Arctic sea ice radar freeboard retrieval from ERS-2 using altimetry: Toward sea ice thickness observation from 1995 to 2021

Bocquet, Marion; Fleury, Sara; Piras, Fanny; Rinne, Eero; Sallila, Heidi; Garnier, Florent; Rémy, Frédérique

doi:10.5194/egusphere-2022-214

Cited by 5 publications

(4 citation statements)

References 37 publications

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“…Furthermore, this will increase the uncertainty of sea ice thickness based on conventional altimeters such as HY-2B. Fortunately, some studies have shown that the sea ice thickness deviation between the traditional altimeter and the CryoSat-2 SAR altimeter can be improved by correction [56][57][58][59]. In the future, we will study the influence of HY-2B sea ice identification results on sea ice thickness deviation.…”

Section: Discussionmentioning

confidence: 99%

Arctic Sea Ice Lead Detection from Chinese HY-2B Radar Altimeter Data

Zhong

Jiang

et al. 2023

Remote Sensing

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Sea ice thickness is one of the essential characteristics of sea ice. Sea ice lead detection is the key to sea ice thickness estimation from radar altimetry data. This research studies ten different surface type classification methods, including supervised learning, unsupervised learning, and threshold methods, being applied to the HY-2B radar altimeter data collected in October 2019 in the Arctic Ocean. The Sentinel-1 Synthetic Aperture Radar (SAR) images were used for training and validation of the classifiers. Compared with other classifiers, the supervised Bagging ensemble learning classifier showed excellent and robust performance with overall accuracy up to 95.69%. In order to assess the performance of the Bagging classifier in practical applications, lead fractions from January 2019 to March 2021 based on the HY-2B radar altimeter data were mapped using the trained Bagging classifier and compared to the CryoSat-2 L2I data product. The results of the lead fraction showed the monthly variability of ice lead, and the ice lead had a reasonable spatial distribution and was consistent with CryoSat-2 L2I data products. According to these results, the Bagging classifier can provide an essential reference for future studies of Arctic sea ice thickness and sea level estimation from HY-2B radar altimeter data.

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Section: Discussionmentioning

confidence: 99%

Arctic Sea Ice Lead Detection from Chinese HY-2B Radar Altimeter Data

Zhong

Jiang

et al. 2023

Remote Sensing

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“…For sea ice thickness, we utilized altimetry-based ice thickness retrievals from 1994 to 2022, as provided by (Bocquet, 2023;Bocquet & Fleury, 2024). This dataset integrates yearround 12.5 km gridded radar freeboard time series from ERS-1, ERS-2, Envisat, and CryoSat-2 missions.…”

Section: Datamentioning

confidence: 99%

Thermodynamics Drive Changes in the Antarctic Sea Ice Seasonal Cycle Following 2016

Himmich,

Vancoppenolle,

Stammerjohn

et al. 2024

Preprint

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Antarctic sea ice extent has been persistently low since late 2016, possibly owing to changes in atmospheric and oceanic conditions. However, the relative contributions of the ocean, the atmosphere and the underlying mechanisms by which they have affected sea ice remain uncertain. To investigate possible causes for this sea-ice decrease, we establish a seasonal timeline of sea ice changes following 2016, using remote sensing observations. Anomalies in the timing of sea ice retreat and advance are examined along with their spatial and interannual relations with various indicators of seasonal sea ice and oceanic changes. They include anomalies in winter ice thickness, spring ice removal rate due to ice melt and transport, and summer sea surface temperature. We find that the ice season has shortened at unprecedented rate and magnitude, due to earlier retreat and later advance. We attribute this shortening to a winter ice thinning, in line with the ice-albedo feedback, with ice transport playing a more minor role. Reduced ice thickness has accelerated spring ice area removal as thinner sea ice requires less time to melt. The consequent earlier sea ice retreat has in turn increased ocean solar heat uptake in summer, ultimately delaying sea ice advance. We speculate that the observed winter sea ice thinning is consistent with previous evidence of subsurface warming of the Southern Ocean.

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“…This can lead to noticeable error in terms of the actual sea ice thickness. Therefore, we followed methods used in previous studies that resampled data over IMB tracks (Bocquet et al, 2022;Kwok et al, 2007;Mu et al, 2018;Stroeve et al, 2020;Xie et al, 2018).…”

Section: Data Griddingmentioning

confidence: 99%

Improving Arctic sea ice thickness retrieved from CryoSat-2: A comprehensive optimization of a retracking algorithm, radar penetration rate, and snow depth

Zhou

Chen

et al. 2023

Preprint

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Abstract. The Arctic sea ice thicknesses retrieved from the CryoSat-2 satellite are significantly influenced by sea ice surface roughness, snow backscatter, and snow depth on the sea ice. This study is the first to improve the retrieval of sea ice thickness from CryoSat-2 data derived by the Alfred Wegener Institute (AWI CS2) through applying a comprehensive optimization of an improved retracking algorithm, corrected radar penetration rate, and new snow depth. The radar freeboard data was obtained from the improved retracking algorithm of the Lognormal Altimeter Retracker Model (LARM). The radar penetration rates were corrected to 0.77 for first-year ice (FYI), 0.96 for multi-year ice (MYI), and 0.91 for all ice types. The new snow depth data was derived from the Chinese satellite Feng Yun-3B with the MicroWave Radiometer Imager (FY3B/MWRI). Three individual and one combined optimization cases were created by focusing on the retracking algorithm, radar penetration rate, and snow depth, which were validated with in situ observations collected from the National Aeronautics and Space Administration Operation IceBridge (OIB), Ice Mass Balance buoys (IMB), CryoSat Validation Experiment (CryoVEX), and Alfred Wegener Institute IceBird Program (AWI IceBird). The assessment results showed that all the optimization cases had the ability to effectively improve the sea ice thickness, with similar correlation coefficients. In the validation with the four kinds of observed data, the optimization cases reduced the RMSE of AWI CS2 up to 0.23 m (25.0 %), 0.27 m (29.7 %), 0.26 m (25.5 %), and 0.22 m (23.9 %). The improved sea ice thickness retrieval retained the major distribution patterns generated by AWI CS2, but generally showed thinner sea ice thickness. In particular, in the MYI region, the difference in thickness became increasingly evident from fall to spring. The differences in the variation trend between the optimization cases and AWI CS2 were significant in some coastal regions and the central Arctic. The experiments revealed that the radar penetration rate calculation was more sensitive to sea ice density than to snow density. The sensitivity experiments suggested that the snow depth of TOPAZ4, in addition to that of FY3B/MWRI, was also applicable in improving the retrieval of sea ice thickness. The updated scheme of sea ice densities (FYI = 925 kg m−3 and MYI = 902 kg m−3) can be combined with the use of comprehensive optimization to improve the retrieval of sea ice thickness. This successful optimization provided new insights into improving the sea ice thickness retrieved from CryoSat-2 and helped to further understand and quantify the spatiotemporal variations of sea ice thickness.

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Arctic sea ice radar freeboard retrieval from ERS-2 using altimetry: Toward sea ice thickness observation from 1995 to 2021

Cited by 5 publications

References 37 publications

Arctic Sea Ice Lead Detection from Chinese HY-2B Radar Altimeter Data

Arctic Sea Ice Lead Detection from Chinese HY-2B Radar Altimeter Data

Thermodynamics Drive Changes in the Antarctic Sea Ice Seasonal Cycle Following 2016

Improving Arctic sea ice thickness retrieved from CryoSat-2: A comprehensive optimization of a retracking algorithm, radar penetration rate, and snow depth

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