Automated Delineation of Dry and Melt Snow Zones in Antarctica

2152 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 44, NO. 8, AUGUST 2006 Automated Delineation of Dry and Melt Snow Zones in Antarctica Using Active and Passive Microwave Observations From Space Hongxing Liu, Member, IEEE, Lei Wang, and Kenneth C. Jezek, Associate Member, IEEE

Abstract—This paper presents the algorithms and analysis re- percolation zone, and the dry snow zone. Long-term variations sults for delineating snow zones using active and passive mi- in areal extent of different snow zones contribute to changes in crowave satellite remote sensing data. With a high-resolution the Earth’s radiation budget, and hence to climate changes [3]. Radarsat synthetic aperture radar (SAR) image mosaic, dry snow The balance between accumulation and melt in different snow zones, percolation zones, wet snow zones, and blue ice patches for the Antarctic continent have been successfully delineated. A zones also affects the runoff and discharge of stream systems competing region growing and merging algorithm is used to fed by snow and glacier melt water. Moreover, snow pack is initially segment the SAR images into a series of homogeneous extremely sensitive to atmospheric temperature. Spatial extent regions. Based on the backscatter characteristics and texture and geographical position of different snow zones indicate property, these image regions are classified into different snow regional climate condition [4]–[6]. zones. The higher level of knowledge about the areal size of and Since the publication of Benson’s [1] classification scheme adjacency relationship between snow zones is incorporated into in the early 1960s, many investigators have employed satel- the algorithms to correct classification errors caused by the SAR image noise and relief-induced radiometric distortions. Mathe- lite remote sensing data to detect these snow zones. Passive matical morphology operations and a line-tracing algorithm are microwave data acquired by the Scanning Multichannel Mi- designed to extract a vector line representation of snow-zone crowave Radiometer (SMMR) onboard the Nimbus-7 satellite boundaries. With the daily passive microwave Special Sensor and the Special Sensor Microwave/Imager (SSM/I) from the Microwave/Imager (SSM/I) data, dry and melt snow zones were Defense Meteorological Satellite Program (DMSP) have been derived using a multiscale wavelet-transform-based method. The widely used to detect snow-melt occurrences and to map melt analysis results respectively derived from Radarsat SAR and SSM/I data were compared and correlated. The complementary snow zones versus dry snow zones on a large geographical scale nature and comparative advantages of frequently repeated passive [7], [8]. Active microwave radar images are demonstrably microwave data and spatially detailed radar imagery for detecting effective for a detailed snow-zone recognition and delineation and characterizing snow zones were demonstrated. [5], [6], and [9]–[11]. Due to their complementary nature in Index Terms—Antarctica, passive microwave, snow zones, spatial and temporal resolution, the combination of active and synthetic aperture radar (SAR). passive microwave data may improve snow-zone detection and interpretation. However, appropriate algorithms are needed to I. INTRODUCTION conflate and assimilate the data sets. Up to date, most previous investigators used visual interpretations and manual tracing HE METAMORPHOSIS of snow and firn is strongly methods to delineate the snow zones. In addition, earlier re- T controlled by the amount of snow accumulation, annual search efforts to map snow zones concentrated on the Green- surface-temperature variation, and the intensity of summer land ice sheet [1], [9]–[11] and some Alpine glaciers [4], [5]. melt, which vary spatially with geographical location, surface Little research in this aspect has been reported for the much elevation, and regional climate. Depending on the interplay larger Antarctic ice sheet, with the exception of the recent work between these factors, distinct snow morphologies arise, which of Rau and Braun [6] on the Antarctic Peninsula. can be used to spatially classify the snow pack into snow zones In this paper, we present numerical algorithms and analysis [1], [2]. Based on field observations from snow pits and cores in results for delineating snow zones in Antarctica with active the Greenland ice sheet, Benson [1] developed a general snow- microwave Radarsat synthetic aperture radar (SAR) imagery zone model. In this model, the surface area of an ice sheet or a and passive microwave SSM/I data. Based on radar-backscatter glacier is subdivided into four distinct snow zones (facies): the characteristics of different snow zones, we developed a numer- superimposed (bare) ice zone, the wet (soaked) snow zone, the ical method to derive snow zones from SAR imagery. With high-resolution Radarsat SAR imagery acquired in 1997, we Manuscript received March 24, 2005; revised January 18, 2006. This work delineated dry snow zones, percolation zones, wet snow zones, was supported in part by the National Aeronautics and Space Administration and blue ice patches over the entire Antarctic ice sheet and under Grant NAG5-10112 and in part by the National Science Foundation under Grant 0126149. surrounding ice shelves. Using the passive microwave data, H. Liu and L. Wang are with the Department of Geography, Texas A&M we also derived the snow-melt onset date, melt end date, and University, College Station, TX 77843 USA (e-mail: [email protected]; melt duration for the period 1991–1997 with a multiscale [email protected]). wavelet-transform-based method [12]. We compared and in- K. C. Jezek is with the Byrd Polar Research Center, The Ohio State Univer- sity, Columbus, OH 43210 USA (e-mail: [email protected]). terpreted the analysis results derived, respectively, from the Digital Object Identifier 10.1109/TGRS.2006.872132 Radarsat SAR data and the SSM/I data.

