Accurate monitoring of seasonal to decadal snow cover dynamics is essential for climate change attribution and sustainable water resource management. As part of the European Space Agency (ESA) Snow Climate Change Initiative (CCI+) Phase-2 project, this study introduces an Advanced Very High Resolution Radiometer (AVHRR) snow cover fraction (SCF) product portfolio, with a particular emphasis on the development of AVHRR10C1.V4-the first 45-year (1979-2023), global, daily SCF product. This dataset achieves unprecedented temporal consistency by addressing long-standing challenges such as orbital drift, inter-sensor inconsistencies, reduced cloud contamination, and enhanced SCF retrieval accuracy. The AVHRR10C1.V4 product was generated by integrating newly calibrated EUMETSAT AVHRR Fundamental Data Record (FDR) data from 16 AVHRR sensors on a 0.05 degrees grid through a calibration method, modified cloud masking, an updated SCF retrieval method, refined post-processing, and a novel consolidation framework. Comprehensive validation against 66 high-resolution Landsat/Sentinel-2 SCF maps and extensive ground-based snow measurements confirms the robust accuracy of the AVHRR10C1.V4 product: root mean square errors of 16-19% for viewable snow cover fraction (SCFV) and 18-28% for ground snow cover fraction (SCFG), with overall accuracy (OA) ranging from 0.80 to 0.92. The consolidation approach remarkably reduces RMSEs by 7%-48% and lowers missing data rates by similar to 30% compared to single-sensor products. The product maintains strong temporal consistency with ERA5-Land SCF product (R > 0.8) and in situ snow measurements. Statistical analysis over the 45-year record and multiple separate periods confirms minimal sensor-drift biases, revealing no statistically significant breakpoints or drift trends (Mann-Kendall p > 0.05). While the SCFV product proves robust to variations in viewing geometry and vegetation conditions, SCFG accuracy is more sensitive to forest cover density-exhibiting substantially increased estimation uncertainties in dense forest canopies (forest cover density > 50%) due to canopy radiative effects and the use of static land cover assumptions. This open-access, climate data record-quality AVHRR10C1.V4 product establishes a critical benchmark for studying snow-climate interactions and long-term cryospheric monitoring. It supports the development of next-generation global SCF products, enabling reliable detection of snow trends, improved hydrological modeling, and informed climate adaptation in a warming climate.

Seasonal alpine snow is subject to fluctuating meteorological conditions with diurnal temperature cycles around the freezing point and a mix of snow and rain. Throughout the season, fresh snow accumulations repeatedly contribute to the snowpack whereas older layers beneath contain snow at various stages of the metamorphosis often including melting and refreezing periods. The increasing complexity of the snowpack throughout the snow season affects the interaction of radar signals with the snowpack and underlying ground. Numerous radar/SAR missions, operating at different frequencies, aim to retrieve snow parameters such as snow mass, snow water equivalent, and snow cover extent. These include missions like CRISTAL, TSMM, ROSE-L, and NISAR, each utilizing specific frequency bands to study the temporal variations in snow properties. Understanding the vertical structure of seasonal snow and its interaction with radar signals at various microwave frequencies from L- to Ka-band is therefore essential. In our study, we investigated tower-mounted rail-based tomographic SAR measurements obtained within the ESA SnowLab project in Davos Laret, Switzerland. The SAR tomography technique provides non-destructive measurements of the vertical structure of the snowpack by means of vertical profiles of radar backscatter, co-polar phase differences, and interferometric phase differences. The measurements were taken with the ESA SnowScat and the ESA Wide-Band Scatterometer, covering a wide range of frequency bands. Additional data on snow characteristics and meteorology complemented the radar measurements. We present time series of SAR tomographic profiles over entire snow seasons at different frequency bands (1-6 GHz, 12-18 GHz, and 28-40 GHz) with reference snow characterizations obtained from snow pits and SnowMicroPen measurements. Detailed analyses include depth-resolved co-polar phase differences, anisotropy, and differential interferometric phase, revealing insights into changes in snow properties over time. The high-resolution SAR tomographic profiles offer valuable information on microwave interactions with seasonal alpine snow. Analysis of vertical radar backscatter profiles indicates relative changes in location and intensity within the snowpack, correlating with factors like melting and refreezing cycles, snow accumulation, and liquid water content. We find that distinctive features of seasonal snow, such as melt-freeze crusts, varying penetration depths, and anisotropy can be tracked over time using a SAR tomography approach. To exploit this information for snow mass and structure retrieval, further research tailored to specific spaceborne SAR mission objectives is required. The ESA SnowLab time series of SAR tomographic profiles is a rich dataset covering a broad spectrum of frequencies and providing an opportunity to advance the understanding of scattering mechanisms in alpine snow for various spaceborne SAR missions. The comprehensive coverage includes frequency bands relevant to existing and future mission concepts.

Time series of SAR tomographic profiling measurements of seasonal snow in the Swiss Alps were acquired with the tower-mounted ESA Wideband Scatterometer (WBScat) during an entire snow season as part of the ESA SnowLab campaign. The wide range of frequency bands (1-40 GHz) covered in combination with the depth-resolving capability of the tomographic SAR measurements provides new insights into relative change of location and intensity of radar backscatter within the snowpack during melting and refreezing cycles as a function of time and various parameters obtained by auxiliary measurements such as snow accumulation, snow mass (SWE), snow surface temperature, and liquid water content. It is found that at both, Ku-band and Ka-band, the tomographic profiles provide evidence of substantial backscatter at melt/freeze crust interfaces within the seasonal snowpack. Varying penetration depths and (re-)appearing backscattering layers can be observed throughout several cycles of snow melting and gradual refreezing.

Abstract. Knowledge on ice surface velocity of glaciers and ice caps contributes to a better understanding of a wide range of processes related to glacier dynamics, mass change and response to climate. Based on the recent release of historical SAR data from various space agencies we compiled nearly complete mosaics of winter ice surface velocities for the 1990’s over the Eastern Arctic (Novaya Zemlya, Franz-Josef-Land, Severnaya Zemlya and Svalbard), a region with sparse optical velocity records from these years. We mainly applied offset-tracking to JERS-1 SAR data and filled data gaps using SAR interferometry and offset-tracking from ERS-1/2 SAR data. We studied the long-term variability of winter ice surface velocity by comparing our 1990’s results to 2008–2011 velocity maps from ALOS-1 PALSAR-1 and 2020–2021 maps from Sentinel-1. A general increase of winter velocities from the 1990’s to present along with a retreat of glacier fronts is obsverved. Exceptions to this general pattern are surges, which are widespread over Svalbard but rarely found in the other three regions. The dense time series of ice surface velocity from Sentinel-1 since 2015 were also considered to infer the representativeness of winter data with respect to mean annual values. We found that for non-surging glaciers short-term seasonal fluctuations are relatively small and winter ice surface velocities are a good representative of mean annual velocities with an underestimation of less than 10 %. Together with consistent datasets of glacier ice thickness and terminus position, the ice surface velocities in the Eastern Arctic provide the basis to quantify the regional decadal average calving flux during the 1990’s. The ice surface velocity data set for the 1990’s over the Eastern Arctic from satellite SAR data can be downloaded from https://doi.pangaea.de/10.1594/PANGAEA.938381 (Strozzi et. al., 2021).

