The ICESat-2 Laser Altimetry Mission

Abdalati, W.; Zwally, H. Jay; Bindschadler, Robert; Csathó, B. M.; Farrell, S. L.; Fricker, H. A.; Harding, David J.; Kwok, R.; Lefsky, M. A.; Markus, T.; Marshak, Alexander; Neumann, Thomas; Palm, Stephen P.; Schutz, B. E.; Smith, Ben; Spinhirne, James D.; Webb, C. E.

doi:10.1109/jproc.2009.2034765

Cited by 371 publications

(219 citation statements)

References 61 publications

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“…NASA's OIB mission began collecting airborne observations of the polar regions in 2009, bridging the gap between NASA's Ice, Cloud, and land Elevation Satellite (ICESat) mission which retired in 2009, and the future ICESat-2 mission (Abdalati et al, 2010) scheduled for launch in 2017. OIB aircraft carry a suite of instruments designed to measure both land and sea ice, including their overlying snow cover.…”

Section: Datamentioning

confidence: 99%

Characterizing Arctic sea ice topography using high-resolution IceBridge data

et al. 2016

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Abstract. We present an analysis of Arctic sea ice topography using high-resolution, three-dimensional surface elevation data from the Airborne Topographic Mapper, flown as part of NASA's Operation IceBridge mission. Surface features in the sea ice cover are detected using a newly developed surface feature picking algorithm. We derive information regarding the height, volume and geometry of surface features from 2009 to 2014 within the Beaufort/Chukchi and Central Arctic regions. The results are delineated by ice type to estimate the topographic variability across first-year and multi-year ice regimes.The results demonstrate that Arctic sea ice topography exhibits significant spatial variability, mainly driven by the increased surface feature height and volume (per unit area) of the multi-year ice that dominates the Central Arctic region. The multi-year ice topography exhibits greater interannual variability compared to the first-year ice regimes, which dominates the total ice topography variability across both regions. The ice topography also shows a clear coastal dependency, with the feature height and volume increasing as a function of proximity to the nearest coastline, especially north of Greenland and the Canadian Archipelago. A strong correlation between ice topography and ice thickness (from the IceBridge sea ice product) is found, using a square-root relationship. The results allude to the importance of ice deformation variability in the total sea ice mass balance, and provide crucial information regarding the tail of the ice thickness distribution across the western Arctic. Future research priorities associated with this new data set are presented and discussed, especially in relation to calculations of atmospheric form drag.

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Section: Datamentioning

confidence: 99%

Characterizing Arctic sea ice topography using high-resolution IceBridge data

et al. 2016

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“…Verification with available OIB data shows that both sea ice thickness and snow depth are retrieved, with the error in both parameters mainly arising from the mismatch between modeled and observed TB values. This algorithm can be applied to the large-scale retrieval of sea ice thickness and snow depth using concurrent L-band satellite remote sensing and satellite altimetry of the sea ice cover such as Abdalati et al (2010).…”

Section: Summary and Discussionmentioning

confidence: 99%

On the retrieval of sea ice thickness and snow depth using concurrent laser altimetry and L-band remote sensing data

Zhou

Liu

et al. 2018

The Cryosphere

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Abstract. The accurate knowledge of sea ice parameters, including sea ice thickness and snow depth over the sea ice cover, is key to both climate studies and data assimilation in operational forecasts. Large-scale active and passive remote sensing is the basis for the estimation of these parameters. In traditional altimetry or the retrieval of snow depth with passive microwave remote sensing, although the sea ice thickness and the snow depth are closely related, the retrieval of one parameter is usually carried out under assumptions over the other. For example, climatological snow depth data or as derived from reanalyses contain large or unconstrained uncertainty, which result in large uncertainty in the derived sea ice thickness and volume. In this study, we explore the potential of combined retrieval of both sea ice thickness and snow depth using the concurrent active altimetry and passive microwave remote sensing of the sea ice cover. Specifically, laser altimetry and L-band passive remote sensing data are combined using two forward models: the L-band radiation model and the isostatic relationship based on buoyancy model. Since the laser altimetry usually features much higher spatial resolution than L-band data from the Soil Moisture Ocean Salinity (SMOS) satellite, there is potentially covariability between the observed snow freeboard by altimetry and the retrieval target of snow depth on the spatial scale of altimetry samples. Statistically significant correlation is discovered based on high-resolution observations from Operation IceBridge (OIB), and with a nonlinear fitting the covariability is incorporated in the retrieval algorithm. By using fitting parameters derived from large-scale surveys, the retrievability is greatly improved compared with the retrieval that assumes flat snow cover (i.e., no covariability). Verifications with OIB data show good match between the observed and the retrieved parameters, including both sea ice thickness and snow depth. With detailed analysis, we show that the error of the retrieval mainly arises from the difference between the modeled and the observed (SMOS) L-band brightness temperature (TB). The narrow swath and the limited coverage of the sea ice cover by altimetry is the potential source of error associated with the modeling of L-band TB and retrieval. The proposed retrieval methodology can be applied to the basin-scale retrieval of sea ice thickness and snow depth, using concurrent passive remote sensing and active laser altimetry based on satellites such as ICESat-2 and WCOM.

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“…Currently, an ICESat2 mission (Abdalati et al 2010) is planned for launch in late 2017. There are currently 6 radar altimetry satellites in operation (http://www.altimetry.info/missions/).…”

Section: Elevation and Volume Changesmentioning

confidence: 99%

Observation-Based Estimates of Global Glacier Mass Change and Its Contribution to Sea-Level Change

Marzeion¹,

Champollion²,

Haeberli³

et al. 2017

Space Sciences Series of ISSI

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Glaciers have strongly contributed to sea-level rise during the past century and will continue to be an important part of the sea-level budget during the twenty-first century. Here, we review the progress in estimating global glacier mass change from in situ measurements of mass and length changes, remote sensing methods, and mass balance modeling driven by climate observations. For the period before the onset of satellite observations, different strategies to overcome the uncertainty associated with monitoring only a small sample of the world's glaciers have been developed. These methods now yield estimates generally reconcilable with each other within their respective uncertainty margins. Whereas this is also the case for the recent decades, the greatly increased number of estimates obtained from remote sensing reveals that gravimetry-based methods typically arrive at lower mass loss estimates than the other methods. We suggest that strategies for better interconnecting the different methods are needed to ensure progress and to increase the temporal and spatial detail of reliable glacier mass change estimates.

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The ICESat-2 Laser Altimetry Mission

Cited by 371 publications

References 61 publications

Characterizing Arctic sea ice topography using high-resolution IceBridge data

Characterizing Arctic sea ice topography using high-resolution IceBridge data

On the retrieval of sea ice thickness and snow depth using concurrent laser altimetry and L-band remote sensing data

Observation-Based Estimates of Global Glacier Mass Change and Its Contribution to Sea-Level Change

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