The Arctic region has been experiencing significant climate change, with the loss of snow and ice accelerating at an alarming rate. Accurate monitoring of the spatiotemporal dynamics of snow and ice is essential for understanding and predicting the impacts of climate change on Arctic ecosystems and their feedback on global climate. In this paper, we use the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) satellite to monitor the spatiotemporal dynamics of snow and ice in polar regions. The VIIRS/Day/Night Band (DNB) is a unique instrument that can provide high-resolution imagery of the Earth's surface at night, with a spatial resolution of 750 m and a sensitivity of 0.01 nW/cm2/sr. This enables the detection of faint moonlight and artificial light and allows for mapping snow and ice in polar winter when no sunlight is available for months.  Our aims demonstrate the potential of moonlight remote sensing for continuous monitoring of snow/ice in the Arctic region and analyse the importance of continuous monitoring and research on the impacts of climate change on the Arctic ecosystem and the potential for Arctic seaway.

How to cite: Liu, D. and Zhang, Q.: Monitoring the Spatiotemporal Dynamics of Arctic Winter Snow/Ice with MoonlightRemote Sensing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1920, https://doi.org/10.5194/egusphere-egu23-1920, 2023.

The study investigates the role of snow for the climate change impacts in hydrological regimes across the Lena river basin in Yakutia, Eastern Siberia using a hydrological model constrained by in-situ and satellite-based snow and river discharge observations. The river runoff observations in large and medium sized rivers show an increase over recent decades that can be associated with increasing air temperature and precipitation, as well as changes in snow, glaciers, and permafrost. We assessed the relation between changes in snow and streamflow using the satellite-based ESA CCI snow data and the hydrological model HYPE. The streamflow trend analysis showed a general pattern of increasing monthly mean and minimum stream flows from October to April, but more frequent in larger river basins, and especially if the last 20 years are included in the trend analysis. This can be explained by the increasing autumn precipitation, but the absence of change in annual maximum flow and streamflow in June also suggests relation to changes in the snow. The snow data shows a pattern of decreasing maximum snow water equivalent in the western part of the study area, and a corresponding decreasing trend of number of days with snow cover. These results are in line with the trends in observed streamflow; a short snow cover period (and increasing amount of autumn and winter rainfall, not shown here) as well as a lower maximum snow water equivalent could contribute both to the increasing winter runoff, and the absence of increasing streamflow in early summer. 

This work was conducted as part of the HYPE-ERAS project funded by FORMAS (project DNR: 2019-02332), RFBR (project No. 20-55-71005), and JST (Grant No. JPMJBF2003) through the Belmont Forum Collaborative Research Action: Resilience in the Rapidly Changing Arctic.

How to cite: Gustafsson, D., Musuuza, J., Klemeth, K., Bozhinova, D., Popp, A., Lebedeva, L., and Hiyama, T.: Role of snow for changes hydrological regimes in the Lena river basin, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16000, https://doi.org/10.5194/egusphere-egu23-16000, 2023.

How to cite: Kollert, A., Mayr, A., Rutzinger, M., and Dullinger, S.: Validation of high resolution remotely sensed and modeled snow cover with webcam imagery, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11618, https://doi.org/10.5194/egusphere-egu23-11618, 2023.

In the past decade, heavy snow has recorded the third-highest disaster damage in Korea after typhoons and heavy rain. In addition, snowfall is one of the important factors in the water cycle, and it directly affects hydrological factors such as evapotranspiration and soil moisture. Due to the topographical features of Korea, snowfall occurs heterogeneously, so it has limitations to use only in-situ data for snow monitoring. Although grid data such as remote sensing and model simulated data has been suggested as an alternative to this, it is also difficult to use only grid data due to the characteristics of snow that influence spectral behavior depending on grain size, age, etc. In this study, snow depth data was evaluated using model simulated data and ground observation data over the South Korea. For data, Local Data Assimilation and Prediction System [LDAPS] (provided with 3 hours of temporal resolution and 1.5 km of spatial resolution), Modern-Era Retrospective analysis for Research and Applications, version 2 [MERRA-2] (provided with 1 hour of temporal resolution and 0.5° × 0.623° of spatial resolution) and Automated Synoptic Observing System [ASOS] (provided with 1 hour of temporal resolution) were used. The applicability of each data was evaluated with topographic data, and long-term trend of snow depth was analyzed. This study can help to predict snow information, with the combination of various reanalysis data and model simulated forecast dataset.

