In this study, we trained a supervised neural network for sea ice classification using X-band SAR images obtained during an Antarctic fieldwork campaign and applied it to Arctic X-band data acquired during sea ice fieldwork campaigns. The results revealed a pronounced correlation between the predicted sea ice class and measured snow depth in the Arctic. This correlation is likely attributed to the Arctic acquisitions taking place during the cold winter conditions characterized by thicker snow layers, which resemble conditions in Antarctica.

The change of glacier surface elevation in Karakoram holds significant relevance to the study of global climate change and regional water resource management. In this work, we focused on understanding glacier dynamics in the Karakoram region through the analysis of Digital Elevation Models (DEMs) generated from a comprehensive 481 TanDEM-X CoSSC products spanning 2011 to 2020. An iterative approach based on the residual phase with respect to the reference DEM was employed to generate high resolution DEMs. The systematic approach offered a robust methodology for extracting precise glacier elevation changes in the Karakoram with high temporal and spatial resolution, provided valuable insights into the understanding of complex glacier dynamics.

Single-pass interferometric synthetic aperture radar (InSAR) offers the potential to generate sea ice digital elevation models (DEMs) despite the inherent dynamics of sea ice. However, the accuracy of sea ice DEM generation from InSAR is hindered by the varying penetration bias across different types of ice. This paper presents a new scheme to generate sea ice DEMs (i.e., snow freeboard) across different ice types from TanDEM-X images. The proposed scheme is applied to generate a sea ice topographic map over a 630 km × 19 km area, revealing a spatial pattern of sea ice elevation. The highest elevations are observed near the Antarctica Peninsula (AP), with a rapid decrease in elevation within approximately 200km from the AP coastline, followed by a gradual increase towards the south.

Mapping seasonal wet snow (WS) is essential for climate research, hydrological assessments and natural hazards management. It is especially crucial in Karakoram as the the water supply in the region is dominated by snow melting. However, the complex terrain in Karakoram brings great challenge when employing synthetic aperture radar (SAR) data for WS mapping. In this work, we present an algorithm that combines SAR and topographic data for WS mapping. The result shows that the proposed algorithm can efficiently reduce the uncertainty and generate robust WS map on a large scale.

Single-pass interferometric synthetic aperture radar (InSAR) is an effective technique for sea ice topographic retrieval despite the dynamics of sea ice. Co-polarimetric phase differences (CPD) provide scattering information concerning the sea ice properties. This study estimates the observational CPD from the dual-polarimetric SAR images over young ice (YI) and undeformed ice (UI) in the Western Weddell Sea. The CPD is further modelled as a function of structural anisotropy, incidence angle, ice volume fracture, and volume thickness. The good agreement between the observational and modelled CPD indicates that CPD can be a promising indication for topographic characterization over the YI and UI.

Interferometric synthetic aperture radar (InSAR) is an effective technique for sea-ice topographic retrieval. However, the penetration of electromagnetic waves into snow-covered thick ice limits the accuracy of InSAR-derived sea-ice topography. In this study, the data were acquired over the sea ice in the western Weddell Sea, Antarctica. A new method is proposed by merging co-polarization coherence into InSAR processing to measure the sea-ice surface topography.

A novel physical paradigm is explored in this paper: synthetic-aperture radar (SAR) ocean speckle is informative. This paper experimentally analyzes the SAR ocean speckle intensity K-distribution model versus sea-surface wind field. It is shown that the normalized intensity moments of the K-distribution measured from actual C-band SAR data well fit the theoretical ones under different wind conditions, i.e., wind regime and relative SAR-wind azimuth direction. In addition, it is observed that the K-distribution shape parameter decreases as wind speed increases. A sensitivity of the K-distribution shape parameter on incidence angle and wind direction is also observed, where the largest variability is experienced at higher incidence angles and under crosswind relative azimuth angle.

The marine atmospheric boundary layer (MABL) roll plays an important role in the turbulent exchange of momentum, sensible heat, and moisture throughout the boundary layer of tropical cyclones. Hence, rolls are believed to be closely related to storm development and intensification. In this study, based on the RADARSAT-2 dataset including various tropical cyclone (TC) intensities, the roll characteristics are retrieved from synthetic aperture radar (SAR) images via fast Fourier transform (FFT). We investigate the roll wavelengths at variance of TC intensities and found that the roll wavelengths are related to the distance with respect to TC center and TC intensities. This study is promising to bring roll-induced effects into hurricane forecasting model.

A new study based on the complex and amplitude cross-pol correlation is made. It exploits Sentinel-1 Single Look Complex (SLC) SAR and reference HSCAT wind data. The behavior of these two polarimetric features vs wind speed is analyzed. Low-to-moderate and high wind speed regimes are considered. The study is focused on three subsets characterized by different incident angle ranges. The study shows that in all cases the two polarimetric features are sensitive to wind speed and better behave with respect to$S$vv.

Performance of ship detection is influenced by synthetic aperture radar (SAR) imaging characteristics and environmental conditions. In this paper, aiming at evaluating vessel detectability for Sentinel-1 SAR data, a model based on a large-scale Sentinel-1A vessel chips database is established. The model sensitivity is analyzed by simulation data. In the experiment, by inputting the parameters of imaging characteristics (incidence angle, polarization, spatial resolution) and environmental conditions (wind speed, wind direction, sea state) of a specific Sentinel-1 image, the minimum detectable vessel length can be estimated. Further validations demonstrate the availability of the estimated minimum detectable vessel length.