Progress in satellite remote sensing for studying physical processes at the ocean surface and its borders with the atmosphere and sea ice

Physical oceanography is the study of physical conditions, processes and variables within the ocean, including temperature–salinity distributions, mixing of the water column, waves, tides, currents and air–sea interaction processes. Here we provide a critical review of how satellite sensors are being used to study physical oceanography processes at the ocean surface and its borders with the atmosphere and sea ice. The paper begins by describing the main sensor types that are used to observe the oceans (visible, thermal infrared and microwave) and the specific observations that each of these sensor types can provide. We then present a critical review of how these sensors and observations are being used to study: (i) ocean surface currents, (ii) storm surges, (iii) sea ice, (iv) atmosphere–ocean gas exchange and (v) surface heat fluxes via phytoplankton. Exciting advances include the use of multiple sensors in synergy to observe temporally varying Arctic sea ice volume, atmosphere–ocean gas fluxes, and the potential for four-dimensional water circulation observations. For each of these applications we explain their relevance to society, review recent advances and capability, and provide a forward look at future prospects and opportunities. We then more generally discuss future opportunities for oceanography-focused remote sensing, which includes the unique European Union Copernicus programme, the potential of the International Space Station and commercial miniature satellites. The increasing availability of global satellite remote-sensing observations means that we are now entering an exciting period for oceanography. The easy access to these high quality data and the continued development of novel platforms is likely to drive further advances in remote sensing of the ocean and atmospheric systems.

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Satellite remote sensing of sea-ice thickness and kinematics: a review

Journal of Glaciology ◽

10.3189/002214311796406167 ◽

2010 ◽

Vol 56 (200) ◽

pp. 1129-1140 ◽

Cited By ~ 50

Author(s):

R. Kwok

Keyword(s):

Remote Sensing ◽

Sea Ice ◽

Satellite Remote Sensing ◽

Satellite Altimetry ◽

Large Scale ◽

Arctic Sea Ice ◽

Sensor Technology ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Temporal Sampling

AbstractObservations of sea-ice thickness and kinematics are essential for understanding changes in sea-ice mass balance, interactions between the ice cover and the ocean and atmosphere and for improving projections of sea-ice response in a warming climate. These parameters are not directly observable with current sensor technology, but are derived from satellite altimetry and imagery. While there is progress in the retrievals of Arctic sea-ice thickness from satellite altimetry, approaches to address Southern Ocean ice thickness require additional attention. On the other hand, procedures to derive sea-ice motion from satellite imagery are more mature and better understood and have been employed to produce useful results for more than a decade. Adequate sampling of sub-daily ice motion, however, remains a challenge. Generally, satellite instruments provide large-scale coverage but the frequency of temporal sampling is limited by orbit characteristics. In this review, I focus on the approaches, uncertainties, sampling limitations and validation issues associated with the estimation of sea-ice thickness and motion. I provide a summary of current and anticipated capabilities for monitoring sea-ice thickness and kinematics from space. The prospects for continuing these measurements into the next decade, from a satellite remote-sensing perspective, are discussed.

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Combined observations of Arctic sea ice with near-coincident colocated X-band, C-band, and L-band SAR satellite remote sensing and helicopter-borne measurements

Journal of Geophysical Research Oceans ◽

10.1002/2016jc012273 ◽

2017 ◽

Vol 122 (1) ◽

pp. 669-691 ◽

Cited By ~ 16

Author(s):

A. M. Johansson ◽

J. A. King ◽

A. P. Doulgeris ◽

S. Gerland ◽

S. Singha ◽

...

