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In this study, the feasibility of current retrieval from Sentinel-1 (S-1) synthetic aperture radar (SAR) in the radar look/range direction is investigated. S-1 Ocean (OCN) products acquired in interferometric wide (IW) mode in the regions with the western boundary current, i.e., the western Pacific and western Atlantic, are collected for the period from 2020 to 2022, which are collocated with the current field from HYbrid Coordinate Ocean Model (HYCOM). The OCN wind, HYCOM current, and Stokes drift estimated from the OCN wave parameters are geometric projected to be the range direction. In addition, the Doppler centroid anomaly (DCA) is estimated using the difference between the radar return Doppler frequency and the predicted Doppler shift, which are derived from the OCN products. The dependences of the upper ocean dynamics in the range direction on the DCA are studied, and it is found that the range Stokes drift, wind speed, and current speed are linearly related to the DCA. Based on deep learning, denoted as multi-layer perceptron, a range current retrieval algorithm from the OCN product is developed using two-thirds of the collocated dataset, and the root mean square error (RMSE) of the range current speed converges to 0.15 ms−1. In particular, the wave-induced surface Stokes drift is considered in the process. Comparison of one-third of the dataset yields an RMSE of 0.14 ms−1 for the range current speed, a correlation coefficient (r) of 0.85, and a bias of −0.001 ms−1. Validation against several moored buoys shows an RMSE of 0.12 ms−1 with a r of 0.74 and a bias of −0.01 ms−1. Under this circumstance, it is believed that the algorithm used in this study is applicable for range current retrieval from the S-1 OCN product. [ABSTRACT FROM AUTHOR]

The SSS (sea surface salinity) is an important factor affecting global climate changes, sea dynamic environments, global water cycles, marine ecological environments, and ocean carbon cycles. Satellite remote sensing is a practical way to observe SSS from space, and the key to retrieving SSS satellite products is to establish an accurate sea surface brightness temperature forward model. However, the calculation results of different forward models, which are composed of different relative permittivity models and SSR (sea surface roughness) brightness temperature increment models, are different, and the impact of this calculation difference has exceeded the accuracy requirement of the SSS inversion, and the existing SSR brightness temperature increment models, which primarily include empirical models and theoretical models, cannot match all the relative permittivity models. In order to address this problem, this paper proposes a universal DNN (deep neural network) model architecture and corresponding training scheme, and provides different SSR brightness temperature increment models for different relative permittivity models utilizing DNN based on offshore experiment data, and compares them with the existing models. The results show that the DNN models perform significantly better than the existing models, and that their calculation accuracy is close to the detection accuracy of a radiometer. Therefore, this study effectively solves the problem of SSR brightness temperature correction under different relative permittivity models, and provides a theoretical support for high-precision SSS inversion research. [ABSTRACT FROM AUTHOR]

In this letter, we examine a new measure for high resolution and wide swath (HRWS) synthetic aperture radar (SAR) imaging based on an elevated frequency diversity array (EFDA). By highly integrating digital beamforming (DBF) and frequency diversity array (FDA) techniques, EFDA–SAR achieves range ambiguity resolution in the spatial frequency domain and range ambiguity suppression outside the observed swath in the range space domain. Moreover, the EFDA–SAR system improves the signal-to-noise ratio (SNR) due to its elevated antenna array design. A model is developed for the time-varying filtering of this novel EFDA-SAR system design. Simulation results are provided to demonstrate the efficiency of the proposed design. Using EFDA–SAR, we can obtain an HRWS SAR image without range ambiguity from the observed swath or outside it. Moreover, by combining DBF on reception, the SNR of the EFDA-SAR image is significantly improved. [ABSTRACT FROM AUTHOR]

Digital beamforming (DBF) is an effective method for improving the signal-to-noise ratio (SNR) in synthetic aperture radar (SAR) images. However, further enhancing the SNR in DBF-SAR is limited by the antenna size and the expensive system cost. To address this problem, in this letter we implement a hybrid strip-map/spotlight mode for DBF. By utilizing the proposed processing method, DBF is adapted for hybrid strip-map/spotlight mode data. Furthermore, the additional synthetic aperture acquired from the hybrid strip-map/spotlight mode is employed to implement multilook processing for further enhancing the SNR of DBF-SAR images. The proposed processing method was verified by an airborne X-band 16-channel DBF-SAR system. The results confirmed that a remarkable additional SNR improvement in DBF-SAR images can be obtained using the proposed method without increasing system complexity. [ABSTRACT FROM AUTHOR]