Drone-based sea ice albedo measurements and photogrammetry during the Arctic freeze-up in the MOSAiC expedition

2021 ◽  
Author(s):  
Roberta Pirazzini ◽  
Henna-Reetta Hannula ◽  
David Brus ◽  
Ruzica Dadic ◽  
Martin Scnheebeli
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Surface Topography ◽  
Near Infrared ◽  
Snow Accumulation ◽  
The Arctic ◽  
Ocean Optics ◽  
Surface Features ◽  
Ice Surface ◽  
Melt Ponds

<p>Aerial albedo measurements and detailed surface topography of sea ice are needed to characterize the distribution of the various surface types (melt ponds of different depth and size, ice of different thicknesses, leads, ridges) and to determine how they contribute to the areal-averaged albedo on different horizontal scales. These measurements represent the bridge between the albedo measured from surface-based platforms, which typically have metre-to-tens-of-meters footprint, and satellite observations or large-grid model outputs.</p><p>Two drones were operated in synergy to measure the albedo and map the surface topography of the sea ice during the leg 5 of the MOSAiC expedition (August-September 2020), when concurrent albedo and surface roughness measurements were collected using surface-based instruments. The drone SPECTRA was equipped with paired Kipp and Zonen CM4 pyranometers measuring broadband albedo and paired Ocean Optics STS VIS (350 – 800 nm) and NIR (650-1100 nm) micro-radiometers measuring visible and near-infrared spectral albedo, and the drone Mavic 2 Pro was equipped with camera to perform photography mapping of the area measured by the SPECTRA drone.</p><p>Here we illustrate the collected data, which show a drastic change in sea ice albedo during the observing period, from the initial melting state to the freezing and snow accumulation state, and demonstrate how this change is related to the evolution of the different surface features, melt ponds and leads above all. From the data analysis we can conclude that the 30m albedo is not significantly affected by the individual surface features and, therefore, it is potentially representative of the sea ice albedo in satellite footprint and model grid areas.</p><p>The Digital Elevation Models (DEMs) of the sea ice surface obtained from UAV photogrammetry are combined with the DEMs based on Structure From Motion technique that apply photos manually taken close to the surface. This will enable us to derive the surface roughness from sub-millimeter to meter scales, which is critical to interpret the observed albedo and to develop correction methods to eliminate the artefacts caused by shadows.</p><p>The UAV-based albedo and surface roughness are highly complementary also to analogous helicopter-based observations, and will be relevant for the interpretation of all the physical and biochemical processes observed at and near the sea ice surface during the transition from melting to freezing and growing.</p>

Importance of variable neutral drag coefficients for ocean-ice and air-ice fluxes in polar regions

2021 ◽  
Author(s):  
Jean Sterlin ◽  
Thierry Fichefet ◽  
Francois Massonnet ◽  
Michel Tsamados
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Surface Fluxes ◽  
The Arctic ◽  
Climate Modelling ◽  
Form Drag ◽  
Drag Coefficients ◽  
Ice Surface ◽  
Melt Ponds ◽  
Ice Surface Roughness

<p>Sea ice features a variety of obstacles to the flow of air and seawater at its top and bottom surfaces. Sea ice ridges, floe edges, ice surface roughness and melt ponds, lead to a form drag that interacts dynamically with the air-ice and ocean-ice fluxes of heat and momentum. In most climate models, surface fluxes of heat and momentum are calculated by bulk formulas using constant drag coefficients over sea ice, to reflect the mean surface roughness of the interfaces with the atmosphere and ocean. However, such constant drag coefficients do not account for the subgrid-scale variability of the sea ice surface roughness. To study the effect of form drag over sea ice on air-ice-ocean fluxes, we have implemented a formulation that estimates drag coefficients in ice-covered areas comprising the effect of sea ice ridges, floe edges and melt ponds, and ice surface skin (Tsamados et al., 2013) into the NEMO3.6-LIM3 global coupled ice-ocean model. In this work, we thoroughly analyse the impacts of this improvement on the model performance in both the Arctic and Antarctic. A particular attention is paid to the influence of this modification on the air-ice-ocean fluxes of heat and momentum, and the characteristics of the oceanic surface layers. We also formulate an assessment of the importance of variable drag coefficients over sea ice for the climate modelling community.</p>

scholarly journals C-Band Polarimetric Coherences and Ratios for Discriminating Sea Ice Roughness

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Mukesh Gupta ◽  
Randall K. Scharien ◽  
David G. Barber
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Two Dimensions ◽  
The Arctic ◽  
Rapid Decline ◽  
Ice Surface ◽  
Ice Roughness ◽  
Direct Measurements ◽  
Ice Surface Roughness ◽  
Spaceborne Radar