Fig. 2. Components of radar backscattering in the snow pack.

B. Radar-Backscattering Characteristics of Different SnowZones Fig. 1. Orthorectified SAR image mosaic for Antarctica, in which source A spaceborne SAR sensor is an active microwave imaging images were acquired in September and October, 1997 by a Radarsat-1 C-band SAR sensor. system. The radar backscatter received by the SAR sensor is the sum of surface scattering at the air/snow interface, volu- metric scattering within the snowpack, and scattering at the II. DELINEATION OF SNOW ZONES USING snow/bedrock interface (Fig. 2). The bedrock backscatter is RADARSAT SAR IMAGERY negligible in Antarctica due to the great depth of the snow pack. In different snow zones, snow grain size and density, A. Orthorectified Radarsat SAR Image Mosaic for Antarctica stratigraphy, surface roughness, and water content of the snow Antarctica is dominated by a vast ice sheet that extends pack are different. These factors affect the surface and volume outward on the surrounding ocean in the form of massive ice backscatter strength of the radar signal, and consequently shelves (Fig. 1). The size of the Antarctic continent approx- the brightness variation in radar imagery. The contrasts in imates the areas of the U.S. and Mexico combined and is radar-backscatter strength between different zones permit snow 6.4 times as large as Greenland. zones to be delineated from SAR image data. The responses of We use the Radarsat-1 SAR image mosaic of Antarctica radar signal to snow zones vary from season to season. Previous for the snow-zone delineation. The mosaic was compiled studies have demonstrated that the winter SAR image acquisi- from data acquired by a C-band (5.6-cm wavelength) SAR tions have much stronger brightness contrasts between adjacent sensor onboard the Canadian Radarsat-1 satellite. The imaging zones than summer acquisitions [5], [6], [11]. The 1997 campaign was conducted from September 9 to October 20, Radarsat SAR image mosaic of Antarctica was compiled based 1997 [13]. The coastal regions were imaged mainly using the on the image data acquired during the austral winter season. standard beam 2 with a nominal incidence angle of 24◦–31◦ at Therefore, the direct effect of surface melting is minimized, a 25-m resolution. The combination of over 4000 SAR image and the radar backscattering is mainly influenced by the grain frames (100 km by 100 km each frame) collected over 30 days size, density, stratigraphy, and surface roughness of snow pack. provides a high-resolution view of the entire Antarctic In this paper, we adopt the terminology and the general snow- continent and offshore ocean. zone model in [1] and [2] as the conceptual framework for our Through block bundle adjustment, terrain correction, and snow-zone recognition and delineation effort. In the general radiometric balancing operations, a complete seamless or- snow-zone model, the superimposed ice zone, the wet snow thorectified SAR image mosaic has been produced [13], [14] zone, the percolation zone, and the dry snow zone form an (Fig. 1). The orthorectified SAR image mosaic is in the polar ordered sequence from the ice front to the interior of the ice stereographic projection with reference to the WGS84 ellipsoid. sheet. The combination of the percolation zone, the wet snow The radiometric balancing operation minimizes the radiometric zone, and the superimposed ice zone represents the total area differences between adjacent image frames and also reduces affected by surface melting and is referred to as the melt snow the radiometric distortions introduced by terrain relief and zone in this paper (Fig. 3). local incidence-angle variations. The orthorectification process In the dry snow zone, melting does not occur even during ensures the positional accuracy of the source SAR images, summers. The prevailing dry-snow metamorphism includes and hence the geographic position and geometric shape of gradual compaction due to its own weight (gravity) and wind the derived snow-zone boundaries. The absolute geolocation action, recrystallization, and depth-hoar development due to accuracy of the SAR image mosaic is estimated to be approx- internal temperature and moisture gradients. Owing to the imately 100 m. This precisely geocoded and terrain-corrected absence of melt events, the upper part of the dry snow zone image mosaic furnishes a planimetrically accurate radar image has a low snow density, uniform crystals of small grain size, base for interpreting and delineating different snow zones at a and a moderately layered snow pack without melt-related continental scale. ice layers. The interaction between the radar signal and the 2154 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 44, NO. 8, AUGUST 2006