Abstract Ice marginal lakes are a dynamic component of terrestrial meltwater storage at the margin of the Greenland Ice Sheet. Despite their significance to the sea level budget, local flood hazards and bigeochemical fluxes, there is a lack of Greenland-wide research into ice marginal lakes. Here, a detailed multi-sensor inventory of Greenland’s ice marginal lakes is presented based on three well-established detection methods to form a unified remote sensing approach. The inventory consists of 3347 ( $$\pm 8$$ ± 8 %) ice marginal lakes ( $$>0.05\,{{\text{ km }}^{2}}$$ > 0.05 km 2 ) detected for the year 2017. The greatest proportion of lakes lie around Greenland’s ice caps and mountain glaciers, and the southwest margin of the ice sheet. Through comparison to previous studies, a $$\sim 75$$ ∼ 75 % increase in lake frequency is evident over the west margin of the ice sheet since 1985. This suggests it is becoming increasingly important to include ice marginal lakes in future sea level projections, where these lakes will form a dynamic storage of meltwater that can influence outlet glacier dynamics. Comparison to existing global glacial lake inventories demonstrate that up to 56% of ice marginal lakes could be unaccounted for in global estimates of ice marginal lake change, likely due to the reliance on a single lake detection method.

The modular Snow Microwave Radiative Transfer (SMRT) model simulates microwave scattering behavior in snow via different selectable theories and snow microstructure representations, which is well suited to intercomparisons analyses. Here, five microstructure models were parameterized from X-ray tomography and thin-section images of snow samples and evaluated with SMRT. Three field experiments provided observations of scattering and absorption coefficients, brightness temperature, and/or backscatter with the increasing complexity of snowpack. These took place in Sodankylä, Finland, and Weissfluhjoch, Switzerland. Simulations of scattering and absorption coefficients agreed well with observations, with higher errors for snow with predominantly vertical structures. For simulation of brightness temperature, difficulty in retrieving stickiness with the Sticky Hard Sphere microstructure model resulted in relatively poor performance for two experiments, but good agreement for the third. Exponential microstructure gave generally good results, near to the best performing models for two field experiments. The Independent Sphere model gave intermediate results. New Teubner–Strey and Gaussian Random Field models demonstrated the advantages of SMRT over microwave models with restricted microstructural geometry. Relative model performance is assessed by the quality of the microstructure model fit to micro-computed tomography (CT) data and further improvements may be possible with different fitting techniques. Careful consideration of simulation stratigraphy is required in this new era of high-resolution microstructure measurement as layers thinner than the wavelength introduce artificial scattering boundaries not seen by the instrument.

WBSCAT is a polarimetric scatterometer developed for the European Space Agency for measuring microwave signatures of snow in support of the SnowLab and SnowLab-NG projects. SnowLab-NG includes feasibility studies for ESA candidate satellite missions operating at L-Band such as ROSE-L and Hydroterra. The WBSCAT instrument is based on a vector network analyzer to cover the 1–40 GHz frequency range, including L-Band. Using ground-based data collected in the near-field to model data collected by satellites is challenging when there is volume scattering from a thick layer such as snowpack. WBSCAT has been constructed to use near-field aperture synthesis to permit focusing of backscatter data. Focusing mitigates the effects of near-field operation by synthesizing a narrow antenna beam that illuminates a smaller volume of the snowpack. Using aperture synthesis, an improvement in azimuth resolution by a factor of 6 is possible with WBSCAT at L-band. Initial results from phase calibration of a calibration target response show the potential for this approach.

