Analysis and retracking of continental ice sheet radar altimeter waveforms

Martin, Thomas; Zwally, H. Jay; Brenner, Anita C.; Bindschadler, Robert

doi:10.1029/jc088ic03p01608

Cited by 248 publications

(154 citation statements)

References 4 publications

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“…The process has to be applied with the help of the whole waveform [23]. Most of the existing retracking methods deduce the surface height elevation by seeking the point of the leading edge corresponding to the average surface [23][24][25]. Finally, due to the kilometric scale footprint, the small-scale topographic features also affect the waveform shape that in turns via the retracking affects the height restitution so that the final precision is limited [26].…”

Section: Radar Altimetrymentioning

confidence: 99%

See 1 more Smart Citation

Antarctic Ice Sheet and Radar Altimetry: A Review

Rémy

Parouty

2009

Remote Sensing

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Abstract:Altimetry is probably one of the most powerful tools for ice sheet observation. Our vision of the Antarctic ice sheet has been deeply transformed since the launch of the ERS1 satellite in 1991. With the launch of ERS2 and Envisat, the series of altimetric observations now provides 19 years of continuous and homogeneous observations that allow monitoring of the shape and volume of ice sheets. The topography deduced from altimetry is one of the relevant parameters revealing the processes acting on ice sheet. Moreover, altimeter also provides other parameters such as backscatter and waveform shape that give information on the surface roughness or snow pack characteristics.

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Section: Radar Altimetrymentioning

confidence: 99%

“…The on-board estimate is thus inaccurate and a re-estimation, named retracking is needed. The process has to be applied with the help of the whole waveform [23]. Most of the existing retracking methods deduce the surface height elevation by seeking the point of the leading edge corresponding to the average surface [23][24][25].…”

Section: Radar Altimetrymentioning

confidence: 99%

Antarctic Ice Sheet and Radar Altimetry: A Review

Rémy

Parouty

2009

Remote Sensing

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“…ICE-2 retracking algorithm is based on the Brown model (Brown, 1977), fitting the altimetric waveform with an error function (erf) for the leading edge and an exponential decrease for the trailing edge slope and deduce the waveform integration, thus providing the parameter estimates (Legresy et al, 2005). Beta-5 retracker was first proposed by (Martin et al, 1983). It is a five parameter functional model designed to derive geophysical parameters for Brown like waveforms obtained over ocean and large water bodies by trying to fit the acquired waveform to the model curve.…”

Section: Resultsmentioning

confidence: 99%

Inland Water Bodies Monitoring using Satellite Altimetry over Indian Region

Chander

Ganguly

Dubey

et al. 2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Satellite altimetry for inland water applications has evolved from investigation of water height retrieval to monitoring since last two decades. Altimetry derived reservoir/ river levels can subsequently be used to deal with key inland water resources problems such as flood, rating curve generation for remote locations, reservoir operations, and calibration of river/lake models. In this work 29 inland water bodies were selected over Indian region to monitor from satellite altimetry. First cut selection of potential water bodies was based on availability of altimeter tracks and geographic locations. Then feasibility study was carried out to check the potential of availability of in-situ measurement and scope of GPS survey for final selection. An algorithm is proposed and tested for the waterlevel retrieval over the Ukai Reservoir which fulfil all the necessary requirements. The methodology is based on averaged high rate waveforms, modified retracker and range corrections. The results were then validated with the GPS survey and in-situ tide gauge dataset. SARAL derived water-level information for six different retrackers were compared with the in-situ tide-gauge dataset installed close to the Ukai Dam. Averaged high rate waveforms were analysed for better performance, i.e. single 40Hz, and multiple 40-Hz. A field trip was conducted on 17 th January 2014, same day on the SARAL pass, using two Dual frequency GPS instruments. New improved retracker work best with overall RMSE within the range of 8 cm. The results supports that AltiKa dataset can be utilized for more accurate water level information over inland water bodies.

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“…These variables are given in CryoSat-2 L1B data; detailed descriptions and processing methods of the variables are well explained in [25]. ∆R is another correction term derived by various retracking methods [36][37][38][39]. The aim of these terms is to determine the range offset between the mid-point of the range bin and a realistic range point of the leading edge of sea ice.…”

Section: Sea Ice Thickness Estimationmentioning

confidence: 99%

Arctic Sea Ice Thickness Estimation from CryoSat-2 Satellite Data Using Machine Learning-Based Lead Detection

Lee

Kim

et al. 2016

Remote Sensing

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Satellite altimeters have been used to monitor Arctic sea ice thickness since the early 2000s. In order to estimate sea ice thickness from satellite altimeter data, leads (i.e., cracks between ice floes) should first be identified for the calculation of sea ice freeboard. In this study, we proposed novel approaches for lead detection using two machine learning algorithms: decision trees and random forest. CryoSat-2 satellite data collected in March and April of 2011-2014 over the Arctic region were used to extract waveform parameters that show the characteristics of leads, ice floes and ocean, including stack standard deviation, stack skewness, stack kurtosis, pulse peakiness and backscatter sigma-0. The parameters were used to identify leads in the machine learning models. Results show that the proposed approaches, with overall accuracy >90%, produced much better performance than existing lead detection methods based on simple thresholding approaches. Sea ice thickness estimated based on the machine learning-detected leads was compared to the averaged Airborne Electromagnetic (AEM)-bird data collected over two days during the CryoSat Validation experiment (CryoVex) field campaign in April 2011. This comparison showed that the proposed machine learning methods had better performance (up to r = 0.83 and Root Mean Square Error (RMSE) = 0.29 m) compared to thickness estimation based on existing lead detection methods (RMSE = 0.86-0.93 m). Sea ice thickness based on the machine learning approaches showed a consistent decline from 2011-2013 and rebounded in 2014.

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Analysis and retracking of continental ice sheet radar altimeter waveforms

Cited by 248 publications

References 4 publications

Antarctic Ice Sheet and Radar Altimetry: A Review

Antarctic Ice Sheet and Radar Altimetry: A Review

Inland Water Bodies Monitoring using Satellite Altimetry over Indian Region

Arctic Sea Ice Thickness Estimation from CryoSat-2 Satellite Data Using Machine Learning-Based Lead Detection

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