The rapid decline of sea ice in the Arctic has resulted in a variable sea ice roughness that necessitates improved methods for efficient observation using high-resolution spaceborne radar. The utility of C-band polarimetric backscatter, coherences, and ratios as a discriminator of ice surface roughness is evaluated. An existing one-dimensional backscatter model has been modified to two-dimensions (2D) by considering deviation in the orientation (i.e., the slopes) in azimuth and range direction of surface roughness simultaneously as an improvement in the model. It is shown theoretically that the circular coherence (ρRRLL) decreases exponentially with increasing surface roughness. The crosspolarized coherence (ρHHVH) is found to be less sensitive to surface roughness, whereas the copolarized coherence (ρVVHH) decreases at far-range incidence angles for all ice types. A complete validation of the adapted 2D model using direct measurements of surface roughness is suggested as an avenue for further research.

scholarly journals Average cosine coefficient and spectral distribution of the light field under sea ice: Implications for primary production

Elem Sci Anth ◽  
2019 ◽  
Vol 7 ◽  
Author(s):  
L. C. Matthes ◽  
J. K. Ehn ◽  
S. L.-Girard ◽  
N. M. Pogorzelec ◽  
M. Babin ◽  
...  
Keyword(s):  
Sea Ice ◽  
Primary Production ◽  
Radiation Field ◽  
Light Field ◽  
The Arctic ◽  
Daily Production ◽  
Ice Surface ◽  
Melt Ponds ◽  
The Impact ◽  
Average Cosine

The Arctic spring phytoplankton bloom has been reported to commence under a melting sea ice cover as transmission of photosynthetically active radiation (PAR; 400–700 nm) suddenly increases with the formation of surface melt ponds. Spatial variability in ice surface characteristics, i.e., snow thickness or melt pond distributions, and subsequent impact on transmitted PAR makes estimating light-limited primary production difficult during this time of year. Added to this difficulty is the interpretation of data from various sensor types, including hyperspectral, multispectral, and PAR-band irradiance sensors, with either cosine-corrected (planar) or spherical (scalar) sensor heads. To quantify the impact of the heterogeneous radiation field under sea ice, spectral irradiance profiles were collected beneath landfast sea ice during the Green Edge ice-camp campaigns in May–June 2015 and June–July 2016. Differences between PAR measurements are described using the downwelling average cosine, μd, a measure of the degree of anisotropy of the downwelling underwater radiation field which, in practice, can be used to convert between downwelling scalar, E0d, and planar, Ed, irradiance. A significantly smallerμd(PAR) was measured prior to snow melt compared to after (0.6 vs. 0.7) when melt ponds covered the ice surface. The impact of the average cosine on primary production estimates, shown in the calculation of depth-integrated daily production, was 16% larger under light-limiting conditions when E0d was used instead of Ed. Under light-saturating conditions, daily production was only 3% larger. Conversion of underwater irradiance data also plays a role in the ratio of total quanta to total energy (EQ/EW, found to be 4.25), which reflects the spectral shape of the under-ice light field. We use these observations to provide factors for converting irradiance measurements between irradiance detector types and units as a function of surface type and depth under sea ice, towards improving primary production estimates.

scholarly journals Arctic Sea Ice Surface Roughness Derived from Multi-Angular Reflectance Satellite Imagery

2018 ◽  
Author(s):  
Anne W. Nolin
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Ice Age ◽  
Ice Surface ◽  
Ice Roughness ◽  
Melt Ponds ◽  
Arctic Sea ◽  
Angular Reflectance ◽  
Ice Surface Roughness

Sea ice surface roughness affects ice-atmosphere interactions, serves as an indicator of ice age, shows patterns of ice convergence and divergence, affects the spatial extent of summer melt ponds, and ice albedo. We have developed a method for mapping sea ice surface roughness using angular reflectance data from the Multi-angle Imaging SpectroRadiometer (MISR) and lidar-derived roughness measurements from the Airborne Topographic Mapper (ATM). Using an empirical data modeling approach, we derived estimates of Arctic sea ice roughness ranging from centimeters to decimeters meters within the MISR 275-m pixel size. Using independent ATM data for validation, we find that histograms of lidar and multi-angular roughness values are nearly identical for areas with roughness <20 cm but that for rougher regions, the MISR-derived roughness has a narrower range of values than the ATM data. The algorithm is able to accurately identify areas that transition between smooth and rough ice. Because of its coarser spatial scale, MISR-derived roughness data have a variance of about half that ATM roughness data.

scholarly journals The potential of using Landsat 7 ETM+ for the classification of sea-ice surface conditions during summer

2002 ◽  
Vol 34 ◽  
pp. 415-419 ◽  
Author(s):  
Thorsten Markus ◽  
Donald J. Cavalieri ◽  
Alvaro Ivanoff
Keyword(s):  
Sea Ice ◽  
Near Infrared ◽  
Color Image ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Surface Conditions ◽  
Melt Ponds ◽  
Wet Snow ◽  
Landsat 7 ◽  
The Individual

