1. Introduction
Active radar remote sensing plays a key role for high-resolution observations of the Arctic sea ice cover in the order of a hundred meters to a kilometer. Synthetic aperture radar (SAR) imagery carries information, for example, about sea ice concentration and extent, sea ice types and sea ice drift, e.g., [
1]. Radar altimetry is the primary tool to estimate sea ice thickness with good accuracy on basin-scale [
2,
3]. The altimeter measures the sea ice freeboard, i.e., the distance between the ocean and the sea ice surface, and ice thickness can then be calculated assuming hydrostatic equilibrium and auxiliary information about sea ice density, i.e., sea ice type, and snow load [
4]. To reduce the dependence on auxiliary information, the direct retrieval of sea ice type from altimeter waveform data has been investigated and showed promising results, but also a large variability of waveform parameters [
5,
6,
7,
8]. The introduction of the delay/Doppler or SAR altimeter has significantly increased the along-track resolution of spaceborne altimetric measurements [
9,
10]. This results in better sensitivity to small features on the surface [
11] and enables the ability to investigate the response of altimeter waveforms to small-scale variations of sea ice types such as large ridges, refrozen leads or multiyear sea ice floes embedded in first-year sea ice on spatial scales from a few hundred meters to a few tens of kilometers.
Altimetric data alone is, however, not sufficient to unambiguously determine the origin of waveform changes over sea ice, because of temporal and spatial variability of waveforms and the impact of off-nadir specular reflectors [
6,
7,
12]. Near-coincidental SAR imagery has proven a valuable source for verification of lead detection algorithms in altimeter data processing, e.g., [
13,
14]. In the late 1980s, two studies have been undertaken with data from Seasat and Geosat to relate SAR backscatter and altimetric data over sea ice, but both were limited to one SAR image as a reference [
15,
16]. Presently however, Copernicus’ Sentinel-1 and Sentinel-3 two satellite constellations in addition to Radarsat-2/Radarsat-Constellation and CryoSat-2 provide an increasing and long term (Copernicus program) data availability especially at lower latitudes (below 81.5
). Furthermore, near real time availability of data [
17] facilitates the use for short term observations of the sea ice cover. This allows us to perform, to the best of our knowledge, the first comprehensive study of the waveform evolution over different sea ice types with SAR imagery as high-resolution validation data.
SAR imagery gives a high-resolution, two-dimensional picture of the current sea ice situation, but sea ice thickness can only be inferred from sea ice signatures with ambiguities [
18]. Meanwhile, altimeter data provides a means to obtain sea ice thickness, but is sparse in nature due to the one-dimensionality of the measurements. To overcome these spatial limitations, averaging in space and time is commonly applied to obtain standard products of hemispherical sea ice freeboard and thickness, e.g., [
19], at the expense of small-scale features. Combining these two information sources therefore has several potential benefits. On the one hand, sea ice type information, necessary for freeboard to thickness conversion [
4], can be provided by SAR imagery in high-resolution [
20] to reduce uncertainties in this critical step. On the other hand, the scarce thickness retrievals could be locally expanded to neighboring areas with similar sea ice signature in the SAR image regarding backscatter intensity and texture.
In this paper, we use near-coincidental Sentinel-1 SAR imagery and CryoSat-2 altimeter data of three winter seasons in the Beaufort Sea to investigate the sensitivity of altimeter waveforms to small-scale (up to 10–20 km) variations of sea ice. We evaluate whether SAR imagery aids the interpretation of the sea ice situation and identification of sea ice types. Furthermore, we derive dimensions of such features to obtain meaningful freeboard estimates. The study area and data are presented in
Section 2. The backscatter and altimeter parameters are presented in
Section 3 and analyzed in
Section 4, focusing first on the distinct waveform properties of the different ice types (4.1) and then on local features (4.2). We conclude with a discussion on how SAR imagery and altimetry can be used to augment each other’s ability for sea ice monitoring.
5. Discussion
Near-coincidental high-resolution SAR imagery significantly aids the interpretation of altimeter waveforms in particular for small-scale features in the sea ice cover. Waveform derived parameters such as PP, IMP and SSD can principally discern between FYI, MYI and leads. The sensitivity of altimeters waveforms to smooth areas within the footprint, however, complicates the classification task. We introduced the IMP parameter to improve the separability between FYI and MYI for cases where the peak power of the two ice types are similar (see
Figure 4). Large temporal and spatial variability of these parameters, however, impedes the establishment of universally valid thresholds. Spatio-temporal changes can have a multitude of sources, e.g., differences in snow depth and composition [
30,
31], changes in surface roughness in the order of the radar wavelength [
24] or temperature variations [
32]. To circumvent this problem, adaptive thresholding has been proposed for larger scale assessments of ice type variability from altimeter data [
7]. For small-scale investigations, the relative spatial evolution of waveforms corresponds well to changes of sea ice surface types as determined from SAR imagery (see
Figure 8,
Figure 9,
Figure 10 and
Figure 11). Therefore, usage of SAR imagery offers possibilities to assess the reasons of sea ice type misclassification from CryoSat-2 data [
7,
8] and inconsistencies in the spatial distribution of waveform parameters and sea ice type products [
6] left unanswered in previous studies.
