Remote sensing supports carbon estimation, allowing the upscaling of field measurements to large extents. Lidar is considered the premier instrument to estimate above ground biomass, but data are expensive and collected on-demand, with limited spatial and temporal coverage. The previous JERS and ALOS SAR satellites data were extensively employed to model forest biomass, with literature suggesting signal saturation at low-moderate biomass values, and an influence of plot size on estimates accuracy. The ALOS2 continuity mission since May 2014 produces data with improved features with respect to the former ALOS, such as increased spatial resolution and reduced revisit time. We used ALOS2 backscatter data, testing also the integration with additional features (SAR textures and NDVI from Landsat 8 data) together with ground truth, to model and map above ground biomass in two mixed forest sites: Tahoe (California) and Asiago (Alps). While texture was useful to improve the model performance, the best model was obtained using joined SAR and NDVI (R² equal to 0.66). In this model, only a slight saturation was observed, at higher levels than what usually reported in literature for SAR; the trend requires further investigation but the model confirmed the complementarity of optical and SAR datatypes. For comparison purposes, we also generated a biomass map for Asiago using lidar data, and considered a previous lidar-based study for Tahoe; in these areas, the observed R² were 0.92 for Tahoe and 0.75 for Asiago, respectively. The quantitative comparison of the carbon stocks obtained with the two methods allows discussion of sensor suitability. The range of local variation captured by lidar is higher than those by SAR and NDVI, with the latter showing overestimation. However, this overestimation is very limited for one of the study areas, suggesting that when the purpose is the overall quantification of the stored carbon, especially in areas with high carbon density, satellite data with lower cost and broad coverage can be as effective as lidar.

Rock glaciers are widespread periglacial landforms in mountain regions like the European Alps. Depending on their ice content, they are characterized by slow downslope displacement due to permafrost creep. These landforms are usually mapped within inventories, but understand their activity is a very difficult task, which is frequently accomplished using geomorphological field evidences, direct measurements, or remote sensing approaches. In this work, a powerful method to analyze the rock glaciers’ activity was developed exploiting the synthetic aperture radar (SAR) satellite data. In detail, the interferometric coherence estimated from Sentinel-1 data was used as key indicator of displacement, developing an unsupervised classification method to distinguish moving (i.e., characterized by detectable displacement) from no-moving (i.e., without detectable displacement) rock glaciers. The original application of interferometric coherence, estimated here using the rock glacier outlines as boundaries instead of regular kernel windows, allows describing the activity of rock glaciers at a regional-scale. The method was developed and tested over a large mountainous area located in the Eastern European Alps (South Tyrol and western part of Trentino, Italy) and takes into account all the factors that may limit the effectiveness of the coherence in describing the rock glaciers’ activity. The activity status of more than 1600 rock glaciers was classified by our method, identifying more than 290 rock glaciers as moving. The method was validated using an independent set of rock glaciers whose activity is well-known, obtaining an accuracy of 88%. Our method is replicable over any large mountainous area where rock glaciers are already mapped and makes it possible to compensate for the drawbacks of time-consuming and subjective analysis based on geomorphological evidences or other SAR approaches.

The kinematic acceleration of rock glaciers observed in recent decades shows that the behavior of these landforms is related to climate change. Velocity variations on yearly to seasonal time scales are frequently investigated, but velocity changes measured on shorter time scales (i.e., on hourly resolutions) are as yet poorly investigated. We used a ground based synthetic aperture radar to investigate, on an hourly time scale, the displacement of a rock glacier located in Val Senales (European Alps, northern Italy). We observed velocity fluctuations occurring at a very regular pace, characterized by phases of sharp acceleration (up to 0.9 mm/hr) lasting 4–11 hr followed by long phases of stagnation lasting 13–20 hr. This study describes an unprecedented observation of an hourly velocity rhythm of an active rock glacier and opens up new perspectives in the analysis and interpretation of rock glacier kinematics.

