Unsupervised classification of Arctic sea ice using neural network

The accurate knowledge of spatial and temporal variations of snow depth over sea ice in the Arctic basin is important for understanding the Arctic energy budget and retrieving sea ice thickness from satellite altimetry. In this study, we develop and validate a new method for retrieving snow depth over Arctic sea ice from brightness temperatures at different frequencies measured by passive microwave radiometers. We construct an ensemble-based deep neural network and use snow depth measured by sea ice mass balance buoys to train the network. First, the accuracy of the retrieved snow depth is validated with observations. The results show the derived snow depth is in good agreement with the observations, in terms of correlation, bias, root mean square error, and probability distribution. Our ensemble-based deep neural network can be used to extend the snow depth retrieval from first-year sea ice (FYI) to multi-year sea ice (MYI), as well as during the melting period. Second, the consistency and discrepancy of snow depth in the Arctic basin between our retrieval using the ensemble-based deep neural network and two other available retrievals using the empirical regression are examined. The results suggest that our snow depth retrieval outperforms these data sets.

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Deep Learning Convolutional Neural Network Applying for the Arctic Acoustic Tomography Current Inversion Accuracy Improvement

Journal of Marine Science and Engineering ◽

10.3390/jmse9070755 ◽

2021 ◽

Vol 9 (7) ◽

pp. 755

Author(s):

Kangkang Jin ◽

Jian Xu ◽

Zichen Wang ◽

Can Lu ◽

Long Fan ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Sea Ice ◽

Convolutional Neural Network ◽

Arctic Sea Ice ◽

The Arctic ◽

Small Scale ◽

Acoustic Tomography ◽

Arctic Sea ◽

Current Inversion

Warm current has a strong impact on the melting of sea ice, so clarifying the current features plays a very important role in the Arctic sea ice coverage forecasting study field. Currently, Arctic acoustic tomography is the only feasible method for the large-range current measurement under the Arctic sea ice. Furthermore, affected by the high latitudes Coriolis force, small-scale variability greatly affects the accuracy of Arctic acoustic tomography. However, small-scale variability could not be measured by empirical parameters and resolved by Regularized Least Squares (RLS) in the inverse problem of Arctic acoustic tomography. In this paper, the convolutional neural network (CNN) is proposed to enhance the prediction accuracy in the Arctic, and especially, Gaussian noise is added to reflect the disturbance of the Arctic environment. First, we use the finite element method to build the background ocean model. Then, the deep learning CNN method constructs the non-linear mapping relationship between the acoustic data and the corresponding flow velocity. Finally, the simulation result shows that the deep learning convolutional neural network method being applied to Arctic acoustic tomography could achieve 45.87% accurate improvement than the common RLS method in the current inversion.

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A Comparison of Machine Learning Algorithms for the Segmentation and Classification of Snow Micro Penetrometer Profiles on Arctic Sea Ice

10.5194/egusphere-egu21-15637 ◽

2021 ◽

Author(s):

Julia Kaltenborn ◽

Viviane Clay ◽

Amy R. Macfarlane ◽

Joshua Michael Lloyd King ◽

Martin Schneebeli

Keyword(s):

Machine Learning ◽

Sea Ice ◽

Arctic Sea Ice ◽

Machine Learning Algorithms ◽

Training Data ◽

Support Vector ◽

Snow Layer ◽

Arctic Sea ◽

Execution Speed

Snow-layer classification is an essential diagnostic task for a wide variety of cryospheric science and climate research applications. Traditionally, these measurements are made in snow pits, requiring trained operators and a substantial time commitment. The SnowMicroPen (SMP), a portable high-resolution snow penetrometer, has been demonstrated as a capable tool for rapid snow grain classification and layer type segmentation through statistical inversion of its mechanical signal. The manual classification of the SMP profiles requires time and training and becomes infeasible for large datasets.Here, we introduce a novel set of SMP measurements collected during the MOSAiC expedition and apply Machine Learning (ML) algorithms to automatically classify and segment SMP profiles of snow on Arctic sea ice. To this end, different supervised and unsupervised ML methods, including Random Forests, Support Vector Machines, Artificial Neural Networks, and k-means Clustering, are compared. A subsequent segmentation of the classified data results in distinct layers and snow grain markers for the SMP profiles. The models are trained with the dataset by King et al. (2020) and the MOSAiC SMP dataset. The MOSAiC dataset is a unique and extensive dataset characterizing seasonal and spatial variation of snow on the central Arctic sea-ice.We will test and compare the different algorithms and evaluate the algorithms&#8217; effectiveness based on the need for initial dataset labeling, execution speed, and ease of implementation. In particular, we will compare supervised to unsupervised methods, which are distinguished by their need for labeled training data.The implementation of different ML algorithms for SMP profile classification could provide a fast and automatic grain type classification and snow layer segmentation. Based on the gained knowledge from the algorithms&#8217; comparison, a tool can be built to provide scientists from different fields with an immediate SMP profile classification and segmentation.&#160;&#160;King, J., Howell, S., Brady, M., Toose, P., Derksen, C., Haas, C., & Beckers, J. (2020). Local-scale variability of snow density on Arctic sea ice. The Cryosphere, 14(12), 4323-4339, https://doi.org/10.5194/tc-14-4323-2020.

