предыдущий|следующий

Time-series surface velocity extraction of Petermann Glacier based on Sentinel-1 pixel offset-tracking and iterative SVD

  • role： First author第一作者
  • Affiliation：

    School of Geospatial Engineering and Science, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China

  • Email：juqi@mail2.sysu.edu.cn
  • Introduction：鞠琦，研究方向为极地遥感。E-mail： juqi@mail2.sysu.edu.cn
JU Qi,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Geospatial Engineering and Science, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China

  • Email：ligang57@mail.sysu.edu.cn
  • Introduction：李刚，研究方向为微波遥感。E-mail： ligang57@mail.sysu.edu.cn
LI Gang*,  
  • Affiliation：

    School of Geospatial Engineering and Science, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China

LI Chaoyue,  
  • Affiliation：

    School of Geospatial Engineering and Science, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China

FENG Xiaoman,  
  • Affiliation：

    School of Geospatial Engineering and Science, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China

CHEN Xiao,  
  • Affiliation：

    School of Geospatial Engineering and Science, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China

YANG Zhibin,  
  • Affiliation：

    School of Geospatial Engineering and Science, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China

CHEN Zhuoqi

реферат

Monitoring the Greenland glacier flow velocity is essential for the quantitative estimation of ice sheet material loss, the assessment of the impact of global climate change on ice sheet dynamics, and the evaluation of Greenland’s contribution to current sea-level rises. The offset-tracking technique is the main method for deriving glacier velocity by using the intensity information of SAR or optical images. Intensity offset tracking is less sensitive to decorrelation than the InSAR method and can be applied to images with long temporal intervals. However, glacier avalanche, ice avalanche, snowfall, and melting–freezing cycles on glaciers still cause changes in the scattering characteristics of the surface, resulting in changes of the SAR image intensity, leading to a loss of correlation in matching between images, especially in summer. To provide more accurate glacier flow velocity field, this research proposes a novel data processing strategy of processing Sentinel-1 SAR data and takes the famous Petermann outlet glacier in Greenland as an example to extract its glacier velocity based on image tracking. Noise and errors in tracking images formed by single pairs of Sentinel-1 images are removed through morphological opening operation, connectivity analysis, adaptive median filtering, etc. Meanwhile, annual and monthly Greenland ice flow velocity products are employed to select datum by taking its low-speed area as reference. We also introduce flow direction of the annual or seasonal glacier flow to filter out wrong matchings. Similar to the small-baseline analysis of the InSAR technique, redundant observation of tracking pairs with 6-, 12-, and 18-day intervals are then applied to the Singular Value Decomposition (SVD) method to solve the time series of glacier velocity and to avoid the possible rank deficit. SVD is iteratively performed to remove the observed coarse error that could not be eliminated in the previous processing by checking residuals of the observation after each iteration. We obtain the time-series glacier velocity for the Petermann Glacier from the year 2018 to 2020 with a temporal resolution of 6 days. Compared with the published glacier velocity products, our derived results are less noisy, more continuous, smoother, and cover more area than the CPOM product, which employs the same data source. Compared with the PROMICE product produced from multitrack SAR, data show that we share similar accuracy and effective data coverage, but the results of this research have higher resolution and are less noisy, especially in summer. We conclude that the proposed algorithm can effectively eliminate the anomalous matching of single offset-tracking pair for forming high spatial and temporal resolution glacier flow velocity time series with redundant matching pairs by using an iterative SVD method, which is essential for monitoring glacier flow velocity for the Greenland Ice Sheet with satellite SAR images.

ключеви́че слова́

remote sensing;Greenland ice sheet;glacier velocity;SAR;Sentinel-1;Offset-Tracking;singular value decomposition

References

  1. 1.
    Alley R B, Clark P U, Huybrechts P and Joughin I. 2005. Ice-sheet and sea-level changes. Science, 310(5747): 456-460
  2. 2.
    Boncori J P M, Andersen M L, Dall J, Kusk A, Kamstra M, Andersen S B, