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In this paper, a method is presented to classify volcano activity into three classes, namely quiet, strombolian, and paroxysm. The method is based on training a six-layered deep neural network (DNN) model using these signals as inputs (features): time series of the number of distances of infrasonic events, radar backscatter power, RMS of tremor in five stations close to craters of the volcano, tilt derivative, and seismic tremor source depth. The method was tested on the data related to a period of five years, and the results were concluded using indexes of precision, recall, F 1 score, and Cohen's Kappa coefficient were calculated to evaluate the qualification of the classification. Also, the results were compared to Bayesian network (BN), K-nearest neighbors (KNN), and decision tree (DT) methods. Decision learning trees and KNN are popular machine learning algorithms belonging to the class of supervised learning algorithms. They mimic the human level thinking and, differing from neural networks, are not black box models. The comparisons reveal the proposed method, especially in classifying both strombolian and paroxysm classes. This advantage makes the presented method a more reliable tool for practical use in the volcano monitoring control rooms.

Investigation of microwave scattering mechanisms is extremely important for developing methods for ocean remote sensing. Recent studies have shown that a common two-scale scattering model accounting for resonance (Bragg) scattering has some drawbacks, in particular it often overestimates the vertical-to-horizontal polarization radar return ratio and underestimates the radar Doppler shifts if the latter are assumed as associated with quasi linear resonance surface waves. It is supposed nowadays that radar backscattering at moderate incidence angles is determined not only by resonance Bragg mechanism but also contains non polarized (non Bragg) component which is associated supposedly with wave breaking but which is still insufficiently studied. Better understanding of the scattering mechanisms can be achieved when studying variations of radar return due to long wind waves. In this paper, results of experiments from an Oceanographic Platform on the Black Sea using dual co-polarized X-band scatterometers working at moderate incidence are presented and variations of Bragg and non-Bragg components (BC and NBC, respectively) and radar Doppler shifts are analysed. It is established that BC and NBC are non-uniformly distributed over profile of dominant (decametre-scale) wind waves (DWW). Variations of BC are characterized by some “background” return weakly modulated with the dominant wind wave periods, while NBC is determined mostly by rare and strong spikes occurred near the crests of the most intense individual waves in groups of DWW. We hypothesize that the spikes are due to intensification of nonlinear structures on the profile of short, decimetre-scale wind waves when the latter are amplified by intense DWW. Bragg scattering in slicks under the experimental conditions was suppressed stronger than NBC and spikes dominated in total radar return. It is obtained that radar Doppler shifts at HH-polarization are larger than at VV-polarization, particularly in slicks, the same relation is for NBC and BC Doppler shifts, thus indicating different scattering mechanisms for these components.

Based on massive processing of heritage radar data from the satellite missions ERS-1/2, JERS-1, ENVISAT ASAR, ALOS PALSAR and Radarsat-2, and in combination with data from the current Sentinel-1 and ALOS-2 PALSAR-2 missions, we compiled a ∼ 30-year time series of radar backscatter over Svalbard. We exploited this data to detect glacier surges by using changes in backscatter as an indicator of increased or decreased surge-related crevassing. In this way, we reconstructed an as consistent as possible time series of surge activity on Svalbard for 1992 to 2025. We recorded 24 surge-type events during the pre Sentinel-1 period 1992–2014 (23 years) and 34 surge-type events during the post Sentinel-1 period 2015–2025 (11 years). This time series shows an approximately threefold increase in surges since 2015, from an average of about one surge per year to more than three surges per year. We show that this increase is unlikely to be explained alone by the better resolution, coverage and quality of the Sentinel-1 data compared to the data from the earlier SAR heritage missions. Simulation results indicate that the observed increase is extremely unlikely to be attributed to random perturbations in surge cyclicity, and instead suggest the influence of an external forcing mechanism. The number of surges during the recent decade seems high, but due to uncertainties in historical records, it remains unclear whether this frequency is exceptional or if earlier decades were unusually quiet. The cause of the observed threefold increase in surge activity also remains uncertain, given our incomplete understanding of surge initiation in relation to climate variability and non-climatic surge controls.

