Studies of the Retrieval of Sea Ice Thickness and Salinity with Wideband Microwave Radiometry

2021 ◽  
Author(s):  
O. Demir ◽  
K. Jezek ◽  
M. Brogioni ◽  
G. Macelloni ◽  
L. Kaleschke ◽  
...  
Keyword(s):  
Sea Ice ◽  
Microwave Radiometry ◽  
Ice Thickness ◽  
Sea Ice Thickness

Remote Sensing of Sea Ice Thickness and Salinity With 0.5–2 GHz Microwave Radiometry

2019 ◽  
Vol 57 (11) ◽  
pp. 8672-8684 ◽  
Author(s):  
Kenneth C. Jezek ◽  
Ronald Kwok ◽  
Lars Kaleschke ◽  
Domenic J. Belgiovane ◽  
Chi-Chih Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Microwave Radiometry ◽  
Ice Thickness ◽  
Sea Ice Thickness

Sea Ice Thickness Retrieval based on Predictive Regression Neural Networks using L-band Microwave Radiometry Data from the FSSCat mission

2021 ◽  
Author(s):  
Christoph Herbert ◽  
Joan Francisc Munoz-Martin ◽  
David LLaveria ◽  
Miriam Pablos ◽  
Adriano Camps
Keyword(s):  
Neural Networks ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Microwave Radiometry ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Predictive Regression ◽  
Arctic Sea ◽  
L Band ◽  
Arctic Sea Ice Thickness

<p>Several approaches have been developed to yield Arctic sea ice thickness based on satellite observations. Microwave radiometry operating at L-band is sensitive to sea ice properties and allows to retrieve thin sea ice up to ~ 0.5 m. Sea ice thickness retrievals above 1 m can be successfully derived using sea ice freeboard data from satellite altimeters. Current inference models are build upon empirically determined assumptions on the physical composition of sea ice and are validated against regionally available data. However, sea ice can exhibit time-dependent non-linear relations between sea ice properties during the process of formation and melting, which cannot be fully addressed by simple inversion models. Until now, an accurate estimation of sea ice thickness requires specific conditions and is only viable during Arctic freeze up from mid-October to mid-April. Neural networks are an efficient model-based learning technique capable of resolving complex systems while recognizing hidden links among large amounts of data. Models have the advantage to be adaptive to new data and can therefore reflect seasonally changing sea ice conditions to make predictions based on the relationship between a set of input features. FSSCat is a two 6-unit CubeSat mission launched on September 3, 2020, which carries the FMPL-2 payload on board the 3Cat-5/A, one out of two spacecrafts. FMPL-2 encompasses the first L-band radiometer and GNSS-Reflectometer on a CubeSat, designed to provide global brightness temperature data suitable for soil moisture retrieval on land and sea ice applications.</p><p>In this work a predictive regression neural network was built to predict thin sea ice thickness up to 0.6 m at Arctic scale based on FMPL-2 observations and ancillary data including sea ice concentration and surface temperature. The network was trained based on CubeSat acquisitions during early Arctic freeze up from October 15 to December 4, 2020, using existing maps of daily estimated sea ice thickness derived from the Soil Moisture and Ocean Salinity (SMOS) mission as ground truth data. Hyperparameters were optimized to prevent the model from overfitting and overgeneralization with the best fit resulting in an overall mean absolute error of 6.5 cm. Preliminary results reveal good performance up to 0.5 m, whereas predicted values are slightly underestimated for higher thickness. The thin ice model allows to produce weekly composites of Arctic sea ice thickness maps. Future work involves the complementation of the input features with sea ice freeboard observations from the Cryosat-2 mission to extend the sensitivity range of the current network and to validate the findings with on-site data. Aim is to apply the model trained on Arctic data to retrieve full-range Arctic and Antarctic sea ice thickness maps. The presented approach demonstrated the potential of neural networks for sea ice parameter retrieval and indicated that data acquired by moderate-cost CubeSat missions can offer scientifically valuable contributions to applications in Earth observation.</p>

scholarly journals An Attempt to Improve Snow Depth Retrieval Using Satellite Microwave Radiometry for Rough Antarctic Sea Ice

