ICE FLOW MAPPING WITH P-BAND SAR

J. Dall1, U. Nielsen1, A. Kusk1, R.S.W. van de Wal2

1 Technical University of Denmark, National Space Institute, Denmark; jd@space.dtu.dk 2 Utrecht University, Institute for Marine and Atmospheric Research Utrecht, The Netherlands

ABSTRACT

Glacier and ice sheet dynamics are currently mapped with X-, C-, and L-band SAR. With the prospect of a P-band SAR, Biomass, to be launched within the next decade it is interesting to look into the potential of P-band for ice velocity mapping. In this paper first results are presented. Airborne P-band SAR data have been acquired in Greenland, and both offset tracking and DInSAR have been applied to the full resolution data as well as to data degraded to the resolution of Biomass. Generally, ice velocity maps are successfully generated, but in the ablation zone, DInSAR fails in the melt season at both resolutions, while feature tracking fails at the coarser resolution.

Index Terms— SAR, ice flow, interferometry, offset tracking, coherence, P-band

1 INTRODUCTION Recently, Biomass was selected as ESA’s seventh Earth Explorer mission [1]. Biomass is a P-band SAR, and ESA’s airborne IceSAR 2012 campaigns have addressed the feasibility and potential advantages of measuring the dynamics of glaciers and ice sheets with P-band SAR, i.e. at a lower frequency than used so far.

Existing velocity maps of the ice sheets and outlet glaciers in Greenland and Antarctica are primarily based on C-band SAR data due to the abundance of data from the ERS-1/2, ENVISAT/ASAR, and RADARSAT satellite SAR systems. For instance, RADARSAT-1 is the principal data source for the velocity maps generated in the Greenland Ice Mapping Project (GIMP) under the NASA MEaSUREs program [2]. GIMP maps from 2000/1 and 2005/6 to 2010/11 are available from the National Snow and Ice Data Center. With X-band TerraSAR-X data, a higher spatial and temporal resolution can be obtained, but in practice regional sized or continental sized maps are not generated due to the narrow swath of TerraSAR-X. In addition, a typical decorrelation time of a few days or even hours [3] implies that interferometric and speckle tracking techniques are generally not applicable, and consequently the use of X-band data is confined to glaciers and other areas rich in surface features [3][4][5]. For large-scale mapping, L-band data are preferable to C- and X-band data [6]. Rignot and

Mouginot [7] have found that “the best mapping performance is obtained using ALOS PALSAR data due to higher levels of temporal coherence at the L-band frequency”. This is a consequence of a deeper penetration into the snow and firn, which in turn makes L-band more robust to surface weathering caused by melt, wind and snow accumulation [6][7][8]. P-band is expected to penetrate even deeper than L-band, and the resulting decrease of temporal decorrelation suggests advantages such as:

1 Increased coverage of the ice sheets. 2 Measurement of seasonal variations of ice velocities. 3. Better velocity precision and spatial resolution, where

(and if) differential SAR interferometry (DInSAR) can replace offset tracking techniques.

In large parts of central Greenland, ice velocity measurements with C-band SAR are difficult [7], and due to a longer revisit time, the problem is more pronounced with ASAR than with RADARSAT (35 days vs. 24 days). Often C-band (and to some extent L-band) fails above the equilibrium line [9], where features in the firn are scarce and where many C-band data sets have insufficient coherence for speckle tracking and DInSAR. However, also ice velocities above the equilibrium line are of interest, e.g. the observation of an immediate speed-up of the ice just above the equilibrium line in a warm year like 2012 suggests that the sliding area of the ice sheet can rapidly extend inland [10]. In order to measure seasonal variations, ice velocities must also be measured in the melt season, where the coherence is lower. Again, P-band might advantageously be used. Finally, DInSAR provides better velocity precision and finer spatial resolution, but calls for higher coherence than offset tracking. P-band might provide sufficient coherence for DInSAR.

In Sections 2 and 3 the airborne SAR and the acquired P-band SAR data are described. Sections 4 and 5 present the results obtained with Offset Tracking and DInSAR, respectively. Both techniques are assessed at the full resolution of the airborne SAR and at the coarser resolution of Biomass. Finally, the conclusions are found in Section 6.

2 AIRBORNE P-BAND SAR Prior to the IceSAR 2012 campaigns, The Technical University of Denmark (DTU) added a SAR capability to

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the ESA/DTU POLarimetric Airborne Radar Ice Sounder (POLARIS) [11] as shown in Figure 1. The result is a left/right ambiguity ratio better than -34 dB, a nadir echo suppression better than -35 dB, and a signal varying less than 20 dB over the swath, all assuming two-way propagation and Lambert scattering. Table 1 lists key POLARIS parameters. For the ice dynamics studies, single aperture, quad pol data were acquired with the maximum 85 MHz bandwidth, and a typical pulse length of 20 µs. For tomography studies, 4-aperture HH pol data were acquired [17]. In-flight configuration of the look direction allowed data to be acquired with the same geometry when flying a track in opposite directions.

POLARIS was flown on the Norlandair TF-POF Twin Otter aircraft. In order to reproduce predefined flight tracks, deviations from a desired track and other steering information was computed from GPS and presented in real-time to the pilots. For long tracks where the surface elevation and hence the desired flight altitude changed significantly (e.g. along the K-transect) a digital elevation model (DEM) was used in combination with the GPS input. For short data acquisitions the rms deviation from the desired track was less than 5 meters.

