The mountainous and ice-free terrains of the
maritime Antarctic generate complex mosaics of snow patches, ranging from
tens to hundreds of metres. These can only be accurately mapped using
high-resolution remote sensing. In this paper we evaluate the application of
radar scenes from TerraSAR-X in High Resolution SpotLight mode for mapping
snow patches at a test area on Fildes Peninsula (King George Island, South
Shetlands). Snow-patch mapping and characterization
of snow stratigraphy were conducted at the time of image acquisition on 12
and 13 January 2012. Snow was wet in all studied snow patches, with
coarse-grain and rounded crystals showing advanced melting and with frequent
ice layers in the snow pack. Two TerraSAR-X scenes in HH and
VV polarization modes were analysed, with the former showing the best results
when discriminating between wet snow, lake water and bare soil. However,
significant overlap in the backscattering signal was found. Average wet-snow
backscattering was
In complex topography terrain, snowmelt patterns are difficult to map accurately due to shadowing and layover effects (Rees and Steel, 2001), especially when a high spatial resolution is necessary (e.g. < 5 m). High-resolution satellite optical imagery is expensive, shows a large revisiting time and is only effective during the day and in cloud-free conditions. Recently, unmanned aerial vehicles have been shown to provide efficient snow mapping results at a low cost (Bühler et al., 2016) but they are still limited by the lack of access to the survey area, as well as by meteorological conditions. In remote locations, such as the maritime Antarctic, with high cloudiness (ci 90 % of the days show cloud cover) and the continuous passage of polar frontal systems, more robust approaches are needed for monitoring snowmelt over large areas.
Late-lying snow patches are known to generate local influences on the ground
thermal regime and on moisture availability, thus being of major significance
for geomorphic processes, ecosystems and permafrost distribution, especially
in the discontinuous permafrost zone (Green and Pickering, 2009). Recent
observations in the maritime Antarctic indicate that snow patches play a key
role in keeping the ground cooler during the summer, determining the presence
of permafrost at sites where, without summer snow, it would not occur (Vieira
et al., 2010). The influence of snow patches on the geomorphological dynamics
gave rise to the use of nivation as an overarching term for the complex set
of geomorphic processes acting in the vicinity of late-lying and perennial
snow patches, with snow being their main driver (Thorn, 1988). Recurring
nivation processes in the same location have been identified as responsible
for the increased erosion and for the development of concavities, named
nivation hollows. Snow is also a major ecological factor especially since it
controls moisture availability during the warm season, but also because snow
traps wind-transported particles that are deposited in snow patches, allowing
for a better development of the vegetation (Brown and Ward, 1996; Erickson et
al., 2005; Hiemstra et al., 2006; Green and Pickering, 2009). Snow also plays
a major role in the distribution of lichen communities, inhibiting the
development of
Synthetic Aperture Radar (SAR) and Advanced Synthetic Aperture Radar (ASAR) imagery, e.g. from ERS, Envisat (C-band) and TerraSAR-X are widely used to characterize snow packs and snow cover (Shi and Dozier, 1997; Baghdadi et al., 1999; Bernier et al., 1999; Nagler and Rott, 2000; Rees and Steel, 2001; Magagi and Bernier, 2003; Vogt and Braun, 2004; Longépé et al., 2009; Falk et al., 2016). Most applications have been developed for regional-scale mapping, but the literature lacks high spatial-resolution case studies. Despite the wide application of C-band imagery, Baghdadi et al. (1997) and Koskinen and Pulliainen (1997) have shown that wet-snow and snow-free terrain can not be distinguished in some types of surfaces or in particular local incidence angles. Mora et al. (2013) tested ENVISAT ASAR C-band imagery at 12 m pixel resolution for mapping snow cover in Deception Island and found that the imagery is only useful at the regional scale and of limited application for snow-patch mapping. In fact, according to some authors, X-band imagery is preferable to detect wet snow (Shi and Dozier, 1995; Strozzi et al., 1998, 1999). It shows a limited penetration capacity in snow and is much more sensitive than other bands to the surficial snow pack (0 to 15 cm), enabling an evaluation of the snow electromagnetic response in a simplified scheme when comparing it with C band (Wiesmann and Mätzler, 1999; Rott et al., 2013).
