In this study, we use the Polar Pathfinder Daily 25km EASE-Grid Sea Ice Motion Vectors product (version 4) archived and distributed by the NASA Snow and Ice Distributed Active Archive Center (DAAC) at the National Snow and Ice Data Center (NSIDC) . Although this product employs optimal interpolation to combine three independent data sources in the Arctic – (i) gridded PMW satellite imagery, (ii) winds from reanalysis fields, and (iii) drifting buoy positions – the Antarctic sea ice motion is derived solely from gridded and optimally interpolated PMW satellite imagery. To generate gridded sea ice drift, this product first computes daily-averaged PMW brightness temperatures on the EASE grid. Then, it uses a feature-tracking algorithm that calculates cross-correlations between spatial patterns on different days, i.e., the maximum cross-correlation (MCC) pattern-matching method . This method involves selecting two spatially coincident and temporally consecutive satellite images, defining a search window around a grid cell, and translating the latter image relative to the earlier one within this window. Then, the spatial offset corresponding to the maximum correlation value is taken as the most probable ice displacement. This offset is converted to ice velocity by dividing the spatial displacement by the time separation between the two images. For the study period (2015–2023), 37H GHz, 37V GHz, 91H GHz, and 91V GHz channels of the Special Sensor Microwave/Imager and Sounder (SSMIS) were used as the PMW imagery sources . However, since the intrinsic PMW sensor footprint is coarse, the resultant sea ice motion field is coarse and noisy. To address this limitation and achieve sub-pixel resolution, the MCC method used for this product incorporates 4 × oversampling, which improves the effective SSMIS sampling interval to 6.25 km d⁻¹.

To generate the daily gridded Antarctic sea ice motion fields, the PMW-based sea ice motion is optimally interpolated (i.e., kriging). In this kriging interpolation, the sea ice motion vector (comprising the u and v velocity components) at a given grid cell is calculated by assigning weights to nearby available sea ice velocity values, and those weighted velocities are averaged to estimate the grid cell value. This weighting factor (w) is inversely dependent on the distance (d) from the source data point, determined by the following exponential function: 1w=Ce-d/D

where D represents the length scale constant, empirically determined to be 417 km , and C is the source-based coefficient (0.8 for SSMIS-based sea ice motion; for the Arctic product, this coefficient is 0.95 for buoy data and 0.45 for wind). The optimal interpolation effectively transforms the sparse and/or noisy individual motion estimates into a smoothly varying combined daily motion grid. It is important to acknowledge that optimal interpolation relies on the ideal assumptions of stationarity, homogeneity, and isotropy in the data field, which are often violated in real-world sea ice dynamics. We refer to for a detailed description of this sea ice motion product.

In this study, we acquire Sentinel-1 SAR images covering the entire Southern Ocean over a 9-year period, from 2015 to 2023. The Sentinel-1 mission consists of two identical satellites, Sentinel-1A (launched in April 2014) and Sentinel-1B (launched in April 2016; operations ended in December 2021). The combined constellation provides a 6 d revisit cycle based on a 12 d revisit cycle for each satellite. Sentinel-1 satellites carry a single C-band SAR with a center frequency of 5.405 GHz and a wavelength of 5.6 cm. We utilize the Level-1 HH polarization band images in Extra Wide Swath (EW) mode from the Ground Range Detected (GRD) scenes, which have a resolution of 40 m. We access and process these images via GEE , where each scene is pre-processed using the Sentinel-1 Toolbox, which includes (i) thermal noise removal, (ii) radiometric calibration, and (iii) terrain correction (orthorectification) (https://developers.google.com/earth-engine/guides/sentinel1, last access: 16 September 2026).

We divide the entire Southern Ocean into multiple 250 km × 250 km super-grids on the EASE projection and collect all available Sentinel-1 images for each super-grid via GEE. For Sentinel-1 images covering more than 30 % of a super-grid cell, we downscale the original ∼ 40 m resolution images to 120 m resolution on the EASE projection. This downscaling is performed to export the images as the same-dimension arrays into a Python environment using the geemap package . This downscaling also serves two purposes: reducing speckle noise in SAR backscatter imagery and reducing the computational load required for processing a large number of images. Additionally, to ensure a reliable estimation of sea ice drift, we only use Sentinel-1 image pairs with a temporal separation of less than 4 d (96 h).