Seasonal snow is an important component of the global climate system. It is highly variable in space and time and sensitive to short term synoptic scale processes and long term climate-induced changes of temperature and precipitation. Current snow products derived from various satellite data applying different algorithms show significant discrepancies in extent and snow mass, a potential source for biases in climate monitoring and modelling. The recently launched ESA CCI+ Programme addresses seasonal snow as one of 9 Essential Climate Variables to be derived from satellite data. In the snow_cci project, scheduled for 2018 to 2021 in its first phase, reliable fully validated processing lines are developed and implemented. These tools are used to generate homogeneous multi-sensor time series for the main parameters of global snow cover focusing on snow extent and snow water equivalent. Using GCOS guidelines, the requirements for these parameters are assessed and consolidated using the outcome of workshops and questionnaires addressing users dealing with different climate applications. Snow extent product generation applies algorithms accounting for fractional snow extent and cloud screening in order to generate consistent daily products for snow on the surface (viewable snow) and snow on the surface corrected for forest masking (snow on ground) with global coverage. Input data are medium resolution optical satellite images (AVHRR-2/3, AATSR, MODIS, VIIRS, SLSTR/OLCI) from 1981 to present. An iterative development cycle is applied including homogenisation of the snow extent products from different sensors by minimizing the bias. Independent validation of the snow products is performed for different seasons and climate zones around the globe from 1985 onwards, using as reference high resolution snow maps from Landsat and Sentinel- 2as well as in-situ snow data following standardized validation protocols. Global time series of daily snow water equivalent (SWE) products are generated from passive microwave data from SMMR, SSM/I, and AMSR from 1978 onwards, combined with in-situ snow depth measurements. Long-term stability and quality of the product is assessed using independent snow survey data and by intercomparison with the snow information from global land process models. The usability of the snow_cci products is ensured through the Climate Research Group, which performs case studies related to long term trends of seasonal snow, performs evaluations of CMIP-6 and other snow-focused climate model experiments, and applies the data for simulation of Arctic hydrological regimes. In this presentation, we summarize the requirements and product specifications for the snow extent and SWE products, with a focus on climate applications. We present an overview of the algorithms and systems for generation of the time series. The 40 years (from 1980 onwards) time series of daily fractional snow extent products from AVHRR with 5 km pixel spacing, and the 20-year time series from MODIS (1 km pixel spacing) as well as the coarse resolution (25 km pixel spacing) of daily SWE products from 1978 onwards will be presented along with first results of the multi-sensor consistency checks and validation activities.

WBSCAT is a new terrestrial microwave scatterometer supporting polarimetric observations over 1 to 40 GHz. This instrument is being developed for the European Space Agency (ESA) to conduct microwave studies of a wide range of ground covers including snow and ice. This instrument is built upon the heritage of SnowScat , operating over the range of 9.2 to 17.8 GHz, that has been used for generating tomographic time-series of snow pack and is part of the ongoing ESA SnowLab project [2]. WBSCAT, like its predecessor, acquires coherent data and can measure polarimetric scattering matrices, interferometric phase, and coherence. Both instruments will be operated in Winter 2018/2019 in Davos Laret, Switzerland mounted on a 10- meter tower and performing multiple daily observations of the snow pack. Either instrument can be attached to a 2.2-meter linear scanner inclined at 45-degrees permitting tomographic snow profiling [1]. The WBSCAT instrument uses radial-scan aperture synthesis to acquire independent observations of the scattering volume and also to restrict the field of view to the undisturbed test site, despite wide antenna beamwidths at low frequencies. The 6 horn antennas, with overlapping frequency ranges of 1 to 6, 2 to 18, and 10 to 40 GHz, are mounted approximately 60 cm radially from the rotation axis of the pan/tilt scanner. The antennas can be scanned between +35 and -45 degrees in elevation and +/- 90 degrees in azimuth, creating a synthetic aperture. The aperture dimensions are mostly determined by the antenna pattern, but at low frequencies, the antenna beamwidth exceeds 90 degrees. Aperture synthesis substantially increases the number of looks for improved radiometric resolution and is a novel approach for ground-based microwave scatterometry. Combining ranging information along with WBSCAT aperture synthesis perpendicular to the line of sight, has the potential for direct 3D imaging of the snow pack.

WBSCAT is a new terrestrial 1-40 GHz polarimetric scatterometer. This instrument, built for the European Space Agency, with additional support from ETH WSL, is currently an element of the ESA SnowLab project for continuous microwave measurements of snowpack in Davos-Laret Switzerland. WBSCAT is based on a compact Vector Network Analyzer (VNA), combined with calibration standards and low-noise amplifiers to increase sensitivity. A pan/tilt positioner provides the angular and spatial diversity required for measurement of radar cross-section, 3D tomographic imaging, and measurements of interferometric coherence. The instrument can apply angular diversity and aperture synthesis to increase radiometric accuracy and suppress clutter. In Davos-Laret, WBSCAT is suspended from a 2.2-meter linear rail positioner that provides additional spatial diversity for high-resolution 3D tomographic imaging.