Fig. 3. Schematic representation of different snow zones. snow pack is dominated by volume scattering from below the snow surface. Surface scattering is insignificant because the snow density is low. Due to the high penetration depth (about 20 m) and predominant volume scattering, the dry snow zone is characterized by low backscatter, and thus generally Fig. 4. Radar-backscatter differences between dry snow zones, percolation appears very dark in radar imagery [5], [6], [9]–[11]. An zones, wet snow zones, and blue ice patches on the Antarctic Peninsula. The exception to this behavior occurs in East Antarctica. There, frozen melt ponds in the wet snow zone are highlighted in the inset image. snow accumulation is very low, and so slowly growing grains and depth hoar remain near the surface. The result is a marked this zone have merged into a continuous ice mass by freezing contrast in brightness between the dry snow zones of East and meltwater. During winters, the bare ice zone exhibits relatively West Antarctica. Based on aprioriinformation and the passive low backscatter compared to that of the firn of the wet snow microwave data that we discuss later, we classify the interior zone [5], [6]. East Antarctic region as a dry snow zone, even though this To sum up, the four snow zones are discernible on radar im- region has a relatively high radar backscatter. agery [11]. The expected brightness-variation pattern on a win- In the snow pack of the percolation zone, occasional or ter SAR image is: dark for the dry snow zone, exceptional bright frequent surface melting occurs during summers. Surface melt- for the percolation zone, intermediate bright for the wet snow water percolates downward, occasionally spreading out into zone, and intermediate dark for bare ice zone. This pattern has layers. The percolation zone is characterized by large grain been recognized by field-based radar measurements in [9] and sizes and numerous subsurface ice pipes and lenses caused by [10] and demonstrated on satellite SAR images in [5] and [11]. melt–freeze cycles. The ice pipes and lenses have dimensions larger than or comparable to the wavelength of the SAR and C. Radar-Backscatter Signatures of Different profoundly affect radar backscatter. In the frozen state, the mas- SnowZones in Antarctica sive subsurface ice layers with large pipes and lenses strongly scatter the radar signal and make the percolation zone appear The winter SAR image mosaic has sufficient brightness exceptional bright in radar imagery [9]–[11]. contrasts for discrimination of different snow zones, and dry In the wet snow zone, surface melting is intensive and snow zones, percolation zones, and wet (soaked) snow zones vigorous. The upper snow pack is damp throughout the summer are present in the Antarctic continent. season. At the lower elevations of the wet snow zone, the As shown in Fig. 4, the strong contrast in radar backscatter seasonal presence of free water may produce ponds and lakes in exists between dry snow zones and percolation zones. This topographical depressions or a slush area. There is strong sea- makes the discrimination between the dry snow zone and the sonal dependence in radar backscatter associated with the melt percolation zone relatively easy and reliable. Differences in occurrence. During summers, the penetration depth of the radar the radar backscatter between the percolation zone and the signal is dramatically reduced to the uppermost 3–4 cm due wet snow zone are also evident. The frozen melt ponds can be to the presence of high liquid-water content in the snow pack. clearly observed in the wet snow zone, as shown in the enlarged The increased absorption of wet snow strongly decreases the inset of Fig. 4. Nevertheless, the overall contrast between the strength of backscatter intensity. Therefore, the damp snow cor- percolation zone and the wet snow zone is not as strong as responds to the darkest tone in radar imagery [5], [9], [10]. Dur- the contrast between the dry snow zone and the percolation ing winters, the frozen wet snow zone is characterized by denser zone. As a consequence, the discrimination of the wet snow and more compacted firn than in the percolation zone. Ice zone from the percolation zone is expected to be more difficult pipes and lenses are less effective backscatters, because of the and less accurate. The superimposed bare ice zone in Benson’s reduced transmission of the radar signal into the higher-density model is absent in Antarctica due to its relatively high latitude. firn. The wet snow zone is of intermediate brightness in the win- Nevertheless, blue ice patches [15] exposed by sublimation and ter radar imagery due to reduced backscattering [5], [9]–[11]. katabatic wind scouring can be observed in both percolation In the superimposed bare ice zone, snow and firn cover zones and dry snow zones as isolated islands. In general, the has ablated away. Due to intensive melting, the ice layers in radar-backscatter strength of blue ice patches is lower than wet LIU et al.: DELINEATION OF SNOW ZONES IN ANTARCTICA USING MICROWAVE OBSERVATIONS FROM SPACE 2155

Fig. 5. Histograms of radar backscatter created from the training sites in different snow zones on the Antarctic Peninsula. (a) Dry snow zone. (b) Percolation zone. (c) Wet snow zone. (d) Blue ice patch. snow zones and higher than dry snow zones in the winter SAR imagery. The spatial extents of blue ice patches are relatively small, and their textures are characterized by ripples, cusps, and stripes caused by katabatic winds and compressive ice flows. To derive a quantitative signature for each type of snow zone, we identified a number of training sites, respectively, for dry snow zones, percolation zones, wet snow zones, and blue ice patches in different parts of Antarctica. The selection of training sites is based on visual interpretation and also referenced to previous studies [6]. Based on the training data sets, the statisti- cal characteristics