Keywords: Snow Depth, LDAPS, MERRA-2, ASOS

Acknowledgment

This research was supported by the BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF-2021R1A6A3A01087645).

How to cite: Jeon, H., Jeong, J., Lee, Y., and Choi, M.: Evaluation of LDAPS Snow information by MERRA-2 and ASOS over the South Korea, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12126, https://doi.org/10.5194/egusphere-egu23-12126, 2023.

The unprecedented precision of the altimetry satellite ICESat-2 and the increasing availability of high-resolution elevation datasets open new opportunities to measure snow depth in the mountains, a critical variable for ecosystems and water resources monitoring. We retrieved snow depth over the upper Tuolumne basin (California, USA) for three years by differencing ICESat-2 ATL06 snow-on elevations and various snow-off elevation sources, including ATL06 and external digital elevation models. The snow presence of each ATL06 segment (i.e. point measurements regularly spaced every 20 m) can be determined from the number of photons returned by the ground surface. Snow depth derived from ATL06 data only (snow-on and snow-off) provided a poor temporal and spatial coverage, limiting its utility. However, using airborne lidar or satellite photogrammetry elevation models as snow-off elevation source yielded an accuracy of ~0.2 m (bias), a precision of ~0.5 m for low slopes and ~1.2 m for steeper areas, compared to eight reference airborne lidar snow depth maps. The snow depth derived from ICESat-2 ATL06 will help address the challenge of measuring the snow depth in unmonitored mountainous areas.

How to cite: Deschamps-Berger, C., Gascoin, S., Shean, D., Besso, H., Guiot, A., and López Moreno, J. I.: A new opportunity to measure snow depth from space: evaluation of retrievals from ICESat-2 using airborne laser-scanning data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7537, https://doi.org/10.5194/egusphere-egu23-7537, 2023.

Cryosphere is an integral part of the terrestrial ecosystem with important linkages and feedbacks generated through its influence on moisture fluxes, hydrology, and climate change due to temporal changes in snow/ice extent, impurity load and albedo. Therefore, snow and ice properties including ice and snow albedo and extent are monitored using ground and satellite instrumentation. The measurements are performed at various temporal and spatial scales using passive and active remote sensing instrumentation in a broad spectral range. The high spatial resolution is highly relevant for studies of cryosphere due to the rapid horizontal variability of snow properties and impurity load (dust outbreaks, algae blooms, structures on the snow surface/sastrugi). The instruments with low spatial resolution are not capable to resolve fine scales of snow variability. The accurate information on specific snow features (e.g., spatial distribution of algae blooms) can be hardly detected using measurements performed on the scale 0.3-1.0km.  The Environmental Mapping and Analysis Program (EnMAP) is a German hyperspectral satellite mission (Guanter et al., 2015), which provides information on evolution of aquatic and terrestrial ecosystems including cryosphere on the spatial scale of 30m, is capable to resolve the fine scale variability of snow properties. The measurements of EnMAP can be also used to assess the sub-pixel snow variability for coarse spatial resolution satellite missions and assess the accuracy of satellite products derived on the coarse spatial grid (e.g., snow fraction). This paper is aimed at the adaptation of the previously proposed snow remote sensing technique (Kokhanovsky et al., 2023) to EnMAP measurements. The retrievals are based on the asymptotic radiative transfer theory valid for weakly absorbing multiply light scattering turbid media (Kokhanovsky, 2021). The local optical properties of snow are calculated using the geometrical optics approximation, which is a valid technique for snow due to large size of ice grains as compared to the wavelength of the incident solar light in the spectral range under study. In particular, the spectral snow albedo and snow grain size are retrieved using EnMAP measurements performed by the SWIR EnMAP detector in the spectral range 900 - 1283nm. The snow specific surface area (SSA) and broadband albedo (BBA) are also derived using EnMAP measurements. The example of retrievals over Concordia station in Antarctica is given. It has been found that the effective ice grain diameter is around 0.23mm, SSA=28m*m/kg, and BBA=0.81, which is similar to the values of snow parameters measured at this location at the same season using both ground and satellite instrumentation.