Seasonal snow is an essential water resource in many mountain regions. However, the spatio-temporal variability in mountain snow depth or snow water equivalent (SWE) at regional to global scales is not well understood due to the lack of high-resolution satellite observations and robust retrieval algorithms. We investigate the ability of the Sentinel-1 mission to monitor snow depth at sub-kilometer (100 m, 500 m, and 1 km) resolutions over the European Alps for 2017–2019. The Sentinel-1 backscatter observations, especially in cross-polarization, show a high correlation with regional model simulations of snow depth over Austria and Switzerland. The observed changes in radar backscatter with the accumulation or ablation of snow are used in an empirical change detection algorithm to retrieve snow depth. The algorithm includes the detection of dry and wet snow conditions. Compared to in situ measurements at 743 sites in the European Alps, dry snow depth retrievals at 500 m and 1 km resolution have a spatio-temporal correlation of 0.89. The mean absolute error equals 20 %–30 % of the measured values for snow depths between 1.5 and 3 m. The performance slightly degrades for retrievals at the finer 100 m spatial resolution as well as for retrievals of shallower and deeper snow. The results demonstrate the ability of Sentinel-1 to provide snow estimates in mountainous regions where satellite-based estimates of snow mass are currently lacking. The retrievals can improve our knowledge of seasonal snow mass in areas with complex topography and benefit a number of applications, such as water resource management, flood forecasting, and numerical weather prediction. However, future research is recommended to further investigate the physical basis of the sensitivity of Sentinel-1 backscatter observations to snow accumulation.

Measuring the radar backscatter characteristics of glacier ice at different frequencies and incidence angles is fundamental to predicting the glacier mapping performance of a sensor. However, such measurements at 94 GHz do not exist. To address this knowledge gap, we collected 94 GHz radar backscatter data from the surface of Rhônegletscher in Switzerland using the All‐Weather Volcano Topography Imaging Sensor (AVTIS2) real‐aperture Frequency Modulated Continuous Wave radar. We determine the mean normalized radar cross section σmean0$\\left({\\sigma }_{\\text{mean}}^{0}\\right)$to be −9.9 dB. The distribution closely follows a log‐normal distribution with a high goodness of fit (R2 = 0.99) which suggests that radar backscatter is diffuse and driven by surface roughness. Further, we quantified the uncertainty of AVTIS2 3D point clouds to be 1.30–3.72 m, which is smaller than other ground‐based glacier surface mapping radars. These results demonstrate that glacier surfaces are an efficient scattering target at 94 GHz, hence demonstrating the suitability of millimeter‐wave radar for glacier monitoring. Plain Language Summary Radar sensors map glacier surfaces by transmitting a signal at a specific frequency and measuring its return strength when reflected back. This returned signal strength, called radar backscatter, is determined by the characteristics of the glacier surface and varies with radar frequency and sensor viewing angle. Millimeter‐wave radars operating at 94 GHz can acquire high resolution measurements of glaciers in most weather conditions. However, there are currently no measurements of radar backscatter from glacier surfaces at this frequency. We therefore acquired the first ever measurements of 94 GHz radar backscatter from glacier ice. The results are consistent with those expected from randomly rough surfaces, hence we conclude that the roughness of the glacier surface is the primary driver of 94 GHz radar backscatter. We also show that 3D glacier surface mapping at this frequency is more accurate than other ground‐based radars that are employed to map glacier geometries. The results overall indicate that 94 GHz radar is an effective tool for glacier monitoring and thus opens up new possibilities for studying glacier processes. Key Points 94 GHz radar backscatter from alpine glacier ice has been characterized for the first time Surface roughness is the primary factor in 94 GHz radar backscatter from glacier ice The uncertainty of 3D glacier mapping using 94 GHz radar has been quantified

For studying the flow of glaciers and their response to climate change it is important to detect glacier surges. Here, we compute within Google Earth Engine the normalized differences between winter maxima of Sentinel-1 C-band radar backscatter image stacks over subsequent years. We arrive at a global map of annual backscatter changes, which are for glaciers in most cases related to changed crevassing associated with surge-type activity. For our demonstration period 2018–2019 we detected 69 surging glaciers, with many of them not classified so far as surge type. Comparison with glacier surface velocities shows that we reliably find known surge activities. Our method can support operational monitoring of glacier surges and some other special events such as large rock and snow avalanches.

Using global Sentinel-1 radar backscatter data, we systematically map the locations of glaciers with surge-type activity during 2017–22. Patterns of pronounced increases or decreases in the strongest backscatter between two winter seasons often indicate large changes in glacier crevassing, which we treat here as a sign of surge-type activity. Validations against velocity time series, terminus advances and crevassing found in optical satellite images confirm the robustness of this approach. We find 115 surge-type events globally between 2017 and 2022, around 100 of which on glaciers already know as surge-type. Our data reveal a pronounced spatial clustering in three regions, (i) Karakoram, Pamirs and Western Kunlun Shan (~50 surges), (ii) Svalbard (~25) and (iii) Yukon/Alaska (~9), with only a few other scattered surges elsewhere. This spatial clustering is significantly more pronounced than the overall global clustering of known surge-type glaciers. The 2017–22 clustering may point to climatic forcing of surge initiation.