TerraSAR-X acquisitions in Spotlight mode show ca. 1 m resolution and are therefore potentially a good source for very detailed snow mapping. The German Aerospace Center (DLR) satellite shows a short revisit time (11 days) and an improved radiometric and geometric resolution, which are key factors to detect the evolution of the snow cover, especially during snowmelt when changing moisture content influences the backscattering signal. TerraSAR-X imagery is frequently used for interferometric applications (Venkataraman and Rao, 2005; Alia et al., 2015; Barboux et al., 2015; Betbeder et al., 2015; Reis et al., 2015), glaciology (Braun, 2001; König et al., 2001; Rott et al., 2011; Schubert et al., 2013) and also for snow mapping (Baghdadi et al., 1997; Malnes and Gunerissen, 2002, 2003; Venkataraman et al., 2008; Falk et al., 2016), but mostly using the coarser-resolution StripMap mode. Most research focuses on the retrieval of snow water equivalent (SWE) and not so much on the detailed mapping of snow extent and melt patterns, topics which are very relevant to the geocryological community. In fact, research on high spatial-resolution mapping using microwave imagery is rarely present in the literature. Malnes et al. (2014) tested the use of TerraSAR-X SpotLight mode, VV-polarization imagery for SWE retrieval in Svalbard using ground-truth data obtained along transects, but in order to reduce speckle noise, the authors used a 10 m pixel resolution, thus losing detail. They have found a good capacity for SWE estimation in dry snow, but in wet snow, due to the complete absorbance of the radar signal in the top layers, the procedure did not work.
Climate scenarios indicate that the recent warming in the Antarctic Peninsula
will be followed by an increase in precipitation and possibly in snowfall
(Thomas, et al., 2008; Steig et al., 2009; Winkelmann et al., 2012; Barrand
et al., 2013). The significance of these changes for the geomorphological and
ecological dynamics of the ice-free areas has not been yet evaluated.
However, since the western Antarctic Peninsula, and especially the South
Shetlands, shows mean annual temperatures just slightly below 0
Location and topography of Fildes Peninsula and the Meseta Norte test site.
This paper deals with evaluating the potential of TerraSAR-X (X-band) imagery acquired in SpotLight mode, to map summer snow-patch distribution with a spatial resolution close to 1 m in the maritime Antarctic. Spatial monitoring of snow cover and snowmelt has proven to be a very difficult task in the region (Mora et al., 2013; de Pablo et al., 2016) and the methodology proposed here aims at bridging this gap and is a step towards making an implementation in operational mode available to the terrestrial ecosystems and permafrost research community working on the western Antarctic Peninsula. For the purpose of testing and validating, we have selected a field site on Fildes Peninsula (King George Island, South Shetlands archipelago).
The Meseta Norte is a mesa-like relief in the north-eastern part of Fildes
Peninsula, King George Island (KGI), located in the South Shetlands, off the
northern tip of the Antarctic Peninsula (Fig. 1). KGI is the largest island
in the archipelago and about 90 % of its surface (1250 km
The study site is located in the Meseta Norte, a plateau bounded by steep slopes with a slightly depressed central area at 100–120 m a.s.l. and a series of small plateaus and scarps (Simonov, 1977; Smellie and López-Martínez, 2002; Fig. 2). Small lakes occur in the interior of the Meseta, an area which stays almost completely snow free in late summer, except for a few perennial snow patches. Vegetation is sparse with rocky outcrops and loose clastic material dominating the landscape. The lower areas are the ones where vegetation cover is more frequent, especially at present or past faunal colonies (Michel, 2011).
Topographical setting of the Meseta Norte test area on Fildes Peninsula with the ground truthing mapped in the field. Snow patches are numbered as in the paper.
The climate is polar oceanic, with an average annual air temperature at
sea level of
Methodology for the evaluation of the potential of Spotlight mode TerraSAR-X imagery for high-resolution snow cover mapping.
The Meseta Norte was selected as a site representative of maritime Antarctic conditions due to its climate, lack of vegetation and fast snowmelt rates during the summer, with frequent late-lying snow patches. The area also shows morphological diversity allowing us to better assess the spatial variability of the backscatter signal across a variety of slope angles and aspects. The presence of lakes and snow-free clast-covered surfaces allowed both for improving geocoding of the TerraSAR-X scenes and for ground truthing.
