Land ice height-retrieval algorithm for NASA's ICESat-2 photon-counting laser altimeter

Smith, B. E.; Fricker, H. A.; Holschuh, Nicholas; Gardner, Alex; Adusumilli, Susheel; Brunt, Kelly M.; Csathó, Beáta; Harbeck, Kaitlin; Huth, Alex; Neumann, Thomas; Nilsson, Johan; Siegfried, Matthew R.

doi:10.1016/j.rse.2019.111352

Cited by 147 publications

(149 citation statements)

References 14 publications

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“…The surface roughness of polar ice sheets is primarily a result of ice dynamics and surface-atmosphere interactions on varying temporal and spatial scales. In general, ice flow over rugged bedrock topography causes roughness features that can extend from several hundreds of kilometers to a few kilometers depending on ice thickness, flow speed, and basal conditions (e.g., Smith et al, 2006, and references therein). These large-scale variations in ice surface topography caused by ice dynamics are not the topic of this analysis.…”

Section: Introductionmentioning

confidence: 99%

Temporal and spatial variability in surface roughness and accumulation rate around 88° S from repeat airborne geophysical surveys

et al. 2020

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Abstract. We use repeat high-resolution airborne geophysical data consisting of laser altimetry, snow, and Ku-band radar and optical imagery acquired in 2014, 2016, and 2017 to analyze the spatial and temporal variability in surface roughness, slope, wind deposition, and snow accumulation at 88∘ S, an elevation bias validation site for ICESat-2 and potential validation site for CryoSat-2. We find significant small-scale variability (<10 km) in snow accumulation based on the snow radar subsurface stratigraphy, indicating areas of strong wind redistribution are prevalent at 88∘ S. In general, highs in snow accumulation rate correspond with topographic lows, resulting in a negative correlation coefficient of r2=-0.32 between accumulation rate and MSWD (mean slope in the mean wind direction). This relationship is strongest in areas where the dominant wind direction is parallel to the survey profile, which is expected as the geophysical surveys only capture a two-dimensional cross section of snow redistribution. Variability in snow accumulation appears to correlate with variability in MSWD. The correlation coefficient between the standard deviations of accumulation rate and MSWD is r2=0.48, indicating a stronger link between the standard deviations than the actual parameters. Our analysis shows that there is no simple relationship between surface slope, wind direction, and snow accumulation rates for the overall survey area. We find high variability in surface roughness derived from laser altimetry measurements on length scales smaller than 10 km, sometimes with very distinct and sharp transitions. Some areas also show significant temporal variability over the course of the 3 survey years. Ultimately, there is no statistically significant slope-independent relationship between surface roughness and accumulation rates within our survey area. The observed correspondence between the small-scale temporal and spatial variability in surface roughness and backscatter, as evidenced by Ku-band radar signal strength retrievals, will make it difficult to develop elevation bias corrections for radar altimeter retrieval algorithms.

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Section: Introductionmentioning

confidence: 99%

Temporal and spatial variability in surface roughness and accumulation rate around 88° S from repeat airborne geophysical surveys

et al. 2020

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“…Hence, to eliminate the influence of single reference data on the validation results, high-precision altimetry data points from Ice, Cloud, and and Elevation Satellite-2 or ICESat-2 were adopted to further verify the accuracy of the derived DEM. These data are accurate enough to be used as ground control points (GCPs) for assessing the InSAR DEMs [24][25][26][27]. The blue dots in Figure 2 (3784 GCPs) and Figure 7 (7269 GCPs) are the ATL08 land data products collected by ICESat-2.…”

Section: Reference Elevation Datamentioning

confidence: 99%

A Framework for Correcting Ionospheric Artifacts and Atmospheric Effects to Generate High Accuracy InSAR DEM

Liu

Zhou

et al. 2020

Remote Sensing

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Repeat-pass interferometric synthetic aperture radar is a well-established technology for generating digital elevation models (DEMs). However, the interferogram usually has ionospheric and atmospheric effects, which reduces the DEM accuracy. In this paper, by introducing dual-polarization interferograms, a new approach is proposed to mitigate the ionospheric and atmospheric errors of the interferometric synthetic aperture radar (InSAR) data. The proposed method consists of two parts. First, the range split-spectrum method is applied to compensate for the ionospheric artifacts. Then, a multiresolution correlation analysis between dual-polarization InSAR interferograms is employed to remove the identical atmospheric phases, since the atmospheric delay is independent of SAR polarizations. The corrected interferogram can be used for DEM extraction. Validation experiments, using the ALOS-1 PALSAR interferometric pairs covering the study areas in Hawaii and Lebanon of the U.S.A., show that the proposed method can effectively reduce the ionospheric artifacts and atmospheric effects, and improve the accuracy of the InSAR-derived DEMs by 64.9% and 31.7% for the study sites in Hawaii and Lebanon of the U.S.A., respectively, compared with traditional correction methods. In addition, the assessment of the resulting DEMs also includes comparisons with the high-precision Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) altimetry data. The results show that the selection of reference data will not affect the validation results.

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“…The ATL06 elevation measurements are derived from the individual photon elevation observations, averaged over a 40 m length segment. The segments overlap by 50% along each of the six ground tracks, thus the ATL06 elevation measurements are separated by 20 m along each ground track (Smith et al 2019). Five cycles exist during the study period but only cycles 3, 4 and 5 are exact repeat cycles.…”

Section: Icesat-datamentioning

confidence: 99%

Mapping the Antarctic Grounding Zone from ICESat-2 Laser Altimetry

Dawson

Chuter

et al. 2020

Preprint

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Abstract. We present the results of mapping the grounding zone of the Antarctic Ice Sheet using laser altimetry from the ICESat-2 satellite, based on a combination of repeat track data and crossover analysis of ascending and descending tracks. We use a new automated method to provide estimates for both the landward limit of ice flexure and the inshore limit of hydrostatic equilibrium by detecting the ocean tidal signals, and is demonstrated on the Larsen C Ice Shelf in the Antarctic Peninsula. The results show a 2 times increase in number of observations of the grounding line location compared to ICESat-1, as well as improved precision. The mean absolute separation and the standard deviation between our ICESat-2 derived grounding line and DInSAR grounding line product are 0.29 km and 0.31 km, respectively. The beam pair structure of ICESat-2 enables us to derive the grounding zone features from a minimum of two repeat cycles. Our results demonstrate that ICESat-2 can provide high precision and density observations of grounding line in both space and time. This new method can improve the efficiency of grounding zone calculation and can be applied to other regions of the Antarctic Ice Sheet.

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Land ice height-retrieval algorithm for NASA's ICESat-2 photon-counting laser altimeter

Cited by 147 publications

References 14 publications

Temporal and spatial variability in surface roughness and accumulation rate around 88° S from repeat airborne geophysical surveys

Temporal and spatial variability in surface roughness and accumulation rate around 88° S from repeat airborne geophysical surveys

A Framework for Correcting Ionospheric Artifacts and Atmospheric Effects to Generate High Accuracy InSAR DEM

Mapping the Antarctic Grounding Zone from ICESat-2 Laser Altimetry

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