References

Guanter, L., H. Kaufmann, K. Segl, et al., 2015: The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation. Remote Sensing 7: 8830-8857.

Kokhanovsky, A., 2021: Snow Optics, Cham: Springer.

Kokhanovsky, A., B. Vandecrux, A. Wehrlé, O. Danne, C. Brockmann, and J. E. Box, 2023: Improved Retrieval of Snow and Ice Properties Using Spaceborne OLCI/S-3 Spectral Reflectance Measurements: Updated Atmospheric Correction and Snow Impurity Load Estimation. Remote Sensing 15: 1-25.

How to cite: Kokhanovsky, A., Brell, M., Chabrillat, S., Förster, S., and Segl, K.: Retrieval of snow grain size and albedo using EnMAP spaceborne observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15479, https://doi.org/10.5194/egusphere-egu23-15479, 2023.

Arctic snow cover dynamics exhibit modification in terms of extent and duration due to recent changes in climate, i.e. increasing temperatures and changing precipitation patterns, i.e. winter rain-on-snow events (WROS). Remote sensing methods based on active radar images (SAR) have demonstrated a significant advantage for snow monitoring, (i) capturing physical and dielectric properties, (ii) overcoming the weakness of optical images limited by cloud cover and polar night.

The aim of this study is dedicated to the analysis of the spatial and temporal variability of snow cover in the Ny-Ålesund area on the BrØgger peninsula, Svalbard (N 78°55’ / E 11° 55’). In-situ snow measurements from two automated weather stations (Ny-Ålesund, and Bayelva), regular snowpits around the village and in spring on the Austre Loéven, were compared with the spaceborne dataset.

The RADARSAT Constellation Mission (RCM) is comprised of three satellites into closely coordinated orbits operating in C-band (5.4 GHz, 5.5 cm). The high temporal (4-day repeat cycle) and spatial resolution of the sensors in Quad-Pol mode (9-m) or Compact-Pol mode (5-m) provide a helpful performance for detecting the spatial variability of snow properties. RCM data are also compared to images of the TerraSAR-X satellite (DLR, Germany) operated in X-band (9.6 GHz, 3.1 cm) at 5-m spatial resolution. Both RCM QP mode and TSX data were acquired with medium incidence angles (33° to 39°) providing better snow penetration for volume backscattering. The RCM CP data were only available under low (23°) and high (53°) beam angles.

The following two snow properties were analyzed:

WROS detection: the focus was set on the 16-17 March 2022 event (+ 5.5 °C, 62 mm). RCM data at cross-polarization VH or HV can clearly detect the impact of rain on snow, indicating an intensity drop of -10 dB, even on the glacier at high elevation.

Snow depth retrieval: the study covers spring 2021 (March-June) and the complete winter season 2021-2022 (November-June).

- Concerning QP mode, better correlation between snow depth and SAR backscattering is observed by the cross-pol VH component, retrieving more volume backscattering information than co-pol configuration or total backscattering power (Span). We observe also that descending orbit images (06 :30 AM) provide a better correlation with snow depth than ascending orbit (15 :30 PM) data.

- Concerning CP mode and Span (RH+RV), the low incidence images (23°) do not match the snow depth observations due to main surface backscattering, contrariwise the high incidence images correlate better with in-situ observations. The analyses of the Stokes vector elements showed a satisfying correlation for the g3 element and the Relative Phase polarimetric decomposition.

Finally, a comparison of Span temporal values between RCM at C-band and TSX at X-band indicates similar time profiles, but clearly lower values of -5 to -10 dB at the C-band.

How to cite: Dedieu, J.-P., Van Noort, J., Montpetit, B., Levistre, M., Vauclare, S., Wendleder, A., Boike, J., Bernard, E., Gallet, J.-C., and Jacobi, H.-W.: Snow cover monitoring in the Arctic (Svalbard) with RADARSAT Constellation Mission (RCM). Comparison with in-situ measurements and TerraSAR-X data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4196, https://doi.org/10.5194/egusphere-egu23-4196, 2023.