The methodological framework followed in the paper is shown in Fig. 3 and consists of (i) a detailed field survey of snow cover characteristics (ground truthing), (ii) SAR imagery analysis (remote sensing) and (iii) evaluation of classification methods.
In order to obtain high-quality ground-truthing data, in January 2012 a field campaign was conducted on Fildes Peninsula which aimed to characterize the snow cover at the time of remote sensing imagery acquisition. The area of the Meseta Norte was selected, accounting for its variable topography and facilitated access from the Chilean Antarctic station, Professor Julio Escudero. Twelve snow patches with varied slope angle and aspect were mapped and snow pits were dug to describe snow characteristics, with observations taking place on 11 January 2012 (Fig. 4). In cases where slush was present, we excluded the area from the snow-patch boundary surveying and in very large snow patches, only partial ground truthing was conducted.
Overview of the snow conditions in the sampled snow patches during the field survey in the Meseta Norte in January 2012.
Snow pits were dug either down to bedrock, or to depths where thick (> 3–5 cm) and difficult to penetrate ice layers occurred. The focus was on the upper 25 cm of the snow pack due to its sensitivity to the propagation of the X-band radar signal. Each of the snow pits was described for snow stratigraphy, grain size and shape and snow density. Pervasive moisture in all snow pits showed that snow was wet.
Grain size was measured by carefully collecting small amounts of snow from
each of the layers of the snow pack and depositing them in a black tissue
for contrast. They were then observed with a 10x magnifier, which allowed
for measuring and describing the grain shape and size. Grain size (or
crystal size) showed variability within each layer and our description
encompasses the mean grain sizes. When variability was large, we added
the more frequent dimensions (i.e. 1–2 mm). Snow density was measured by
carefully collecting snow from each snow layer without disturbing the snow
pack, using a metal box with a volume of 212 cm
Due to a failure in the thermometer, no temperature depth profiles were measured. As a workaround, ibutton DS1922L single-channel temperature miniloggers were installed at shallow depth (ca. 5 cm) near each snow pit, inside 50 mm cylindrical white plastic photographic film cases. Snow temperature was recorded at 1 h intervals during a period of several days, which included the dates of satellite imagery acquisition. Fast snowmelt and the high radiation absorption of the cases induced extraordinary diurnal heating inside the cases and daily maximum temperatures were abnormal, thus could not be used to accurately describe snow temperature. Surface melting also induced surfacing of the miniloggers, which had to be reinserted into the snowpack in the morning. Mean daily air temperature data were obtained for the Russian Weather Station of Bellingshausen from NNDC/NCDC Climate Data Online (NOAA) and was used for a general characterization of the days of image acquisition.
Characteristics of the TerraSAR-X scenes used for snow mapping on Fildes Peninsula.
In order to improve geocoding and for a better analysis of the radar imagery, the boundaries of several snow patches were mapped using a Leica Viva Differential Global Positioning System (DGPS) in Real Time Kinematic (RTK) mode with a local base station and a rover, allowing for an accuracy of ca. 2 cm for each GPS point. Lake boundaries for improving georeferencing of the satellite images and ground truthing of water surfaces, as well as bare-soil areas (mainly frost-shattered debris), were also mapped with DGPS.
Three TerraSAR-X SpotLight SSC (Single Look Slant Range Complex) mode scenes
were acquired. SSC products offer a single look of the focused radar signal,
with a scene size of 10
Since the goal was to evaluate the discriminating potential of TerraSAR-X for very high-resolution mapping of snow cover, two scenes were acquired for the summer season on 12 and 13 January 2012, the former in HH and the latter in VV polarization (Table 1). These dates coincided with the ground-truthing campaign. An additional early spring scene (28 September 2012, HH polarization) was used in order to assess the backscattering for dry-snow conditions. The results presented in this paper respect the area of the Meseta Norte, corresponding to the field validation area, which is a subsector of the larger TerraSAR-X scene.
The typical speckle noise (salt and pepper), present in radar images due to
the constructive and destructive electromagnetic interference associated with
the scatter, implies choosing an adequate filtering phase to each specific
area, compensating for the noise or emphasizing textures. In this case, Lee
filtering with a 9
Geocoding and terrain correction of the scenes were performed using ranging Doppler analysis and an external digital elevation model of 5 m resolution derived from the Chilean Antarctic Institute topographical map of Fildes Peninsula. This procedure detects geometrical deformations of the original scene due to the off-nadir swapping. Geometric distortions such as layover and shadowing are therefore compensated for in the final product. Image processing was conducted using the ESA SNAP 3.0 software.