A Permafrost Information System (PerSys) has been setup as part of the GlobPermafrost ESA DUE GlobPermafrost project (2016-2019, www.globpermafrost.info). This includes a data catalogue as well as a WebGIS, both linked to the Pangaea repository for easy data access. The thematic products available include InSAR-based land surface deformation maps, rock glacier velocity fields, spatially distributed permafrost model outputs, land surface properties and changes, and ground-fast lake ice. Extended permafrost modelling (time series) is implemented in the new ESA CCI+ Permafrost project (2018-2021), which will provide the key for our understanding of the changes of surface features over time. Special emphasis in CCI+ Permafrost will be on the evaluation and development of land surface models to gain better understanding of the impact of climate change on permafrost and land-atmosphere exchange. Additional focus will be on documentation of kinematics from rock glaciers in several mountain regions across the world. We will present an overview on technical developments made within GlobPermafrost and demonstrate its utility and challenges for an area prone to change of permafrost features. We will focus on the central Yamal Peninsula and the unusually warm years of 2012 and 2016. Conditions of 2012 triggered widespread retrogressive thaw slumps and the development of a gas emission crater. Thaw slumps have been reactivated in 2016, the first year with extensive coverage of Sentinel-1 as well as Sentinel-2 data. We present the documentation of these developments based on InSAR subsidence, Landsat trend analyses, ground fast lake ice, Sentinel-2 landcover information as well as a time series of the first version of ground temperatures from the ESA CCI+ Permafrost project. While landcover documents the occurrence of disturbances, InSAR provides insight into soil properties and impacts of unusually warm conditions during the unfrozen period. These space-based observations have been evaluated by in situ measurements at the long-term monitoring site Vaskiny Datchi. Ground fast lake ice and ground temperature modelling results provide additional insight into interannual variability.

The aim of the ESA SnowLab project is to provide a comprehensive multi-frequency, multi-polarisation, multi-temporal dataset of active microwave measurements over snow-covered grounds to investigate the relationship between effective snow- and ground parameters and the resultant signals detected by microwave radar. An important part for the development of microwave models is the microstructural characterisation. This characterisation can only be done by repeated measurements by SnowMicroPen and more completely, but also much more expensive, by X-ray micro-tomography. Within this project we complemented the microwave measurements of Alpine snow in Switzerland with extensive effective snow- and ground parameters and meteorological data. Microwave backscatter measurements were conducted using the 9 - 18 GHz ESA SnowScat instrument and since December 2018 the recently built ESA WBScat instrument. WBScat allows to extend the spectral coverage to 1 - 40 GHz.

Knowledge on ice surface velocity of glaciers and ice cap contributes to a better understanding of a wide range of processes related to glacier dynamics, for example glacier mass flux, flow modes and flow instabilities (e.g. surges), subglacial processes (e.g. erosion), supraand intra-glacial mass transport, and the development of glacier lakes and associated hazards. In addition, the comparison of the spatio-temporal variations of glacier velocities will improve understanding of climate change impacts.

As part of the ESA SnowLab campaign the SnowScat device, a terrestrial stepped-frequency continuous-wave (SFCW) scatterometer which supports fully-polarimetric measurements within a frequency band from 9.2 to 17.8 GHz, was operated in tomographic profiling mode. In this tomographic profiling mode the SnowScat device is subsequently displaced in elevation direction to obtain a high-resolution not only in range direction but also along elevation. This leads to two-dimensional vertical profiles of a snowpack, which means that radar backscatter, co-polar phase difference, interferometric phase and coherence can be distinguished also along the vertical dimension of the snowpack. In this paper, we provide a summary and a few examples of a time series of tomographic measurements of snow obtained within the ESA SnowLab campaign at two different locations in the Swiss Alps during three snow seasons.

