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Landslides, resulting from disturbances in slope equilibrium, pose a significant threat to landscapes, infrastructure, and human life. Triggered by factors such as intense precipitation, seismic activities, or volcanic eruptions, these events can cause extensive damage and endanger nearby communities. A comprehensive understanding of landslide characteristics, including spatio-temporal patterns, dimensions, and morphology, is vital for effective landslide disaster management. Existing remote sensing approaches mostly use either optical or synthetic aperture radar sensors. Integrating information from both these types of sensors promises greater accuracy for identifying and locating landslides. This study proposes a novel approach, the ML-LaDeCORsat (Machine Learning-based coseismic Landslide Detection using Combined Optical and Radar Satellite Imagery), that integrates freely available Sentinel-1, Palsar-2, and Sentinel-2 imagery data in Google Earth Engine (GEE). The approach also integrates relevant spectral indices and suitable bands used in a machine learning-based classification of coseismic landslides. The approach includes a robust and reproducible training and validation strategy and allows one to choose between five classifiers (CART, Random Forest, GTB, SVM, and Naive Bayes). Using landslides from four different earthquake case studies, we demonstrate the superiority of our approach over existing solutions in coseismic landslide identification and localization, providing a GTB-based detection accuracy of 87–92%. ML-LaDeCORsat can be adapted to other landslide events (GEE script is provided). Transfer learning experiments proved that our model can be applied to other coseismic landslide events without the need for additional training data. Our novel approach therefore facilitates quick and reliable identification of coseismic landslides, highlighting its potential to contribute towards more effective disaster management. [ABSTRACT FROM AUTHOR]

Arctic tundra landscapes are highly complex and are rapidly changing due to the warming climate. Datasets that document the spatial and temporal variability of the landscape are needed to monitor the rapid changes. Synthetic Aperture Radar (SAR) imagery is specifically suitable for monitoring the Arctic, as SAR, unlike optical remote sensing, can provide time series regardless of weather and illumination conditions. This study examines the potential of seasonal backscatter mechanisms in Arctic tundra environments for improving land cover classification purposes by using a time series of HH/HV TerraSAR-X (TSX) imagery. A Random Forest (RF) classification was applied on multi-temporal Sigma Nought intensity and multi-temporal Kennaugh matrix element data. The backscatter analysis revealed clear differences in the polarimetric response of water, soil, and vegetation, while backscatter signal variations within different vegetation classes were more nuanced. The RF models showed that land cover classes could be distinguished with 92.4% accuracy for the Kennaugh element data, compared to 57.7% accuracy for the Sigma Nought intensity data. Texture predictors, while improving the classification accuracy on the one hand, degraded the spatial resolution of the land cover product. The Kennaugh elements derived from TSX winter acquisitions were most important for the RF model, followed by the Kennaugh elements derived from summer and autumn acquisitions. The results of this study demonstrate that multi-temporal Kennaugh elements derived from dual-polarized X-band imagery are a powerful tool for Arctic tundra land cover mapping. [ABSTRACT FROM AUTHOR]

Radar penetration in brine-wetted snow-covered sea ice is almost nil, yet reports exist of a correlation between snow depth or ice thickness and SAR parameters. This article presents a description of snow depth and first-year sea ice thickness distributions in three fjords of the Hudson Strait and of their tenuous correlation with SAR backscattering in the C- and X-band. Snow depth and ice thickness were directly measured in three fjords of the Hudson Strait from 2015 to 2018 in April or May. Bayesian linear regression analysis was used to investigate their relationship with RADARSAT-2 (C-band) or TerraSAR-X (X-band). Polarimetric ratios and the Cloude–Pottier decomposition parameters were explored along with the HH, HV and VV bands. Linear correlations were generally no higher than 0.3 except for a special case in May 2018. The co-polarization ratio did not perform better than the backscattering coefficients. [ABSTRACT FROM AUTHOR]