In order to assess the discriminating potential of TerraSAR-X imagery for high-resolution mapping of wet-snow distribution, we have first characterized the backscattering signal of the three selected scenes in the ground-truthing areas. For this purpose, the field-mapped boundaries of the snow patches, lakes and bare soil were integrated in a GIS, first as a point layer and then transformed into polygons. Backscattering at the ground-truthing areas was retrieved for the three scenes and analysed in order to identify differences in polarization modes and respective potential for the three surface types. Statistical analyses were conducted to detect backscattering similarities between snow patches and to compare with their topographical setting and snow characteristics obtained from the pits.
The previous approach allowed for the identification of backscattering thresholds for the different surfaces and these data, together with ground-truthing, were used to test three mapping approaches: (i) simple threshold-based surface classification mapping, (ii) ratio-based mapping using a dry-snow and wet-snow scene, and (iii) object-oriented mapping. The results of the three classifications are then evaluated by comparison with the reference data.
Snow pit description for the studied snow patches on Fildes Peninsula.
For extracting the feature values and training the classifiers, we have used a random sample set of half of the ground-truthing area obtained in the field survey for snow patches, lakes and bare soil and the other half to make the validation of the classification.
Twelve snow pits were dug in different snow patches to depths between 45 and
70 cm, at altitudes from 86 to 117 m, at different aspects, located within
an area of ca. 0.6 km
Surficial snow density (kg m
Snow-patch temperatures measured at 5 cm depth from 12 to 13 January 2012. The peaks in the maxima relate to anomalous overheating of the minilogger case. SP1–SP12 are the snow-patch numbers. SP13 was not monitored.
Mean daily air temperatures in 2012 in the Bellingshausen Station (NNDC/NCDC – NOAA). Arrows indicate the day with image acquisitions.
Snow-patch subsurface temperatures at 5 cm depth on 12 and 13 January 2012
showed ca. 0
Radar backscattering of snow, water and soil ground-truthing areas
for the three selected TerraSAR-X scenes:
The three TerraSAR-X scenes (HH – summer, VV – summer, HH – early spring) show different discriminating potential for snow cover when compared to bare ground and open water surfaces (Fig. 9).
The summer HH-polarization scene showed the best separability between the
three cover types, with statistically significant differences and clear
curves peaking at different backscattering values. Snow peaks between bare
soil and water with an average of
The summer VV-polarization scene shows no discriminating capacity, with a
complete mixture between snow and water, with average values of
The early spring scene of 28 September 2012 was selected for analysis due to
the full snow cover conditions and for preceding the onset of snowmelt
conditions, therefore representing dry snow. This fact is confirmed by the
mean air temperatures at Bellingshausen, which from 26 to 28 September varied
between
The HH polarization scene of 12 January 2012 provided the best results for
snow-patch discrimination and therefore was selected for further analysis
and classification. The backscattering for individual snow patches shows
significant differences, with average dB ranging from
Backscattering characteristics of the sampled snow patches, lakes and bare-soil areas. SD is standard deviation.
Snow-patch characteristics and backscattering in HH polarization (12 January 2012). Italics indicate the members of group 1 and bold indicates the members of group 2. All other snow patches are integrated into group 3.
ANOVA analysis and Kruskal–Wallis non-parametric tests were conducted to
identify groups of equivalent response to backscattering. An evaluation of
correlation (
Box and whisker plots of backscattering of individual snow patches in the HH polarization scene from 12 January 2012.
Scatter plots of snow density
Figure 11a shows the lack of correlation between snow density and
backscattering, both in the HH and VV scenes from January 2012. Figure 11b
shows that at HH polarization a weak positive correlation (
Performances for the three tested classifications. Classification
Threshold A: backscattering threshold water – wet snow at
In order to evaluate the applicability of single-band HH-polarized data for wet-snow mapping we have tested several methods. An expert-based visual inspection of the HH backscattering sigma-nought scene allows for a relatively easy delimitation of wet snow patches by comparing greyscale values with feature shapes in the terrain evidencing the potential of the scene (Fig. 12). The purpose of this section is to confront pixel- and object-based classifiers, comparing their performances and potentialities.