Current methods for retrieving SWE (snow water equivalent) from space rely on passive microwave sensors. Observations are limited by poor spatial resolution, ambiguities related to separation of snow microstructural properties from the total snow mass, and signal saturation when snow is deep (~>80 cm). The use of SAR (Synthetic Aperture Radar) at suitable frequencies has been suggested as a potential observation method to overcome the coarse resolution of passive microwave sensors. Nevertheless, suitable sensors operating from space are, up to now, unavailable. Active microwave retrievals suffer, however, from the same difficulties as the passive case in separating impacts of scattering efficiency from those of snow mass. In this study, we explore the potential of applying active (radar) and passive (radiometer) microwave observations in tandem, by using a dataset of co-incident tower-based active and passive microwave observations and detailed in situ data from a test site in Northern Finland. The dataset spans four winter seasons with daily coverage. In order to quantify the temporal variability of snow microstructure, we derive an effective correlation length for the snowpack (treated as a single layer), which matches the simulated microwave response of a semi-empirical radiative transfer model to observations. This effective parameter is derived from radiometer and radar observations at different frequencies and frequency combinations (10.2, 13.3 and 16.7 GHz for radar; 10.65, 18.7 and 37 GHz for radiometer). Under dry snow conditions, correlations are found between the effective correlation length retrieved from active and passive measurements. Consequently, the derived effective correlation length from passive microwave observations is applied to parameterize the retrieval of SWE using radar, improving retrieval skill compared to a case with no prior knowledge of snow-scattering efficiency. The same concept can be applied to future radar satellite mission concepts focused on retrieving SWE, exploiting existing methods for retrieval of snow microstructural parameters, as employed within the ESA (European Space Agency) GlobSnow SWE product. Using radar alone, a seasonally optimized value of effective correlation length to parameterize retrievals of SWE was sufficient to provide an accuracy of <25 mm (unbiased) Root-Mean Square Error using certain frequency combinations. A temporally dynamic value, derived from e.g., physical snow models, is necessary to further improve retrieval skill, in particular for snow regimes with larger temporal variability in snow microstructure and a more pronounced layered structure.

Permafrost cannot be directly detected from space, but many surface features of permafrost terrains and typical periglacial landforms are observable with a variety of EO sensors ranging from very high to medium resolution at various wavelengths. In addition, landscape dynamics associated with permafrost changes and geophysical variables relevant for characterizing the state of permafrost, such as land surface temperature or freeze-thaw state can be observed with spaceborne Earth Observation. Suitable regions to examine environmental gradients across the Arctic have been defined in a community white paper (Bartsch et al. 2014, hdl:10013/epic.45648.d001). These transects have been revised and adjusted within the DUE GlobPermafrost initiative of the European Space Agency. The ESA DUE GlobPermafrost project develops, validates and implements Earth Observation (EO) products to support research communities and international organisations in their work on better understanding permafrost characteristics and dynamics. Prototype product cases will cover different aspects of permafrost by integrating in situ measurements of subsurface and surface properties, Earth Observation, and modelling to provide a better understanding of permafrost today. The project will extend local process and permafrost monitoring to broader spatial domains, support permafrost distribution modelling, and help to implement permafrost landscape and feature mapping in a GIS framework. It will also complement active layer and thermal observing networks. Both lowland (latitudinal) and mountain (altitudinal) permafrost issues are addressed. The status of the Permafrost Information System and first results will be presented. Prototypes of GlobPermafrost datasets include: Modelled mean annual ground temperature by use of land surface temperature and snow water equivalent from satellites Land surface characterization including shrub height, land cover and parameters related to surface roughness Trends from Landsat time-series over selected transects For selected sites: subsidence, ground fast lake ice, land surface features and rock glacier monitoring