of radar backscatter are analyzed for each Fig. 6. Data-processing procedure for automated extraction of snow zones type of snow zone. Fig. 5 shows the frequency distributions from SAR imagery. of the radar backscatter in decibels for different snow zones on the Antarctic Peninsula. For the training data sites, the D. Automated Algorithms for Snow-Zone Extraction average normalized backscatter coefficient (sigma naught) and We designed a chain of image-processing steps to automate the standard deviation are computed (Fig. 5). the extraction of snow-zone boundaries from the SAR imagery The radar backscattering in mountainous areas is not only based on the image segmentation. The key processing steps influenced by the physical properties of the snow pack but also include: image segmentation with a competing region growing influenced by the topographical effect. The relief-induced ra- and merging algorithm; region classification based on the diometric distortions complicate the recognition of snow zones. backscattering and texture properties; and postclassification The areas with a steep slope facing the radar sensor appear correction based on higher level knowledge about size and as bright strips in radar images due to the small local inci- adjacency relationships between snow zones. For the sake of dence angle and the radiometric compression brought about by clarity, a flow chart is given in Fig. 6 to outline our algorithms foreshortening or layover effects. Mountainous areas affected and processing chain. We have implemented the algorithms by foreshortening and layover tend to have high radar returns using the C++ programming language. A working example is similar to percolation zones. The areas with a steep slope facing shown in Fig. 7 to demonstrate the major processing steps. away from the SAR sensor have very low radar returns and First, the SAR image is clipped by the Antarctic coastline form radar shadows. The radar shadows appear as dark as dry [14] to mask out the ocean water portion. Then, we utilize an snow zones in a radar image. In terms of radar-backscattering anisotropic diffusion algorithm [16] to filter and suppress radar strength, it is difficult to differentiate radar-foreshortening af- speckle [Fig. 7(b)]. Next, the SAR imagery is down sampled fected areas from percolation zones and radar shadows from from 25- to 100-m pixel size in order to reduce data volume dry snow zones. Nevertheless, both radar-foreshortening and and subsequent computation. shadow affected areas have a relatively smaller spatial extent 1) Image Segmentation With a Competing Region Growing and a coarser texture, compared with percolation zones and and Merging Algorithm: We developed a competing region dry snow zones. It should be noted that foreshortening compli- growing and merging algorithm to segment the filtered SAR cations were minimized in the 1997 Antarctic Radarsat SAR image into a set of relatively homogeneous regions [Fig. 7(c)]. image mosaic by using a digital elevation model (DEM) to The objective of the segmentation is to partition the entire perform terrain correction and orthorectification. Layover and image R under processing into n regions {Ri : i =1, 2,...,n} shadowing effects were minimized by replacing affected pixels so that with data from different beams and with data from the opposite- looking direction [13], [14]. Residual artifacts remain partly n ∪ Ri = R, (Ri is a connected region,i=1, 2,...,n) (1) because of the quality of the data used to construct the DEM i=1 for mountainous regions. Ri ∩Rj = φ, if i=j (φ is the null set). (2) 2156 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 44, NO. 8, AUGUST 2006

Fig. 7. Classification and postclassification steps for extracting snow zones. (a) Original SAR image. (b) After filtering and clipping. (c) Segmented image. (d) Initially classified image. (e) Postclassification correction after applying rule 1). (f) Postclassification correction after applying rules 2)–5). (g) After mathematical morphology operations. (h) Final snow-zone boundaries. LIU et al.: DELINEATION OF SNOW ZONES IN ANTARCTICA USING MICROWAVE OBSERVATIONS FROM SPACE 2157

Condition (1) requires that the segmentation must be com- regions whose mean brightness-value difference is less than the plete, and condition (2) indicates that the regions must be threshold T . A region adjacency graph (RAG) data structure disjoint and not overlap each other. Our competing region [17] is utilized to realize this competing merging scheme. growing procedure groups individual pixels into image regions. The competing region growing and merging algorithm grows It starts with a set of regularly distributed seed points. The and merges regions in a least cost (minimum-difference) di- spacing between these seed points is adjustable. In our analysis, rection. Therefore, it ensures that the boundaries and shapes seed points are deployed at ten-pixel intervals both in row of the resulting regions closely follow the natural margins of and column directions. These seed points can be considered as various snow zones [Fig. 7(c)]. Our experiments show that one-pixel regions Ri (i =1, 2,...,n). We grow the territory this competing region growing and merging algorithm over- of these regions by appending their neighbor pixels according comes the segmentation biases and artifacts that often come to