In order to more quantitatively compare the differences between radar reflectivity and snowfall intensity against ground-observed snow depth, snow depth ground observation data and weather radar observation data were analyzed. For radar observation data, cumulative reflectivity and precipitation intensity derived from reflectivity, differential reflectivity, and specific differential phase (Quantitative Precipitation Estimation) were compared. As a result of the analysis, it was found that the precipitation intensity was similar to the variation according to the snow depth and time compared to the radar reflectivity. However, although the initial accumulation tendency of snow fall was very well matched due to the characteristics of snow cover, which is sensitive to temperature and has accumulation and melting characteristics, the melting tendency from daytime showed a difference. Therefore, it is judged that more accurate snow depth can be estimated only when precipitation intensity estimation method according to temperature is derived and used in addition to methods such as accumulation of reflectivity.

Acknowledgement

This research was supported by a grant(2022-MOIS61-003) of Development Risk Prediction Technology of Storm and Flood for Climate Change based on Artificial Intelligence funded by Ministry of Interior and Safety(MOIS, Korea).

How to cite: Kang, N., Hwang, S., and Yoon, J.: Comparison of correlations between radar reflectivity and radar precipitation intensity for snow depth, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10436, https://doi.org/10.5194/egusphere-egu23-10436, 2023.

How to cite: Jans, J.-F., Beernaert, E., Lievens, H., and Verhoest, N.: Dynamics in mountain SNOW water resources by MODEs of climate variability assessed from satellite observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6176, https://doi.org/10.5194/egusphere-egu23-6176, 2023.

Seasonal snow is an essential source of water, especially in mountainous regions. However, accurate satellite observations of the snow water equivalent (SWE), i.e., snow depth multiplied by the snow density, are still lacking. Therefore, new and robust remote sensing techniques are urgently needed. This study presents a novel method for SWE retrieval in mountainous regions at sub-weekly temporal and 500-m spatial resolution, based on snow depth observations from the ESA and Copernicus Sentinel-1 (S1) satellite mission and model simulations of snow density. The snow depth observations rely on a change detection algorithm which translates the temporal changes in the S1 radar backscatter measurements into the accumulation or ablation of snow. The snow density estimates are obtained from different modeling approaches, including empirical methods (e.g., based on the day of the year, the snow depth, snow climate class, etc.) and a physics-based mass and energy balance model. The performance of the different snow density modeling approaches is here compared, both with respect to their ability to accurately simulate in situ measurements of snow density, as well as their ability to accurately simulate in situ measurements of SWE after combination with the S1 snow depth observations. The performance is evaluated over the European Alps, using a large dataset of in situ time series measurements for the period 2015-2022. The results show that the physics-based snow density modeling approach outperforms the empirical approaches, yielding high spatio-temporal correlation between S1 SWE retrievals and in situ measurements. Therefore, the study demonstrates the capability of the Sentinel-1 satellite mission, in combination with a physics-based snow model, to accurately represent the spatio-temporal distribution of SWE in mountainous regions, which can benefit a large range of applications, including hydropower generation, water management, flood forecasting, and numerical weather prediction.

How to cite: Boeykens, L., Lievens, H., Beernaert, E., Jans, J.-F., and Verhoest, N.: SWE retrieval in the European Alps based on Sentinel-1 snow depth observations and modeled snow density, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7050, https://doi.org/10.5194/egusphere-egu23-7050, 2023.

The mass of seasonal snow is a challenging parameter to measure from space. This is a significant observational gap as information on snow mass would be required by diverse applications such as flood prevention, and water resource management. Snow Water Equivalent (SWE) describes the amount of liquid water that is stored in the snowpack. A promising technique to measure changes in SWE over time is repeat-pass Interferometric SAR (InSAR), since it provides high spatial resolution and reasonable temporal resolution. The retrieval technique relies on the phase difference induced by the increase in propagation path due to snow accumulation since snow has a higher permittivity than air [1]. The retrieval has been demonstrated using a wide range of sensors [1-3]. In a recent work [4], the usability of L-, S-, C-, and X- frequency bands (1-10 GHz) was analysed in the context of coherence conservation and SWE retrieval. L-band emerged as a solid candidate, as this band appeared more resilient against temporal decorrelation in snow while enabling retrieval of large amounts of SWE.

