Antarctic Sea-Ice Thickness Retrieval from ICESat: Inter-Comparison of Different Approaches

We present freeboard measurements from airborne laser scanner (ALS), the Airborne Synthetic Aperture and Interferometric Radar Altimeter System (ASIRAS), and CryoSat‐2 SIRAL radar altimeter; ice thickness measurements from both helicopter‐borne and ground‐based electromagnetic‐sounding; and point measurements of ice properties. This case study was carried out in April 2015 during the N‐ICE2015 expedition in the area of the Arctic Ocean north of Svalbard. The region is represented by deep snow up to 1.12 m and a widespread presence of negative freeboards. The main scattering surfaces from both CryoSat‐2 and ASIRAS are shown to be closer to the snow freeboard obtained by ALS than to the ice freeboard measured in situ. This case study documents the complexity of freeboard retrievals from radar altimetry. We show that even under cold (below −15°C) conditions the radar freeboard can be close to the snow freeboard on a regional scale of tens of kilometers. We derived a modal sea‐ice thickness for the study region from CryoSat‐2 of 3.9 m compared to measured total thickness 1.7 m, resulting in an overestimation of sea‐ice thickness on the order of a factor 2. Our results also highlight the importance of year‐to‐year regional scale information about the depth and density of the snowpack, as this influences the sea‐ice freeboard, the radar penetration, and is a key component of the hydrostatic balance equations used to convert radar freeboard to sea‐ice thickness.

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“…The second approach is the “modified ice density” method, also described in Kern et al (), where the modified ice density

ρ_{i} *

can be defined:

ρ_{i} * = \frac{h_{i} ρ_{i} + h_{s} ρ_{s}}{h_{i} + h_{s}}

…”

Section: Methodsmentioning

confidence: 99%

Comparison of Freeboard Retrieval and Ice Thickness Calculation From ALS, ASIRAS, and CryoSat‐2 in the Norwegian Arctic to Field Measurements Made During the N‐ICE2015 Expedition

et al. 2018

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“…Kurtz and Markus (2012) combined ICESat freeboards with in situ density measurements and made the "zero ice freeboard" assumption that the snow depth was equal to the snow freeboard, and thus no independent snow depth measurements were required. Kern et al (2016) compared multiple methods of computing thickness using ICESat freeboard data by calculating snow depths from both AMSR-E and a static but seasonally varying snow-depth-to-thickness ratio. A new one-layer method was developed by Li et al (2018) to compute thickness using ICESat data that built on the static ratio used by Kern et al (2016) and incorporated a dynamic snow depth-to-thickness ratio for every data point.…”

Section: Introductionmentioning

confidence: 99%

“…Kern et al (2016) compared multiple methods of computing thickness using ICESat freeboard data by calculating snow depths from both AMSR-E and a static but seasonally varying snow-depth-to-thickness ratio. A new one-layer method was developed by Li et al (2018) to compute thickness using ICESat data that built on the static ratio used by Kern et al (2016) and incorporated a dynamic snow depth-to-thickness ratio for every data point. As these studies show, a large limitation to calculating Antarctic sea ice thickness from laser altimetry, regardless of the method used, is the uncertainty in the snow depth distribution on sea ice.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of snow freeboard of Antarctic sea ice using waveform fitting of CryoSat-2 returns

