An Algorithm for Sea Ice Drift Retrieval Based on Trend of Ice Drift Constraints from Sentinel-1 SAR Data

Zhang, Xi; Zhu, Yixun; Zhang, Jie; Meng, Junmin; Li, Xiaona; Li, Xingxing

doi:10.2112/si102-015.1

Cited by 9 publications

(5 citation statements)

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“…The study of Zhang et al [37] provided an algorithm that can improve the accuracy of sea ice drift retrieval and the spatial density of ice drift vectors, as well as reducing computation time. According to the results of Wang et al [38], Gaofen-4 not only provides a good sea ice/water contrast but is also useful for sea ice drift detection in the Bohai Sea of China.…”

Section: Discussionmentioning

confidence: 99%

“…The automated retrieval of ice drift and deformation is valuable for operational monitoring of ice conditions in the context of regional sea ice climatologies, which have also to consider the change of ice dynamics over seasons and its possible trends over years. With our analyses and improvements of drift retrieval and studies of possible errors, as discussed in Sections 3.1 and 4.1 [35][36][37][38], we took the first steps towards the development of sea ice climatology products that also include information on ice drift and deformation.…”

Section: Overall Discussionmentioning

confidence: 99%

“…Dierking et al [36] extended the investigations on the statistical errors that occur when retrieving drift vectors from SAR image pairs and their effect on the calculation of different deformation parameters (divergence/convergence, shear, vorticity, and total deformation). Zhang et al [37] studied an automatic Sentinel-1 SAR sea ice drift detection method based on multi-scale observation. The proposed method detected the ice cracks or lead structures from down-sampled sequential SAR images firstly.…”

Section: Research Approachmentioning

confidence: 99%

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Results of the Dragon 4 Project on New Ocean Remote Sensing Data for Operational Applications

et al. 2021

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This paper provides an overview of the Dragon 4 project dealing with operational monitoring of sea ice and sea surface salinity (SSS) and new product developments for altimetry data. To improve sea ice thickness retrieval, a new method was developed to match the Cryosat-2 radar waveform. Additionally, an automated sea ice drift detection scheme was developed and tested on Sentinel-1 data, and the sea ice drifty capability of Gaofen-4 geostationary optical data was evaluated. A second topic included implementation and validation of a prototype of a Fully-Focussed SAR processor adapted for Sentinel-3 and Sentinel-6 altimeters and evaluation of its performance with Sentinel-3 data over the Yellow Sea; the assessment of sea surface height (SSH), significant wave height (SWH), and wind speed measurements using different altimeters and CFOSAT SWIM; and the fusion of SSH measurements in mapping sea level anomaly (SLA) data to detect mesoscale eddies. Thirdly, the investigations on the retrieval of SSS include simulations to analyse the performances of the Chinese payload configurations of the Interferometric Microwave Radiometer and the Microwave Imager Combined Active and Passive, SSS retrieval under rain conditions, and the combination of active and passive microwave to study extreme winds.

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Section: Discussionmentioning

confidence: 99%

Section: Overall Discussionmentioning

confidence: 99%

Section: Research Approachmentioning

confidence: 99%

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Results of the Dragon 4 Project on New Ocean Remote Sensing Data for Operational Applications

et al. 2021

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“…Currently, obtaining wide-ranging sea ice movement data using satellite information has become the primary method due to the advantages of broad observation coverage, rapid imaging, and periodic observations. Satellite sensors that provide sea ice movement research data mainly include passive microwave [12]- [14], optical [15]- [17], and synthetic aperture radar (SAR) [18]- [24] imagery. Currently, the main sea ice movement products published by leading international institutions include NSIDC (National Snow and Ice Data Center), OSI SAF (Ocean and Sea Ice Satellite Application Facility), and Ifremer (French Research Institute for the Exploitation of the Seas).…”

Section: Introductionmentioning

confidence: 99%

Improvement of Sea Ice Drift Extraction Based on Feature Tracking from C-SAR/01 Imagery

Yang,

Xie,

Sun

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In this study, the extraction of sea ice drift from imagery captured by the 1-meter C-SAR 01 satellite (C-SAR/01) was facilitated utilizing the Oriented fast and Rotated Brief (ORB) algorithm within the Feature Tracking (FT) procedure, thus addressing the previously unexplored area of sea ice drift extraction using C-SAR/01 imagery. The retained keypoints and Nearest Neighbor Distance Ratio test (NNDR) for sea ice drift extracted from C-SAR/01 imagery was compared, indicating high reliability with 300000 and 0.75, respectively. Additionally, the Local Outlier Factor (LOF) algorithm is proposed in this paper, which can effectively remove erroneous sea ice drift vectors. The sea ice drift extracted from C-SAR/01 was validated against manually extracted sea ice drift, revealing an uncertainty of 0.271 cm/s in speed and 8.331° in direction. Furthermore, the sea ice drift obtained from the algorithm in this study, when compared with sea ice drift from IABP buoys, exhibits high accuracy, reflecting the robustness of the algorithm.

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“…In recent decades, SIM data have been extracted from various types of remote sensing platforms, including passive microwave [5][6] [7], radiometer [8], scatterometer [9], thermal infrared [10] [11], optical [12][13] [14], and synthetic aperture radar (SAR) [15][16] [17] [18][19] [20] imagery. These data are used to generate different types of SIM products.…”

Section: Introductionmentioning

confidence: 99%

Application of the Combined Feature Tracking and Maximum Cross-Correlation Algorithm to the Extraction of Sea Ice Motion Data From GF-3 Imagery

Zhou

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In this study, an algorithm combining feature tracking and maximum cross-correlation (FT-MCC) for the extraction of sea ice motion (SIM) vectors was applied to Gaofen-3 (GF-3) imagery, filling the gap of SIM extraction using GF-3 imagery. The locally consistent (LC) flow field filtering method is proposed to replace the filtering method based on the correlation coefficient threshold in FT-MCC to improve filtering effectiveness of SIM results extracted by FT-MCC. A comparison of the probability density distributions (PDDs) of the correlation coefficients of SIM vectors extracted by FT-MCC from images with different resolutions revealed high reliability for SIM vectors extracted for images with an 80 m spatial resolution. A comparison of the PDDs of the correlation coefficients of SIM vectors obtained from images with different polarization modes showed more reliable SIM vectors were extracted from vertical transmit horizontal receive (VH) polarization images than from corresponding vertical transmit vertical receive (VV) polarization images. The SIM vectors extracted from GF-3 images by two methods (FT(A-KAZE)-MCC and FT(ORB)-MCC) derived from the FT-MCC algorithm were highly consistent in terms of accuracy and reliability. SIM vectors extracted manually and from Sentinel-1 images were used as reference data to verify the SIM results extracted from GF-3 images, for which the uncertainties in the magnitude and direction of the extracted SIM vectors were found to be 0.119 cm/s-0.287 cm/s (103 m/d-248 m/d) and 4.119°-5.930°, respectively.

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An Algorithm for Sea Ice Drift Retrieval Based on Trend of Ice Drift Constraints from Sentinel-1 SAR Data

Cited by 9 publications

References 0 publications

Results of the Dragon 4 Project on New Ocean Remote Sensing Data for Operational Applications

Results of the Dragon 4 Project on New Ocean Remote Sensing Data for Operational Applications

Improvement of Sea Ice Drift Extraction Based on Feature Tracking from C-SAR/01 Imagery

Application of the Combined Feature Tracking and Maximum Cross-Correlation Algorithm to the Extraction of Sea Ice Motion Data From GF-3 Imagery

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