For all collected Sentinel-1 image pairs within each super-grid, we retrieve sea ice drift using the motion retrieval algorithm developed by Nansen Environmental and Remote Sensing Center (NERSC) . This algorithm achieves high accuracy (reported positional error below 300 m) and computational efficiency (processing time of less than one minute per Sentinel-1 image pair) by combining feature tracking and pattern matching methods . The initial feature-tracking algorithm, called ORB (Oriented FAST and Rotated BRIEF), is adopted and tuned for retrieving a first-guess sea ice drift from the Sentinel-1 SAR images. Then, the next pattern-matching algorithm, based on MCC calculation, is used further to derive sea ice drift on a regular grid. First, feature tracking automatically identifies keypoints in a pair of Sentinel-1 SAR images as a vector of 256 binary descriptors ; the number of keypoints is set to 20 000 based on empirical experiments. For each keypoint on the first image, a ratio between the smallest and the second smallest Hamming distance (number of the descriptors in the vector with different values; ) to the keypoints on the second image is computed. If the ratio is below a threshold (set to 0.7 in this study), the keypoint with the smallest Hamming distance is considered matched. If this ice displacement exceeds 40 km d⁻¹, this vector is omitted as incorrect . Since these keypoints are heterogeneously distributed in space, the feature-tracking results are approximated on a regular grid using linear interpolation.

Next, this approximated ice drift on a regular grid serves as the first guess for the subsequent, more precise pattern-matching method. A template t is defined around a point of interest in the first image, and the corresponding x and y coordinates in the second image are estimated based on the initial feature-tracking ice drift approximation. A larger sub-image s is then extracted from the second image, centered at these coordinates. Then, the template t is rotated within a range of angles (rotated template tROT), and the rotation angle that provides the maximum cross-correlation between the rotated template tROT and s within a searching range D is determined. After the optimal rotation angle between two image scenes is found, the position of the maximum cross-correlation (rMAX) is identified as the resultant positional offset from the first image to the second image. If this maximum cross-correlation rMAX falls below a threshold of 0.4, we disregard this drift vector as unreliable. Based on the results from and our trial-and-error experiments, we set the template size (T) to 20 pixels and the searching range D to 35 pixels. A template size of 20 pixels results in a nominal SAR sea ice drift resolution of approximately 2.4 km (120 m downsampled SAR resolution × 20 pixels). As illustrated in Fig. , the resultant SAR-derived sea ice drifts appear reliable and show good agreement with buoy sea ice drift (Fig. ). More details about this SAR-based ice drift retrieval algorithm are described in , and the open source software of this algorithm is publicly available (https://github.com/nansencenter/sea_ice_drift, last access: 16 September 2026).

For the comprehensive validation of PMW and SAR sea ice drift, we utilize position data from drifting buoys deployed across the Southern Ocean as part of the International Programme for Antarctic Buoys (IPAB). These buoys record precise latitude and longitude positions every 1–3 h, providing accurate and precise sea ice drift information. We found 99 available drifting buoys spanning from 2015 to 2023, with the majority concentrated in the Weddell Sea region (Fig. ). Buoys deployed in the Weddell Sea typically follow the clockwise circulation of the Weddell Gyre (Fig. ). However, upon exiting the Weddell Sea, these buoys are often transported by the Antarctic Circumpolar Current, moving outside the primary sea ice boundary. To ensure that buoy motion accurately represents sea ice drift, we exclude any buoy data points where the coincident sea ice concentration is below 15 %. Although some buoys are available in the Ross Sea near the McMurdo Sound, their limited distribution prevents them from fully representing regional sea ice drift. We calculate the velocity vectors from the recorded buoy position changes to serve as the reference sea ice drift, which is then compared with coincident PMW and SAR drift observations.

It is essential to note that the PMW, SAR, and buoy sea ice drift observations possess distinct spatial and temporal scales. Therefore, we employ three different spatiotemporal sampling schemes to facilitate robust comparisons: (i) SAR-buoy, (ii) PMW-buoy, and (iii) PMW-SAR.

SAR-buoy comparison: Sentinel-1 SAR provides gridded sea ice drifts between two image acquisition times, whereas buoys provide regular point-to-point track movement every 1–3 h. To match these data, we first extract the buoy displacement vector over the exact time interval between two SAR image scenes. The resulting x and y displacements, divided by the time difference, constitute the buoy-derived sea ice drift velocity. For the corresponding SAR sea ice drift, we calculate the mean of SAR vectors located within a 25 km radius of the buoy location. Following the recommendation of to ensure reliable drift estimates, this comparison is restricted to areas where the sea ice concentration was greater than 85 %.

