Height variation detection in polar regions from ICESat satellite altimetry

Alberti, Mauro; Biscaro, Debbie

doi:10.1016/j.cageo.2009.07.002

Cited by 12 publications

(9 citation statements)

References 9 publications

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“…The Surface Elevation Reconstruction and Change (SERAC) method [171] combines elements of RT and XO [99]. Alberti and Biscaro [172] summarize the ICESat orbital parameters in detail and describe a flexible algorithm for determining RT and XO points from them.…”

Section: Laser Altimetry Sensors Methods and Datasetsmentioning

confidence: 99%

Satellite Remote Sensing of the Greenland Ice Sheet Ablation Zone: A Review

Cooper

Smith

2019

Remote Sensing

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The Greenland Ice Sheet is now the largest land ice contributor to global sea level rise, largely driven by increased surface meltwater runoff from the ablation zone, i.e., areas of the ice sheet where annual mass losses exceed gains. This small but critically important area of the ice sheet has expanded in size by~50% since the early 1960s, and satellite remote sensing is a powerful tool for monitoring the physical processes that influence its surface mass balance. This review synthesizes key remote sensing methods and scientific findings from satellite remote sensing of the Greenland Ice Sheet ablation zone, covering progress in (1) radar altimetry, (2) laser (lidar) altimetry, (3) gravimetry, (4) multispectral optical imagery, and (5) microwave and thermal imagery. Physical characteristics and quantities examined include surface elevation change, gravimetric mass balance, reflectance, albedo, and mapping of surface melt extent and glaciological facies and zones. The review concludes that future progress will benefit most from methods that combine multi-sensor, multi-wavelength, and cross-platform datasets designed to discriminate the widely varying surface processes in the ablation zone. Specific examples include fusing laser altimetry, radar altimetry, and optical stereophotogrammetry to enhance spatial measurement density, cross-validate surface elevation change, and diagnose radar elevation bias; employing dual-frequency radar, microwave scatterometry, or combining radar and laser altimetry to map seasonal snow depth; fusing optical imagery, radar imagery, and microwave scatterometry to discriminate between snow, liquid water, refrozen meltwater, and bare ice near the equilibrium line altitude; combining optical reflectance with laser altimetry to map supraglacial lake, stream, and crevasse bathymetry; and monitoring the inland migration of snowlines, surface melt extent, and supraglacial hydrologic features.

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Section: Laser Altimetry Sensors Methods and Datasetsmentioning

confidence: 99%

Satellite Remote Sensing of the Greenland Ice Sheet Ablation Zone: A Review

Cooper

Smith

2019

Remote Sensing

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show abstract

“…The Surface Elevation Reconstruction and Change (SERAC) method [163] combines elements of RT and XO [99]. Alberti and Biscaro [164] summarize the ICESat orbital parameters in detail and describe a flexible algorithm for determining RT and XO points from them. The XO method calculates / at the ascending and descending orbital crossover points.…”

Section: Laser Altimetry Sensors Methods and Datasetsmentioning

confidence: 99%

Satellite Remote Sensing of the Greenland Ice Sheet Ablation Zone: A Review

Cooper¹,

Smith²

2019

Preprint

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The Greenland Ice Sheet is now the largest land ice contributor to global sea level rise, largely driven by increased surface meltwater runoff from the ablation zone, i.e. areas of the ice sheet where annual mass losses exceed gains. This small but critically important area of the ice sheet has expanded in size by ~50% since the early 1960s, and satellite remote sensing is a powerful tool for monitoring the physical processes that influence its surface mass balance. This review synthesizes key remote sensing methods and scientific findings from satellite remote sensing of the Greenland Ice Sheet ablation zone, covering progress in 1) radar altimetry, 2) laser (lidar) altimetry, 3) gravimetry, 4) multispectral optical imagery and, 5) microwave and thermal imagery. Physical characteristics and quantities examined include surface elevation change, gravimetric mass balance, reflectance, albedo, and mapping of surface melt extent and glaciological facies and zones. The review concludes that future progress will benefit most from methods that combine multi-sensor, multi-wavelength, and cross-platform datasets designed to discriminate the widely varying surface processes in the ablation zone. Specific examples include fusing laser altimetry, radar altimetry, and optical stereophotogrammetry to enhance spatial measurement density, cross-validate surface elevation change, and diagnose radar elevation bias; fusing optical imagery, radar imagery, and microwave scatterometry to discriminate between snow, liquid water, refrozen meltwater, and bare ice near the equilibrium line altitude; combining optical reflectance with laser altimetry to map supraglacial lake, stream, and crevasse bathymetry; and monitoring the inland migration of snowlines, surface melt extent, and supraglacial hydrologic features.

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“…In contrast, LiDAR sensors can directly acquire the surface height information by measuring the time of the transmitted and received pulses [15,16]. Its high-frequency sampling makes great promises of high vertical accuracy [17][18][19][20][21][22]. Therefore, the space-borne LiDAR has become the most promising technique for accurately measuring the terrain characteristics on a global scale [21,22].…”

Section: Introductionmentioning

confidence: 99%

Application and Validation of a Model for Terrain Slope Estimation Using Space-Borne LiDAR Waveform Data

et al. 2018

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The terrain slope is one of the most important surface characteristics for quantifying the Earth surface processes. Space-borne LiDAR sensors have produced high-accuracy and large-area terrain measurement within the footprint. However, rigorous procedures are required to accurately estimate the terrain slope especially within the large footprint since the estimated slope is likely affected by footprint size, shape, orientation, and terrain aspect. Therefore, based on multiple available datasets, we explored the performance of a proposed terrain slope estimation model over several study sites and various footprint shapes. The terrain slopes were derived from the ICESAT/GLAS waveform data by the proposed method and five other methods in this study. Compared with five other methods, the proposed method considered the influence of footprint shape, orientation, and terrain aspect on the terrain slope estimation. Validation against the airborne LiDAR measurements showed that the proposed method performed better than five other methods (R2 = 0.829, increased by ~0.07, RMSE = 3.596°, reduced by ~0.6°, n = 858). In addition, more statistics indicated that the proposed method significantly improved the terrain slope estimation accuracy in high-relief region (RMSE = 5.180°, reduced by ~1.8°, n = 218) or in the footprint with a great eccentricity (RMSE = 3.421°, reduced by ~1.1°, n = 313). Therefore, from these experiments, we concluded that this terrain slope estimation approach was beneficial for different terrains and various footprint shapes in practice and the improvement of estimated accuracy was distinctly related with the terrain slope and footprint eccentricity.

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Height variation detection in polar regions from ICESat satellite altimetry

Cited by 12 publications

References 9 publications

Satellite Remote Sensing of the Greenland Ice Sheet Ablation Zone: A Review

Satellite Remote Sensing of the Greenland Ice Sheet Ablation Zone: A Review

Satellite Remote Sensing of the Greenland Ice Sheet Ablation Zone: A Review

Application and Validation of a Model for Terrain Slope Estimation Using Space-Borne LiDAR Waveform Data

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