Articles | Volume 15, issue 4
https://doi.org/10.5194/tc-15-1907-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/tc-15-1907-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Apr 2021
Research article |
| 20 Apr 2021
| 20 Apr 2021
Mapping potential signs of gas emissions in ice of Lake Neyto, Yamal, Russia, using synthetic aperture radar and multispectral remote sensing data
b.geos, Korneuburg, Austria
Austrian Polar Research Institute, Vienna, Austria
Department of Geoinformatics – Z_GIS, DK GIScience, Paris Lodron University of Salzburg, Salzburg, Austria
Annett Bartsch
b.geos, Korneuburg, Austria
Austrian Polar Research Institute, Vienna, Austria
Department of Geoinformatics – Z_GIS, DK GIScience, Paris Lodron University of Salzburg, Salzburg, Austria
Yury A. Dvornikov
Department of Landscape Design and Sustainable Ecosystems, Agrarian-Technological Institute, Peoples’ Friendship University of Russia, Moscow, Russia
Alexei V. Kouraev
LEGOS, Université de Toulouse, CNES, CNRS, IRD, UPS, Toulouse, France
Department of Geology and Geography, Tomsk State University, Tomsk, Russia
Related authors
Annett Bartsch, Helena Bergstedt, Georg Pointner, Xaver Muri, Kimmo Rautiainen, Leena Leppänen, Kyle Joly, Aleksandr Sokolov, Pavel Orekhov, Dorothee Ehrich, and Eeva Mariatta Soininen
The Cryosphere, 17, 889–915, https://doi.org/10.5194/tc-17-889-2023, https://doi.org/10.5194/tc-17-889-2023, 2023
Short summary
Short summary
Rain-on-snow (ROS) events occur across many regions of the terrestrial Arctic in mid-winter. In extreme cases ice layers form which affect wildlife, vegetation and soils beyond the duration of the event. The fusion of multiple types of microwave satellite observations is suggested for the creation of a climate data record. Retrieval is most robust in the tundra biome, where records can be used to identify extremes and the results can be applied to impact studies at regional scale.
Clemens von Baeckmann, Annett Bartsch, Helena Bergstedt, Aleksandra Efimova, Barbara Widhalm, Dorothee Ehrich, Timo Kumpula, Alexander Sokolov, and Svetlana Abdulmanova
EGUsphere, https://doi.org/10.5194/egusphere-2024-699, https://doi.org/10.5194/egusphere-2024-699, 2024
Short summary
Short summary
Lakes are common features in Arctic permafrost areas. Landcover change following their drainage needs to be monitored since it has implications for ecology and the carbon cycle. Satellite data are key in this context. We compared a common vegetation index approach with a novel landcover monitoring scheme. Landcover information provides specifically information on wetland features. We also showed that the bioclimatic gradients play a significant role after drainage within the first 10 years.
Qing Ying, Benjamin Poulter, Jennifer D. Watts, Kyle A. Arndt, Anna-Maria Virkkala, Lori Bruhwiler, Youmi Oh, Brendan M. Rogers, Susan M. Natali, Hilary Sullivan, Luke D. Schiferl, Clayton Elder, Olli Peltola, Annett Bartsch, Amanda Armstrong, Ankur R. Desai, Eugénie Euskirchen, Mathias Göckede, Bernhard Lehner, Mats B. Nilsson, Matthias Peichl, Oliver Sonnentag, Eeva-Stiina Tuittila, Torsten Sachs, Aram Kalhori, Masahito Ueyama, and Zhen Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-84, https://doi.org/10.5194/essd-2024-84, 2024
Preprint under review for ESSD
Short summary
Short summary
We present daily methane fluxes of northern wetlands at 10-km resolution during 2016–2022 (WetCH4) derived from a novel machine-learning framework with improved accuracy. We estimated an average annual CH4 emissions of 20.8 ±2.1 Tg CH4 yr-1. Emissions were intensified in 2016, 2020, and 2022, with the largest interannual variations coming from West Siberia. Continued, all-season tower observations and improved soil moisture products are needed for future improvement of CH4 upscaling.
Cecile B. Menard, Sirpa Rasmus, Ioanna Merkouriadi, Gianpaolo Balsamo, Annett Bartsch, Chris Derksen, Florent Domine, Marie Dumont, Dorothee Ehrich, Richard Essery, Bruce C. Forbes, Gerhard Krinner, David Lawrence, Glen Liston, Heidrun Matthes, Nick Rutter, Melody Sandells, Martin Schneebeli, and Sari Stark
EGUsphere, https://doi.org/10.5194/egusphere-2023-2926, https://doi.org/10.5194/egusphere-2023-2926, 2024
Short summary
Short summary
Computer models, like those used in climate change studies, are written by modelers who have to decide how best to construct the models in order to satisfy the purpose they serve. Using snow modeling as an example, we examine the process behind the decisions to understand what motivates or limits modelers in their decision-making. We found that the context in which research is undertaken is often more crucial than scientific limitations. We argue for more transparency into our research practice.
Annett Bartsch, Aleksandra Efimova, Barbara Widhalm, Xaver Muri, Clemens von Baeckmann, Helena Bergstedt, Ksenia Ermokhina, Gustaf Hugelius, Birgit Heim, and Marina Leibmann
EGUsphere, https://doi.org/10.5194/egusphere-2023-2295, https://doi.org/10.5194/egusphere-2023-2295, 2023
Short summary
Short summary
Wetness gradients and landcover diversity for the entire Arctic tundra have been assessed using a novel satellite data based map. Patterns of lakes, wetlands, general soil moisture conditions and vegetation physiognomy are represented at 10 m. About 40 % of the area north of the treeline falls into three units of dry types with limited shrub growth. Wetter regions have higher landcover diversity than drier regions.
Annett Bartsch, Helena Bergstedt, Georg Pointner, Xaver Muri, Kimmo Rautiainen, Leena Leppänen, Kyle Joly, Aleksandr Sokolov, Pavel Orekhov, Dorothee Ehrich, and Eeva Mariatta Soininen
The Cryosphere, 17, 889–915, https://doi.org/10.5194/tc-17-889-2023, https://doi.org/10.5194/tc-17-889-2023, 2023
Short summary
Short summary
Rain-on-snow (ROS) events occur across many regions of the terrestrial Arctic in mid-winter. In extreme cases ice layers form which affect wildlife, vegetation and soils beyond the duration of the event. The fusion of multiple types of microwave satellite observations is suggested for the creation of a climate data record. Retrieval is most robust in the tundra biome, where records can be used to identify extremes and the results can be applied to impact studies at regional scale.
