Doppler Centroid Analysis (DCA) technique is one of the major techniques that do permit a direct retrieval of ocean surface velocity from synthetic aperture radar (SAR) data. However, azimuth ambiguities in the SAR images severely restrict the capability of DCA technique to obtain accurate ocean surface Doppler velocities. Therefore, it is necessary to investigate how the azimuth ambiguities impact the Doppler velocity estimation performance and to evaluate how significant the impact is. In this paper, a model for ocean surface Doppler velocity estimation affected by azimuth ambiguities is developed resorting to jointly circular Gaussian processes, and its statistic is derived. The impact of azimuth ambiguities on Doppler velocity estimation performance in terms of Doppler centroid estimation bias and the standard deviation of Doppler centroid estimates is analyzed. The theoretical results are validated through simulation and Doppler velocities retrieved from Chinese Gaofen-3 (GF-3) SAR Doppler centroid estimates affected by azimuth ambiguities. This study will help researchers better understand the impact of azimuth ambiguities on Doppler velocity estimation, and will provide a theoretical reference for subsequent research on how to reduce the impact of azimuth ambiguities more effectively.

Synthetic aperture radar (SAR) provides high-resolution observations of radial sea surface currents. By combining radial currents observed from different viewing directions, the trajectory-crossing method can reconstruct the sea surface current vector field. In practice, observations from ascending and descending passes are acquired at different times, so the spatial structure of the surface current field may evolve between acquisitions. Directly combining multitemporal observations can therefore introduce spatial mismatch and reduce reconstruction accuracy. This study proposes a trajectory-crossing method for reconstructing sea surface current vectors from multitemporal Sentinel-1 Doppler observations. Maximum cross-correlation (MCC) is applied to gradient images of the radial current fields to estimate displacement and spatially register observations acquired at different times before vector reconstruction. The method is evaluated using Sentinel-1 data acquired over the Gulf Stream region and compared with geostrophic currents from the Copernicus Marine Environment Monitoring Service (CMEMS), Surface Water and Ocean Topography (SWOT) observations, and Global Drifter Program (GDP) drifter measurements. Results show that the proposed method reduces mismatch effects and improves the accuracy and stability of sea surface current vector reconstruction. It provides a practical approach for deriving surface current vector fields from multitemporal SAR Doppler observations.

Synthetic aperture radar (SAR) data contains Doppler information on ocean surface movement, and the Doppler centroid anomaly (DCA) can be used to quantitatively retrieve ocean surface currents in the line‐of‐sight of radar. However, non‐geophysical errors (NGE) in Doppler information significantly affect retrieval accuracy. Generally, existing methods rely on large‐area land data that are spatially and temporally close to the imaging region for NGE correction. However, these methods inherently limit the applicability and accuracy of ocean surface current retrieval. To address this issue, this study systematically analyzes the variation patterns of NGE in Gaofen‐3 (GF‐3) satellite data over the Amazon rainforest from 2019 to 2024 and proposes a novel NGE fitting method based on Doppler shift variation characteristics. The proposed method formulates the NGE fitting equation by estimating the range bias in advance and then calculating the constant offset based on nearby land regions. This study validates the proposed method using GF‐3 data over the Agulhas region and compares the results with HYCOM and SWOT data. The results indicate that the proposed method effectively corrects the NGE and significantly reduces the retrieval error of ocean surface currents. In addition, the proposed method allows flexible selection of small‐area land data in temporal and spatial proximity for NGE correction, thereby reducing the land region requirements for NGE fitting and further expanding the applicability of SAR‐based ocean surface current retrieval.