Discrimination of different sea ice types from CryoSat-2 satellite data using an Object-based Random Forest (ORF)

Su, Shu Lan; Zhou, Xinghua; Shen, Xueqin; Liu, Zhanchi; Tang, Qiuhua; Li, Haili; Ke, Chang‐Qing; Li, Jie

doi:10.1080/01490419.2019.1671560

Cited by 14 publications

(36 citation statements)

References 27 publications

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“…As an ML method, the decision tree (DT) method has been widely applied to sea ice monitoring [41,42]. Another powerful ML algorithm employed for classification is random forest (RF), which creates a variety of individual decision trees that operate as an ensemble [43]. Although the DT and RF algorithms have been applied to monitor sea ice using satellite remote sensing data, such as MODIS and CryoSat-2, there is a lack of information about how DT and RF algorithms can be utilized for monitoring sea ice using spaceborne GNSS-R data.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Aided Sea Ice Monitoring Using Feature Sequences Extracted from Spaceborne GNSS-Reflectometry Data

Zhu

Tao

et al. 2020

Remote Sensing

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Two effective machine learning-aided sea ice monitoring methods are investigated using 42 months of spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) data collected by the TechDemoSat-1 (TDS-1). The two-dimensional delay waveforms with different Doppler spread characteristics are applied to extract six features, which are combined to monitor sea ice using the decision tree (DT) and random forest (RF) algorithms. Firstly, the feature sequences are used as input variables and sea ice concentration (SIC) data from the Advanced Microwave Space Radiometer-2 (AMSR-2) are applied as targeted output to train the sea ice monitoring model. Hereafter, the performance of the proposed method is evaluated through comparing with the sea ice edge (SIE) data from the Special Sensor Microwave Imager Sounder (SSMIS) data. The DT- and RF-based methods achieve an overall accuracy of 97.51% and 98.03%, respectively, in the Arctic region and 95.46% and 95.96%, respectively, in the Antarctic region. The DT- and RF-based methods achieve similar accuracies, while the Kappa coefficient of RF-based approach is slightly larger than that of the DT-based approach, which indicates that the RF-based method outperforms the DT-based method. The results show the potential of monitoring sea ice using machine learning-aided GNSS-R approaches.

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Section: Introductionmentioning

confidence: 99%

Machine Learning-Aided Sea Ice Monitoring Using Feature Sequences Extracted from Spaceborne GNSS-Reflectometry Data

Zhu

Tao

et al. 2020

Remote Sensing

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“…For classifying the MYI and FYI, we use four classifiers that have already been applied to Ku-band altimeter measurements [15,17,27,28]. The classifiers are trained separately for each year.…”

Section: Classificationmentioning

confidence: 99%

“…A k-nearest neighbour (KNN) classifier finds k objectives in a provided training dataset that are closest to the test point; the class is based on a majority vote among these k objects and the distance is based on the ordinary Euclidean metric; based on this distance, the classification output is determined [17,28]. The training data used are the same for all supervised classifiers (and threshold-based), and the features used are the waveform parameters (which particular parameters selected are described in Section 4).…”

Section: K-nearest Neighbour (Knn) Classificationmentioning

confidence: 99%

“…With these findings, the possibility of retrieving information on sea ice and sea ice type classification from radar altimeter data has been discussed in several studies (e.g., [22][23][24]). Currently, sea ice thickness retrieval algorithms only employ a method for distinguishing leads from ice floes to estimated freeboard, which is the sea ice above local sea surface level [25], but recent studies have investigated the possibility of discriminating between sea ice types with airborne [15] and spaceborne SAR radar altimeters [17,[26][27][28]. The assumptions are based on the shape of the waveform from conventional altimeters acquired over ice and snow to characterise the underlying surface in order to differentiate based on the sea ice type.…”

Section: Introductionmentioning

confidence: 99%

“…In comparison, MYI (and heavily deformed FYI) will often exhibit lower backscatter and a slower decay [27]. While recent studies have investigated spaceborne Ku-band SAR altimeters (CryoSat-2) (e.g., [17,27,28]), there have been no such studies investigating the possibility of discriminating sea ice types from spaceborne Ka-band radar altimeters, to the best of our knowledge. Thus, we will investigate the possibility in this paper.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Sea Ice Types in the Arctic by Radar Echoes from SARAL/AltiKa

Hansen

Rinne

Skourup³

2021

Remote Sensing

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An important step in the sea ice freeboard to thickness conversion is the classification of sea ice types, since the ice type affects the snow depth and ice density. Studies using Ku-band CryoSat-2 have shown promise in distinguishing FYI and MYI based on the parametrisation of the radar echo. Here, we investigate applying the same classification algorithms that have shown success for Ku-band measurements to measurements acquired by SARAL/AltiKa at the Ka-band. Four different classifiers are investigated, i.e., the threshold-based, Bayesian, Random Forest (RF) and k-nearest neighbour (KNN), by using data from five 35 day cycles during Arctic mid-winter in 2014–2018. The overall classification performance shows the highest accuracy of 93% for FYI (Bayesian classifier) and 39% for MYI (threshold-based classifier). For all classification algorithms, more than half of the MYI cover falsely classifies as FYI, showing the difference in the surface characteristics attainable by Ka-band compared to Ku-band due to different scattering mechanisms. However, high overall classification performance (above 90%) is estimated for FYI for three supervised classifiers (KNN, RF and Bayesian). Furthermore, the leading-edge width parameter shows potential in discriminating open water (ocean) and sea ice when visually compared with reference data. Our results encourage the use of waveform parameters in the further validation of sea ice/open water edges and discrimination of sea ice types combining Ka- and Ku-band, especially with the planned launch of the dual-frequency altimeter mission Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) in 2027.

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Semantic image segmentation for sea ice parameters recognition using deep convolutional neural networks

Zhang

Chen

2022

International Journal of Applied Earth Observation and Geoinfor

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Discrimination of different sea ice types from CryoSat-2 satellite data using an Object-based Random Forest (ORF)

Cited by 14 publications

References 27 publications

Machine Learning-Aided Sea Ice Monitoring Using Feature Sequences Extracted from Spaceborne GNSS-Reflectometry Data

Machine Learning-Aided Sea Ice Monitoring Using Feature Sequences Extracted from Spaceborne GNSS-Reflectometry Data

Classification of Sea Ice Types in the Arctic by Radar Echoes from SARAL/AltiKa

Semantic image segmentation for sea ice parameters recognition using deep convolutional neural networks

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