Elena Zakharova, Svetlana Agafonova, Claude Duguay, Natalia Frolova, and Alexei Kouraev
The Cryosphere, 15, 5387–5407, https://doi.org/10.5194/tc-15-5387-2021, https://doi.org/10.5194/tc-15-5387-2021, 2021
Short summary
Short summary
The paper investigates the performance of altimetric satellite instruments to detect river ice onset and melting dates and to retrieve ice thickness of the Ob River. This is a first attempt to use satellite altimetry for monitoring ice in the challenging conditions restrained by the object size. A novel approach permitted elaboration of the spatiotemporal ice thickness product for the 400 km river reach. The potential of the product for prediction of ice road operation was demonstrated.
Alexei V. Kouraev, Elena A. Zakharova, Andrey G. Kostianoy, Mikhail N. Shimaraev, Lev V. Desinov, Evgeny A. Petrov, Nicholas M. J. Hall, Frédérique Rémy, and Andrey Ya. Suknev
The Cryosphere, 15, 4501–4516, https://doi.org/10.5194/tc-15-4501-2021, https://doi.org/10.5194/tc-15-4501-2021, 2021
Short summary
Short summary
Giant ice rings are a beautiful and puzzling natural phenomenon. Our data show that ice rings are generated by lens-like warm eddies below the ice. We use multi-satellite data to analyse lake ice cover in the presence of eddies in April 2020 in southern Baikal. Unusual changes in ice colour may be explained by the competing influences of atmosphere above and the warm eddy below the ice. Tracking ice floes also helps to estimate eddy currents and their influence on the upper water layer.
Lydia Stolpmann, Caroline Coch, Anne Morgenstern, Julia Boike, Michael Fritz, Ulrike Herzschuh, Kathleen Stoof-Leichsenring, Yury Dvornikov, Birgit Heim, Josefine Lenz, Amy Larsen, Katey Walter Anthony, Benjamin Jones, Karen Frey, and Guido Grosse
Biogeosciences, 18, 3917–3936, https://doi.org/10.5194/bg-18-3917-2021, https://doi.org/10.5194/bg-18-3917-2021, 2021
Short summary
Short summary
Our new database summarizes DOC concentrations of 2167 water samples from 1833 lakes in permafrost regions across the Arctic to provide insights into linkages between DOC and environment. We found increasing lake DOC concentration with decreasing permafrost extent and higher DOC concentrations in boreal permafrost sites compared to tundra sites. Our study shows that DOC concentration depends on the environmental properties of a lake, especially permafrost extent, ecoregion, and vegetation.
Elena Shevnina, Ekaterina Kourzeneva, Yury Dvornikov, and Irina Fedorova
The Cryosphere, 15, 2667–2682, https://doi.org/10.5194/tc-15-2667-2021, https://doi.org/10.5194/tc-15-2667-2021, 2021
Short summary
Short summary
Antarctica consists mostly of frozen water, and it makes the continent sensitive to warming due to enhancing a transition/exchange of water from solid (ice and snow) to liquid (lakes and rivers) form. Therefore, it is important to know how fast water is exchanged in the Antarctic lakes. The study gives first estimates of scales for water exchange for five lakes located in the Larsemann Hills oasis. Two methods are suggested to evaluate the timescale for the lakes depending on their type.
Zhen Zhang, Etienne Fluet-Chouinard, Katherine Jensen, Kyle McDonald, Gustaf Hugelius, Thomas Gumbricht, Mark Carroll, Catherine Prigent, Annett Bartsch, and Benjamin Poulter
Earth Syst. Sci. Data, 13, 2001–2023, https://doi.org/10.5194/essd-13-2001-2021, https://doi.org/10.5194/essd-13-2001-2021, 2021
Short summary
Short summary
The spatiotemporal distribution of wetlands is one of the important and yet uncertain factors determining the time and locations of methane fluxes. The Wetland Area and Dynamics for Methane Modeling (WAD2M) dataset describes the global data product used to quantify the areal dynamics of natural wetlands and how global wetlands are changing in response to climate.
Alexander Savvichev, Igor Rusanov, Yury Dvornikov, Vitaly Kadnikov, Anna Kallistova, Elena Veslopolova, Antonina Chetverova, Marina Leibman, Pavel A. Sigalevich, Nikolay Pimenov, Nikolai Ravin, and Artem Khomutov
Biogeosciences, 18, 2791–2807, https://doi.org/10.5194/bg-18-2791-2021, https://doi.org/10.5194/bg-18-2791-2021, 2021
Short summary
Short summary
Microbial processes of the methane cycle were studied in four lakes of the central part of the Yamal Peninsula in an area of continuous permafrost: two large, deep lakes and two small and shallow ones. It was found that only small, shallow lakes contributed significantly to the overall diffusive methane emissions from the water surface during the warm summer season. The water column of large, deep lakes on Yamal acted as a microbial filter preventing methane emissions into the atmosphere.
Michael Kern, Robert Cullen, Bruno Berruti, Jerome Bouffard, Tania Casal, Mark R. Drinkwater, Antonio Gabriele, Arnaud Lecuyot, Michael Ludwig, Rolv Midthassel, Ignacio Navas Traver, Tommaso Parrinello, Gerhard Ressler, Erik Andersson, Cristina Martin-Puig, Ole Andersen, Annett Bartsch, Sinead Farrell, Sara Fleury, Simon Gascoin, Amandine Guillot, Angelika Humbert, Eero Rinne, Andrew Shepherd, Michiel R. van den Broeke, and John Yackel
The Cryosphere, 14, 2235–2251, https://doi.org/10.5194/tc-14-2235-2020, https://doi.org/10.5194/tc-14-2235-2020, 2020
Short summary
Short summary
The Copernicus Polar Ice and Snow Topography Altimeter will provide high-resolution sea ice thickness and land ice elevation measurements and the capability to determine the properties of snow cover on ice to serve operational products and services of direct relevance to the polar regions. This paper describes the mission objectives, identifies the key contributions the CRISTAL mission will make, and presents a concept – as far as it is already defined – for the mission payload.