The Copernicus Radar Observation System for Europe in L-band (ROSE-L), estimated to be launched in 2028, is one of the six Copernicus high-priority Sentinel Expansion missions selected for implementation. The mission will consist of two satellites with a 180 degrees orbit phasing, allowing a temporal baseline of 6 days. We present an analysis of L-band ALOS2 imagery over Sodankylä, in northern Finland, applied for SWE retrieval using the InSAR method. The landscape is dominated by coniferous forest, presenting a challenge for large-scale retrieval of SWE. Due to ALOS2 revisit time of 14 days, it is prone to suffer from temporal decorrelation. We analysed the coherence conservation considering environmental events, land cover, canopy cover and topography. We introduce SnowModel [5], a high-resolution, spatially distributed physical snow evolution model, for comparison to InSAR SWE retrievals. SnowModel simulations were used to calibrate the interferometric phase, allowing a comparison between the two and demonstrating in which areas and under which conditions the retrieval works.

[1] T. Guneriussen, K. A. Hogda, H. Johnsen and I. Lauknes, "InSAR for estimation of changes in snow water equivalent of dry snow," in IEEE Transactions on Geoscience and Remote Sensing, vol. 39, no. 10, pp. 2101-2108, Oct. 2001, doi: 10.1109/36.957273.

[2] T. Nagler et al., "Airborne Experiment on Insar Snow Mass Retrieval in Alpine Environment," IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 2022, pp. 4549-4552, doi: 10.1109/IGARSS46834.2022.9883809.

[3] S. Leinss, A. Wiesmann, J. Lemmetyinen and I. Hajnsek, "Snow Water Equivalent of Dry Snow Measured by Differential Interferometry," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 8, pp. 3773-3790, Aug. 2015.

[4] J. J. Ruiz et al., "Investigation of Environmental Effects on Coherence Loss in SAR Interferometry for Snow Water Equivalent Retrieval," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022, Art no. 4306715, doi: 10.1109/TGRS.2022.3223760.

[5] Liston, Glen E.; Elder, Kelly. 2006. A distributed snow-evolution modeling system (SnowModel). Journal of Hydrometeorology. 7(6): 1259-1276

How to cite: Jorge Ruiz, J., Lemmetyinen, J., Merkouriadi, I., Cohen, J., Kontu, A., Pulliainen, J., and Praks, J.: Analysis of ALOS2 L-band repeat-pass InSAR for the retrieval of Snow Water Equivalent over boreal forest., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15295, https://doi.org/10.5194/egusphere-egu23-15295, 2023.

In a low-relief, agricultural landscape we integrate detailed measurements from plane-based L-band SAR (UAVSAR), drone-based LiDAR and photogrammetry, cosmic ray neutron sensor (CRNS), and field assessments to disentangle and quantify how topography, wind, and vegetation influence the spatial distribution snow cover and water equivalent. Seasonal snow in prairie and temperate grasslands environments helps sustain agriculture, socio-environmental systems, and aquifers while also exacerbating flooding in wetter years. Because these expansive landscapes cover roughly 10% of the earth’s surface, quantifying snow and snow water equivalent (SWE) is critical to better resolve water and energy budgets from local to global scales. Present day, remotely-sensed observations and conventional automated ground-based observations (e.g. SWE scales) of seasonal snow in these biomes contain considerable uncertainty. Optical imagery can detect the presence/absence of snowpack, but lacks the capacity to provide estimates of SWE. Synthetic Aperture Radar (SAR) provides a potential path forward to quantify SWE in grassland and agricultural environments, but current measurements are poorly constrained, especially in prairie environments. The Central Agricultural Research Center (CARC) in central Montana, USA (47ºN, 110ºW) served as field site for NASA’s SnowEx 2021 Mission and was distinct from other campaign locations due to its prairie landscape, controlled agricultural vegetation patterns, and ephemeral snow cover. The CRNS measures an integrated snow signal over several hectares, allowing for continuous estimations of SWE that are less influenced than smaller scale observations by the significant spatial heterogeneity of prairie snow. Initial results show that CRNS effectively quantifies an integrated SWE signal at the study site (R² ≥ 0.90).  Interferometric UAVSAR products and drone flights provide complementary high resolution snow information for narrow time periods that effectively identify snow presence across areas with different crop types (wheat, barley, peas) and stubble heights (0-0.6 m) . The limited number of UAVSAR flights in 2021 preclude a full season or multi-year analysis. However our integrated sensing approach and analysis provides a framework to reduce uncertainty in future efforts, and better constrain measurements from the upcoming L-band NISAR mission that is expected to be launched in January 2024.   