Keyword(s):

Remote Sensing ◽

Sea Ice ◽

Satellite Remote Sensing ◽

Arctic Sea Ice ◽

X Band ◽

Arctic Sea ◽

L Band

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Methods of satellite remote sensing of sea ice

Sea Ice ◽

10.1002/9781118778371.ch9 ◽

2016 ◽

pp. 239-260 ◽

Cited By ~ 3

Author(s):

Gunnar Spreen ◽

Stefan Kern

Keyword(s):

Remote Sensing ◽

Sea Ice ◽

Satellite Remote Sensing

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Variability scaling and consistency in airborne and satellite altimetry measurements of Arctic sea ice

The Cryosphere ◽

10.5194/tc-14-751-2020 ◽

2020 ◽

Vol 14 (2) ◽

pp. 751-767

Author(s):

Shiming Xu ◽

Lu Zhou ◽

Bin Wang

Keyword(s):

Remote Sensing ◽

Sea Ice ◽

Snow Depth ◽

Spatial Scales ◽

Ice Cover ◽

Arctic Sea Ice ◽

The Arctic ◽

First Year ◽

Noise Levels ◽

Wide Range

Abstract. Satellite and airborne remote sensing provide complementary capabilities for the observation of the sea ice cover. However, due to the differences in footprint sizes and noise levels of the measurement techniques, as well as sea ice's variability across scales, it is challenging to carry out inter-comparison or consistently study these observations. In this study we focus on the remote sensing of sea ice thickness parameters and carry out the following: (1) the analysis of variability and its statistical scaling for typical parameters and (2) the consistency study between airborne and satellite measurements. By using collocating data between Operation IceBridge and CryoSat-2 (CS-2) in the Arctic, we show that consistency exists between the variability in radar freeboard estimations, although CryoSat-2 has higher noise levels. Specifically, we notice that the noise levels vary among different CryoSat-2 products, and for the European Space Agency (ESA) CryoSat-2 freeboard product the noise levels are at about 14 and 20 cm for first-year ice (FYI) and multi-year ice (MYI), respectively. On the other hand, for Operation IceBridge and NASA's Ice, Cloud, and land Elevation Satellite (ICESat), it is shown that the variability in snow (or total) freeboard is quantitatively comparable despite more than a 5-year time difference between the two datasets. Furthermore, by using Operation IceBridge data, we also find widespread negative covariance between ice freeboard and snow depth, which only manifests on small spatial scales (40 m for first-year ice and about 80 to 120 m for multi-year ice). This statistical relationship highlights that the snow cover reduces the overall topography of the ice cover. Besides this, there is prevalent positive covariability between snow depth and snow freeboard across a wide range of spatial scales. The variability and consistency analysis calls for more process-oriented observations and modeling activities to elucidate key processes governing snow–ice interaction and sea ice variability on various spatial scales. The statistical results can also be utilized in improving both radar and laser altimetry as well as the validation of sea ice and snow prognostic models.

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Application of Satellite Remote Sensing and Image Processing Techniques to Understand the Sea Ice Variability in the Weddell Sea Sector of Antarctica

2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA) ◽

10.1109/iceca.2018.8474797 ◽

2018 ◽

Author(s):

Steffie Tom ◽

H G Virani

Keyword(s):

Remote Sensing ◽

Image Processing ◽

Sea Ice ◽

Satellite Remote Sensing ◽

Weddell Sea ◽

Image Processing Techniques ◽

Processing Techniques

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Sea ice circulation in the Laptev Sea and ice export to the Arctic Ocean: Results from satellite remote sensing and numerical modeling

Journal of Geophysical Research Atmospheres ◽

10.1029/2000jc900029 ◽

2000 ◽

Vol 105 (C7) ◽

pp. 17143-17159 ◽

Cited By ~ 27

Author(s):

Vitaly Y. Alexandrov ◽

Thomas Martin ◽

Josef Kolatschek ◽

Hajo Eicken ◽

Martin Kreyscher ◽

...

Keyword(s):

Remote Sensing ◽

Numerical Modeling ◽

Sea Ice ◽

Arctic Ocean ◽

Satellite Remote Sensing ◽

The Arctic ◽

Laptev Sea ◽

The Arctic Ocean ◽

The Laptev Sea

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Air-Sea Fluxes following the Unusually Early Ice Retreats in the Arctic

10.5194/egusphere-egu2020-20077 ◽

2020 ◽

Author(s):

Dongxiao Zhang ◽

Chidong Zhang ◽

Jessica Cross ◽

Calvin Mordy ◽

Edward Cokelet ◽

...