Jaroslav Obu, Sebastian Westermann, Gonçalo Vieira, Andrey Abramov, Megan Ruby Balks, Annett Bartsch, Filip Hrbáček, Andreas Kääb, and Miguel Ramos
The Cryosphere, 14, 497–519, https://doi.org/10.5194/tc-14-497-2020, https://doi.org/10.5194/tc-14-497-2020, 2020
Short summary
Short summary
Little is known about permafrost in the Antarctic outside of the few research stations. We used a simple equilibrium permafrost model to estimate permafrost temperatures in the whole Antarctic. The lowest permafrost temperature on Earth is −36 °C in the Queen Elizabeth Range in the Transantarctic Mountains. Temperatures are commonly between −23 and −18 °C in mountainous areas rising above the Antarctic Ice Sheet, between −14 and −8 °C in coastal areas, and up to 0 °C on the Antarctic Peninsula.
Christine Kroisleitner, Annett Bartsch, and Helena Bergstedt
The Cryosphere, 12, 2349–2370, https://doi.org/10.5194/tc-12-2349-2018, https://doi.org/10.5194/tc-12-2349-2018, 2018
Short summary
Short summary
Knowledge about permafrost extent is required with respect to climate change. We used borehole temperature records from across the Arctic for the assessment of surface status information (frozen or unfrozen) derived from space-borne microwave sensors for permafrost extent mapping. The comparison to mean annual ground temperature (MAGT) at the coldest sensor depth revealed that not only extent but also temperature can be obtained from C-band-derived surface state with a residual error of 2.22 °C.
Sarah E. Chadburn, Gerhard Krinner, Philipp Porada, Annett Bartsch, Christian Beer, Luca Belelli Marchesini, Julia Boike, Altug Ekici, Bo Elberling, Thomas Friborg, Gustaf Hugelius, Margareta Johansson, Peter Kuhry, Lars Kutzbach, Moritz Langer, Magnus Lund, Frans-Jan W. Parmentier, Shushi Peng, Ko Van Huissteden, Tao Wang, Sebastian Westermann, Dan Zhu, and Eleanor J. Burke
Biogeosciences, 14, 5143–5169, https://doi.org/10.5194/bg-14-5143-2017, https://doi.org/10.5194/bg-14-5143-2017, 2017
Short summary
Short summary
Earth system models (ESMs) are our main tools for understanding future climate. The Arctic is important for the future carbon cycle, particularly due to the large carbon stocks in permafrost. We evaluated the performance of the land component of three major ESMs at Arctic tundra sites, focusing on the fluxes and stocks of carbon.
We show that the next steps for model improvement are to better represent vegetation dynamics, to include mosses and to improve below-ground carbon cycle processes.
Sina Muster, Kurt Roth, Moritz Langer, Stephan Lange, Fabio Cresto Aleina, Annett Bartsch, Anne Morgenstern, Guido Grosse, Benjamin Jones, A. Britta K. Sannel, Ylva Sjöberg, Frank Günther, Christian Andresen, Alexandra Veremeeva, Prajna R. Lindgren, Frédéric Bouchard, Mark J. Lara, Daniel Fortier, Simon Charbonneau, Tarmo A. Virtanen, Gustaf Hugelius, Juri Palmtag, Matthias B. Siewert, William J. Riley, Charles D. Koven, and Julia Boike
Earth Syst. Sci. Data, 9, 317–348, https://doi.org/10.5194/essd-9-317-2017, https://doi.org/10.5194/essd-9-317-2017, 2017
Short summary
Short summary
Waterbodies are abundant in Arctic permafrost lowlands. Most waterbodies are ponds with a surface area smaller than 100 x 100 m. The Permafrost Region Pond and Lake Database (PeRL) for the first time maps ponds as small as 10 x 10 m. PeRL maps can be used to document changes both by comparing them to historical and future imagery. The distribution of waterbodies in the Arctic is important to know in order to manage resources in the Arctic and to improve climate predictions in the Arctic.
Barbara Widhalm, Annett Bartsch, Marina Leibman, and Artem Khomutov
The Cryosphere, 11, 483–496, https://doi.org/10.5194/tc-11-483-2017, https://doi.org/10.5194/tc-11-483-2017, 2017
Short summary
Short summary
The active layer above the permafrost, which seasonally thaws during summer, is an important parameter for monitoring the state of permafrost. Its thickness is typically measured locally. The relationship between active-layer thickness (ALT) and X-band SAR backscatter of TerraSAR-X has been investigated in order to explore the possibility of delineating ALT with continuous and larger spatial coverage.
Annett Bartsch, Barbara Widhalm, Peter Kuhry, Gustaf Hugelius, Juri Palmtag, and Matthias Benjamin Siewert
Biogeosciences, 13, 5453–5470, https://doi.org/10.5194/bg-13-5453-2016, https://doi.org/10.5194/bg-13-5453-2016, 2016
Short summary
Short summary
A new approach for the estimation of soil organic carbon (SOC) pools north of the tree line has been developed based on synthetic aperture radar (SAR) data from the ENVISAT satellite. It can be shown that measurements of C-band SAR under frozen conditions represent vegetation and surface structure properties which relate to soil properties, specifically SOC. The approach provides the first spatially consistent account of soil organic carbon across the Arctic.
Klaus Haslinger and Annett Bartsch
Hydrol. Earth Syst. Sci., 20, 1211–1223, https://doi.org/10.5194/hess-20-1211-2016, https://doi.org/10.5194/hess-20-1211-2016, 2016
Short summary
Short summary
Gridded fields of daily max. and min. temperatures for the Austrian domain are used to calculate ET0 based on a re-calibrated Hargreaves method. Newly derived, station-based calibration parameters, with Penman–Monteith ET0 as a reference, show a distinct altitude and seasonal dependence. Theses features are used to interpolate the new calibration values in space and time onto the temperature grids. The ET0 is then calculated based on the entire gridded temperature data starting back in 1961.
