The intensity of a Sentinel-1 Terrain Observation with Progressive Scans synthetic aperture radar image is disturbed by additive thermal noise, particularly in the cross-polarization channel. Although the European Space Agency provides calibrated noise vectors for noise power subtraction, residual noise contributions are significant when considering the relatively narrow backscattering distribution of the cross-polarization channel. In this paper, we investigate the characteristics of noise and propose an efficient method for noise reduction based on a three-step correction process comprised of azimuth descalloping, noise scaling and interswath power balancing, and local residual noise power compensation. The core idea is to find the optimal correction coefficients resulting in the most noise-uncorrelated gentle backscatter profile over a homogeneous region and to combine them with the scalloping gain for a reconstruction of the complete 2-D noise field. Denoising is accomplished by subtracting the reconstructed noise field from the original image. The performance improvement in some applications by adopting the denoising procedure shows the effectiveness of the proposed method.

A new Sentinel-1 image-based sea ice classification algorithm using a machine-learning-based model trained in a semi-automated manner is proposed to support daily ice charting. Previous studies mostly rely on manual work in selecting training and validation data. We show that the readily available ice charts from the operational ice services can reduce the amount of manual work in preparation of large amounts of training/testing data. Furthermore, they can feed highly reliable data to the trainer by indirectly exploiting the best ability of the sea ice experts working at the operational ice services. The proposed scheme has two phases: training and operational. Both phases start from the removal of thermal, scalloping, and textural noise from Sentinel-1 data and calculation of grey level co-occurrence matrix and Haralick texture features in a sliding window. In the training phase, the weekly ice charts are reprojected into the SAR image geometry. A random forest classifier is trained with the texture features on input and labels from the rasterized ice charts on output. Then, the trained classifier is directly applied to the texture features from Sentinel-1 images operationally. Test results from the two datasets spanning winter (January–March) and summer (June–August) seasons acquired over the Fram Strait and the Barents Sea showed that the classifier is capable of retrieving three generalized cover types (open water, mixed first-year ice, old ice) with overall accuracies of 87 % and 67 % in winter and summer seasons, respectively. For the summer season, the classifier failed in distinguishing mixed first-year ice from old ice with accuracy of only 12 %; however, it performed rather like an ice–water discriminator with high accuracy of 98 % as the misclassification between the mixed first-year ice and old ice was between them. The accuracy for five cover types (open water, new ice, young ice, first-year ice, old ice) in the winter season was 60 %. The errors are attributed both to incorrect manual classification on the ice charts and to the semi-automated algorithm. Finally, we demonstrate the potential for near-real-time service of the ice map using daily mosaicked Sentinel-1 images.

The thermal noise-induced distortions in a Sentinel-1 terrain observation with progressive scans synthetic aperture radar image cannot be corrected by the noise equivalent sigma nought (NESZ) subtraction only. Since the thermal noise is scaled during synthetic aperture radar processing, it resides not only as an additive noise in each pixel but also as a multiplicative noise in the interpixel contrast. In this paper, we investigate the noise characteristics and propose an efficient method for the multiplicative textural noise correction. The core ideas are to find the optimal coefficient of the noise-induced standard deviation (SD) and model the noise contribution to the local SD as a function of the NESZ and the signal-to-noise ratio. Denoising is accomplished by a subwindow-wise adaptive rescaling of the pixel values. The improvements in the first- and second-order statistical textural features demonstrate the effectiveness of the proposed method.

An updated algorithm for the removal of thermal noise in Sentinel-1 synthetic aperture radar (SAR) level-1 ground range detected (GRD) data in cross-polarization is presented. The algorithm is comprised of two steps: correction of the annotated thermal noise magnitude (previously proposed in Park et al. , 2018) and a novel correction of the annotated thermal noise range dependence. The magnitude of the annotated thermal noise is corrected by applying scale and offset coefficients tuned on a few hundred of Sentinel-1 data acquired over surfaces with low backscatter in interferometric wide and extra-wide swath modes in HV and VH polarizations. The values of coefficients for all modes and polarizations for data processing with the Instrument Processing Facility (IPF) version 3.1–3.3 are provided. The range dependence is corrected by minimizing a cost function between the annotated range profiles of thermal noise and antenna pattern gain (APG). An objective validation metric based on a comparison of averaged backscatter at interswath boundaries is proposed. Validation is performed on hundreds of Sentinel-1 scenes acquired over the open ocean, doldrums, deserts, and sea ice. It shows that the new algorithm outperforms the standard thermal noise removal algorithm proposed by European Space Agency in almost all cases. Analysis shows that the new algorithm worsens noise correction in cases when the range dependence of the annotated APG does not match with the observed signal, indicating either problems with signal processing on IPF or imprecise annotation of APG.

Estimating the sea ice drift field is of importance in both scientific study and activities in the polar ocean. Ice motion is being tracked at large scale (10 km and larger) on a daily basis; however, a higher resolution product is desirable for more reliable monitoring of rapid changes in sea ice. The use of wide-swath SAR has been extensively studied; yet, recent high-resolution X-band SAR sensors have not been tested enough. We examine the feasibility of KOMPSAT-5 and COSMO-SkyMed for retrieving sea ice motion by using the dataset of the MOSAiC expedition. The ice drift match-ups extracted from consecutive SAR image pairs and buoys for more than seven months in the central Arctic were used for a performance evaluation and validation. In addition to individual tests for KOMPSAT-5 and COSMO-SkyMed, a cross-sensor combination of two sensors was tested to overcome the drawback, a relatively long revisit time of high-resolution SAR. The experimental results show that higher accuracies are achievable from both single- and cross-sensor configurations of high-resolution X-band SARs compared to wide-swath C-band SARs, and that sub-daily monitoring is feasible from the cross-sensor approach.

Ground moving targets distort normally-focused synthetic aperture radar (SAR) images. Since most high-resolution SAR data providers only offer single-look complex (SLC) data rather than raw signals to general users, they need to apply a simple and efficient residual SAR focusing to SLC data containing moving targets. This paper presents an efficient and effective SAR residual focusing method that is practically applicable to SLC data. The residual Doppler spectrum of the moving target is derived from a general SAR configuration and normal SAR focusing. The processing steps are simple and straightforward, with a limited size of the processing window, e.g., 64 × 64. Application results using simulation data and actual TerraSAR-X SLC data with a speed-controlled vehicle demonstrate the effectiveness of the method, which particularly improves the −3 dB width, integrated sidelobe ratio, and symmetry of the reconstructed signals. In particular, the azimuthal symmetry becomes seriously distorted when the target speed is higher than 8 m/s (or 28.8 km/h), and the symmetry is well recovered by the proposed method.