PMW-buoy comparison: The PMW product provides daily 25 km gridded sea ice drifts, which must be reconciled with the hourly (or 3-hourly) buoy track movements. To achieve temporal consistency, we first calculate the daily displacement of the buoys. This daily buoy displacement is then compared with the mean of the PMW sea ice drift vectors located within a 25 km radius of the buoy's central location. Unlike the SAR-buoy comparison, this PMW-buoy comparison is conducted across the entire available product area, where the sea ice concentration is greater than 15 % . We note that both SAR and PMW ice drift vectors represent the average ice drift within a large area of radius 25 km, whereas the buoy vectors represent point-like ice drift at deployed ice floes. This scale mismatch, alongside spatial and temporal collocation errors, introduces a representativeness error that may partly explain the observed discrepancies between satellite-derived and buoy-measured ice drift . Since this representativeness error is difficult to fully separate from intrinsic retrieval errors, the validation statistics reported in this study likely contain contributions from both error sources.

PMW-SAR comparison: Although both PMW and SAR provide large-scale sea ice drift fields, the PMW product has coarse spatial resolution (25 km) and a regular daily record, while the SAR product has a finer spatial resolution (∼ 2.4 km) and irregular sampling time (between image acquisitions). For each 25 km PMW grid cell, we calculate the mean SAR-derived sea ice drift if there are more than 10 available SAR vectors within a 25 km radius of the PMW cell center. We also calculate the average PMW-derived sea ice displacement at that PMW cell between the Sentinel-1 image acquisition times using the daily PMW sea ice drift. Consequently, we compare SAR-derived and PMW-derived sea ice drifts between SAR acquisition times for 25 km PMW grid cells. Given the requirement for high-quality SAR drift input, this comparison is also limited to regions with sea ice concentration greater than 85 %. We also note that we present the results only from March to November due to low sea ice cover in the summer months (December to February).

Validation metrics: To quantify the accuracy of sea ice drift estimates from each comparison scheme, we compute two primary vector metrics between ice drift vectors: (1) angle difference and (2) speed difference. Additionally, for the direct comparisons against the buoy reference data (SAR-buoy and PMW-buoy comparisons), we also calculate the root mean square errors (RMSEs), mean difference (MD), and correlation coefficient (R) between u and v component vectors. We note that these u and v vector components are the horizontal and vertical components on the EASE grid, not the typical zonal and meridional components.

Figure  presents the comparison between SAR and buoy sea ice drift. The SAR ice drift demonstrates an RMSE of 2.0 km d⁻¹ and R of 0.92 in u velocity component and an RMSE of 3.3 km d⁻¹ and R of 0.85 in v velocity component (Fig. a and b). The MDs of the u and v components between SAR and buoy velocities (εu and εv) are -0.1±2.0 and -0.1±3.2 km d⁻¹, respectively, which indicates minimal biases in SAR-derived ice drift compared to the buoys (Fig. c). Furthermore, the angle difference (-5.9±38.6°) and speed difference (0.1±2.9 km d⁻¹) between SAR and buoy vectors are close to zero. These results confirm that SAR imagery is an effective and accurate tool for deriving sea ice drift where buoy measurements are unavailable in the Southern Ocean (Fig. d and e). Spatially, there are no significant patterns observed in the errors of the SAR ice velocity (Fig. f and g), although the available SAR-buoy comparison pairs are predominantly located in the Weddell Sea.

Figure  illustrates the comparison results between PMW and buoy sea ice drift. As expected, the PMW sea ice drift exhibits relatively larger errors compared to the SAR sea ice drift product discussed previously. The PMW ice drift yields an RMSE of 7.1 km d⁻¹ for u velocity component and 7.9 km d⁻¹ for v velocity component (Fig. a and b). These error magnitudes are significantly greater than the RMSE of 2.90–2.93 km d⁻¹ reported for the Arctic . The larger RMSE in the Antarctic is primarily attributed to the omission of buoy data in the optimal interpolation scheme used for the Antarctic sea ice drift product. Nevertheless, the MDs of the u and v components (εu and εv) are found to be 0.2±7.1 and -0.6±7.8 km d⁻¹, respectively (Fig. c). These low mean bias values are consistent with those reported for the Arctic product and prior Antarctic validation studies . Analysis of the vector metrics indicates that the PMW sea ice drift is negligibly biased in direction (3–4° angle difference) and consistently underestimates the sea ice speed by an average of 2–3 km d⁻¹ (Fig. d and e).