Keyword(s):

Sea Ice ◽

Numerical Models ◽

Green Energy ◽

Arctic Sea Ice ◽

Heat Fluxes ◽

The Arctic ◽

Energy Fluxes ◽

The Pacific ◽

Arctic Sea ◽

Ice Retreat

The Arctic has been rapidly changing over the last decade, with more frequent unusually early ice retreats in late spring and summer. Vast Arctic areas that were usually covered by sea ice are now exposed to the atmosphere because of earlier ice retreat and later arrival. Assessment of consequential changes in the energy cycle of the Arctic and their potential feedback to the variability of Arctic sea ice and marine ecosystems critically depends on the accuracy of surface flux estimates. In the Pacific sector of the Arctic, earlier ice retreat generally follows the warm Pacific water inflow into the Arctic through the Bering and Chukchi Seas. Due to ice coverage and irregularity of seasonal ice retreats, air-sea flux measurements following the ice retreats has been difficult to plan and execute. A recent technology development is the Unmanned Surface Vehicles (USVs): The long-range USV saildrones are powered by green energy with wind for propulsion and solar energy for instrumentation and vehicle control. NOAA/PMEL and University of Washington scientists have made surface measurements of the ocean and atmosphere in the Pacific Arctic using saildrones for the past several years. In 2019, for the 1st time a fleet of six saildrones capable of measuring both turbulent and radiative heat fluxes, wind stress, air-sea CO2 flux and upper ocean currents was deployed to follow the ice retreat from May to October, with five of the USVs into the Chukchi and Beaufort Seas while one staying in the Bering Sea. These in situ measurements provide rare opportunities of estimating air-sea energy fluxes during a period of rapid reduction in Arctic sea ice in different scenarios: open water after ice melt, free-floating ice bands, and marginal ice zones. In this study, Arctic air-sea heat and momentum fluxes measured by the saildrones are compared to gridded flux products based on satellite data and numerical models to investigate the circumstances under which they agree and differ, and the main sources of their discrepancies. The results will quantify the uncertainty margins in the gridded flux products and provide insights needed to improve their accuracy. We will also discuss the feasibility of using USVs in sustained Arctic observing system to collect benchmark datasets of the changing surface energy fluxes due to rapid sea ice reduction and provide real time data for improved weather and ocean forecasts.&#160;&#160;

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Permeability of growing sea ice - observations, modelling and some implications for thinning Arctic sea ice

10.5194/egusphere-egu2020-5863 ◽

2020 ◽

Author(s):

Sönke Maus

Keyword(s):

Sea Ice ◽

Pore Space ◽

Arctic Sea Ice ◽

Heat Fluxes ◽

Onset Of Convection ◽

Permeability Model ◽

Electrically Conducting ◽

Melt Pond ◽

Starting Point

The permeability of sea ice is an important property with regard to the role of sea ice in the earth system. It controls fluid flow within sea ice, and thus processes like melt pond drainage, desalination and to some degree heat fluxes between the ocean and the atmosphere. It also impacts the role of sea ice in hosting sea ice algae and organisms, and the uptake and release of nutrients and pollutants from Arctic surface waters. However, as it is difficult to measure in the field, observations of sea ice permeability are sparse and vary, even for similar porosity, over orders of magnitude. Here I present progress on this topic in three directions. First, I present results from numerical simulations of the permeability of young sea ice based on 3-d X-ray microtomographic images (XRT). These results provide a relationship between permeability and brine porosity of young columnar sea ice for the porosity range 2 to 25 %. The simulations also show that this ice type is permeable and electrically conducting down to a porosity of 2 %, considerably lower than what has been proposed in previous work. Second, the XRT-based simulations are compared to predictions based on a novel crystal growth modelling approach, finding good agreement. Third, the permeability model provides a relationship between sea ice growth velocity and permeability. Based on this relationshiop interesting aspects of the growth of permeable sea ice can be deduced: The predictions consistently explain observations of the onset of convection from growing sea ice. They also allow for an evaluation of expected permeability changes for a thinning sea ice cover in a warmer climate. As the model is strictly valid for growing and cooling sea ice, the results are mostly relevant for sea ice desalination processes during winter. Modelling permeability of summer ice (and melt pond drainage) will require more observations of the pore space evolution in warming sea ice, for which the present results can be considered as a resonable starting point.

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