Accelerating melt on the Greenland ice sheet leads to dramatic changes at a global scale. Especially in the last decades, not only the monitoring, but also the quantification of these changes has gained considerably in importance. In this context, Interferometric Synthetic Aperture Radar (InSAR) systems complement existing data sources by their capability to acquire 3D information at high spatial resolution over large areas independent of weather conditions and illumination. However, penetration of the SAR signals into the snow and ice surface leads to a bias in measured height, which has to be corrected to obtain accurate elevation data. Therefore, this study purposes an easy transferable pixel-based approach for X-band penetration-related elevation bias estimation based on single-pass interferometric coherence and backscatter intensity which was performed at two test sites on the Northern Greenland ice sheet. In particular, the penetration bias was estimated using a multiple linear regression model based on TanDEM-X InSAR data and IceBridge laser-altimeter measurements to correct TanDEM-X Digital Elevation Model (DEM) scenes. Validation efforts yielded good agreement between observations and estimations with a coefficient of determination of R2 = 68% and an RMSE of 0.68 m. Furthermore, the study demonstrates the benefits of X-band penetration bias estimation within the application context of ice sheet elevation change detection. [ABSTRACT FROM AUTHOR]

Continuous monitoring of glacier changes supports our understanding of climate related glacier behavior. Remote sensing data offer the unique opportunity to observe individual glaciers as well as entire mountain ranges. In this study, we used synthetic aperture radar (SAR) data to monitor the recession of wet snow area extent per season for three different glacier areas of the Rofental, Austria. For four glaciological years (GYs, 2014/2015–2017/2018), Sentinel-1 (S1) SAR data were acquired and processed. For all four GYs, the seasonal snow retreated above the elevation range of perennial firn. The described processing routine is capable of discriminating wet snow from firn areas for all GYs with sufficient accuracy. For a short in situ transect of the snow—firn boundary, SAR derived wet snow extent agreed within an accuracy of three to four pixels or 30–40 m. For entire glaciers, we used optical remote sensing imagery and field data to assess reliability of derived wet snow covered area extent. Differences in determination of snow covered area between optical data and SAR analysis did not exceed 10% on average. Offsets of SAR data to results of annual field assessments are below 10% as well. The introduced workflow for S1 data will contribute to monitoring accumulation area extent for remote and hazardous glacier areas and thus improve the data basis for such locations. [ABSTRACT FROM AUTHOR]

The Baltoro Glacier is one of the largest glaciers in the Karakoram mountain range. Long-term monitoring of glacier dynamics provides key information on glacier evolution in a changing climate, which is essential for regional water resource and natural hazard management. On large glaciers, detailed field based mass balance is not feasible. Ice dynamic variations quantify changes in mass transport and possibly the influence of environmental parameters on the evolution of the glacier. Although velocity variations of Baltoro Glacier during winter and summer are linked to seasonally enhanced basal sliding, little is known about differences in timing and magnitude of (intra-)seasonal velocity variations and their determining mechanisms. We present time series of annual, seasonal, and intra-seasonal glacier surface velocities by means of intensity offset tracking applied on multi-mission Synthetic Aperture Radar (SAR) data for a period of 25 years from 1992 to 2017. Supraglacial lakes forming on the downstream glacier surface in summer were mapped from 1991 to 2017 based on the Normalized Difference Water Index (NDWI), calculated from multi-spectral Landsat and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) imagery. Additionally, precipitation data of the Tropical Rainfall Measurement Mission (TRMM) and temperature data of ERA-Interim were used to derive the Standardized Precipitation Index (SPI) and Standardized Temperature Index (STI) from 1998 to 2017. Linking surface velocities to the SPI confirmed a strong correlation between heavy precipitation events in winter and the magnitude and the timing of glacier acceleration in summer. Downstream extensions of summer acceleration that have been found since 2015 may be explained by additional water draining from an increased number of supraglacial lakes through crevasses that have been formed in consequence of higher initial velocities, evoked by strong winter precipitation. The warmer melt seasons observed in the years 2015 to 2017 additionally affects the formation of a supraglacial lake, so stronger summer acceleration events in recent years may be indirectly related to global warming. [ABSTRACT FROM AUTHOR]