AbstractDuring spring and summer, the surface of the Arctic sea-ice cover undergoes rapid changes that greatly affect the surface albedo and significantly impact the further decay of the sea ice. These changes are primarily the development of a wet snow cover and the development of melt ponds. As melt ponds generally do not exceed a couple of meters in diameter, the spatial resolutions of sensors like the Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer are too coarse for their identification. Landsat 7, on the other hand, has a spatial resolution of 30 m (15 m for the panchromatic band) and thus offers the best chance to map the distribution of melt ponds from space. The different wavelengths (bands) from blue to near-infrared offer the potential to distinguish among different surface conditions. Landsat 7 data for the Baffin Bay region for June 2000 have been analyzed. The analysis shows that different surface conditions, such as wet snow and melt-ponded areas, have different signatures in the individual Landsat bands. Consistent with in situ albedo measurements, melt ponds show up as blueish, whereas dry and wet ice have a white to gray appearance in the Landsat true-color image. These spectral differences enable areas with high fractions of melt ponds to be distinguished.

scholarly journals Evolution of Backscattering Coefficients of Drifting Multi-Year Sea Ice during End of Melting and Onset of Freeze-up in the Western Beaufort Sea

2020 ◽  
Vol 12 (9) ◽  
pp. 1378
Author(s):  
Seung Hee Kim ◽  
Hyun-Cheol Kim ◽  
Chang-Uk Hyun ◽  
Sungjae Lee ◽  
Jung-Seok Ha ◽  
...  
Keyword(s):  
Sea Ice ◽  
Transition Period ◽  
Incidence Angle ◽  
Beaufort Sea ◽  
Reanalysis Data ◽  
The Arctic ◽  
Angle Dependence ◽  
Ice Surface ◽  
Melt Ponds ◽  
Increasing Trend

Backscattering coefficients of Sentinel-1 synthetic aperture radar (SAR) data of drifting multi-year sea ice in the western Beaufort Sea during the transition period between the end of melting and onset of freeze-up are analyzed, in terms of the incidence angle dependence and temporal variation. The mobile sea ice surface is tracked down in a 1 km by 1 km region centered at a GPS tracker, which was installed during a field campaign in August 2019. A total of 24 Sentinel-1 images spanning 17 days are used and the incidence angle dependence in HH- and HV-polarization are −0.24 dB/deg and −0.10 dB/deg, respectively. Hummocks and recently frozen melt ponds seem to cause the mixture behavior of surface and volume scattering. The normalized backscattering coefficients in HH polarization gradually increased in time at a rate of 0.15 dB/day, whereas the HV-polarization was relatively flat. The air temperature from the ERA5 hourly reanalysis data has a strong negative relation with the increasing trend of the normalized backscattering coefficients in HH-polarization. The result of this study is expected to complement other previous studies which focused on winter or summer seasons in other regions of the Arctic Ocean.

scholarly journals Influence of Melt Ponds on the SSMIS-Based Summer Sea Ice Concentrations in the Arctic

2021 ◽  
Vol 13 (19) ◽  
pp. 3882
Author(s):  
Jiechen Zhao ◽  
Yining Yu ◽  
Jingjing Cheng ◽  
Honglin Guo ◽  
Chunhua Li ◽  
...  
Keyword(s):  
Sea Ice ◽  
The Arctic ◽  
High Concentration ◽  
Sea Ice Concentration ◽  
Melt Pond ◽  
Ice Surface ◽  
Melt Ponds ◽  
Ice Concentration ◽  
Microwave Imager

As a long-term, near real-time, and widely used satellite derived product, the summer performance of the Special Sensor Microwave Imager/Sounder (SSMIS)-based sea ice concentration (SIC) is commonly doubted when extensive melt ponds exist on the ice surface. In this study, three SSMIS-based SIC products were assessed using ship-based SIC and melt pond fraction (MPF) observations from 60 Arctic cruises conducted by the Ice Watch Program and the Chinese Icebreaker Xuelong I/II. The results indicate that the product using the NASA Team (SSMIS-NT) algorithm and the product released by the Ocean and Sea Ice Satellite Application Facility (SSMIS-OS) underestimated the SIC by 15% and 7–9%, respectively, which mainly occurred in the high concentration rages, such as 80–100%, while the product using the Bootstrap (SSMIS-BT) algorithm overestimated the SIC by 3–4%, usually misestimating 80% < SIC < 100% as 100%. The MPF affected the SIC biases. For the high MPF case (e.g., 50%), the estimated biases for the three products increased to 20% (SSMIS-NT), 7% (SSMIS-BT), and 20% (SSMIS-OS) due to the influence of MPF. The relationship between the SIC biases and the MPF observations established in this study was demonstrated to greatly improve the accuracy of the 2D SIC distributions, which are useful references for model assimilation, algorithm improvement, and error analysis.