2019 ◽  
Vol 11 (19) ◽  
pp. 2323
Author(s):  
Stefan Kern ◽  
Burcu Ozsoy
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Satellite Altimetry ◽  
Hybrid Approach ◽  
Microwave Radiometry ◽  
Major Constituent ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Basin Scale ◽  
Satellite Microwave

Snow depth on sea ice is a major constituent of the marine cryosphere. It is a key parameter for the derivation of sea-ice thickness from satellite altimetry. One way to retrieve the basin-scale snow depth on sea ice is by satellite microwave radiometry. There is evidence from measurements and inter-comparison studies that current retrievals likely under-estimate the snow depth over deformed, rough sea ice. We follow up on an earlier study, where satellite passive microwave data were combined with information on the sea-ice topography from the satellite laser altimeter on board the Ice, Cloud and land Elevation Satellite (ICESat) in a hybrid approach. Such topography information is spatiotemporally limited because of ICESat’s operation mode. In this paper, we aim to derive a proxy for this topography information from satellite microwave radiometry. For this purpose, we co-locate parameters describing the sea-ice deformation taken from visual ship-based observations and the surface elevation standard deviation derived from ICESat laser altimetry with the microwave brightness temperatures (TB) measured via the Advanced Microwave Scanning Radiometer aboard Earth Observation Satellite (AMSR-E) and aboard Global Change Observation Mission-Water 1 (GCOM-W1) (AMSR2). We find that the TB polarization ratio at 6.9 GHz and the TB gradient ratio between 10.7 GHz (horizontal polarization) and 6.9 GHz (vertical polarization), might be suited as such a proxy. Using this proxy, we modify the above-mentioned hybrid approach and compute the snow depths on sea ice from the AMSR-E and AMSR2 data. We compare our snow depths with those of the commonly used approach, the hybrid approach, with the ship-based observations for the years 2002 through 2015 and with the measurements made by drifting buoys for the period of 2014 through 2018. We find a convincing overall agreement with the hybrid approach and some improvement over the common approach. However, our approach is sensitive to the presence of thin ice—here, the retrieved snow depths are too large; and our approach performs sub-optimally over old ice—here, the retrieved snow depths are too small. More investigations and, in particular, more evaluations are required to optimize our approach so that the snow depths retrieved for the combined AMSR-E/AMSR2 period could serve as a data set for sea-ice thickness retrieval based on satellite altimetry.

In-situ automatic observations of ice thickness of seas

2012 ◽  
Vol 19 (3) ◽  
pp. 583-592 ◽  
Author(s):  
Yinke Dou ◽  
Xiaomin Chang
Keyword(s):  
Sea Ice ◽  
New Technologies ◽  
Capacitive Sensor ◽  
Automatic Monitoring ◽  
Ice Thickness ◽  
In Situ Observation ◽  
Glacier Surface ◽  
Sea Ice Thickness ◽  
Surface Ice

Abstract Ice thickness is one of the most critical physical indicators in the ice science and engineering. It is therefore very necessary to develop in-situ automatic observation technologies of ice thickness. This paper proposes the principle of three new technologies of in-situ automatic observations of sea ice thickness and provides the findings of laboratory applications. The results show that the in-situ observation accuracy of the monitor apparatus based on the Magnetostrictive Delay Line (MDL) principle can reach ±2 mm, which has solved the “bottleneck” problem of restricting the fine development of a sea ice thermodynamic model, and the resistance accuracy of monitor apparatus with temperature gradient can reach the centimeter level and research the ice and snow substance balance by automatically measuring the glacier surface ice and snow change. The measurement accuracy of the capacitive sensor for ice thickness can also reach ±4 mm and the capacitive sensor is of the potential for automatic monitoring the water level under the ice and the ice formation and development process in water. Such three new technologies can meet different needs of fixed-point ice thickness observation and realize the simultaneous measurement in order to accurately judge the ice thickness.