To improve our understanding of how snow properties influence sea ice thickness retrievals from presently operational and upcoming satellite radar altimeter missions, as well as to investigate the potential for combining dual frequencies to simultaneously map snow depth and sea ice thickness, a new, surface-based, fully polarimetric Ku- and Ka-band radar (KuKa radar) was built and deployed during the 2019–2020 year-long MOSAiC international Arctic drift expedition. This instrument, built to operate both as an altimeter (stare mode) and as a scatterometer (scan mode), provided the first in situ Ku- and Ka-band dual-frequency radar observations from autumn freeze-up through midwinter and covering newly formed ice in leads and first-year and second-year ice floes. Data gathered in the altimeter mode will be used to investigate the potential for estimating snow depth as the difference between dominant radar scattering horizons in the Ka- and Ku-band data. In the scatterometer mode, the Ku- and Ka-band radars operated under a wide range of azimuth and incidence angles, continuously assessing changes in the polarimetric radar backscatter and derived polarimetric parameters, as snow properties varied under varying atmospheric conditions. These observations allow for characterizing radar backscatter responses to changes in atmospheric and surface geophysical conditions. In this paper, we describe the KuKa radar, illustrate examples of its data and demonstrate their potential for these investigations.

The fracturing of glaciers and ice shelves in Antarctica influences their dynamics and stability. Hence, data on the evolving distribution of crevasses are required to better understand the evolution of the ice sheet, though such data have traditionally been difficult and time-consuming to generate. Here, we present an automated method of mapping crevasses on grounded and floating ice with the application of convolutional neural networks to Sentinel-1 synthetic aperture radar backscatter data. We apply this method across Antarctica to images acquired between 2015 and 2022, producing a 7.5-year record of composite fracture maps at monthly intervals and 50 m spatial resolution and showing the distribution of crevasses around the majority of the ice sheet margin. We develop a method of quantifying changes to the density of ice shelf fractures using a time series of crevasse maps and show increases in crevassing on Thwaites and Pine Island ice shelves over the observational period, with observed changes elsewhere in the Amundsen Sea dominated by the advection of existing crevasses. Using stress fields computed using the BISICLES ice sheet model, we show that much of this structural change has occurred in buttressing regions of these ice shelves, indicating a recent and ongoing link between fracturing and the developing dynamics of the Amundsen Sea sector.

Accurate snow information at high spatial and temporal resolution is needed to support climate services, water resource management, and environmental prediction services. However, snow remains the only element of the water cycle without a dedicated Earth observation mission. The snow scientific community has shown that Ku-band radar measurements provide quality snow information with its sensitivity to snow water equivalent and the wet/dry state of snow. With recent developments of tools like the snow micropenetrometer (SMP) to retrieve snow microstructure data in the field and radiative transfer models like the Snow Microwave Radiative Transfer (SMRT) model, it becomes possible to properly characterize the snow and how it translates into radar backscatter measurements. An experiment at Trail Valley Creek (TVC), Northwest Territories, Canada, was conducted during the winter of 2018/19 in order to characterize the impacts of varying snow geophysical properties on Ku-band radar backscatter at a 100 m scale. Airborne Ku-band data were acquired using the University of Massachusetts radar instrument. This study shows that it is possible to calibrate SMP data to retrieve statistical information on snow geophysical properties and properly characterize a representative snowpack at the experiment scale. The tundra snowpack measured during the campaign can be characterize by two layers corresponding to a rounded snow grain layer and a depth hoar layer. Using RADARSAT-2 and TerraSAR-X data, soil background roughness properties were retrieved (msssoil=0.010±0.002), and it was shown that a single value could be used for the entire domain. Microwave snow grain size polydispersity values of 0.74 and 1.11 for rounded and depth hoar snow grains, respectively, were retrieved. Using the geometrical optics surface backscatter model, the retrieved effective soil permittivity increased from C-band (εsoil=2.47) to X-band (εsoil=2.61) and to Ku-band (εsoil=2.77) for the TVC domain. Using the SMRT and the retrieved soil and snow parameterizations, an RMSE of 2.6 dB was obtained between the measured and simulated Ku-band backscatter values when using a global set of parameters for all measured sites. When using a distributed set of soil and snow parameters, the RMSE drops to 0.9 dB. This study thus shows that it is possible to link Ku-band radar backscatter measurements to snow conditions on the ground using a priori knowledge of the snow conditions to retrieve snow water equivalent (SWE) at the 100 m scale.