Inter-comparison of melt pond products with melt/freeze-up dates and sea ice concentration data

2020 ◽  
Author(s):  
Sanggyun Lee ◽  
Julienne Stroeve ◽  
Michel Tsamados
Keyword(s):  
Machine Learning ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Solar Heat ◽  
Melt Pond ◽  
Ice Surface ◽  
Melt Ponds ◽  
Ice Concentration

&lt;p&gt;&amp;#160;Melt ponds are a dominant feature on the Arctic sea ice surface in summer, occupying up to about 50 &amp;#8211; 60% of the sea ice surface during advanced melt. Melt ponds normally begin to form around mid-May in the marginal ice zone and expand northwards as the summer melt season progresses. Once melt ponds emerge, the scattering characteristics of the ice surface changes, dramatically lowering the sea ice albedo. Since 96% of the total annual solar heat into the ocean through sea ice occurs between May and August, the presence of melt ponds plays a significant role in this transfer of solar heat, influencing not only the sea ice energy balance, but also the amount of light available under the sea ice and ocean primary productivity. Given the importance melt ponds play in the coupled Arctic climate-ecosystem, mapping and quantification of melt pond variability on a Pan-Arctic basin scale are needed. Satellite-based observations are the only way to map melt ponds and albedo changes on a pan-Arctic scale. R&amp;#246;sel et al. (2012) utilized a MODIS 8-day average product to map melt ponds on a pan-Arctic scale and over several years. In another approach, melt pond fraction and surface albedo were retrieved based on the physical and optical characteristics of sea ice and melt ponds without a priori information using MERIS.Here, we propose a novel machine learning-based methodology to map Arctic melt ponds from MODIS 500m resolution data. We provide a merging procedure to create the first pan-Arctic melt pond product spanning a 20-year period at a weekly temporal resolution. Specifically, we use MODIS data together with machine learning, including multi-layer neural network and logistic regression to test our ability to map melt ponds from the start to the end of the melt season. Since sea ice reflectance is strongly dependent on the viewing and solar geometry (i.e. sensor and solar zenith and azimuth angles), we attempt to minimize this dependence by using normalized band ratios in the machine learning algorithms. Each melt pond retrieval algorithm is different and validation ways are different as well producing somewhat dissimilar melt pond results. In this study, we inter-compare melt ponds products from different institutes, including university of Hamburg, university of Bremen, and university college London. The melt pond maps are compared with melt onset and freeze-up dates data and sea ice concentration. The melt pond maps are evaluated by melt pond fraction statistics from high resolution satellite (MEDEA) images that have not been used for the evaluation in melt pond products.&amp;#160;&lt;/p&gt;

scholarly journals Mapping Sea Ice Surface Topography in High Fidelity with ICESat-2

2020 ◽  
Author(s):  
Sinéad Farrell ◽  
Kyle Duncan ◽  
Ellen Buckley ◽  
Jacqueline Richter-Menge ◽  
Ruohan Li
Keyword(s):  
Sea Ice ◽  
Surface Topography ◽  
High Fidelity ◽  
Ice Surface

scholarly journals A Field Study of Brine Drainage and Oil Entrainment in First-Year Sea Ice

1979 ◽  
Vol 22 (88) ◽  
pp. 473-502 ◽  
Author(s):  
Seelye Martin
Keyword(s):  
Sea Ice ◽  
Growth And Development ◽  
Salt Transport ◽  
First Year ◽  
Field Observations ◽  
Melt Pond ◽  
Ice Surface ◽  
Melt Ponds ◽  
Brine Channel ◽  
Horizontal Layers

AbstractFrom field observations this paper describes the growth and development of first-year sea ice and its interaction with petroleum. In particular, when sea ice initially forms, there is an upward salt transport so that the ice surface has a highly saline layer, regardless of whether the initial ice is frazil, columnar, or slush ice. When the ice warms in the spring, because of the eutectic condition, the surface salt liquifies and drains through the ice, leading to the formation of top-to-bottom brine channels and void spaces in the upper part of the ice. If oil is released beneath winter ice, then the oil becomes entrained in thin lenses within the ice. In the spring, this oil flows up to the surface through the newly-opened brine channels and distributes itself within the brine-channel feeder systems, on the ice surface, and in horizontal layers in the upper part of the ice. The paper shows that these layers probably form from the interaction of the brine drainage with the percolation of melt water from surface snow down into the ice and the rise of the oil from below. Finally in the summer, the oil on the surface leads to melt-pond formation. The solar energy absorbed by the oil on the surface of these melt ponds eventually causes the melt pond to melt through the ice, and the oil is again released into the ocean.

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