a competition rule. For each one-pixel region, we examine with conventional segmentation methods like merge, split, and the radar brightness differences between the seed point and merge-and-split algorithms [18], which use an arbitrary or fixed its surrounding neighbors. The minimum difference between growing and merging order. the seed point and its neighbors is recorded. We first grow 2) Region Classification Based on Radar Backscatter and the one-pixel region that has the smallest radar brightness Texture Properties: The image regions derived from the seg- difference from its neighbors. After a region is expanded, the mentation process are subsequently classified into six classes: radar brightness differences between the edge pixels of the dry snow zones, percolation zones, wet snow zones, blue ice region and the neighbors of the edge pixels are calculated, patches, radar-shadow affected areas, and radar-foreshortening and the minimum difference is recorded for the region. For affected areas [Fig. 7(d)]. each region, we keep track of the minimum radar brightness In terms of radar-backscattering property, considerable clas- difference (di) between its edge pixels and their neighboring sification ambiguity exists between dry snow zones and radar pixels during the competing growing process shadows, between percolation zones and radar-foreshortening affected areas, and between blue ice patches and dry snow l zones or wet snow zones. To increase the separability between di = min min Ek − Nk ,k=1, 2,...,m (3) k l these features, an image texture measure is introduced in the classification. The texture measure used in our analysis is the contrast index C based on the gray-level co-occurrence matrix where Ek is the radar brightness value of the kth edge pixel of l (GLCM) computed from a 7 × 7 window [19], [20] region i, Nk is the radar brightness value of the lth neighbor of the edge pixel k, and m is the number of edge pixels of N−1 N−1 region i. If the region i is expanded, its di value is updated. C = |i − j|P (i, j) (4) Among all regions Ri (i =1, 2,...,n), we select the region i=0 j=0 Rp for next round of expansion, if dp ≤ di (i =1, 2,...,p− 1,p+1,...,n). Repeat the above competing growing process where N is the number of gray levels (brightness) within until all candidate neighbor pixels have been appended into the the window; i, j are different gray levels (radar brightness image regions. values); and P (i, j) represents the relative frequency (the joint Since the selection of spatial spacing of seed points is quite probability) that a pair of co-occurring pixels with brightness arbitrary, the above growing process may create too many values i and j appears in the window in separation of one regions. In the competition process, some seed pixels may not pixel in 0◦,45◦,90◦, and 135◦ directions. The contrast index get a chance to germinate and develop, resulting in some small C is relatively high for radar-foreshortening and radar-shadow regions. To improve the segmentation result, a region merging affected areas, medium for blue ice patches, and relatively low step follows the competing growing process. For two adjacent for percolation zones, wet snow zones, and dry snow zones. regions Ri and Rj, if the difference of their mean radar bright- The classification is based on two feature variables: the mean ness values (Mi and Mj) are smaller than an appropriately radar brightness value and the median value of contrast index selected threshold value T , namely, if |Mi − Mj|

Fig. 10. Snow-melt pattern for the austral summer during 1996–1997. (a) Melt onset date. (b) Melt end date.

Fig. 11. Annual variability in snow-melt extent and duration (cumulative melt days) during 1991–1997 detected with the SSM/I data. LIU et al.: DELINEATION OF SNOW ZONES IN ANTARCTICA USING MICROWAVE OBSERVATIONS FROM SPACE 2161

mechanisms to detect and map snow zones. Passive microwave sensors depend on the daily variations in snow emissivity and brightness temperature, which are induced by the changes in the liquid-water content of the snow pack. Therefore, melt snow zones detected from passive microwave data represent the melt extent at the time of data acquisition. In contrast, active microwave SAR sensors rely more on spatial differences in radar-backscatter strength, which are affected by relatively long-term and cumulative variations of the grain size, density, subsurface ice layers, stratigraphy, and surface roughness of Fig. 12. Snow-zone boundaries derived from the 1997 SAR image mo- the snow pack. Consequently, melt snow zones detected from saic compared with snow-melt extents derived from passive microwave data. SAR imagery, including percolation zones and wet snow zones, (a) The shaded area is the snow-melt extent during the 1996/1997 austral summer. (b) The shaded area is comprised of pixels whose average annual represent the cumulative melt extent over a number of years melt duration in the past six austral summers from 1991 to 1997 is longer than prior to the image acquisition date. three days. Since active microwave SAR imagery has a high spatial SAR-derived melt zone extent and the passive microwave- resolution (25 m), relatively small snow zones like dry snow derived melt extent for the 1996/1997 summer is only 61%. zones on the tops of ice rises and narrow percolation zones The discrepancies suggest that