The impact of snow depth, snow density and ice density on sea ice thickness retrieval from satellite radar altimetry: results from the ESA-CCI Sea Ice ECV Project Round Robin Exercise

Kern, Stefan; Khvorostovsky, K.; Skourup, Henriette; Rinne, Eero; Parsakhoo, Z. S.; Djepa, V.; Wadhams, Peter; Sandven, Stein

doi:10.5194/tc-9-37-2015

Cited by 75 publications

(72 citation statements)

References 58 publications

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“…This result suggests that unlike for the native Envisat radar freeboard estimates (Kern et al, 2015), the Envisat/PP should allow a realistic seasonal ice growth cycle to be derived.…”

Section: Toward An Improved Envisat Freeboardmentioning

confidence: 98%

“…Following this methodology, sea-ice thickness has been estimated with the pulselimited altimeters RA on board ERS-1 and RA-2 on board ERS-2 and Envisat (Laxon et al, 2003;Giles et al, 2008). While the Envisat ice thickness estimates are promising, the current prototype product (http://icdc.cen.uni-hamburg.de/1/ projekte/esa-cci-sea-ice-ecv0.html) has a positive bias of 0.5 to 1.5 m and does not reproduce accurately the seasonal cycle of ice growth (Kern et al, 2015). It is the goal of the European Space Agency (ESA) Sea Ice Climate Change Initiative project (SI-CCI) to improve RA-2 freeboard retrievals and to provide accurate time series of ice thickness over the Envisat mission lifetime .…”

Section: K Guerreiro Et Al: Comparison Of Cryosat-2 and Envisat Radmentioning

confidence: 99%

“…However, the study by Kern et al (2015) shows that even when using a similar parameterisation of snow depth and ice density as in CryoSat-2 studies (Laxon et al, 2013;Kwok and Cunningham, 2015), the Envisat ice thickness estimates remain relatively inaccurate. In particular, the authors do not succeed in reproducing the seasonal ice growth cycle.…”

Section: K Guerreiro Et Al: Comparison Of Cryosat-2 and Envisat Radmentioning

confidence: 99%

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Comparison of CryoSat-2 and ENVISAT radar freeboard over Arctic sea ice: toward an improved Envisat freeboard retrieval

et al. 2017

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Abstract. Over the past decade, sea-ice freeboard has been monitored with various satellite altimetric missions with the aim of producing long-term time series of ice thickness. While recent studies have demonstrated the capacity of the CryoSat-2 mission (2010-present) to provide accurate freeboard measurements, the current estimates obtained with the Envisat mission (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) still require some large improvements.In this study, we first estimate Envisat and CryoSat-2 radar freeboard by using the exact same processing algorithms. We then analyse the freeboard difference between the two estimates over the common winter periods (November 2010-April 2011 and November 2011-March 2012. The analysis of along-track data and gridded radar freeboard in conjunction with Envisat pulse-peakiness (PP) maps suggests that the discrepancy between the two sensors is related to the surface properties of sea-ice floes and to the use of a threshold retracker.Based on the relation between the Envisat pulse peakiness and the radar freeboard difference between Envisat and CryoSat-2, we produce a monthly CryoSat-2-like version of Envisat freeboard. The improved Envisat data set freeboard displays a similar spatial distribution to CryoSat-2 (RMSD = 1.5 cm) during the two ice growth seasons and for all months of the period of study.The comparison of the altimetric data sets with in situ ice draught measurements during the common flight period shows that the improved Envisat data set (RMSE = 12-28 cm) is as accurate as CryoSat-2 (RMSE = 15-21 cm) and much more accurate than the uncorrected Envisat data set (RMSE = 178-179 cm).The comparison of the improved Envisat radar freeboard data set is then extended to the rest of the Envisat mission to demonstrate the validity of PP correction from the calibration period. The good agreement between the improved Envisat data set and the in situ ice draught data set (RMSE = 13-32 cm) demonstrates the potential of the PP correction to produce accurate freeboard estimates over the entire Envisat mission lifetime.