I. Gouttevin, A. Bartsch, G. Krinner, and V. Naeimi
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-10-11241-2013, https://doi.org/10.5194/hessd-10-11241-2013, 2013
Manuscript not accepted for further review
Related subject area
Discipline: Other | Subject: Remote Sensing
Co-registration and residual correction of digital elevation models: a comparative study
Ice thickness and water level estimation for ice-covered lakes with satellite altimetry waveforms and backscattering coefficients
Semi-automated tracking of iceberg B43 using Sentinel-1 SAR images via Google Earth Engine
Brief communication: Glacier run-off estimation using altimetry-derived basin volume change: case study at Humboldt Glacier, northwest Greenland
Recent changes in pan-Antarctic region surface snowmelt detected by AMSR-E and AMSR2
CryoSat Ice Baseline-D validation and evolutions
Theoretical study of ice cover phenology at large freshwater lakes based on SMOS MIRAS data
Tao Li, Yuanlin Hu, Bin Liu, Liming Jiang, Hansheng Wang, and Xiang Shen
The Cryosphere, 17, 5299–5316, https://doi.org/10.5194/tc-17-5299-2023, https://doi.org/10.5194/tc-17-5299-2023, 2023
Short summary
Short summary
Raw DEMs are often misaligned with each other due to georeferencing errors, and a co-registration process is required before DEM differencing. We present a comparative analysis of the two classical DEM co-registration and three residual correction algorithms. The experimental results show that rotation and scale biases should be considered in DEM co-registration. The new non-parametric regression technique can eliminate the complex systematic errors, which existed in the co-registration results.
Xingdong Li, Di Long, Yanhong Cui, Tingxi Liu, Jing Lu, Mohamed A. Hamouda, and Mohamed M. Mohamed
The Cryosphere, 17, 349–369, https://doi.org/10.5194/tc-17-349-2023, https://doi.org/10.5194/tc-17-349-2023, 2023
Short summary
Short summary
This study blends advantages of altimetry backscattering coefficients and waveforms to estimate ice thickness for lakes without in situ data and provides an improved water level estimation for ice-covered lakes by jointly using different threshold retracking methods. Our results show that a logarithmic regression model is more adaptive in converting altimetry backscattering coefficients into ice thickness, and lake surface snow has differential impacts on different threshold retracking methods.
YoungHyun Koo, Hongjie Xie, Stephen F. Ackley, Alberto M. Mestas-Nuñez, Grant J. Macdonald, and Chang-Uk Hyun
The Cryosphere, 15, 4727–4744, https://doi.org/10.5194/tc-15-4727-2021, https://doi.org/10.5194/tc-15-4727-2021, 2021
Short summary
Short summary
This study demonstrates for the first time the potential of Google Earth Engine (GEE) cloud-computing platform and Sentinel-1 synthetic aperture radar (SAR) images for semi-automated tracking of area changes and movements of iceberg B43. Our novel GEE-based iceberg tracking can be used to construct a large iceberg database for a better understanding of the behavior of icebergs and their interactions with surrounding environments.
Laurence Gray
The Cryosphere, 15, 1005–1014, https://doi.org/10.5194/tc-15-1005-2021, https://doi.org/10.5194/tc-15-1005-2021, 2021
Short summary
Short summary
A total of 9 years of ice velocity and surface height data obtained from a variety of satellites are used to estimate the water run-off from the northern arm of the Humboldt Glacier in NW Greenland. This represents the first direct measurement of water run-off from a large Greenland glacier, and it complements the iceberg calving flux measurements also based on satellite data. This approach should help improve mass loss estimates for some large Greenland glaciers.
Lei Zheng, Chunxia Zhou, Tingjun Zhang, Qi Liang, and Kang Wang
The Cryosphere, 14, 3811–3827, https://doi.org/10.5194/tc-14-3811-2020, https://doi.org/10.5194/tc-14-3811-2020, 2020
Short summary
Short summary
Snowmelt plays a key role in mass and energy balance in polar regions. In this study, we report on the spatial and temporal variations in the surface snowmelt over the Antarctic sea ice and ice sheet (pan-Antarctic region) based on AMSR-E and AMSR2. Melt detection on sea ice is improved by excluding the effect of open water. The decline in surface snowmelt on the Antarctic ice sheet was very likely linked with the enhanced summer Southern Annular Mode.
Marco Meloni, Jerome Bouffard, Tommaso Parrinello, Geoffrey Dawson, Florent Garnier, Veit Helm, Alessandro Di Bella, Stefan Hendricks, Robert Ricker, Erica Webb, Ben Wright, Karina Nielsen, Sanggyun Lee, Marcello Passaro, Michele Scagliola, Sebastian Bjerregaard Simonsen, Louise Sandberg Sørensen, David Brockley, Steven Baker, Sara Fleury, Jonathan Bamber, Luca Maestri, Henriette Skourup, René Forsberg, and Loretta Mizzi
The Cryosphere, 14, 1889–1907, https://doi.org/10.5194/tc-14-1889-2020, https://doi.org/10.5194/tc-14-1889-2020, 2020
Short summary
Short summary
This manuscript aims to describe the evolutions which have been implemented in the new CryoSat Ice processing chain Baseline-D and the validation activities carried out in different domains such as sea ice, land ice and hydrology.
This new CryoSat processing Baseline-D will maximise the uptake and use of CryoSat data by scientific users since it offers improved capability for monitoring the complex and multiscale changes over the cryosphere.
Vasiliy Tikhonov, Ilya Khvostov, Andrey Romanov, and Evgeniy Sharkov
The Cryosphere, 12, 2727–2740, https://doi.org/10.5194/tc-12-2727-2018, https://doi.org/10.5194/tc-12-2727-2018, 2018
Short summary
Short summary
The paper presents a theoretical analysis of seasonal brightness temperature variations at a number of large freshwater lakes retrieved from data of the Soil Moisture and Ocean Salinity satellite. Three distinct seasonal time regions corresponding to different phenological phases of the lake surfaces, complete ice cover, ice melt and deterioration, and open water, were revealed. The paper demonstrates the possibility of determining the beginning of ice cover deterioration from satellite data.
