A physical algorithm to measure sea ice concentration from passive microwave remote sensing data

Tikhonov, V. I.; Репина, И. А.; Raev, M. D.; Шарков, Е. А.; Ivanov, Vladimir; Boyarskii, D. A.; Alexeeva, Tatyana A.; Komarova, N. Yu.

doi:10.1016/j.asr.2015.07.009

Cited by 14 publications

(7 citation statements)

References 56 publications

(101 reference statements)

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“…Linear Equations (14)- (18) are the approximations of theoretical tangents calculated for SSM/I radiometer channels (19.35, 37, and 85.5 GHz). However, further calculations demonstrate their validity also for SSMIS (channels 19.35, 37, and 91.655 GHz) and AMSR2 (channels 18.7, 36.5, and 89 GHz) [37].…”

mentioning

confidence: 72%

“…The initially developed algorithm VASIA was found to underestimate SIC in the warm season. The shortcoming was identified and successfully eliminated and the modified algorithm got the name VASIA 2 [36,37].…”

Section: The Vasia2 Algorithmmentioning

confidence: 99%

“…The open area between the two straight lines (1) corresponds to ice free of snow or covered with dry and wet snow. The region inside the shaded triangle (2) indicates ice with SWM in various stages including melt ponds formation [36,37]. Each of these four regions was averaged by a linear function depending only on the SIC.…”

Section: The Vasia2 Algorithmmentioning

confidence: 99%

“…The lower boundary of region 1 ( Figure 1C) is selected as a criterion for the presence or absence of SWM on ice. If the value of tg(37-19 V) at a certain SIC lies below this boundary, then SWM is present on ice [36,37]. The lower boundary of region 1 ( Figure 1C) is approximated by a linear function that depends only on I:…”

Section: The Vasia2 Algorithmmentioning

confidence: 99%

“…This is explained by the fact that, as melt develops, the snow becomes saturated with water and the number of melt ponds on ice increases. The area of ice under the melt ponds is "mistaken" by microwave radiometers for open water, and, consequently, the SIC is underestimated [36,37,61,62]. The horizontal axis denotes the difference C smr -C so (tenths), the vertical axis the melt stage (points).…”

Section: Nt Asi Vasia2mentioning

confidence: 99%

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Comparison of Arctic Sea Ice Concentrations from the NASA Team, ASI, and VASIA2 Algorithms with Summer and Winter Ship Data

et al. 2019

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The paper presents a comparison of sea ice concentration (SIC) derived from satellite microwave radiometry data and dedicated ship observations. For the purpose, the NASA Team (NT), Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI), and Variation Arctic/Antarctic Sea Ice Algorithm 2 (VASIA2) algorithms were used as well as the database of visual ice observations accumulated in the course of 15 Arctic expeditions. The comparison was performed in line with the SIC gradation (in tenths) into very open (1–3), open (4–6), close (7–8), very close and compact (9–10,10) ice, separately for summer and winter seasons. On average, in summer NT underestimates SIC by 0.4 tenth as compared to ship observations, while ASI and VASIA2 by 0.3 tenth. All three algorithms overestimate total SIC in regions of very open ice and underestimate it in regions of close, very close, and compact ice. The maximum average errors are typical of open ice regions that are most common in marginal ice zones. In winter, NT and ASI also underestimate SIC on average by 0.4 and 0.8 tenths, respectively, while VASIA2, on the contrary, overestimates by 0.2 tenth against the ship data, however, for open and close ice the average errors are significantly higher than in summer. In the paper, we also estimate the impact of ice melt stage and presence of new ice and nilas on SIC derived from NT, ASI, and VASIA2.

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mentioning

confidence: 72%

Section: The Vasia2 Algorithmmentioning

confidence: 99%

Section: The Vasia2 Algorithmmentioning

confidence: 99%

Section: The Vasia2 Algorithmmentioning

confidence: 99%

Section: Nt Asi Vasia2mentioning

confidence: 99%

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Comparison of Arctic Sea Ice Concentrations from the NASA Team, ASI, and VASIA2 Algorithms with Summer and Winter Ship Data

et al. 2019

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Essential gaps and uncertainties in the understanding of the roles and functions of Arctic sea ice

Gerland

Barber

Meier

et al. 2019

Environ. Res. Lett.

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While Arctic sea ice is changing, new observation methods are developed and process understanding improves, whereas gaps in observations and understanding evolve. Some previous gaps are filled, while others remain, or come up new. Knowing about the status of observation and knowledge gaps is important for interpreting observation and research results, interpretation and use of key climate indicators, and for research and observation planning. This paper deals with identifying some of the important current gaps connected to Arctic sea ice and related climate indicators, including their role and functions in the sea ice and climate systems. Subtopics that are discussed here include Arctic seaice extent, concentration, and thickness, sea-ice thermodynamics, age and dynamic processes, and biological implications of changing sea ice. Among crucial gaps are few in situ observations during the winter season, limited observational data on snow and ice thickness from the Arctic Basin, and wide gaps in biological rate measurements in or under sea ice. There is a need to develop or improve analyzes and products of remote sensing, especially for new sensors and technology such as remotely operated vehicles. Potential gaps in observations are inevitably associated with interruptions in longterm observational time series due to sensor failure or cuts in observation programmes.

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Satellite microwave radiometry of sea ice of polar regions: a review

Tikhonov

Raev

Шарков

et al. 2016

Izv. Atmos. Ocean. Phys.

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A physical algorithm to measure sea ice concentration from passive microwave remote sensing data

Cited by 14 publications

References 56 publications

Comparison of Arctic Sea Ice Concentrations from the NASA Team, ASI, and VASIA2 Algorithms with Summer and Winter Ship Data

Comparison of Arctic Sea Ice Concentrations from the NASA Team, ASI, and VASIA2 Algorithms with Summer and Winter Ship Data

Essential gaps and uncertainties in the understanding of the roles and functions of Arctic sea ice

Satellite microwave radiometry of sea ice of polar regions: a review

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