the melt snow zone from SAR on the grounded ice sheet have been detected and mapped imagery does not represent the melt extent for the year when the (see inset maps of Fig. 8). Even though the SSM/I data were × SAR imagery was acquired. Notably, no surface melting was resampledinto25km 25 km pixels for the 19-GHz channel, observed from the passive SSM/I data in many small ice shelves the natural resolution (the effective ﬁeld of view) is 70 km along the coast of Queen Maud Land. However, the percolation along track and 45 km cross track. The brightness temperature of each data pixel represents the average snow condition over zones were detected in these regions from the 1997 winter SAR 2 imagery. An extensive percolation zone is delineated from the approximately 3150 km . Consequently, individual snow zones SAR imagery on the Ross ice shelf, where only brief melting for smaller than this size cannot be reliably detected. Many small- two small areas was detected by the passive microwave sensor. size dry and percolation zones that are discernable on the SAR In addition, the effect of the short-lived melting in the rim of image mosaic were missed by the SSM/I data. the Ronne–Filchner ice shelf in the 1996/1997 austral summer The comparative advantage of the passive microwave sensor detected from passive microwave SSM/I data does not show up lies in its high temporal resolution. The daily observations of on the 1997 winter SAR imagery. This is likely because the brightness temperatures enable the determination of the melt melting is not intensive enough to develop the ice pipes and onset date, melt end date, and the length of melt duration. ice lenses, or because new snowfall after the summer precluded The timing of melt occurrence cannot be derived from a one- the melting effect from being detected by the SAR sensor. It time snapshot of active microwave radar imagery. The spa- is known that the Ronne–Filchner ice shelf has a much higher tial progression of melting, spatial variation in melt duration, snow-accumulation rate than the Ross ice shelf [23]. and interannual variability in the extent of melt snow zones As shown in Fig. 12(b), the averaged snow-melt extent dur- can be detected and monitored with the passive microwave ing 1991–1997 derived from the passive microwave SSM/I data data (Fig. 11). has a much better correspondence with that derived from the The wet snow zone delineated from the 1997 winter SAR 1997 winter SAR image mosaic. The shaded area in Fig. 12(b) image mosaic represents the areas where the most intensive represents the cumulative melt area with averaged annual melt surface melting occurred in the Antarctic continent. By cor- duration of at least three days during the austral summers relating the surface-melt information derived from the passive from 1991 to 1997. The matching rate between the snow-melt microwave data, we determined that the average annual melt extent derived from the 1997 SAR image and the averaged duration in the wet snow zone is 80 days (with a standard snow-melt extent during 1991–1997 detected by the passive deviation of 12 days) during 1991–1997, 23 days longer than microwave data is 93%. The greatly improved match suggests the adjacent percolation zone on the Antarctic Peninsula. The that the snow-melt zone derived from SAR imagery represents annual melt duration of 80 days may be regarded as a threshold a composite signal of intermittent melt events summed over to recognize the wet snow zone with the passive microwave many years. In areas with low accumulation (less than the data. The differentiation between the percolation zone and the radar penetration depth) in intermittent melt years, the radar- wet snow zone are valuable for investigating the mass balance backscatter signal may still be strong even though the last and stability of the glacial system. In the percolation zone, re- melt event may have occurred years ago. In the case of the distribution of melt water is limited horizontally and vertically Ross ice shelf, a short period but spatially extensive melting near the surface, and melt processes yield no mass loss to the was detected during the 1991/1992 austral summer, and no ice sheet and ice shelves. In contrast, melt water produced in the substantial surface melting was detected afterwards until 1997 wet snow zone can be potentially discharged from the ice sheet (Fig. 11). The melting effect was still detected by the SAR to the sea, resulting in a net loss of mass. In the Antarctic con- sensor after six years, and hence, an extensive percolation zone tinent, the detected wet snow zone was located in the northern was delineated. This is attributable to the extremely low snow part of the Larsen ice shelf on the Antarctic Peninsula. Most of accumulation on the Ross ice shelf [23]. this wet snow zone catastrophically disintegrated in 2002 [24]. The above observation can be explained by the fact that This suggests that the wet snow zone is the most vulnerable part active and passive microwave sensors rely on different physical in the Antarctic glacial system. Scambos et al. [25] suggested 2162 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 44, NO. 8, AUGUST 2006 that mysterious break-ups of this wet snow zone were correlated EASE-Grid brightness-temperature data for this research with intensive melting and the ponded melt water. project.

