Retrieval of Arctic Sea Ice Parameters by Satellite Passive Microwave Sensors: A Comparison of Eleven Sea Ice Concentration Algorithms

Ivanova, N. P.; Johannessen, Ola M.; Pedersen, Leif Toudal; Tonboe, Rasmus

doi:10.1109/tgrs.2014.2310136

Cited by 151 publications

(134 citation statements)

References 30 publications

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“…The NASA Team algorithm (NT, Cavalieri et al, 1984) uses the polarisation ratio at 19 GHz and the gradient ratio of 19V and 37V. ASI (The Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice Algorithm), a non-linear algorithm (Kaleschke et al, 2001), and Near 90 GHz linear (N90, Ivanova et al, 2013) use the polarisation difference at near 90 GHz, both based on Svendsen et al (1987). These are also called near 90 GHz or highfrequency algorithms.…”

Section: Selected Algorithmsmentioning

confidence: 99%

“…Finally we consider combinations of algorithms (hybrid algorithms), where one of the algorithms is expected to have low sensitivity to atmospheric effects over open water, and the other is expected to have a better performance over ice. This group includes the NT+CV algorithm (Ivanova et al, 2013): an average of NT and CV, the CV+N90 algorithm (Ivanova et al, 2013): an www.the-cryosphere.net/9/1797/2015/ The Cryosphere, 9, 1797-1817, 2015 …”

Section: Selected Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Inter-comparison and evaluation of sea ice algorithms: towards further identification of challenges and optimal approach using passive microwave observations

Ivanova¹,

et al. 2015

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Abstract. Sea ice concentration has been retrieved in polar regions with satellite microwave radiometers for over 30 years. However, the question remains as to what is an optimal sea ice concentration retrieval method for climate monitoring. This paper presents some of the key results of an extensive algorithm inter-comparison and evaluation experiment. The skills of 30 sea ice algorithms were evaluated systematically over low and high sea ice concentrations. Evaluation criteria included standard deviation relative to independent validation data, performance in the presence of thin ice and melt ponds, and sensitivity to error sources with seasonal to inter-annual variations and potential climatic trends, such as atmospheric water vapour and water-surface roughening by wind. A selection of 13 algorithms is shown in the article to demonstrate the results. Based on the findings, a hybrid approach is suggested to retrieve sea ice concentration globally for climate monitoring purposes. This approach consists of a combination of two algorithms plus dynamic tie points implementation and atmospheric correction of input brightness temperatures. The method minimizes inter-sensor calibration discrepancies and sensitivity to the mentioned error sources.

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Section: Selected Algorithmsmentioning

confidence: 99%

Section: Selected Algorithmsmentioning

confidence: 99%

Inter-comparison and evaluation of sea ice algorithms: towards further identification of challenges and optimal approach using passive microwave observations

Ivanova¹,

et al. 2015

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“…SIC data can contain errors from multiple sources mentioned above and errors in retrieval algorithms (e.g., underestimating summer SIC in the NASA Team algorithm) [6,8,27,28], but in this study, we assumed that the SIC data provided by the NSIDC are of enough quality.…”

Section: Data Description and Preprocessingmentioning

confidence: 99%

“…Passive microwave satellite data collection has been widely used because of its extended coverage and temporal resolution, despite its low spatial resolution [6,7]. Satellite data is often processed using sea ice retrieval algorithms to determine the values of various sea ice parameters, such as age, concentration, extent, thickness, and others [8]. Most sea ice data products are publicly available online.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Arctic Sea Ice Concentration Using a Fully Data Driven Deep Neural Network

Chi

Kim

2017

Remote Sensing

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Abstract:The Arctic sea ice is an important indicator of the progress of global warming and climate change. Prediction of Arctic sea ice concentration has been investigated by many disciplines and predictions have been made using a variety of methods. Deep learning (DL) using large training datasets, also known as deep neural network, is a fast-growing area in machine learning that promises improved results when compared to traditional neural network methods. Arctic sea ice data, gathered since 1978 by passive microwave sensors, may be an appropriate input for training DL models. In this study, a large Arctic sea ice dataset was employed to train a deep neural network and this was then used to predict Arctic sea ice concentration, without incorporating any physical data. We compared the results of our methods quantitatively and qualitatively to results obtained using a traditional autoregressive (AR) model, and to a compilation of results from the Sea Ice Prediction Network, collected using a diverse set of approaches. Our DL-based prediction methods outperformed the AR model and yielded results comparable to those obtained with other models.

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“…Several widely used sea ice algorithms are the NASA Team (NT) algorithm [14], the enhanced NT algorithm [15], the Bootstrap (BS) algorithm [16], and the ARTIST Sea Ice (ASI) algorithm [17]. More details on the microwave sea ice products can be found in [18,19] and references therein. Advantages of passive microwave data include its high temporal coverage and all weather capability.…”

Section: Introductionmentioning

confidence: 99%

Sea and Freshwater Ice Concentration from VIIRS on Suomi NPP and the Future JPSS Satellites

2016

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Information on ice is important for shipping, weather forecasting, and climate monitoring. Historically, ice cover has been detected and ice concentration has been measured using relatively low-resolution space-based passive microwave data. This study presents an algorithm to detect ice and estimate ice concentration in clear-sky areas over the ocean and inland lakes and rivers using high-resolution data from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar Orbiting Partnership (S-NPP) and on future Joint Polar Satellite System (JPSS) satellites, providing spatial detail that cannot be obtained with passive microwave data. A threshold method is employed with visible and infrared observations to identify ice, then a tie-point algorithm is used to determine the representative reflectance/temperature of pure ice, estimate the ice concentration, and refine the ice cover mask. The VIIRS ice concentration is validated using observations from Landsat 8. Results show that VIIRS has an overall bias of´0.3% compared to Landsat 8 ice concentration, with a precision (uncertainty) of 9.5%. Biases and precision values for different ice concentration subranges from 0% to 100% can be larger.

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Retrieval of Arctic Sea Ice Parameters by Satellite Passive Microwave Sensors: A Comparison of Eleven Sea Ice Concentration Algorithms

Cited by 151 publications

References 30 publications

Inter-comparison and evaluation of sea ice algorithms: towards further identification of challenges and optimal approach using passive microwave observations

Inter-comparison and evaluation of sea ice algorithms: towards further identification of challenges and optimal approach using passive microwave observations

Prediction of Arctic Sea Ice Concentration Using a Fully Data Driven Deep Neural Network

Sea and Freshwater Ice Concentration from VIIRS on Suomi NPP and the Future JPSS Satellites

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