With the rapid growth of Sentinel-1 synthetic aperture radar (SAR) data, how to exploit Sentinel-1 imagery and achieve effective and robust marine surveillance are crucial problems. In this paper, we present the OpenSARShip, a dataset dedicated to Sentinel-1 ship interpretation. The OpenSARShip, providing 11 346 SAR ship chips integrated with automatic identification system messages, owes five essential properties: specificality, large scale, diversity, reliability, and public availability. These properties make sure that the OpenSARShip achieves its objectives. The first is to provide researchers a benchmark dataset to develop applicable and adaptive ship interpretation algorithms and push the performance ceilings of data analysis. The other is to provide a dataset for performing application-oriented quality assessment for Sentinel-1 imagery, which can boost their applications in a targeting way. The construction and the organization of the OpenSARShip are discussed, which show the inside of the dataset and ensure the essential properties. The elaborate geometric and scattering analyses, the benchmark for classification, and the imagery applicability assessment by using the OpenSARShip all demonstrate the applicability and potential of the dataset.

The Sentinel-1 synthetic aperture radar (SAR) allows sufficient resources for cross-pol wind speed retrievals over the ocean. In this paper, we present technical evaluation on wind retrieval from both Sentinel-1A and Sentinel-1B IW cross-pol images. Algorithms are based on the existing theoretical and empirical ones derived from the RADARSAT-2 cross-pol data. First, to better understand the Sentinel-1 observed normalized radar cross section (NRCS) values under various environmental conditions, we constructed a dataset that integrates SAR images with wind field information from scatterometer measurements. There are 11,883 matchup data in the experimental dataset. We then calculated the systemic noise floor of Sentinel-1 IW mode, and presented its unique noise characteristics among different sub-bands. Based on the calculated NESZ measurements, the noise is removed for all matchup data. Empirical relationships among the noise free NRCS ${\sigma }_{\mathrm{VH}}^0$ , wind speed, wind direction, and radar incidence angle are analyzed for each sub-band, and a piecewise model is proposed. We showed that a larger correlation coefficient, r, is achieved by including both wind direction and incidence terms in the model. Validation against scatterometer measurements showed the suitability of the proposed model.

Single-pass interferometric synthetic aperture radar (InSAR) enables the possibility for sea ice topographic retrieval despite the inherent dynamics of sea ice. InSAR digital elevation models (DEMs) are measuring the radar scattering center height. The height bias induced by the penetration of electromagnetic waves into snow and ice leads to inaccuracies of the InSAR DEM, especially for thick and deformed sea ice with snow cover. In this study, an elevation difference between the satellite-measured InSAR DEM and the airborne-measured optical DEM is observed from a coordinated campaign over the western Weddell Sea in Antarctica. The objective is to correct the penetration bias and generate a precise sea ice topographic map from the single-pass InSAR data. With the potential of retrieving sea ice geophysical information by the polarimetric-interferometry (Pol-InSAR) technique, a two-layer-plus-volume model is proposed to represent the sea ice vertical structure and its scattering mechanisms. Furthermore, a simplified version of the model is derived, to allow its inversion with limited a priori knowledge, which is then applied to a topographic retrieval scheme. The experiments are performed across four polarizations: HH, VV, Pauli 1 (HH + VV), and Pauli 2 (HH − VV). The model-retrieved performance is validated with the optically derived DEM of the sea ice topography, showing an excellent performance with root-mean-square error as low as 0.26 m in Pauli-1 (HH + VV) polarization.

Single-pass interferometric synthetic aperture radar (InSAR) is an effective technique for sea ice topographic retrieval despite the inherent dynamics of sea ice. However, the penetration of microwaves into snow-covered thick ice and the achievable height sensitivity for tens-of-centimeters thin ice are two major issues which limit the accuracy of InSAR-derived sea ice topography. Polarimetry provides scattering information concerning the sea ice properties and has the potential, in combination with interferometry, to achieve an accurate reconstruction of a sea ice digital elevation model (DEM). This paper studies the relation between polarimetric signatures and sea ice topography, and explores the possibility to compensate the penetration bias by merging co-polar coherence into InSAR processing. The newly generated topographic map has a root-mean-square error under 0.3 m. For thin ice below 1 m, a positive relation between co-polar phase coPol and surface height is observed, suggesting that coPol can effectively characterize thin sea ice topography. For thick ice with ridges, the maximum polarimetric phase difference δ maxPol reveals a particular shape of the coherence region, which can be interpreted as oriented volume scattering. It suggests that the model-based approach using polarimetric SAR interferometry assuming an oriented volume scattering model is promising in measuring the scattering centers in thick and deformed sea ice. The study of polarimetric behaviour for the InSAR DEM is therefore a step forward toward accurate modelling of sea ice topography from polarimetric single-pass InSAR data.

Sea ice elevation plays a crucial role in sea ice dynamic processes driven by winds and waves, for which satellite radar remote sensing becomes indispensable to monitor snow‐covered sea ice across the vast polar regions regardless of darkness and clouds. To measure sea ice elevation, a theory of polarimetric interferometry for both monostatic and bistatic radars is developed based on analytic solutions of Maxwell's equations, accounting for realistic and complicated properties of snow, sea ice, and seawater. This analytic method inherently preserves phase information that is imperative for radar polarimetry and interferometry. Among a multitude of complex radar coefficients in the general polarimetric interferometric covariance matrix, the symmetry group theory is utilized to identify and select appropriate terms pertaining to the retrieval of sea ice elevation while avoiding radar parameters that may inadvertently introduce non‐uniqueness and excessive uncertainty. Theoretical calculations compare well with field observations for rough and old sea ice encountered in the Operation‐IceBridge and TanDEM‐X Antarctic Science Campaign over the Western Weddell Sea. The results show that the magnitude of the coefficient of normalized correlation between co‐polarized horizontal and co‐polarized vertical radar returns is inversely related to sea ice elevation, while the associate phase term is nonlinear and noisy and should be excluded. From these analyses, a protocol is set up to measure Antarctic sea ice elevation to be presented in the next companion paper.