V. C ONCLUSION REFERENCES In this paper, we presented numerical algorithms for delin- [1] C. S. Benson, “Stratigraphic studies in the snow and firn of the Greenland eating snow zones with active microwave SAR image data. ice sheet,” U.S. Army Corps Eng., Hannover, NH, SIPRE Res. Rep. 70, 1962. A segmentation-based classification method is developed to [2] W. S. B. Paterson, The Physics of Glaciers, 3rd ed. New York: Perga- extract snow zones from the SAR imagery. The innovative mon, 1994. utilization of the higher level knowledge about the size and [3] K. Steffen, “Surface energy exchange at the equilibrium line on the adjacency properties of snow zones in the postclassification Greenland ice sheet during onset of melt,” Ann. Glaciol., vol. 21, no. 1, pp. 13–18, 1995. processing greatly reduces classification errors and minimizes [4] R. S. J. Williams, D. K. Hall, and C. S. Benson, “Analysis of glacier facies human editing effort. The algorithm used with the passive using satellite techniques,” J. Glaciol., vol. 37, no. 125, pp. 120–128, microwave data is based on multiscale wavelet transform of 1991. [5] K. C. Partington, “Discrimination of glacier facies using multi-temporal daily brightness temperatures. The successful delineation of SAR data,” J. Glaciol., vol. 44, no. 146, pp. 42–53, 1998. snow zones over the entire Antarctic continent demonstrates [6] F. Rau and M. Braun, “The regional distribution of the dry-snow zone ◦ that our algorithms are effective and efficient. on the Antarctic Pensinula north of 70 S,” Ann. Glaciol., vol. 34, no. 1, pp. 95–100, Jan. 2002. Our analysis shows that surface melting in the Antarctic [7] W. Abdalati and K. Steffen, “Greenland ice sheet melt extent: continent is relatively sparse and short lived. While the whole 1979–1999,” J. Geophys. Res., vol. 106, no. D24, pp. 33 983–33 989, sequence of snow zones has been identified for the Green- 2001. [8] M. Joshi, C. J. Merry, K. C. Jezek, and J. F. Bolzan, “An edge detec- land ice sheet [1], [11], the superimposed bare ice zone is tion technique to estimate melt duration, season and melt extent on the absent in Antarctica due to its high latitude and less-intensive Greenland ice sheet using passive microwave data,” Geophys. Res. Lett., surface melting. The dry snow zone dominates the Antarctic vol. 28, no. 18, pp. 3497–3500, 2001. continent, and melt snow zones account for only 10.2% of its [9] K. C. Jezek, M. R. Drinkwater, J. P. Crawford, R. Bindschadler, and R. Kwok, “Analysis of synthetic aperture radar data collected over south- snow surface. western Greenland ice sheet,” J. Glaciol., vol. 39, no. 131, pp. 119–132, We demonstrate that both active and passive satellite mi- 1993. crowave sensors are useful for the discrimination of the melt [10] K. C. Jezek, P. Gogineni, and M. Shanableh, “Radar measurements of melt zones on the Greenland ice sheet,” Geophys. Res. Lett., vol. 21, no. 1, snow zone from the dry snow zone. The active microwave pp. 33–36, 1994. SAR image data have advantages in spatial resolution (25 m), [11] M. A. Fahnestock, R. Bindschadler, R. Kwok, and K. C. Jezek, “Green- but provide less-frequent coverage. In contrast, the passive land ice sheet surface properties and ice dynamics from ERS-1 SAR imagery,” Science, vol. 262, no. 5139, pp. 1530–1534, 1993. microwave satellite data span more than 25 years and provide [12] H. Liu, L. Wang, and K. Jezek, “Wavelet-transform based edge detection temporally frequent observations on a daily basis, but the spatial approach to derivation of snowmelt onset, end and duration from satellite resolution is coarse (25 km). Passive microwave data can only passive microwave measurements,” Int. J. Remote Sens., vol. 26, no. 21, provide a coarse discrimination of melt snow zones from dry pp. 4639–4660, 2005. [13] K. C. Jezek, “Radarsat-1 Antarctic mapping project: Change detection and snow zones. The active microwave SAR imagery supports surface velocity campaign,” Ann. Glaciol., vol. 34, no. 1, pp. 263–268, further differentiation between the percolation zone and the wet Jan. 2002. snow zone within a melt snow zone. The blue ice patches and [14] H. Liu and K. C. Jezek, “A complete high-resolution coastline of Antarc- tica extracted from orthorectified Radarsat SAR imagery,” Photogramm. fields can be also reliably delimited from the SAR imagery. Eng. Remote Sens., vol. 70, no. 5, pp. 605–616, 2004. Passive microwave data offer a strong capability in determining [15] R. S. Williams, T. K. Meunier, and J. G. Ferrigno, “Blue ice, meteor- the timing (onset date and end date) and duration of melt occur- ites, and satellite imagery in Antarctica,” Polar Rec., vol. 22, no. 134, pp. 493–496, 