imagery and lack of stable ground within the frame poses a challenge for co-registration which could affect the feasibility of broad-scale applications. Despite this, we show that sub-metre resolution SAR imagery enables us to observe and analyse glacier dynamics at temporal and spatial resolutions that were previously impossible using satellite-based methods. We suggest that such imagery may be used alongside in-situ methods to improve our understanding of fine-scale glaciological processes which may have a significant impact on broader scale glaciological systems. 

How to cite: Izzard, J., Quincey, D. J., Elliott, J. R., and Wendleder, A.: Feature tracking of sub-metre resolution Capella SAR imagery to measure mountain glacier ice flow, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11685, https://doi.org/10.5194/egusphere-egu24-11685, 2024.

Glaciers serve as sensitive indicators of climate change, making accurate glacier boundary delineation crucial for understanding their response to environmental and local factors. However, traditional semi-automatic remote sensing methods for glacier extraction lack precision and fail to fully leverage multi-source data. In this study, we propose a Transformer-based deep learning approach to address these limitations. Our method employs a U-Net architecture with a Local-Global Transformer (LGT) encoder and multiple Local-Global CNN Blocks (LGCB) in the decoder. The model design aims to integrate both global and local information. Training data for the model were generated using Sentinel-1 Synthetic Aperture Radar (SAR) data, Sentinel-2 multispectral data, High Mountain Asia (HMA) Digital Elevation Model (DEM), and Shuttle Radar Topography Mission(SRTM) DEM. The ground truth was obtained for a glaciated area of 1498.06 km² in the Qilian mountains using classic band ratio and manual delineation based on 2 m resolution GaoFen (GF) imagery. A series of experiments including the comparison between different models, model modules and data combinations were conducted to evaluate the model accuracy. The best overall accuracy achieved was 0.972. Additionally, our findings highlight the significant contribution of Sentinel-2 data to glacier extraction.

How to cite: Peng, Y., He, J., Yuan, Q., Wang, S., Chu, X., and Zhang, L.: Automated glacier extraction using a Transformer based deep learning approach from multi-sensor remote sensing imagery, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2353, https://doi.org/10.5194/egusphere-egu24-2353, 2024.

Snow plays a critical role in alpine areas, influencing the local climate and serving as a crucial water reservoir for downstream ecosystems and human activities. The surface temperature of snow provides many insights about the current state of the snowpack and helps water storage estimations. While satellites are regularly used to measure surface temperature of snow over alpine areas, accurate measurements are still difficult to retrieve from space, and calibration-validation initiatives over snow-covered areas are scarce. In this context, we produced a two-winter timeseries of approximately 130,000 maps of the radiative surface temperature of snow acquired with an uncooled Thermal Infrared camera. TIR images were acquired November 2021 to May 2022 and February to May 2023 at the Col du Lautaret, 2057 m a.sl. in the French Alps. During the first season, the camera operated in the off-the-shelf configuration, with a rough thermal regulation (7°C - 39°C) resulted in timeseries of snow surface temperature maps with an absolute accuracy <1.25 K. The large variations of the camera’s internal temperature were identified as the main source of error. An improved setup using a thermoelectric cooler to stabilize the internal temperature was therefore developed for the second campaign, while comprehensive laboratory experiments led to a thorough characterization of the physical properties of the TIR camera and its calibration. A meticulous processing includes radiometric processing, orthorectification and a filter for foggy and snowy images. The validation against precision TIR radiometers deployed in the camera’s field of view results in an estimated absolute accuracy <0.7 K for spring 2023. The efforts to stabilize the internal temperature of the TIR camera therefore led to a notable improvement of the accuracy. This methodology represents a significant advance in the capacity to map the snow surface temperature over complex terrain, overcoming the issues found to get accurate thermal infrared images of absolute temperature discussed in previous studies. The methodology, as well as the resulting timeseries, will be useful for the investigation of the surface energy budget of snow and for the calibration/validation of satellite thermal infrared products such as Landsat, ECOSTRESS and, starting in 2025, TRISHNA over snow.

How to cite: Arioli, S., Picard, G., Arnaud, L., Gascoin, S., Alonso-González, E., Poizat, M., and Irvine, M.: How to obtain a highly accurate dataset of the snow surface temperature with a thermal infrared camera?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18646, https://doi.org/10.5194/egusphere-egu24-18646, 2024.