Fons

2019

The Cryosphere

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Abstract. In this paper we develop a CryoSat-2 algorithm to retrieve the surface elevation of the air–snow interface over Antarctic sea ice. This algorithm utilizes a two-layer physical model that accounts for scattering from a snow layer atop sea ice as well as scattering from below the snow surface. The model produces waveforms that are fit to CryoSat-2 level 1B data through a bounded trust region least-squares fitting process. These fit waveforms are then used to track the air–snow interface and retrieve the surface elevation at each point along the CryoSat-2 ground track, from which the snow freeboard is computed. To validate this algorithm, we compare retrieved surface elevation measurements and snow surface radar return power levels with those from Operation IceBridge, which flew along a contemporaneous CryoSat-2 orbit in October 2011 and November 2012. Average elevation differences (standard deviations) along the flight lines (IceBridge Airborne Topographic Mapper, ATM – CryoSat-2) are found to be 0.016 cm (29.24 cm) in 2011 and 2.58 cm (26.65 cm) in 2012. The spatial distribution of monthly average pan-Antarctic snow freeboard found using this method is similar to what was observed from NASA's Ice, Cloud, and land Elevation Satellite (ICESat), where the difference (standard deviation) between October 2011–2017 CryoSat-2 mean snow freeboard and spring 2003–2007 mean freeboard from ICESat is 1.92 cm (9.23 cm). While our results suggest that this physical model and waveform fitting method can be used to retrieve snow freeboard from CryoSat-2, allowing for the potential to join laser and radar altimetry data records in the Antarctic, larger (∼30 cm) regional differences from ICESat and along-track differences from ATM do exist, suggesting the need for future improvements to the method. Snow–ice interface elevation retrieval is also explored as a potential to obtain snow depth measurements. However, it is found that this retrieval method often tracks a strong scattering layer within the snow layer instead of the actual snow–ice interface, leading to an overestimation of ice freeboard and an underestimation of snow depth in much of the Southern Ocean but with promising results in areas such as the East Antarctic sector.

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“…Uncertainties in S are estimated via Gaussian error propagation with uncertainties of the regression lines (see [6]) and of F (see [16,20]) as input. The used coefficients are summarized in Table 3.…”

Section: Methodsmentioning

confidence: 99%

Satellite Remote Sensing of Snow Depth on Antarctic Sea Ice: An Inter-Comparison of Two Empirical Approaches

Kern

Özsoy

2016

Remote Sensing

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Snow on Antarctic sea ice plays a key role for sea ice physical processes and complicates retrieval of sea ice thickness using altimetry. Current methods of snow depth retrieval are based on satellite microwave radiometry, which perform best for dry, homogeneous snow packs on level sea ice. We introduce an alternative approach based on in-situ measurements of total (sea ice plus snow) freeboard and snow depth, which we use to compute snow depth on sea ice from Ice, Cloud, and land Elevation Satellite (ICESat) total freeboard observations. We compare ICESat snow depth for early winter and spring of the years 2004 through 2006 with the Advanced Scanning Microwave Radiometer aboard EOS (AMSR-E) snow depth product. We find ICESat snow depths agree more closely with ship-based visual and air-borne snow radar observations than AMSR-E snow depths. We obtain average modal and mean ICESat snow depths, which exceed AMSR-E snow depths by 5-10 cm in winter and 10-15 cm in spring. We observe an increase in ICESat snow depth from winter to spring for most Antarctic regions in accordance with ground-based observations, in contrast to AMSR-E snow depths, which we find to stay constant or to decrease. We suggest satellite laser altimetry as an alternative method to derive snow depth on Antarctic sea ice, which is independent of snow physical properties.

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Antarctic Sea-Ice Thickness Retrieval from ICESat: Inter-Comparison of Different Approaches

Cited by 42 publications

References 58 publications

Comparison of Freeboard Retrieval and Ice Thickness Calculation From ALS, ASIRAS, and CryoSat‐2 in the Norwegian Arctic to Field Measurements Made During the N‐ICE2015 Expedition

Comparison of Freeboard Retrieval and Ice Thickness Calculation From ALS, ASIRAS, and CryoSat‐2 in the Norwegian Arctic to Field Measurements Made During the N‐ICE2015 Expedition

Retrieval of snow freeboard of Antarctic sea ice using waveform fitting of CryoSat-2 returns

Satellite Remote Sensing of Snow Depth on Antarctic Sea Ice: An Inter-Comparison of Two Empirical Approaches

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