TerraSAR-X SpotLight mode HH polarization backscattering (sigma-nought) scene from 12 January 2012 with examples of visual interpretation of bare ground (BG), water (W) and snow patches (SP). Contour lines at 5 m equidistance.
Classification results using
The best quality of the discrimination in the HH-polarization scene of 12 January, when compared to the VV scene of 13 January, led us to its
selection for assessing the application of backscattering thresholds and
band maths for the classification. This is the simplest way of evaluating the
applicability of the single-polarization backscattering signal to map wet
snow. Since data have shown a significant overlap between the three classes,
but especially between wet snow and water (see Fig. 9), no accurate
threshold is able to discriminate between them. However, once the objective
is differentiating wet snow from bare-soil surfaces, a valid option is using
the threshold between these two classes, which may be defined using the
average
Both classifications provide a very good general assessments of wet snow cover distribution, but with the typical noise of pixel-based classifiers (Fig. 13). Most of the wet snow is classified correctly, or as water, but numerous pixels wrongly classified as snow are displayed as clumped patterns forming fuzzy clusters, with shapes not as clearly defined as the observed snow patches. The confusion matrix shows an overall accuracy of 81.0 % for Threshold A, and 81.1 % for Threshold B.
Discrimination of wet snow, bare soil and water using simple band
maths:
Differences in backscattering when comparing a snow scene with a snow-free scene, or with a dry-snow scene, have been used by several authors in order to classify ground conditions (Rott and Nagler, 1994; Nagler and Rott, 2000; Malnes et al., 2014). For this purpose, the HH-polarization scene from 28 September 2012 showing a fully snow covered terrain and dry snow was used as reference, while the HH-polarization scene from 12 January 2012 was the target scene for the classification. Figure 13 shows that from the three studied surface types, bare soil is clearly differentiated from lakes, but wet snow shows a significant overlap with both lakes and bare soils, which shows the same limitations as the threshold methods evaluated in panel a.
Another approach is the classification using a band ratio between the dry-snow scene and the wet-snow scene, aiming at detecting thresholds between surface classes (Nagler and Rott, 2000; Valenti et al., 2008). However, the ratio between the HH-pol September and the HH-pol January scenes show very poor discriminating potential with significant mixing between the three surface types (Fig. 14). Given the poor results, no image classification for evaluation was attempted using simple band maths.
The pixel-based classifications produced limited results, with the threshold-based maps showing two different patterns of pixels classified as wet snow: homogeneous patches, with clearly defined limits, which mostly coincided with the ground-truthing snow patches, and small diffuse clusters of pixels, wrongly classified as wet snow. Given the differences in spatial patterns and the relatively straightforward manual delimitation of the wet snow by visual inspection on the imagery, new classification tests were conducted using an algorithm which is object-oriented and constituted by three main processing steps: filtering, segmentation and classification.
The filtering intends to attenuate the speckle of the radar image in order
to enhance the spatial coherence of the image texture or of the structures
of the surface. To achieve this goal, a series of mathematical morphology-based filters were tested (Soille, 2004). These are region-based filters,
which rely on the reconstruction of a “classic” filter, i.e. by opening or
closing (Salembier and Wilkinson, 2009). The filter that performed better is
based on the removal of the image extrema with a contrast criterion, that
is, on suppressing all maxima and minima with height/depth lower than a
given threshold level
Initial steps on the object-oriented classification scheme:
The segmentation consists of delineating the homogeneous regions, often referred to as objects (Blaschke, 2010), of the filtered image. The underlying idea is to classify the basic elements of the texture (the objects) later instead of the basic elements of the digital image (the pixels), since the availability of additional descriptors of the image can greatly improve the decision performance. The segmentation is based on the watershed transform (Soille, 2004), followed by a post-processing task to merge similar adjacent regions. The final watershed lines corresponding to the segmentation of the filtered image are shown superimposed in Fig. 15b.