An interesting and conspicuous finding is the strong dependence of the PMW sea ice drift underestimation on sea ice concentration (SIC) (Figs. h and a). In regions with high SIC (95 %–100 %), which comprise approximately 64 % of the total comparison data, the PMW sea ice velocity exhibits an underestimation (negative bias) of only ∼ 1.52 km d⁻¹. For SIC > 85 %, the negative bias is ∼ 1.97 km d⁻¹. However, this negative speed bias increases substantially as SIC decreases, reaching an underestimation of ∼ 11.47 km d⁻¹ on average in areas with SIC below 50 % (Fig. a). In contrast, the angle difference shows no significant trend or dependence on SIC (Fig. b). This finding suggests two potential hypotheses: (i) the PMW-based feature tracking method inherently yields lower accuracy under low SIC conditions; and (ii) the optimal interpolation scheme used in the product introduces a systemic underestimation of sea ice speed, particularly near the sea ice edge. We will discuss the potential impact of interpolation and sea ice edge dynamics in detail in Sect. .

The SAR-buoy sea ice drift comparison in Sect.  established the high accuracy of the SAR product, confirming its suitability as a reliable, high-resolution reference for sea ice drift. By using this SAR-derived sea ice drift, we extend the uncertainty assessment of the PMW product beyond the spatial and temporal limitations imposed by the buoy data alone (Sect. ). Figure a and b illustrate the temporal variation of the angle difference and speed difference, respectively. These metrics are presented for several key sectors of the Southern Ocean: the Weddell Sea (WS), Ross Sea (RS), Amundsen and Bellingshausen Sea (ABS), and the entire Southern Ocean (TT).

First, regarding the angle difference (Fig. a), the mean angle difference generally centers near zero, ranging from -20 to 20°. In both RS and WS, this angle difference remains consistent, without a significant temporal trend across months or years. The ABS sector shows somewhat larger fluctuations compared to the other regions, which might be attributed to the relatively lower number of comparison pairs available in this region (Figs. c and a). We also note that the angle difference appears relatively stable from 2017 to 2021, with fewer fluctuations in ABS. This stability potentially implies lower uncertainties in the SAR-derived sea ice velocity during these years, corresponding to the period when the full Sentinel-1A and 1B constellation was operational (April 2016–December 2021). As shown in Fig. c, the 2017–2021 period shows lower temporal gaps between SAR image pairs for drift retrievals, a factor that is known to reduce uncertainties in SAR-based sea ice drift. Regarding the spatial distribution of angle difference (Fig. ), the majority of the WS, RS, and ABS sectors exhibit a mean angle difference within 20° (represented by yellowish colors). However, it is notable that the east WS consistently shows a slightly larger clockwise bias (bluish color) in the PMW sea ice drift relative to the SAR ice drift for all years.

The results of the speed difference show that the PMW sea ice drift tends to underestimate the SAR reference drift speed by 0.5–1.0 km d⁻¹ for all months from March to November throughout the entire 2015–2023 study period (Fig. b). Additionally, this 0.5–1.0 km d⁻¹ underestimation is consistent across all regions, including WS, RS, and ABS. While prior comparisons with buoy records only confirmed a similar underestimation tendency primarily around the WS sector due to limited buoy data availability, our extensive comparison with SAR sea ice drift demonstrates that this underestimation is a pan-Antarctic feature, prevalent across RS and ABS as well. This underestimation pattern is also observed in the annual maps of speed difference (Fig. b). Most of the Southern Ocean is characterized by a negative speed difference of 0–1 km d⁻¹ (reddish color), but this underestimation becomes more pronounced in the east WS and west RS, where the difference reaches approximately -3 km d⁻¹. Given that the east WS and west RS can be characterized as fast ice movement and dynamic ice edge conditions, we conjecture that the optimal interpolation is responsible for this large localized underestimation. We will provide a more detailed discussion on the impact of this interpolation method in Sect. .

The uncertainty inherent in the NSIDC sea ice drift product is a composite of the errors stemming from (i) PMW feature tracking and (ii) the optimal interpolation used for gap filling and smoothing. In this section, we investigate the impact of the interpolation by comparing the raw PMW feature tracking velocity (PMW_raw), which is derived without optimal interpolation, against both buoys and the SAR-derived drift.