Assimilation of SMOS sea ice thickness in the regional ice prediction system

2021 ◽  
Vol 42 (12) ◽  
pp. 4583-4606
Author(s):  
Mukesh Gupta ◽  
Alain Caya ◽  
Mark Buehner
Keyword(s):  
Sea Ice ◽  
Ice Thickness ◽  
Prediction System ◽  
Sea Ice Thickness

scholarly journals Antarctic sea-ice thickness and volume estimates from ice charts between 1995 and 1998

2015 ◽  
Vol 56 (69) ◽  
pp. 383-393 ◽  
Author(s):  
E. Rachel Bernstein ◽  
Cathleen A. Geiger ◽  
Tracy L. Deliberty ◽  
Mary D. Lemcke-Stampone
Keyword(s):  
Sea Ice ◽  
Regional Scale ◽  
Weighted Average ◽  
Ice Thickness ◽  
Average Thickness ◽  
Sea Ice Thickness ◽  
Subjective Analysis ◽  
Stage Of Development ◽  
Volume Estimates ◽  
The Impact

AbstractThis work evaluates two distinct calculations of central tendency for sea-ice thickness and quantifies the impact such calculations have on ice volume for the Southern Ocean. The first calculation, area-weighted average thickness, is computed from polygonal ice features and then upscaled to regions. The second calculation, integrated thickness, is a measure of the central value of thickness categories tracked across different scales and subsequently summed to chosen regions. Both methods yield the same result from one scale to the next, but subsequent scales develop diverging solutions when distributions are strongly non-Gaussian. Data for this evaluation are sea-ice stage-of-development records from US National Ice Center ice charts from 1995 to 1998, as proxy records of ice thickness. Results show regionally integrated thickness exceeds area-weighted average thickness by as much as 60% in summer with as few as five bins in thickness distribution. Year-round, the difference between the two calculations yields volume differences consistently >10%. The largest discrepancies arise due to bimodal distributions which are common in ice charts based on current subjective-analysis protocols. We recommend that integrated distribution be used for regional-scale sea-ice thickness and volume estimates from ice charts and encourage similar testing of other large-scale thickness data archives.

scholarly journals Helicopter-borne measurements of sea ice thickness, using a small and lightweight, digital EM system

2009 ◽  
Vol 67 (3) ◽  
pp. 234-241 ◽  
Author(s):  
Christian Haas ◽  
John Lobach ◽  
Stefan Hendricks ◽  
Lasse Rabenstein ◽  
Andreas Pfaffling
Keyword(s):  
Sea Ice ◽  
Ice Thickness ◽  
Sea Ice Thickness

The implications of selected processing methods on satellite altimetry derived sea ice thickness state and trends in the seasonal ice zone

2021 ◽  
Author(s):  
Isolde Glissenaar ◽  
Jack Landy ◽  
Alek Petty ◽  
Nathan Kurtz ◽  
Julienne Stroeve
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Satellite Altimetry ◽  
Region Of Interest ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Baffin Bay ◽  
The Impact

<p>The ice cover of the Arctic Ocean is increasingly becoming dominated by seasonal sea ice. It is important to focus on the processing of altimetry ice thickness data in thinner seasonal ice regions to understand seasonal sea ice behaviour better. This study focusses on Baffin Bay as a region of interest to study seasonal ice behaviour.</p><p>We aim to reconcile the spring sea ice thickness derived from multiple satellite altimetry sensors and sea ice charts in Baffin Bay and produce a robust long-term record (2003-2020) for analysing trends in sea ice thickness. We investigate the impact of choosing different snow depth products (the Warren climatology, a passive microwave snow depth product and modelled snow depth from reanalysis data) and snow redistribution methods (a sigmoidal function and an empirical piecewise function) to retrieve sea ice thickness from satellite altimetry sea ice freeboard data.</p><p>The choice of snow depth product and redistribution method results in an uncertainty envelope around the March mean sea ice thickness in Baffin Bay of 10%. Moreover, the sea ice thickness trend ranges from -15 cm/dec to 20 cm/dec depending on the applied snow depth product and redistribution method. Previous studies have shown a possible long-term asymmetrical trend in sea ice thinning in Baffin Bay. The present study shows that whether a significant long-term asymmetrical trend was found depends on the choice of snow depth product and redistribution method. The satellite altimetry sea ice thickness results with different snow depth products and snow redistribution methods show that different processing techniques can lead to different results and can influence conclusions on total and spatial sea ice thickness trends. Further processing work on the historic radar altimetry record is needed to create reliable sea ice thickness products in the marginal ice zone.</p>

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