3 P-BAND DATA

During the three IceSAR 2012 campaigns, data intended for studies of offset tracking, DInSAR, and tomography were acquired along the K-transect, a line extending 150 km eastward from the ice edge at Kangerlussuaq in western Greenland [12]. Two 5 km by 5 km sites on the K-transect were mapped intensively: SHR in the ablation zone (67.10°N, 49.94°W, 700 m elevation) and S10 above the equilibrium line in the accumulation zone (67.07°N 47.02°W 1850 m elevation). In the period of the IceSAR campaigns, GPS data (and other in-situ data) were acquired at all K-transect stations including SHR and S10.

The temporal baseline of the POLARIS data acquired in April and May is almost the same as that of Biomass’ primary option (17 days), but also data with longer baselines were acquired (May-June: 33 days; April-June: 51 days).

Table 1 Key parameters of the airborne P-band SAR

Center frequency (P-band) 435 MHz Bandwidth ≤85 MHz Polarization quad Number of antenna apertures ≤4 Peak power 100 W Pulse length ≤50 µs Pulse repetition frequency ≤20 kHz Look angle, near range 20° Look angle, far range 45° Look direction right or left Flight altitude above ice typically 4000 m

Figure 1 POLARIS with the horizontally mounted antenna under the fuselage and the uneven length cables to the four subapertures for the electronic beamforming.

The spatial baselines were designed to match the vertical

wavenumber, kz, of Biomass i.e. to have the same sensitivity to elevation and volume decorrelation. This means that the length of the desired baselines is comparable with the precision of the flight control, and consequently the tracks were planned for zero baselines. The tracks dedicated for DInSAR were flown perpendicular to the K-transect in order to have a range component of the ice velocity, which in turn is almost parallel to the transect.

The raw POLARIS data have been focused using the back-projection algorithm in combination with a surface DEM [13], and trihedral reflectors have been used to complement the internal calibration. The trihedrals also verify an azimuth resolution better than 1 m. The analysis in this paper is based on HH polarized data.

4 OFFSET TRACKING

The term Offset Tracking (OT) refers to a family of methods estimating the spatially varying ice displacement from the peak position of the cross-correlation of corresponding patches in a pair of data acquired with a temporal separation [14][15]. Offset Tracking is also termed feature tracking or speckle tracking, depending on the primary origin of the cross-correlation peak. In this paper intensity data are used, but complex Offset Tracking has also been studied.

At full resolution, Offset Tracking provides ice velocities that are in good agreement with the GPS data, for all temporal baselines and both at SHR and at S10, see Figure 2.

In order to assess the performance of Biomass, the POLARIS data have been degraded by reducing the range and azimuth bandwidths. In the SAR imagery the S10 ice appears featureless, but the speckle pattern is sufficiently stable to ensure proper cross-correlation peaks. At SHR

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Figure 2 Validation of Offset Tracking in the accumulation zone (S10) based on full resolution SAR data.

Offset Tracking fails, presumably because the spatial resolution of the simulated Biomass images is insufficient to resolve crevasses and other ice features. The patch pairs that are cross-correlated are apparently not coherent, because otherwise the speckle pattern would have ensured a peak like at S10. For results involving June data, speckle tracking was expected to fail, as surface melt amounts to 50-70 cm prior to the June data acquisition.

Although cross-correlation can be successfully applied to the coarse resolution data from S10, the accuracy of the resulting ice velocities is not satisfactory. Speckle tracking accuracies depend on the spatial resolution (and the coherence), and due to the coarse resolution of Biomass and the low velocity of the accumulation zone ice, a low relative accuracy results.

The Offset Tracking results are summarized in the upper half of Table 2.

5 INTERFEROMETRY

For DInSAR to work properly, the scene must be

sufficiently stabile on a scale comparable with the wave-

Table 2 Applicability of Offset Tracking and DInSAR.

Ablation zone SHR

Accumulation zone (S10)

POLARIS OT Applicable Applicable

Biomass OT

Not applicable (feature size)

Applicable (poor accuracy)

POLARIS DInSAR

Not applicable (melt etc.)

Applicable

Biomass DInSAR

Not applicable (melt etc.)

Applicable

Figure 3 Interferometric coherence in the accumulation zone (S10) based on full resolution SAR data.

length, and the minimum required coherence is higher for DInSAR than for Offset Tracking. At SHR, the coherence is too low for DInSAR, and this applies to all IceSAR data with non-zero temporal baselines. Naturally, the June data cannot be combined with April or May data due to excessive surface melt, but apparently also surface weathering occurring between the April and May data acquisitions prevent DInSAR applications.

For S10, Figure 3 shows the coherence as a function of kz and the incidence angle θ, for all the temporal baselines, TB. Since Biomass’ incidence angles range between 23° and 35°, and the kz values are between 0.06 m-1 and 0.1 m-1, the figure suggests that DInSAR can be successfully applied in the ablation zone. Indeed, at S10 DInSAR works fine, both at full resolution and at the coarser resolution of Biomass. As an example, an interferogram based on data with Biomass resolution is shown in Figure 4. The nominal number of looks is 64 (the equivalent number of looks somewhat lower).

Figure 5 shows that for all temporal baselines DInSAR provides ice velocities that are in good agreement with the GPS data, even at the coarser resolution. Table 2 includes the DInSAR results for SHR and S10.

6 CONCLUSIONS The study presented in this paper and summarized in Table 2 suggests that above the equilibrium line, where it is often difficult to measure ice velocities at higher frequencies, space-based P-band DInSAR might offer a valuable complementary capability. Ionospheric scintilla-tions, however, are likely to inhibit such applications if they cannot be sufficiently mitigated [18].

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Figure 4 Interferogram representing ice motion (flattened with a DEM) from the accumulation zone (S10) at Biomass resolution. The temporal baseline is 18 days (April–May).

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