Finally, in the third step, the classification of the segmented objects is
performed. It is a supervised classification approach, meaning that typical
features of the objects are used to train a classifier. In the current
situation, the classifier that achieved better results is the support vector
machine (SVM). SVM is a supervised kernel method (Vapnik, 1995) that uses an
implicit transformation to a higher dimensional space in order to achieve a
good separability by means of a linear classifier. It also has the ability
to handle data with unknown statistical distributions using small training
sets. The classification of the segmented objects is based on a set of
intensity, geometric and textural descriptors of each object. The SVM kernel
selected is the RBF (radial basis function) with the parameters gamma
The confusion matrix shows the good quality of the classification, with an overall accuracy of 92 % and Kappa equal to 0.88 (Table 3) (Congalton, 1991), indicating the adequacy of the proposed method to separate water, snow and soil in radar images of ice-free regions in maritime Antarctica. The integration into the same processing sequence of some of the most appropriate filters to deal with the spatial arrangement of textures, the a priori delineation of the objects constituting the landscape and the use of one of the most robust classifiers, are the keys for the performances obtained. The only issues in the classification arise in snow patches facing west to north-west, where a significant part of the area was classified as bare soil.
Snow characteristics in the Meseta Norte at Fildes Peninsula on 12 and
13 January 2012 have been described by field mapping of test snow patches and
by analysing snow pits. Snow distribution showed a typical maritime Antarctic
summer melt pattern with snow patches from tens to hundreds of metres long,
concentrating on concavities and prevailing on south-facing slopes. The snow
was in advanced melting stage, with isothermal near 0
Distribution of wet snow in the Meseta Norte using an object-oriented classification with SVM (support vector machine).
X-band radar backscatter is essentially influenced by the characteristics of
the upper 15 cm of the snow pack (Rees, 2006; Rott et al., 2013). Near the
surface, most of the snow patches showed a lack of ice layers, coarse-grained
snow and high densities, ranging from 470 to 600 kg m
Backscattering depends on the dryness of the snow, on the incident angle and
on the roughness of the surface. The HH polarization scene showed a better
discriminating potential between wet snow, bare soil and water than the
VV-pol scene, which completely merged the water and snow signals, while also
showing important overlap with bare soil. These results agree with those
presented by other authors, which have also found better results for snow
cover classification when using HH-polarization scenes in C-band (Baghdadi
et al., 1998; Mora et al., 2013). Holah et al. (2005), in a case study on
soils using in C-band and multiple incidence angles, also show that HH
provided higher sensitivity to surface roughness than VV. Additionally,
VV-polarization is more sensitive to water roughness changes. The
differences in incident angle between the HH (45.6
Snow backscattering in the HH-pol scene of 12 January 2012 showed values from
Given the limitations of the VV-pol scene, the HH-pol scene was selected to test the use of single-band classification methods for identifying wet snow. The results showed a significant overlap between the signature of wet snow and lake water. Wet snow showed higher dB and visual inspection shows that spatial distribution of values in snow patches is more uniform, whilst lakes show higher speckle. The mixed signal between wet snow and water generates a large number of errors when conducting a pixel-based image classification, with numerous pixels classified as water in slope sites where bare soil occurs and a significant mix of snow and water. One of the reasons for the poor discrimination potential is the high moisture content of snow, and another is the high moisture of soils during the summer, with saturation occurring in many locations. The tests carried out with simple thresholds and band maths did not provide robust results. However, the threshold-based maps allow for identifying the snow patches, although with significant noise and too much snow in bare-soil areas.
Supported by the visual inspection of the HH-polarization scene and with the terrain-based expert knowledge suggesting that snow-patch boundaries could be easily identified in the scene, an object-oriented approach was tested as an alternative to the limited performing pixel-based methods. Filtering and image segmentation were important steps for cleaning the noisy areas and the classification results improved very significantly, with an overall accuracy of 92 %. The resulting classification was very good. Noisy areas were removed and a very good overall performance was obtained.
Problems occurred in snow patches facing north-west, which have been misclassified as bare soil. This problem has also been found when analysing the VV-polarization scene and is probably related to artefacts associated with the geometry of acquisition, along a right-looking ascending orbit. Slope and aspect effects on backscattering have been assessed by different authors to develop geometric corrections (e.g. Mi et al., 2014; Small et al., 2010). Here, we have adopted a simple but robust approach through Range Doppler Terrain Correction, taking into account the advantage of a 5 m digital elevation model. In this procedure, the incidence angle and terrain slope are both considered in the absolute radiometric calibration to sigma nought in ESA-SNAP software (Kellndorfer et al., 1998), and in the subsequent phase of Range Doppler Terrain Correction. Using imagery showing multiple SAR incident angle backscattering responses and both ascending and descending passes would be the best approach to inferring a more complete radiometric performance of the terrain signal and is recommended for regionalizing the results. Unfortunately, in this case only two scenes were available. The synergistic use of an ascending and a descending scene with a short time interval (1 day) should also be an adequate option to better identify the snowmelt patterns and fill the spatial gaps.