Cited articles
Antonova, S., Duguay, C. R., Kääb, A., Heim, B., Langer, M., Westermann, S.,
and Boike, J.: Monitoring Bedfast Ice and Ice Phenology in Lakes of the Lena
River Delta Using TerraSAR-X Backscatter and Coherence Time Series, Remote
Sensing, 8, 903, https://doi.org/10.3390/rs8110903, 2016. a
Atwood, D. K., Gunn, G. E., Roussi, C., Wu, J., Duguay, C., and Sarabandi, K.:
Microwave Backscatter From Arctic Lake Ice and Polarimetric
Implications, IEEE Transactions on Geoscience and Remote Sensing, 53,
5972–5982, https://doi.org/10.1109/TGRS.2015.2429917, 2015. a, b, c
Anonymous: Interactive comment on “Mapping potential signs of gas emissions
in ice of lake Neyto, Yamal,Russia using synthetic aperture radar and
multispectral remote sensing data” by Georg Pointner et al., The Cryosphere
Discuss., https://doi.org/10.5194/tc-2020-226-RC2, 2020. a
Bartsch, A., Pointner, G., Leibman, M. O., Dvornikov, Y. A., Khomutov, A. V.,
and Trofaier, A. M.: Circumpolar Mapping of Ground-Fast Lake Ice,
Front. Earth Sci., 5, 12, https://doi.org/10.3389/feart.2017.00012, 2017. a
Bastviken, D., Cole, J., Pace, M., and Tranvik, L.: Methane emissions from
lakes: Dependence of lake characteristics, two regional assessments, and a
global estimate, Global Biogeochem. Cy., 18, GB4009,
https://doi.org/10.1029/2004GB002238, 2004. a
Bastviken, D., Tranvik, L. J., Downing, J. A., Crill, P. M., and Enrich-Prast,
A.: Freshwater Methane Emissions Offset the Continental Carbon Sink, Science,
331, 50–50, https://doi.org/10.1126/science.1196808, 2011. a
Bogoyavlensky, V. I., Sizov, O. S., Bogoyavlensky, I. V., and Nikonov, R. A.:
Technologies for Remote Detection and Monitoring of the Earth
Degassing in the Arctic: Yamal Peninsula, Neito Lake, Arctic:
Ecology and Economy, 2, 83–93, https://doi.org/10.25283/2223-4594-2018-2-83-93, 2018 (in
Russian). a, b, c, d, e, f, g, h, i
Bogoyavlensky, V. I., Bogoyavlensky, I. V., Kargina, T. N., Nikonov, R. A., and
Sizov, O. S.: Earth degassing in the Artic: remote and field studies of the
thermokarst lakes gas eruption, Arctic: Ecology and Economy, 2, 31–47,
https://doi.org/10.25283/2223-4594-2019-2-31-47, 2019a (in Russian). a, b, c, d, e, f, g, h
Bogoyavlensky, V. I., Sizov, O., Bogoyavlensky, I., Nikonov, R., and Kargina,
T.: Earth Degassing in the Arctic: Comprehensive Studies of the Distribution
of Frost Mounds and Thermokarst Lakes with Gas Blowout Craters on the Yamal
Peninsula, Arctic: Ecology and Economy, 4, 52–68,
https://doi.org/10.25283/2223-4594-2019-4-52-68, 2019b (in Russian). a, b
Bogoyavlensky, V. I., Sizov, O., Mazharov, A., Bogoyavlensky, I., Nikonov, R.,
Kishankov, A., and Kargina, T.: Earth degassing in the Arctic: remote and
field studies of the Seyakha catastrophic gas blowout on the Yamal Peninsula,
Arctic: Ecology and Economy, 1, 88–105,
https://doi.org/10.25283/2223-4594-2019-1-88-105, 2019c (in Russian). a, b
Chicco, D. and Jurman, G.: The advantages of the Matthews correlation
coefficient (MCC) over F1 score and accuracy in binary classification
evaluation, BMC genomics, 21, 6, https://doi.org/10.1186/s12864-019-6413-7, 2020. a, b
Cohen, J.: A Coefficient of Agreement for Nominal Scales, Educ. Psychol. Meas., 20, 37–46, https://doi.org/10.1177/001316446002000104,
1960. a, b
Dice, L. R.: Measures of the amount of ecologic association between species,
Ecology, 26, 297–302, https://doi.org/10.2307/1932409, 1945. a
Drusch, M., Bello, U. D., Carlier, S., Colin, O., Fernandez, V., Gascon, F.,
Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F.,
Sy, O., Marchese, F., and Bargellini, P.: Sentinel-2: ESA's Optical
High-Resolution Mission for GMES Operational Services, Remote
Sens. Environ., 120, 25–36, https://doi.org/10.1016/j.rse.2011.11.026, 2012. a
Duguay, C. R. and Lafleur, P. M.: Determining depth and ice thickness of
shallow sub-Arctic lakes using space-borne optical and SAR data,
Int. J. Remote Sens., 24, 475–489,
https://doi.org/10.1080/01431160304992, 2003. a
Duguay, C. R. and Pietroniro, A.: Ice Characteristics and Processes, and Remote Sensing of Frozen Rivers and Lakes, in: Remote Sensing in Northern Hydrology:
Measuring Environmental Change, Washington DC American Geophysical
Union Geophysical Monograph Series, 163, 63–90, https://doi.org/10.1029/GM163, 2005. a, b, c
Duguay, C. R., Pultz, T. J., Lafleur, P. M., and Drai, D.:
RADARSAT backscatter characteristics of ice growing on
shallow sub-Arctic lakes, Churchill, Manitoba, Canada, Hydrol.