From the comparison of the altimeter data with the SAR images we observed that in areas of predominantly FYI, waveforms with high PP and peak power are common that are not linked to visible features in the SAR data. The increased peak power can be attributed to sub-resolution specular reflectors, e.g., thin smooth ice and leads that dominate the waveform. These findings are in line with observations from conventional (non-SAR) altimetry over the marginal ice zone [
33]. Similarly, we found that in mixtures of FYI and MYI, waveforms tend to be more similar to FYI waveforms. The return from the smoother components are still strong enough to dominate the waveform in those cases. Difficulties in waveform interpretation over non-homogeneous surfaces are in accordance with earlier observations from Seasat data [
15,
34].
The complexity of altimeter waveform interpretation of mixed surfaces also showed an impact on freeboard estimates. Close to boundaries of distinct MYI floes in FYI, mixtures of ice types within a floe or small floes of MYI in FYI, freeboard estimates are lower compared to the interior of a homogeneous floe and similar to FYI freeboard measurements. While freeboard may vary over such a floe, this behavior is observed for nearly all floes in our study. Thus, the measurements most likely reflect the range to the thinner component either at nadir or off-nadir positions [
35]. The latter ones often cause highly negative freeboard values because the range estimate is no longer associated with the nadir return. Underestimation of freeboard for mixed ice cases has been demonstrated in a simulation study based on CryoSat-2 parameters [
36]. To reduce biases in freeboard retrievals due to ambiguous waveforms, returns with increased PP and peak power over FYI or waveforms with widened leading edge are usually discarded for sea ice thickness retrievals in standard gridded products [
3,
37]. Filtering of waveforms and spatial averaging reduces the effect of outliers but especially in the transition from FYI and MYI more research is necessary to quantify those effects on sea ice thickness retrievals. This is of great importance to reduce uncertainties in sea ice thickness estimates. While ambiguous waveforms introduce errors to freeboard estimates, local changes of sea ice characteristics leading to such irregular waveforms contain information about small-scale features of the sea ice surface.
Leads of thin ice or open water even smaller than the resolution of the SAR imagery are easily discerned by the altimeter due to their high PP and peak power. Caution needs to be taken to separate real leads and lead-like waveforms that are characterized by lower peak powers [
38]. Lead detection is a necessary step in all sea ice freeboard retrievals and crucial to determine an optimal local sea surface height as a reference for the freeboard [
39]. FYI within MYI floes most likely originates from refrozen leads or cracks in the ice and even small features are easily detected by waveform changes towards peakier more powerful returns. Freeboard estimates can be biased by off-nadir impact of the smoother reflectors [
29,
35]. Small-scale features with an increase in backscatter intensity in the SAR imagery, i.e., large ridges and old ice floes, are characterized by a decrease in PP and peak power in the altimeter waveform sequence. Features a few hundred meters in size can usually be identified but can only be unambiguously assigned to the correct cause by the SAR imagery. Freeboard measurements are, however, not significantly impacted by these small features compared to the surrounding FYI because of the reasons explained above for mixed ice cases. Acquisition of robust freeboard estimates over such features therefore requires this feature on the one hand to be large enough that the altimetric footprint is not affected by surrounding areas and on the other hand to provide enough valid freeboard measurements to obtain a meaningful average value. Based on the analysis of many MYI floes in our data set, we suggest that a floe size in the order of 10 km in along-track and a few kilometers to both sides across-track with a minimum of 10 valid freeboard measurements should act as a guideline for robust estimates of floe freeboard and hence ice thickness.
Altimetric waveforms can reflect small-scale changes of the sea ice surface in size scales from a fraction of the altimeter footprint for leads to a few hundred meters for ridges and old ice floes. The complexity of the waveform and the leading edge in particular introduced by non-homogeneous altimeter footprints, however, impede reliable freeboard estimates on these small scales. SAR imagery as additional data aids the interpretation of the altimeter data and facilitates the understanding of waveform responses to surface changes.