Recently, it was demonstrated that low-frequency wavelength-resolution synthetic aperture radar (SAR) images could be considered to follow an additive mixing model due to their backscatter characteristics. This simplification allows for the use of source separation methods, such as robust principal component analysis (RPCA) via principal component pursuit (PCP), for detecting changes in those images. In this manuscript, a change detection method for wavelength-resolution SAR images based on image stack through RPCA is proposed. The method aims to explore both the temporal and flight heading diversity of a set of wavelength-resolution multitemporal SAR images in order to detect concealed targets in forestry areas. A heuristic based on three rules for better exploring the RPCA results is introduced, and a new configurable parameter for false alarm reduction based on the analysis of image windows is proposed. The method is evaluated using real data obtained from measurements of the ultrawideband (UWB) very high-frequency (VHF) SAR system CARABAS-II. Experiments for stacks of four and seven reference images are conducted, and the use of reference images acquired with different flight headings is explored. The results indicate that a gain in performance can be achieved by using large image stacks containing, at least, one image of each possible flight heading of the data set, which can result in a probability of detection (PD) above 99% for a false alarm rate (FAR) as low as one false alarm per three square kilometers. Furthermore, it is demonstrated that high PD and low FAR can be achieved, also considering images from similar flight headings as reference images.

In this letter, we propose a method to reduce the number of false alarms in a wavelength-resolution synthetic aperture radar (SAR) change detection scheme by using a convolutional neural network (CNN). The detection is performed in two steps: change analysis and object classification. A simple technique for wavelength-resolution SAR change detection is implemented to extract potential targets from the image of interest. A CNN is then used for classifying the change map detections as either a target or nontarget, further reducing the false alarm rate (FAR). The scheme is tested for the CARABAS-II data set, where only three false alarms over a testing area of 96 km² are reported while still sustaining a probability of detection above 96%. We also show that the network can still reduce the FAR even when the flight heading of the SAR system measurement campaign differs by up to 100° between the images used for training and test.

Diagenetic snow facies represent distinct zones of snow and ice characterized by unique snow physical properties and attributes. These facies serve as indicators of changes in the surface mass balance of ice sheets, making them particularly relevant for monitoring the response of snow and firn cover to climatic changes. In this study, we propose a novel, fully unsupervised deep learning method based on convolutional neural networks (CNNs) to monitor snow zones on ice sheets using a decade of single-pass, bistatic interferometric synthetic aperture radar (InSAR) TanDEM-X data over Greenland. To do so, we develop an innovative iterative training approach to effectively manage a large variety of InSAR acquisition geometries with the goal of optimizing a robust, geometry-invariant model. The proposed approach achieves an average classification accuracy of 93.4% across varying acquisition geometries, segmenting the Greenland ice sheet into five distinct partitions. By analyzing these partitions in terms of elevation, snow density, and cumulative melt data, we link them to classical diagenetic snow facies and uncover trends correlated with climate events over the past decade, estimating a loss of over 454,000 km2 in dry snow extent on the Greenland ice sheet due to the 2012 extreme melt event, with a continuing trend of expanding percolation facies. The proposed method is adaptable to current and future single-pass SAR missions, such as the ESA 10th Earth Explorer Harmony mission, enhancing data robustness against varying acquisition geometries and demonstrating the potential of spaceborne single-pass InSAR missions for long-term monitoring of ice sheet dynamics.

TanDEM-X radar penetration into polar ice introduces significant uncertainties in digital elevation models (DEMs), consequently affecting the accuracy of glacial mass balance estimations. This limitation arises from the variable penetration depth of radar signals in snow and ice, which can cause the DEM surface to deviate from the true physical surface. X-band radars, operating in the 8–12 GHz range, interact with ice through absorption, reflection, and transmission. These interactions heavily depend on variables such as ice temperature, water content, snow density, and salinity, which directly influence microwave penetration depth and signal behaviour. Advancements in remote sensing have leveraged X-band synthetic aperture radar (SAR) to characterize snow and ice surfaces, facilitating applications such as sea ice classification and snow layer analysis. However, X-band SAR signals often exhibit biases in elevation measurements due to their partial penetration into snow and ice. This bias complicates efforts to integrate SAR-derived digital elevation models (DEMs) with laser altimetry data like ICESat-2, where penetration is negligible. Therefore, correcting X-band biases is critical for achieving high-accuracy surface elevation models and reliable glaciological assessments. Here we address these challenges by integrating neural network techniques with TanDEM-X (TDX) DEMs, Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) altimetry data, and environmental parameters from European Centre for Medium-Range Weather Forecasts (ECMWF). We leverage about 300,000 ICESat-2 pointwise measurements acquired within 30 days of TDX measurements spanning 2021–2024 in Antarctica. Additionally, we consider a diverse dataset of snow and atmospheric variables including temperature, snowfall, snow depth, and wind speed to model and predict X-band penetration biases across Antarctica. This approach advances existing methods by automating bias correction and enhancing the integration of SAR and altimetry datasets achieving mean bias correction of the order 1 cm with a Root Mean Square Error (RMSE) of about 1 m and maximum errors of the order of 10 m. Findings from this work provide actionable insights for improving elevation model accuracy in different ways. First, we offer the retrieved pointwise TanDEM-X, ICESat-2 and ECMWF dataset open access for future studies through a dedicated Zenodo page. Second, we provide both our trained network weights and our algorithms in the form of Jupyter Notebooks on Github for improved reproducibility. Most importantly, we discuss the broader efforts in the NASA Surface Topography Elevation (STV) decadal survey incubation study and polar ice monitoring by addressing penetration biases which is one of the most critical uncertainties in radar remote sensing of snow and ice.