How to cite: Sproles, E. A., Palomaki, R. T., Woodley, M., and Tuttle, S. E.: An integrated remote-sensing approach for prairie snowpack, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16822, https://doi.org/10.5194/egusphere-egu23-16822, 2023.

The spatial and temporal quantification of the liquid water content (LWC) of the snowpack in alpine regions provides information on the short-term availability of water, which could eventually lead to wet snow avalanches or river floods. The monitoring and forecasting of snow wetness are hence of paramount importance in many fields, from operational avalanche forecasting to hydropower production and flood prediction, when combined with hydrological models. 
Remote sensing is an essential tool for snow monitoring as it offers observations of the snowpack's physical properties. For instance, Sentinel-1 satellites provide C-band synthetic aperture radar (SAR) data at high temporal and spatial resolutions and are capable to detect the presence of wet snow.  On the other side, many snow models were built in literature to simulate snowpack mass dynamics in space and time (see e.g., Crocus and HyS model) and can provide predictions of variables of interest in snow hydrology, such as the LWC. 
In the present work, we aim at identifying and quantifying the differences between satellite products and model snow estimates. In particular, the comparison is led among (1) Sentinel-1-based wet-snow products, (2) HSAF products, coming from the processing of data from Earth observation satellites and revealing the wet or dry status of the snow mantle, and (3) simulations of the liquid water content from HyS model, a temperature-index model, leveraged in both its one-layer and two-layer version. The case study is the Mallero basin, a middle-size alpine basin, whose flow regime is strongly influenced by snow melting and glacier ablation in the spring and summer seasons. 
The comparison returns a good agreement between Sentinel-1 products and HyS simulations. The short period of mismatches between the two outputs is analyzed to identify the physical processes that the model is not able to reproduce. On the other side HSAF products have a coarser resolution if compared to Sentinel-1 products and for this, they can just provide a qualitative overview of the snow mantle status, over a middle-size basin. Moreover, such products are also limited by the effect of cloud covering that makes it impossible to have information on the snow wetness when it is present.

How to cite: Cazzaniga, G., Arslan, A. N., and De Michele, C.: Liquid water content in a seasonal snowpack: a comparison between satellite products and model simulations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15926, https://doi.org/10.5194/egusphere-egu23-15926, 2023.

Environment and Climate Change Canada (ECCC) and the Canadian Space Agency (CSA) continue to advance a new satellite Ku-band radar mission focused on providing moderate resolution (500 m) information on seasonal snow mass. Like many regions of the northern hemisphere, estimates of the amount of water stored as seasonal snow are highly uncertain across Canada. To address this gap, a technical concept capable of providing dual-polarization (VV/VH), moderate resolution (500 m), wide swath (~250 km), and high duty cycle (~25% SAR-on time) Ku-band radar measurements at two frequencies (13.5; 17.25 GHz) is under development. Parallel to engineering studies to address the technical readiness, a range of activities are in progress to advance scientific readiness. In this presentation, we will review how recent progress within the mission science team and across the snow community has provided a sound science foundation for the mission, and identify risks to meeting the required level of readiness within the required timeline for full mission implementation. Key areas include:

• Implementation of computationally efficient SWE retrieval techniques, including parameterized forward model simulations for prediction of snow volume scattering, physical snow modeling to provide initial estimates of snow microstructure, and consideration of background characteristics;
• Incorporation of land surface model SWE estimates to infill gaps with no radar-derived SWE information due to dense forest, wet snow, and swath gaps;
• Direct assimilation of Ku-band backscatter into environmental prediction systems (analogous to how SMOS and SMAP data have improved soil moisture analysis through radiance-based assimilation);
• Segmentation of wet from dry snow;
• Continued advancement of the understanding of the physics of Ku-band backscatter response to variations snow through new experimental tower and airborne measurements.

