###### Abstract
Conversely, the DCA technique [16] relies on the analysis of the Doppler Centroid Anomaly, i.e., the anomaly that accounts for the motion of surface roughness elements on the ocean surface corrected for the Earth's rotation, allowing one to derive the estimates of the surface current velocity. Indeed, DCA computes the differences between the Doppler Centroid (DC) estimated from the SAR data and the frequencies predicted from the relative motion between the satellite and the Earth's rotation, which reflect the geophysical information about the motion of the marine surface. Alternatively, the Doppler frequencies can be estimated using time- and frequency-domain algorithms. Generally, the currently available SAR systems lack ATI mode; hence, DCA is preferred in practical cases since it exploits the direct measure of the surface velocity using single-look complex (SLC) images from one single SAR acquisition. Unlike ATI, DCA is, in principle, applicable to data acquired by any SAR mission. A quality comparison between the ATI and DCA techniques for retrieving surface current fields with TerraSAR-X and TanDEM-X SAR data was already presented in [21]. The pioneering work of [PERSON] [30] firstly addressed the problem of retrieving DC estimates from SAR images by referring to three distinctive algorithms. The former was based on calculating the azimuth Fourier spectrum and evaluating that frequency, i.e., the DC, to which the spectrum is symmetric. This operation was performed by splitting the azimuth spectrum into two non-overlapped slices and calculating their energy difference. DC is the frequency at which the energy is perfectly balanced. This frequency-based energy balance method was found to obtain DC estimates with a variance proportional to the scene contrast under the simplified assumption that the imaged scene is quasi-stationary. The second method proposed in [30] operated in the time domain and was based on calculating the complex correlation function between two SAR image samples displaced for a given number of pixels along the azimuth direction. It was proven that the phase of correlation estimate is dependent on the DC, estimated with a variance that, also in this case, is almost proportional to the scene contrast. This method works in the time domain, and it is preferable to the energy balance method, especially for real-time (or quasi-real-time) applications, when it can directly work on raw data without the need to preliminarily apply a focusing operation. A third method, also operating in the time domain, was also proposed in the same work [30], relying on the straightforward check of the Doppler sign alone to calculate the DC, speeding up the whole process. Comprehensive calculations on the DC estimates precision were then presented by [PERSON] in [31], in which SAR signal statistical properties were exploited to derive a general formulation of the standard deviation of DC measurements. At the Cramer-Rao bound, it has been shown that DC standard deviation is proportional to the Pulse Repetition Frequency (PRF) and inversely proportional to the square root of the number of pixels in the box used to compute the correlation and/or the azimuth spectrum. These calculations were primarily carried out for stationary scenes with a uniform background. However, its primary outcomes can be extended to non-homogeneous targets by multiplying the box pixels by the scene contrast. Inaccurate estimates of the DC values arise in regions with low signal-to-noise ratio (SNR) and/or when the background is not uniform. These errors have been the subject of some investigations over the last decades [21; 32; 33]. In particular, the work of [PERSON] in 2004 [34] proposed two approaches to obtain reliable estimates of DC by adaptively selecting some spatially uniform boxes in the imaged scene to circumvent the problems arising in low SNR regions, partially.