1983. rences, which cannot be derived from active microwave radar [16] H. Liu and K. Jezek, “Automated extraction of coastline from a satellite imagery. Due to the complementary nature, the combination of imagery by integrating Canny edge detection and locally adaptive thresh- active and passive microwave data provides a better view of the olding methods,” Int. J. Remote Sens., vol. 25, no. 5, pp. 937–958, 2004. [17] M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis and properties of the snow pack in different snow zones. Machine Vision, 2nd ed. Pacific Grove, CA: Brooks/Cole, 1999. It should be emphasized that the melt snow zones derived [18] R. C. Gonzalez and R. Wintz, Digital Image Processing. Reading, MA: from active and passive microwave data need to be interpreted Addison-Wesley, 1987. differently. Our analysis suggests that the melt snow zone ex- [19] R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textual features for image classification,” IEEE Trans. Syst., Man, Cybern., vol. SMC-3, no. 6, tracted from active SAR imagery does not necessarily coincide pp. 610–621, Nov. 1973. with the snow-melt extent for the year when the satellite SAR [20] J. R. Parker, Algorithms for Image Processing and Computer Vision. imagery was acquired. Instead, it represents the snow-melt New York: Wiley, 1997. [21] H. J. Zwally and P. Gloersen, “Passive microwave images of polar extent integrated over many years prior to the SAR image regions and research applications,” Polar Rec., vol. 18, no. 116, pp. 431– acquisition. In contrast, melt snow zones delineated from the 450, 1977. passive microwave data at a much coarser spatial resolution [22] F. T. Ulaby, R. K. Moore, and A. Fung, Microwave Remote Sensing: Active and Passive, vol. III. Norwood, MA: Artech House, 1986. indicate snow-melt extent directly corresponding to the acqui- [23] M. B. Giovinetto and H. J. Zwally, “Spatial distribution of net surface sition date of the passive microwave data. accumulation on Antarctic ice sheet,” Ann. Glaciol., vol. 31, no. 1, pp. 171–178, Jan. 2000. [24] D. R. MacAyeal, T. A. Scambos, C. L. Hulbe, and M. A. Fahnestock, “Catastrophic ice-shelf break-up by an ice-shelf-fragment-capsize mech- ACKNOWLEDGMENT anism,” J. Glaciol., vol. 49, no. 164, pp. 22–36, 2003. [25] T. A. Scambos, C. Hulbe, M. Fahnestock, and J. Bohlander, “The link The authors would like to thank the National Snow between climate warming and break-up of ice shelves in the Antarctic and Ice Data Center (Boulder, CO) for providing the SSM/I Peninsula,” J. Glaciol., vol. 46, no. 154, pp. 516–530, 2000. LIU et al.: DELINEATION OF SNOW ZONES IN ANTARCTICA USING MICROWAVE OBSERVATIONS FROM SPACE 2163

Hongxing Liu (M’02–A’02–M’04) received the Kenneth C. Jezek (A’92) received the Ph.D. degree Ph.D. degree in geography from The Ohio State in geophysics from the University of Wisconsin, University, Columbus, in 1999. Madison, in 1980. He is currently an Associate Professor in the De- He was the Director of the Byrd Polar Research partment of Geography, Texas A&M University, Col- Center, The Ohio State University, Columbus, from lege Station. His research interests include remote 1989 to 1999. He is currently a Professor at the sensing, geographical information science, hydro- Byrd Polar Research Center and in the Department of logical modeling, terrain analysis, coastal mapping, Geology, The Ohio State University. Now, he leads and change studies. His recent research projects have a research team studying the Earth’s polar regions been funded by the National Aeronautics and Space using satellite remote sensing techniques. Administration, the National Science Foundation, and the National Oceanographic and Atmospheric Administration Sea Grant Program. Dr. Liu has received a number of prestigious awards, including the NASA Group Achievement Award, the Best Paper Award from Computers and Geo- science, and the Distinguished Achievement Award in Teaching from the College of Geoscience at Texas A&M University.

Lei Wang was born in Lan Zhou, China, on October 3, 1974. He received the B.S. degree in geography from Beijing University, Beijing, China, in 1997, and the M.S. degree in cartography and geographic information system from the Institute of Remote Sensing Applications of the Chinese Academy of Sciences, Beijing, in 2000. He is currently working toward the Ph.D. degree at Texas A&M University, College Station. His Ph.D. dissertation research has been supported by a National Aeronautics and Space Administration Earth System Science Fellowship. His research interests include remote sensing, image analysis and processing, geographic information science, and hydrological modeling.