Process., 16, 1631–1644, https://doi.org/10.1002/hyp.1026, 2002. a, b, c
Dvornikov, Y. A., Leibman, M. O., Khomutov, A. V., Kizyakov, A. I., Semenov,
P., Bussmann, I., Babkin, E. M., Heim, B., Portnov, A., Babkina, E. A.,
Streletskaya, I. D., Chetverova, A. A., Kozachek, A., and Meyer, H.:
Gas-emission craters of the Yamal and Gydan peninsulas: A proposed mechanism
for lake genesis and development of permafrost landscapes, Permafrost
Perigl., 30, 146–162, https://doi.org/10.1002/ppp.2014, 2019. a, b, c
ECMWF (European Centre for Medium-Range Weather Forecasts): Copernicus Climate Change Service (C3S) Climate Data Store, available at: https://cds.climate.copernicus.eu, last access: 19 January 2021. a
Edelstein, K., Alabyan, A., Gorin, S., and Popryadukhin, A.: Hydrological And
Hydrochemical Features Of The Largest Lakes Of The Yamal Peninsula,
Proceedings of the Karelian Research Centre of the Russian Academy of
Sciences, 10, 3–16, https://doi.org/10.17076/lim571, in Russian, 2017. a, b, c
Engram, M., Arp, C. D., Jones, B. M., Ajadi, O. A., and Meyer, F. J.: Analyzing
floating and bedfast lake ice regimes across Arctic Alaska using 25 years of
space-borne SAR imagery, Remote Sens. Environ., 209, 660–676,
https://doi.org/10.1016/j.rse.2018.02.022, 2018. a
Galaziy, G. I.: Baikal in questions and answers, Eastern-Siberian Publishing, Irkutsk,
p. 380, 1987 (in Russian). a
Grunblatt, J. and Atwood, D.: Mapping lakes for winter liquid water
availability using SAR on the North Slope of Alaska,
Int. J. Appl. Earth Obs., 27,
63–69, https://doi.org/10.1016/j.jag.2013.05.006, 2014. a
Gunn, G. E., Duguay, C. R., Atwood, D. K., King, J., and Toose, P.: Observing
Scattering Mechanisms of Bubbled Freshwater Lake Ice Using Polarimetric
RADARSAT-2 (C-Band) and UW-Scat (X- and Ku-Bands), IEEE T.
Geosci. Remote Sens., 56, 2887–2903,
https://doi.org/10.1109/TGRS.2017.2786158, 2018. a, b
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P.,
Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R.,
Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A.,
Fernández del Río, J., Wiebe, M., Peterson, P., Gérard-Marchant, P.,
Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant,
T. E.: Array programming with NumPy, Nature, 585, 357–362,
https://doi.org/10.1038/s41586-020-2649-2, 2020. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A.,
Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers,
D., Simmons, A., Soci, C., and Dee, D., and Thépaut, J.-N.: ERA5 hourly data
on single levels from 1979 to present, Copernicus Climate Change Service
(C3S) Climate Data Store (CDS), https://doi.org/10.24381/cds.adbb2d47, 2018. a, b, c, d, e, f
Hoekstra, P. and Delaney, A.: Dielectric properties of soils at UHF and
microwave frequencies, J. Geophys. Res., 79,
1699–1708, https://doi.org/10.1029/JB079i011p01699, 1974. a
Jeffries, M. O., Morris, K., Weeks, W. F., and Wakabayashi, H.: Structural
and stratigraphie features and ERS 1 synthetic aperture radar
backscatter characteristics of ice growing on shallow lakes in NW
Alaska, winter 1991–1992, J. Geophys. Res.-Oceans, 99,
22459–22471, https://doi.org/10.1029/94JC01479, 1994. a
Kankaku, Y., Suzuki, S., and Osawa, Y.: ALOS-2 mission and development status,
in: 2013 IEEE International Geoscience and Remote Sensing Symposium-IGARSS,
2396–2399, IEEE, https://doi.org/10.1109/IGARSS.2013.6723302, 2013. a, b, c
Kazantsev, V. S., Krivenok, L. A., and Cherbunina, M. Y.: Methane emissions
from thermokarst lakes in the southern tundra of Western Siberia, Geography,
Environment, Sustainability, 11, 58–73,
https://doi.org/10.24057/2071-9388-2018-11-1-58-73, 2018. a
Kazantsev, V. S., Krivenok, L. A., and Dvornikov, Y. A.: Preliminary data on
the methane emission from lake seeps of the Western Siberia permafrost zone,
IOP Conference Series: Earth and Environmental Science, 606, 012022,
https://doi.org/10.1088/1755-1315/606/1/012022, 2020. a, b, c
Kizyakov, A., Zimin, M., Sonyushkin, A., Dvornikov, Y., Khomutov, A., and
Leibman, M.: Comparison of Gas Emission Crater Geomorphodynamics on Yamal and
Gydan Peninsulas (Russia), Based on Repeat Very-High-Resolution Stereopairs,
Remote Sensing, 9, 1023, https://doi.org/10.3390/rs9101023, 2017. a
Kizyakov, A., Leibman, M., Zimin, M., Sonyushkin, A., Dvornikov, Y., Khomutov,
A., Dhont, D., Cauquil, E., Pushkarev, V., and Stanilovskaya, Y.: Gas
Emission Craters and Mound-Predecessors in the North of West Siberia,
Similarities and Differences, Remote Sensing, 12, 2182, https://doi.org/10.3390/rs12142182,
2020. a
Kouraev, A. V., Zakharova, E. A., Rémy, F., Kostianoy, A. G., Shimaraev,
M. N., Hall, N. M. J., and Suknev, Ya. A.: Giant ice rings on lakes Baikal
and Hovsgol: Inventory, associated water structure and potential formation
mechanism, Limnol. Oceanogr., 61, 1001–1014,
https://doi.org/10.1002/lno.10268, 2016. a
Kouraev, A. V., Zakharova, E. A., Rémy, F., Kostianoy, A. G., Shimaraev,
M. N., Hall, N. M. J., Zdorovennov, R. E., and Suknev, A. Y.: Giant ice rings
on lakes and field observations of lens-like eddies in the Middle Baikal
(2016–2017), Limnol. Oceanogr., 64, 2738–2754,
https://doi.org/10.1002/lno.11338, 2019. a
Landis, J. R. and Koch, G. G.: The Measurement of Observer Agreement for
Categorical Data, Biometrics, 33, 159–174, https://doi.org/10.2307/2529310, 1977. a, b
Lee, J.-S., Wen, J.-H., Ainsworth, T. L., Chen, K.-S., and Chen, A. J.:
Improved Sigma Filter for Speckle Filtering of SAR Imagery, IEEE T. Geosci. Remote Sens., 47, 202–213,
https://doi.org/10.1109/TGRS.2008.2002881, 2008. a
Leibman, M., Kizyakov, A., Plekhanov, A., and Streletskaya, I.: New permafrost
feature: deep crater in Central Yamal, West Siberia, Russia as a response to
local climate fluctuations, Geography, Environment, Sustainability, 7,
68–80, https://doi.org/10.24057/2071-9388-2014-7-4-68-79, 2014. a
Lindgren, P. R., Grosse, G., Walter Anthony, K. M., and Meyer, F. J.: Detection and spatiotemporal analysis of methane ebullition on thermokarst lake ice using high-resolution optical aerial imagery, Biogeosciences, 13, 27–44, https://doi.org/10.5194/bg-13-27-2016, 2016. a
Lindgren, P. R., Grosse, G., Meyer, F. J., and Walter Anthony, K. M.: An
Object-Based Classification Method to Detect Methane Ebullition Bubbles in
Early Winter Lake Ice, Remote Sensing, 11, 822, https://doi.org/10.3390/rs11070822, 2019. a
Matthews, B. W.: Comparison of the predicted and observed secondary structure
of T4 phage lysozyme, Biochimica et Biophysica Acta (BBA)-Protein Structure,
405, 442–451, https://doi.org/10.1016/0005-2795(75)90109-9, 1975. a
Meissner, T. and Wentz, F. J.: The complex dielectric constant of pure and
sea water from microwave satellite observations, IEEE T.