Following the methodology of Sect.  (PMW-buoy comparison), we first compare the PMW_raw sea ice velocity with the buoy observations (Fig. ). The PMW_raw product shows slightly lower RMSE for both u and v velocity components (around 6.3–6.5 km d⁻¹ RMSE), compared to the interpolated PMW product (Fig. a and b). More significantly, while the interpolated PMW product shows a negative speed bias (underestimation) of 2–3 km d⁻¹ relative to the buoys, the PMW_raw product shows virtually no speed bias, with a mean speed difference close to zero (Fig. e and g). For SIC > 85 %, PMW_raw exhibits only ∼ 0.6 km d⁻¹ of speed bias against buoy measurements. Furthermore, the PMW_raw drift demonstrates high directional accuracy, indicated by a mean angle difference of approximately 4–5° (Fig. d and f).

This compelling contrast demonstrates that the systematic underestimation of sea ice speed observed in the NSIDC Polar Pathfinder sea ice motion product is primarily attributed to the optimal interpolation scheme, rather than the intrinsic uncertainty of the PMW-based MCC feature tracking. However, we must note that the PMWraw product still exhibits a tendency to underestimate sea ice speed under low SIC conditions (Figs. , g and h). Although PMWraw comparisons are overwhelmingly concentrated in high-SIC areas (with SIC > 90 % in over 90 % of the total data points) (Fig. a), this residual bias implies that a feature-tracking-related bias remains. In low SIC areas, the PMW brightness temperature signal becomes increasingly contaminated by open-water emission, reducing the contrast and spatial coherence and making cross-correlation tracking challenging for coarse-resolution PMW images . Similar to the interpolated PMW results, the PMWraw sea ice drift direction remains robust and is not substantially affected by SIC conditions (Fig. b).

Similar results are observed in the comparison between PMW_raw and SAR sea ice drift (Figs.  and ), further supporting the findings from the PMW_raw-buoy comparison. In terms of angle difference, the PMW_raw sea ice drift direction aligns well with the SAR sea ice drift, showing a near-zero mean angle difference within a range from -20 to 20°. Additionally, although a slight clockwise bias in the PMW_raw sea ice drift direction is still observed near the east WS, the magnitude of this bias is noticeably less severe than that found in the interpolated PMW product (Figs. a and a). On the other hand, in contrast to the systematic underestimation of the interpolated PMW sea ice drift speed (Fig. b), the mean speed difference for PMW_raw is close to zero, indicating no significant bias by time or region (Figs. b and b).

These extensive comparisons between PMW_raw and SAR sea ice drift strongly suggest that the PMW-based MCC feature tracking successfully reproduces sea ice velocity without a significant intrinsic speed bias, whereas the optimal interpolation systematically introduces a negative bias (i.e., underestimation of sea ice speed) into the final gridded sea ice motion product (Table ). We propose two possible, non-exclusive reasons for the widespread underestimation of sea ice speed caused by the interpolation: (1) Smoothing effect: Optimal interpolation is designed to produce a smooth velocity field, which inherently acts to reduce the magnitude of real ice velocity, particularly in highly dynamic, higher-speed regions. (2) Bias at ice edge: Sea ice edges typically exhibit higher speeds than the middle of the ice pack. However, velocity at the ice edge is often estimated through interpolation of lower velocity values from the interior pack ice, leading to a substantial underestimation of the true speed near the ice edge.

Another critical aspect concerning the uncertainty assessment of the PMW sea ice drift product involves its proximity to the coast and the marginal ice zone (MIZ). Figure  illustrates the change in the PMW-SAR ice speed difference as a function of the distance to the ice edge and the coast for the WS, RS, and ABS sectors. In all three regions, the magnitude of the error in ice speed consistently increases when moving closer to the MIZ (particularly less than 200 km distance from the ice edge). Conversely, the error in ice speed appears to increase with increasing distance from the coast. However, given that greater distance from the coast often correlates with closer proximity to the MIZ, the relationship between ice speed difference and coastal distance is likely an indirect effect driven by the MIZ proximity. Therefore, this spatial analysis supports two key conclusions: (1) The uncertainty of the PMW-based interpolated sea ice velocity estimation significantly increases near the MIZ. (2) The proximity to the coast, after accounting for the MIZ relationship, does not significantly affect the uncertainty of PMW sea ice velocity.