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“…This result suggests that unlike for the native Envisat radar freeboard estimates (Kern et al, 2015), the Envisat/PP should allow a realistic seasonal ice growth cycle to be derived.…”

Section: Toward An Improved Envisat Freeboardmentioning

confidence: 98%

Section: K Guerreiro Et Al: Comparison Of Cryosat-2 and Envisat Radmentioning

confidence: 99%

Section: K Guerreiro Et Al: Comparison Of Cryosat-2 and Envisat Radmentioning

confidence: 99%

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Comparison of CryoSat-2 and ENVISAT radar freeboard over Arctic sea ice: toward an improved Envisat freeboard retrieval

et al. 2017

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“…In addition to sea ice thickness (Kern et al, 2015), this requires reliable estimates of sea ice concentration (SIC). Consistency in sea ice climate records is crucial for understanding of internal variability and external forcing (e.g.…”

Section: Introductionmentioning

confidence: 99%

Inter-comparison and evaluation of sea ice algorithms: towards further identification of challenges and optimal approach using passive microwave observations

Ivanova¹,

et al. 2015

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Abstract. Sea ice concentration has been retrieved in polar regions with satellite microwave radiometers for over 30 years. However, the question remains as to what is an optimal sea ice concentration retrieval method for climate monitoring. This paper presents some of the key results of an extensive algorithm inter-comparison and evaluation experiment. The skills of 30 sea ice algorithms were evaluated systematically over low and high sea ice concentrations. Evaluation criteria included standard deviation relative to independent validation data, performance in the presence of thin ice and melt ponds, and sensitivity to error sources with seasonal to inter-annual variations and potential climatic trends, such as atmospheric water vapour and water-surface roughening by wind. A selection of 13 algorithms is shown in the article to demonstrate the results. Based on the findings, a hybrid approach is suggested to retrieve sea ice concentration globally for climate monitoring purposes. This approach consists of a combination of two algorithms plus dynamic tie points implementation and atmospheric correction of input brightness temperatures. The method minimizes inter-sensor calibration discrepancies and sensitivity to the mentioned error sources.

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“…Knowledge of the spatial distribution of leads and polynias is vital to assessing basin-wide estimates of ice thickness and mass change. Altimeter data over sea-ice have been collected in NASA's Operation IceBridge airborne campaigns (see, e.g., Farrell et al [20], Kern et al [21]) and by European Space Agency's CryoSat-2 (Laxon et al [22]). Collection of high-resolution data from unmanned aerial systems, equipped with altimeters, can provide valuable local and regional information (Crocker et al [23], Herzfeld et al [24]).…”

Section: Motivation For Sea-ice Classificationmentioning

confidence: 99%

Geostatistical and Statistical Classification of Sea-Ice Properties and Provinces from SAR Data

et al. 2016

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Recent drastic reductions in the Arctic sea-ice cover have raised an interest in understanding the role of sea ice in the global system as well as pointed out a need to understand the physical processes that lead to such changes. Satellite remote-sensing data provide important information about remote ice areas, and Synthetic Aperture Radar (SAR) data have the advantages of penetration of the omnipresent cloud cover and of high spatial resolution. A challenge addressed in this paper is how to extract information on sea-ice types and sea-ice processes from SAR data. We introduce, validate and apply geostatistical and statistical approaches to automated classification of sea ice from SAR data, to be used as individual tools for mapping sea-ice properties and provinces or in combination. A key concept of the geostatistical classification method is the analysis of spatial surface structures and their anisotropies, more generally, of spatial surface roughness, at variable, intermediate-sized scales. The geostatistical approach utilizes vario parameters extracted from directional vario functions, the parameters can be mapped or combined into feature vectors for classification. The method is flexible with respect to window sizes and parameter types and detects anisotropies. In two applications to RADARSAT and ERS-2 SAR data from the area near Point Barrow, Alaska, it is demonstrated that vario-parameter maps may be utilized to distinguish regions of different sea-ice characteristics in the Beaufort Sea, the Chukchi Sea and in Elson Lagoon. In a third and a fourth case study the analysis is taken further by utilizing multi-parameter feature vectors as inputs for unsupervised and supervised statistical classification. Field measurements and high-resolution aerial observations serve as basis for validation of the geostatistical-statistical classification methods. A combination of supervised classification and vario-parameter mapping yields best results, correctly identifying several sea-ice provinces in the shore-fast ice and the pack ice. Notably, sea ice does not have to be static to be classifiable with respect to spatial structures. In consequence, the geostatistical-statistical classification may be applied to detect changes in ice dynamics, kinematics or environmental changes, such as increased melt ponding, increased snowfall or changes in the equilibrium line.

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The impact of snow depth, snow density and ice density on sea ice thickness retrieval from satellite radar altimetry: results from the ESA-CCI Sea Ice ECV Project Round Robin Exercise

Cited by 75 publications

References 58 publications

Comparison of CryoSat-2 and ENVISAT radar freeboard over Arctic sea ice: toward an improved Envisat freeboard retrieval

Comparison of CryoSat-2 and ENVISAT radar freeboard over Arctic sea ice: toward an improved Envisat freeboard retrieval

Inter-comparison and evaluation of sea ice algorithms: towards further identification of challenges and optimal approach using passive microwave observations

Geostatistical and Statistical Classification of Sea-Ice Properties and Provinces from SAR Data

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