Geosci. Remote Sens., 42, 1836–1849,
https://doi.org/10.1109/TGRS.2004.831888, 2004. a
Mätzler, C. and Wegmüller, U.: Dielectric properties of freshwater ice at
microwave frequencies, J. Physics D, 20, 1623–1630,
https://doi.org/10.1088/0022-3727/20/12/013, 1987. a, b
Nisbet, E. G., Dlugokencky, E. J., and Bousquet, P.: Methane on the
Rise–Again, Science, 343, 493–495, https://doi.org/10.1126/science.1247828, 2014. a
Nisbet, E. G., Manning, M. R., Dlugokencky, E. J., Fisher, R. E., Lowry, D.,
Michel, S. E., Myhre, C. L., Platt, S. M., Allen, G., Bousquet, P., Brownlow,
R., Cain, M., France, J. L., Hermansen, O., Hossaini, R., Jones, A. E.,
Levin, I., Manning, A. C., Myhre, G., Pyle, J. A., Vaughn, B. H., Warwick,
N. J., and White, J. W. C.: Very Strong Atmospheric Methane Growth in the
4 Years 2014–2017: Implications for the Paris Agreement, Global
Biogeochem. Cy., 33, 318–342, https://doi.org/10.1029/2018GB006009, 2019. a
Obu, J., Westermann, S., Barboux, C., Bartsch, A., Delaloye, R., Grosse, G.,
Heim, B., Hugelius, G., Irrgang, A., Kääb, A., Kroisleitner, C., Matthes,
H., Nitze, I., Pellet, C., Seifert, F., Strozzi, T., Wegmüller, U.,
Wieczorek, M., and Wiesmann, A.: ESA Permafrost Climate Change
Initiative (Permafrost_cci): Permafrost Ground Temperature for the
Northern Hemisphere, v2.0, Centre for Environmental Data Analysis,
https://doi.org/10.5285/6ebcb73158b14cd5a321b7c0bc6ed393, 2020. a
Otsu, N.: A Threshold Selection Method from Gray-Level Histograms, IEEE
T. Syst. Man Cyb., 9, 62–66,
https://doi.org/10.1109/TSMC.1979.4310076, 1979. a
Padwick, C., Deskevich, M., Pacifici, F., and Smallwood, S.: WorldView-2
pan-sharpening, in: Proceedings of the ASPRS 2010 Annual Conference, San
Diego, CA, USA, vol. 2630, available at:
https://www.semanticscholar.org/paper/WORLDVIEW-2-PAN-SHARPENING-Padwick-Scientist/cef54a1a117157ab3ec336ff83acc62eaafdd3c2 (last access: 14 April 2021),
2010. a
Petrov, E. A.: The Baikal seal, ECOS, Ulan-Ude, p. 176, 2009 (in Russian). a
PGC (Polar Geospatial Center): ArcticDEM – Polar Geospatial Center, available at: https://www.pgc.umn.edu/data/arcticdem, last access: 19 January 2021. a
Porter, C., Morin, P., Howat, I., Noh, M.-J., Bates, B., Peterman, K., Keesey,
S., Schlenk, M., Gardiner, J., Tomko, K., Willis, M., Kelleher, C., Cloutier,
M., Husby, E., Foga, S., Nakamura, H., Platson, M., Wethington, Michael, J.,
Williamson, C., Bauer, G., Enos, J., Arnold, G., Kramer, W., Becker, P.,
Doshi, A., D'Souza, C., Cummens, P., Laurier, F., and Bojesen, M.:
ArcticDEM, Harvard Dataverse, https://doi.org/10.7910/DVN/OHHUKH, 2018. a
Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C. E., Allen, R. G., Anderson, M. C., Helder, D., Irons, J. R., Johnson, D. M., Kennedy, R., Scambos, T. A., Schaaf, C. B., Schott, J. R., Sheng, Y., Vermote, E. F., Belward, A. S., Bindschadler, R., Cohen, W. B., Gao,F., Hipple, J. D., Hostert, P., Huntington, J., Justice, C. O., Kilic, A., Kovalskyy, V., Lee, Z. P., Lymburner, L., Masek, J. G., McCorkel, J., Shuai, Y., Trezza, R., Vogelmann, J., Wynne, R. H., and Zhu, Z.: Landsat-8: Science and product vision for terrestrial global change
research, Remote Sens. Environ., 145, 154–172,
https://doi.org/10.1016/j.rse.2014.02.001, 2014. a, b
Schwietzke, S., Sherwood, O. A., Bruhwiler, L. M. P., Miller, J. B., Etiope, G., Dlugokencky, E. J., Englund Michel, S., Arling, V. A., Vaughn, B. H., White, J. W. C., and Tans, P. P.: Upward revision of global fossil fuel methane emissions based
on isotope database, Nature, 538, 88–91, https://doi.org/10.1038/nature19797, 2016. a
Serco and GAEL Systems consortium: Copernicus Open Access Hub, available at: https://scihub.copernicus.eu/dhus, last access: 19 January 2021. a
Sørensen, T.: A method of establishing groups of equal amplitude in plant
sociology based on similarity of species content and its application to
analyses of the vegetation on Danish commons, available at:
http://www.royalacademy.dk/Publications/High/295_S%F8rensen, Thorvald.pdf (last access: 14 April 2021),
1948. a
Surdu, C. M., Duguay, C. R., Brown, L. C., and Fernández Prieto, D.: Response of ice cover on shallow lakes of the North Slope of Alaska to contemporary climate conditions (1950–2011): radar remote-sensing and numerical modeling data analysis, The Cryosphere, 8, 167–180, https://doi.org/10.5194/tc-8-167-2014, 2014. a
Surdu, C. M., Duguay, C. R., Pour, H. K., and Brown, L. C.: Ice Freeze-up
and Break-up Detection of Shallow Lakes in Northern Alaska with
Spaceborne SAR, Remote Sensing, 7, 6133–6159,
https://doi.org/10.3390/rs70506133, 2015. a
