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

Guerreiro, Kévin; Fleury, Sara; Zakharova, Elena; Kouraev, Alexei V.; Rémy, Frédérique; Maisongrande, Philippe

doi:10.5194/tc-11-2059-2017

Cited by 59 publications

(74 citation statements)

References 30 publications

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“…While the differences between CryoSat-2 and Envisat freeboard appear to be comparable between both studies, the average monthly sea-ice freeboard estimates for both sensors are between about 3 cm and 10 cm larger in our study compared to the results shown in Guerreiro et al (2017).…”

contrasting

confidence: 52%

“…Hence, we chose a threshold of 95 % for leads from Envisat waveforms. In a very recent study by Guerreiro et al (2017), the use of a 50 % for lead-type wave forms resulted in initial average conditions where the lead surface elevation was detected above that of the surrounding sea-ice floes. However, when we used a single fixed threshold for the 30 range retrieval over sea ice similar to that for CryoSat-2 (i.e., a 50 % threshold from the first local maximum peak power), our Envisat sea-ice-freeboard estimates featured an overall smaller variation and range than CryoSat-2 estimates.…”

mentioning

confidence: 99%

“…A comparison to the results of Guerreiro et al (2017) is constrained by the different methods used. Besides the pulsepeakiness correction of the Envisat monthly freeboard, Guerreiro et al (2017) also apply a 25 km along-track median smoothing to all freeboard estimates and afterward discard all freeboard estimates below -1 m and above 2 m, respectively.…”

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confidence: 99%

“…Due to the already mentioned larger footprint of Envisat and hence increased mixing of different surface types, it appears to be necessary to treat waveforms differently according to the wave-form shape (and hence surface properties) by means of retracking the main scattering horizon. Guerreiro et al (2017) proposed in their study a correction scheme deriving a relationship between monthly pulse peakiness and the monthly freeboard difference 5 between CryoSat-2 and Envisat based on a third order polynomial fit. In contrast to applying a similar post-retracking correction to the resulting freeboard estimates, we apply our correction already during waveform retracking.…”

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confidence: 99%

“…Besides the pulsepeakiness correction of the Envisat monthly freeboard, Guerreiro et al (2017) also apply a 25 km along-track median smoothing to all freeboard estimates and afterward discard all freeboard estimates below -1 m and above 2 m, respectively. The resulting values are then used to compile the monthly gridded data set.…”

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confidence: 99%

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Consistent CryoSat-2 and Envisat Freeboard Retrieval of Arctic and Antarctic Sea Ice

Paul

Hendricks

Ricker

et al. 2018

Preprint

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Abstract. In order to derive long-term changes in sea-ice volume, a multi-decadal sea-ice thickness record is required. CryoSat-2 has showcased the potential of radar altimetry for sea-ice mass-balance estimation over the last years. However, precursor altimetry missions such as Envisat have not been exploited to the same extent so far. Combining both missions to acquire a decadal sea-ice volume data set requires a method to overcome the discrepancies due to different foot-print sizes from either pulse-limited or beam-sharpened radar echoes. In this study, we implemented an inter-mission consistent surface-type classi-5 fication scheme for both hemispheres, based on the waveform pulse peakiness, leading-edge width, and sea-ice backscatter.In order to achieve a consistent retracking procedure, we adapted the Threshold First Maximum Retracker Algorithm, previously used only for CryoSat-2, to develop an adaptive retracker threshold that depends on waveform characteristics. With our method, we produce a global and consistent freeboard data set for CryoSat-2 and Envisat. This novel data set features a maximum monthly difference in the mission-overlap period of 2.2 cm (2.7 cm) for the Arctic (Antarctic) based on all gridded 10 values with spatial resolution of 25 km × 25 km and 50 km × 50 km for the Arctic and Antarctic, respectively.

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confidence: 52%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Consistent CryoSat-2 and Envisat Freeboard Retrieval of Arctic and Antarctic Sea Ice

Paul

Hendricks

Ricker

et al. 2018

Preprint

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Investigation of the Arctic Sea ice volume from 2002 to 2018 using multi‐source data

Shen

et al. 2020

Intl Journal of Climatology

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The Arctic sea ice volume (SIV) was investigated by applying sea ice concentration (SIC) and multi‐source sea ice thickness (SIT) products from the Pan‐Arctic Ice‐Ocean Modelling and Assimilation System (PIOMAS), Envisat and CryoSat‐2 (CS‐2) products. The SIV was estimated during the sea ice growth season (October–April) from October 2002 to December 2018. During the Envisat period (October 2002–April 2010), negative SIV trends were estimated by a hybrid Envisat and PIOMAS SIT dataset (defined as Envi‐PIO); the declining trends for both the maximum/minimum SIV were 360 and 177 km3⋅year−1, respectively; similar SIV trends were obtained by applying only the PIOMAS SIT data. During the CS‐2 period (October 2010–December 2018), no clear trends in the SIV were estimated by either CS‐2 or PIOMAS, except for clear increases in the SIV in northern Greenland and the Canadian Arctic Archipelago (CAA) using the CS‐2 SIT data. The age of sea ice plays an important role in SIV variability. For example, the SIV trend was found to be similar to the multi‐year ice trend between 2003 and 2007. The correlation coefficients between the monthly mean SIV and surface air temperature (SAT) and sea surface temperature (SST) were −0.60 and −0.82, respectively. The decreasing trend in the SIV during the Envisat period was influenced by the increase in the annual maximum SST and minimum SAT. The significant increase in the SIV in northern Greenland and the CAA during the CS‐2 period was related to ice deformation.

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Sea Ice Remote Sensing—Recent Developments in Methods and Climate Data Sets

Sandven¹,

Spreen

Heygster

et al. 2023

Surv Geophys

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Sea ice monitoring by polar orbiting satellites has been developed over more than four decades and is today one of the most well-established applications of space observations. This article gives an overview of data product development from the first sensors to the state-of-the-art regarding retrieval methods, new products and operational data sets serving climate monitoring as well as daily operational services including ice charting and forecasting. Passive microwave data has the longest history and represents the backbone of global ice monitoring with already more than four decades of consistent observations of ice concentration and extent. Time series of passive microwave data is the primary climate data set to document the sea ice decline in the Arctic. Scatterometer data is a valuable supplement to the passive microwave data, in particular to retrieve ice displacement and distinguish between firstyear and multiyear ice. Radar and laser altimeter data has become the main method to estimate sea ice thickness and thereby fill a gap in the observation of sea ice as an essential climate variable. Data on ice thickness allows estimation of ice volume and masses as well as improvement of the ice forecasts. The use of different altimetric frequencies also makes it possible to measure the depth of the snow covering the ice. Synthetic Aperture Radar (SAR) has become the work horse in operational ice observation on regional scale because high-resolution radar images are delivered year-round in nearly all regions where national ice services produce ice charts. Synthetic Aperture Radar data are also important for sea ice research because the data can be used to observe a number of sea ice processes and phenomena, like ice type development and sea ice dynamics, and thereby contribute to new knowledge about sea ice. The use of sea ice data products in modelling and forecasting services as well as in ice navigation is discussed. Finally, the article describes future plans for new satellites and sensors to be used in sea ice observation.

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

Cited by 59 publications

References 30 publications

Consistent CryoSat-2 and Envisat Freeboard Retrieval of Arctic and Antarctic Sea Ice

Consistent CryoSat-2 and Envisat Freeboard Retrieval of Arctic and Antarctic Sea Ice

Investigation of the Arctic Sea ice volume from 2002 to 2018 using multi‐source data

Sea Ice Remote Sensing—Recent Developments in Methods and Climate Data Sets

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