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Arctic Sea ICE Mapping Using Sentinel-1 SAR Scenes with a Convolutional Neural Network

10.1109/igarss47720.2021.9553206 ◽

2021 ◽

Author(s):

Dmitrii Murashkin ◽

Anja Frost

Keyword(s):

Neural Network ◽

Sea Ice ◽

Convolutional Neural Network ◽

Arctic Sea Ice ◽

Arctic Sea

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Lead detection in Arctic sea ice from CryoSat-2: quality assessment, lead area fraction and width distribution

The Cryosphere ◽

10.5194/tc-9-1955-2015 ◽

2015 ◽

Vol 9 (5) ◽

pp. 1955-1968 ◽

Cited By ~ 27

Author(s):

A. Wernecke ◽

L. Kaleschke

Keyword(s):

Sea Ice ◽

Arctic Sea Ice ◽

Maximum Power ◽

Area Fraction ◽

The Arctic ◽

Regional Study ◽

Arctic Sea ◽

Width Distribution ◽

Spot Image

Abstract. Leads cover only a small fraction of the Arctic sea ice but they have a dominant effect on the turbulent exchange between the ocean and the atmosphere. A supervised classification of CryoSat-2 measurements is performed by a comparison with visual MODIS scenes. For several parameters thresholds are optimized and tested in order to reproduce this prior classification. The maximum power of the waveform shows the best classification properties amongst them, including the pulse peakiness. The sea surface height is derived and its spread is clearly reduced for a classifier based on the maximum power compared to published ones. Lead area fraction estimates based on CryoSat-2 show a major fracturing event in the Beaufort Sea in 2013. The resulting Arctic-wide lead width distribution follows a power law with an exponent of 2.47 ± 0.04 for the winter seasons from 2011 to 2014, confirming and complementing a regional study based on a high-resolution SPOT image.

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Artificial Neural Network for the Short-Term Prediction of Arctic Sea Ice Concentration

Remote Sensing ◽

10.3390/rs11091071 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1071

Author(s):

Minjoo Choi ◽

Liyanarachchi Waruna Arampath De Silva ◽

Hajime Yamaguchi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Sea Ice ◽

Arctic Sea Ice ◽

Short Term ◽

Sea Ice Concentration ◽

Arctic Sea ◽

Ice Concentration ◽

Short Term Prediction ◽

Data Scheme

In this paper, we applied an artificial neural network (ANN) to the short-term prediction of the Arctic sea ice concentration (SIC). The prediction was performed using encoding and decoding processes, in which a gated recurrent unit encodes sequential sea ice data, and a feed-forward neural network model decodes the encoded input data. Because of the large volume of Arctic sea ice data, the ANN predicts the future SIC of each cell individually. The limitation of these singular predictions is that they do not use information from other cells. This results in low accuracy, particularly when there are drastic changes during melting and freezing seasons. To address this issue, we present a new data scheme including global and local SIC information, where the global information is represented by sea ice statistics. We trained ANNs using different data schemes and network architectures, and then compared their performances quantitatively and visually. The results show that, compared with a data scheme that uses only local sea ice information, the newly proposed scheme leads to a significant improvement in prediction accuracy.

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Applying high-resolution visible imagery to satellite melt pond fraction retrieval: A neural network approach

Satellite Oceanography and Meteorology ◽

10.18063/som.v3i3.692 ◽

2018 ◽

Vol 3 (3) ◽

Cited By ~ 1

Author(s):

Qi Liu 1 ◽

Yawen Zhang 1

Keyword(s):

Neural Network ◽

Sea Ice ◽

Satellite Data ◽

Arctic Sea Ice ◽

The Arctic ◽

Neural Network Approach ◽

Melt Pond ◽

Melt Ponds ◽

Arctic Sea ◽

Visible Imagery

During summer, melt ponds have a significant influence on Arctic sea-ice albedo. The melt pond fraction (MPF) also has the ability to forecast the Arctic sea-ice in a certain period. It is important to retrieve accurate melt pond fraction (MPF) from satellite data for Arctic research. This paper proposes a satellite MPF retrieval model based on the multi-layer neural network, named MPF-NN. Our model uses multi-spectral satellite data as model input and MPF information from multi-site and multi-period visible imagery as prior knowledge for modeling. It can effectively model melt ponds evolution of different regions and periods over the Arctic. Evaluation results show that the MPF retrieved from MODIS data using the proposed model has an RMSE of 3.91% and a correlation coefficient of 0.73. The seasonal distribution of MPF is also consistent with previous results.

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