The Copernicus Sentinel-1 mission provides synthetic aperture radar (SAR) acquisitions over large areas with high temporal and spatial resolution. This new generation of satellites providing open-data products has enhanced the capabilities for continuously studying Earth surface changes. Over the past two decades, several studies have demonstrated the potential of differential synthetic aperture radar interferometry (DInSAR) for detecting and quantifying land surface deformation. DInSAR limitations and challenges are linked to the SAR properties and the field conditions (especially in mountainous environments) leading to spatial and temporal decorrelation of the SAR signal. High temporal decorrelation can be caused by changes in vegetation (particularly in nonurban areas), atmospheric conditions, or high ground surface velocity. In this study, the kinematics of the complex and vegetated Corvara landslide, situated in Val Badia (South Tyrol, Italy), are monitored by a network of three permanent and 13 monthly measured benchmark points measured with the differential global navigation satellite system (DGNSS) technique. The slope displacement rates are found to be highly unsteady and reach several meters a year. This paper focuses firstly on evaluating the performance of DInSAR changing unwrapping and coherence parameters with Sentinel-1 imagery, and secondly, on applying DInSAR with DGNSS measurements to monitor an active and complex landslide. To this end, 41 particular SAR images, coherence thresholds, and 2D and 3D unwrapping processes give various results in terms of reliability and accuracy, supporting the understanding of the landslide velocity field. Evolutions of phase changes are analysed according to the coherence, the changing field conditions, and the monitored ground-based displacements.

From the wide range of methods available to landslide researchers and practitioners for monitoring ground displacements, remote sensing techniques have increased in popularity. Radar interferometry methods with their ability to record movements in the order of millimeters have been more frequently applied in recent years. Multi-temporal interferometry can assist in monitoring landslides on the regional and slope scale and thereby assist in assessing related hazards and risks. Our study focuses on the Corvara landslides in the Italian Alps, a complex earthflow with spatially varying displacement patterns. We used radar imagery provided by the COSMO-SkyMed constellation and carried out a validation of the derived time-series data with differential GPS data. Movement rates were assessed using the Permanent Scatterers based Multi-Temporal Interferometry applied to 16 artificial Corner Reflectors installed on the source, track and accumulation zones of the landslide. The overall movement trends were well covered by Permanent Scatterers based Multi-Temporal Interferometry, however, fast acceleration phases and movements along the satellite track could not be assessed with adequate accuracy due to intrinsic limitations of the technique. Overall, despite the intrinsic limitations, Multi-Temporal Interferometry proved to be a promising method to monitor landslides characterized by a linear and relatively slow movement rates.

This study aims at estimating the dry snow water equivalent (SWE) by using X-band synthetic aperture radar (SAR) data from the COSMO-SkyMed (CSK) satellite constellation. The time series of CSK acquisitions have been collected during the dry snow period in the Alto Adige test site, in the Italian Alps, during the winter seasons from 2013 to 2015 and from 2019 to 2021. The SAR data have been analyzed and compared with the in situ measurements to understand the X-band SAR sensitivity to SWE, which has been further assessed by dense media radiative transfer (DMRT) model simulations. The sensitivity analysis provided the basis for addressing the SWE retrieval from the CSK data, by exploiting two different machine learning (ML) techniques, namely, artificial neural networks (ANNs) and support vector regression (SVR). To ensure statistical independence of training and validation processes, the algorithms are trained and tested using SWE predictions of the fully distributed snow model AMUNDSEN as reference data and are subsequently validated on the experimental dataset. Due to its influence on the CSK estimates, the effect of forest canopy was accounted for in the analysis. Depending on the algorithm, the validation resulted in a correlation coefficient $0.78\le R \le0.91$ and a root-mean-square error (RMSE) 55.5 mm $\le $ RMSE $\le87.4$ mm between estimated and in situ SWE. Further analysis and validation are needed; however, the obtained results seem suggesting the CSK constellation as an effective tool for the retrieval of the dry SWE in alpine areas.