Turetsky, M. R., Abbott, B. W., Jones, M. C., Walter Anthony, K., Olefeldt, D., Schuur, E. A. G., Grosse, G., Kuhry, P., Hugelius, G., Koven, C., Lawrence, D. M., Gibson, C., Sannel, A. B. K., and McGuire, A. D.: Carbon
release through abrupt permafrost thaw, Nat. Geosci., 13, 138–143,
https://doi.org/10.1038/s41561-019-0526-0, 2020. a
Updike, T. and Comp, C.: Radiometric use of WorldView-2 imagery, Technical
Note, 1–17, available at:
https://dg-cms-uploads-production.s3.amazonaws.com/uploads/document/file/104/Radiometric_Use_of_WorldView-2_Imagery.pdf (last access: 14 April 2021),
2010. a
USGS (United States Geological Survey): EarthExplorer, available at: https://earthexplorer.usgs.gov, last access: 19 January 2021. a
van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F.,
Warner, J. D., Yager, N., Gouillart, E., Yu, T., and the scikit-image
contributors: scikit-image: image processing in Python, PeerJ, 2, e453,
https://doi.org/10.7717/peerj.453, 2014. a, b
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T.,
Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van
der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson,
A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng,
Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R.,
Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro,
A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors:
SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python,
Nat. Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Wakabayashi, H., Weeks, W. F., and Jeffries, M. O.: A C-band
backscatter model for lake ice in Alaska, in: Proceedings of IGARSS '93 –
IEEE International Geoscience and Remote Sensing Symposium, Tokyo, Japan, Vol. 3,
1264–1266, https://doi.org/10.1109/IGARSS.1993.322103, IEEE (Institute of Electrical and Electronics Engineers), Piscataway, NJ, USA,
1993. a
Walter, K. M.: Methane emissions from lakes in northeast Siberia and Alaska,
PhD thesis, available at: http://hdl.handle.net/11122/8900 (last access: 14 April 2021), 2006. a
Walter, K. M., Zimov, S. A., Chanton, J. P., Verbyla, D., and Chapin, F. S.:
Methane bubbling from Siberian thaw lakes as a positive feedback to
climate warming, Nature, 443, 71–75, https://doi.org/10.1038/nature05040, 2006. a, b
Walter Anthony, K. M., Vas, D. A., Brosius, L., Chapin, F. S., Zimov, S. A.,
and Zhuang, Q.: Estimating methane emissions from northern lakes using
ice-bubble surveys, Limnol. Oceanogr.: Methods, 8, 592–609,
https://doi.org/10.4319/lom.2010.8.0592, 2010. a
Wik, M., Crill, P. M., Bastviken, D., Danielsson, Å., and Norbäck, E.:
Bubbles trapped in arctic lake ice: Potential implications for methane
emissions, J. Geophys. Res.-Biogeo., 116, G03044,
https://doi.org/10.1029/2011JG001761, 2011. a
Wik, M., Varner, R. K., Anthony, K. W., MacIntyre, S., and Bastviken, D.:
Climate-sensitive northern lakes and ponds are critical components of
methane release, Nat. Geosci., 9, 99–105, https://doi.org/10.1038/ngeo2578, 2016. a
Woodhouse, I. H.: Introduction to microwave remote sensing, CRC press, Boca Raton,
https://doi.org/10.1201/9781315272573, 2005. a
Yen, J.-C., Chang, F.-J., and Chang, S.: A new criterion for automatic
multilevel thresholding, IEEE T. Image Process., 4, 370–378,
https://doi.org/10.1109/83.366472, 1995. a
Zhang, L., Shi, J., Zhang, Z., and Zhao, K.: The estimation of dielectric
constant of frozen soil-water mixture at microwave bands, in: IGARSS 2003.
2003 IEEE International Geoscience and Remote Sensing Symposium, Toulouse, France, Proceedings
(IEEE Cat. No.03CH37477), Vol. 4, 2903–2905,
https://doi.org/10.1109/IGARSS.2003.1294626, IEEE (Institute of Electrical and Electronics Engineers), Piscataway, NJ, USA, 2003. a, b
Zuhlke, M., Fomferra, N., Brockmann, C., Peters, M., Veci, L., Malik, J., and
Regner, P.: SNAP (sentinel application platform) and the ESA sentinel 3
toolbox, in: Sentinel-3 for Science Workshop, Vol. 734, available at:
https://ui.adsabs.harvard.edu/abs/2015ESASP.734E..21Z/abstract (last access: 14 April 2021),
2015. a
Short summary
This study presents strong new indications that regions of anomalously low backscatter in C-band synthetic aperture radar (SAR) imagery of ice of Lake Neyto in northwestern Siberia are related to strong emissions of natural gas. Spatio-temporal dynamics and potential scattering and formation mechanisms are assessed. It is suggested that exploiting the spatial and temporal properties of Sentinel-1 SAR data may be beneficial for the identification of similar phenomena in other Arctic lakes.
This study presents strong new indications that regions of anomalously low backscatter in C-band...