Ku-band radar is a viable approach for a terrestrial snow mass mission because these measurements are sensitive to SWE through the volume scattering properties of dry snow and can discriminate the wet versus dry state of snow cover. To justify investment in such a mission, however, the scientific pieces must be in place. Balanced and honest assessments of the state of scientific readiness, the likelihood for emerging capabilities, and the level of engagement across the snow community are essential to ensure a healthy mission development process.

How to cite: Derksen, C., Montpetit, B., Vionnet, V., Fortin, V., Lemmetyinen, J., Kelly, R., and Thompson, A.: Advancing science readiness for a new snow mass radar mission, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8530, https://doi.org/10.5194/egusphere-egu23-8530, 2023.

Wet snow is a critical component of the cryosphere, and its spatial and temporal distribution has important implications for water resources, natural hazards, and the regional climate. However, mapping wet snow in alpine regions such as the Karakoram is challenging due to complex topography, harsh weather conditions, and limited in-situ observations.

Previous studies have shown that synthetic aperture radar (SAR) can effectively detect wet snow surfaces using the backscattering ratio between the current and reference images (e.g. the average of summer acquisitions). However, its regional application on a large-scale and complex terrain is hampered, as the ratio value is easily affected by the land cover, local topography, surface roughness, and snow wetness.

In this study, we present a new approach for mapping wet snow in the Karakoram using a combination of SAR data and topographic information. The SAR data used in the analysis were obtained from Sentinel-1, and the topographic data included a digital elevation model (DEM), slope angle, and slope aspect ratio. We first used a Gaussian Mixture Model to classify the ratio image of Sentinel-1 into wet snow (WS) and non-wet snow (NWS) classes, then transformed the two classes into a logistic function to characterize the probability of WS based on the backscattering ratio. Secondly, we categorized the image based on the topography and calculated the likelihood of WS for each topographic bin using the WS probability. The joint WS likelihood map was finally obtained by multiplying the WS probability on the backscattering ratio with the WS likelihood on topography, and a binary WS map was generated by setting a threshold on the joint likelihood map.

The proposed method was validated using snow maps generated from Sentinel-2 images. Compared with the traditional method of using only the SAR backscattering ratio, our method significantly reduced false negative detections and preserved the high true positive rate, indicating an improvement of robustness and accuracy by combining SAR and topographic data for regional wet snow mapping.

This study demonstrates a practical method of merging SAR backscattering features and topographic information for robust regional wet snow mapping in complex mountain ranges. It also provides new insights into the incorporation of different datasets using a probabilistic framework. With the proposed method, the operational monitoring of wet snow distribution in the Karakoram using SAR becomes feasible and reliable.

How to cite: Li, S., Huang, L., Bernhard, P., and Hajnsek, I.: Wet Snow Mapping in the Karakoram using SAR and Topographic Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12702, https://doi.org/10.5194/egusphere-egu23-12702, 2023.

Rain-on-Snow (ROS) events change snow pack properties and in extreme cases ice layers form which affect wildlife, vegetation and soils beyond the duration of the event. Active as well as passive microwave sensors have been used in the past to document ROS on regional scale. Either wet snow during a ROS event or the formation of crust afterwards are identified in most cases. The fusion of both approaches is promising for circumpolar monitoring.

C-band radar is of special interest due to good data availability including a range of nominal spatial resolution (10 m–12.5 km). Previous studies indicated that radar backscatter is suitable to identify snow structure change. As an example L-band passive microwave observations from SMOS and C-band backscatter from Metop ASCAT have been jointly analysed and compared to snowpit observations in Scandinavia and Northwestern Siberia.

A circumpolar dataset of potential ROS has been created. The gridded information has been eventually aggregated for events. Larger mid-winter events have been eventually extracted for 2012-2021. They occur mostly in the NE part of northern Eurasia (mostly November) and across Alaska (mostly December). The spatiotemporal patters of these events and the magnitude of snow structure change will be presented and discussed.

How to cite: Bartsch, A., Bergstedt, H., Muri, X., Rautiainen, K., Leppänen, L., Joly, K., Sokolov, A., Orekhov, P., Ehrich, D., and Soininen, E. M.: Monitoring the impact of rain-on-snow events across the Arctic with satellite data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8142, https://doi.org/10.5194/egusphere-egu23-8142, 2023.