Although showing good classification results due to the low backscattering in the analysed scenes, under strong winds, the water bodies may show high brightness due to Bragg scattering, making them difficult to distinguish from snow. In order to avoid this issue, a lake mask obtained either from a windless scene or from an optical scene or map may be used.
TerraSAR-X imagery shows clear advantages in high cloudiness environments when compared to optical images, since the radar signal traverses the cloud cover and is not dependent on daylight. However, the radar signal structure is very dependent on the topography and the dielectric variables of the terrain and, in the case of the snow, on grain size and the equivalent snow water, implying a large variability in backscattering according to local factors and time. The use of Single Look Slant Range Complex (SSC) images permitted a sophisticated terrain correction, reducing layover and shadowing effects with a precise external DEM. The High Resolution Spotlight Mode and a refined speckle filtering proved to be useful for determining the limits of snow patches, which were validated with terrain data.
In the present study we conducted a very detailed survey of snow conditions over 2 days in the austral summer of 2012, with simultaneous acquisition of two TerraSAR-X scenes in SpotLight mode in HH and VV polarization modes, and a third HH polarization scene was obtained in 28 September 2012 as a reference dry-snow scene. Snow patches were in advanced melting stage, with wet and coarse-grained snow at all studied sites and ponding in the downslope sectors of some snow patches. As a consequence of snowmelt, and also of active layer thaw, the bare soils of Fildes Peninsula showed significant moisture content.
The analysis of the TerraSAR-X scenes and the comparison with ground-truthing from snow patches, lakes and bare-soil test areas showed that the only scene with potential for discrimination of the three surface classes was the one obtained with HH polarization. The lack of quality of the VV scene may have been emphasized by the low incidence angle of the acquisition. Despite different average backscattering in the HH scene, significant mixing still occurred between the three classes. With the objective of mapping wet-snow distribution, we tested single-band pixel-based classification methods and an object-oriented approach. After several tests with the latter, this has proven to be the one providing best classification results, with overall accuracies of 92 %. Some inaccurate classifications were obtained in north-west- to west-facing snow patches, and especially in steeper slopes. The reason for this is probably associated with the geometry of image acquisition and further research is needed to mitigate this issue.
The method presented here, using SpotLight mode imagery together with detailed synchronous reference data, offers a very high-resolution mapping of snow patches in the maritime Antarctic for the first time, allowing us to identify features on a scale of a few metres. Given the lack of knowledge of snowmelt in the ice-free terrains of the Antarctic Peninsula, the present results show that X-band imagery in HH-pol and with a high incident angle, can be used as a good approach for monitoring snowmelt patterns during the summer in key areas. Such an approach is especially useful for monitoring ecosystem dynamics (i.e. at GTN-P, CALM-S or LTER observatories), modelling permafrost and active layer thaw, but also for remotely assessing snow conditions before opening summer research stations and thus implementing better planning for deploying equipment and personnel.
The remote sensing imagery used belong to the German Aerospace Center (DLR) acquired in the framework of the contract VIEIRA_LAN1276 and cannot be made available by the authors. Image references are TDX1_SAR_SSC_HS_S_SRA_20120112T233259_ 20120112T233300, TDX1_SAR_SSC_HS_S_SRA_ 20120113T231558_20120113T231559 and TSX1_SAR_ SSC_HS_S_SRA_20120928T083959_20120928T084000.
The authors declare that they have no conflict of interest.
This research has been funded by the Portuguese Polar Programme and the Fundação para a Ciência e a Tecnologia under the projects SNOWCHANGE and PERMANTAR-3 (PTDC/AAG-GLO/3908/2012). Imagery was obtained through the DLR TerraSAR-X project LAN1276. The authors warmly thank the Instituto Antártico Chileno for the logistical support provided at Professor Julio Escudero research station on Fildes Peninsula. Christian Haas, John Yackel, an anonymous referee and Marco Jorge are thanked for the comments and insights, which contributed to clarifying the final version of the manuscript. Edited by: C. Haas Reviewed by: J. Yackel and one anonymous referee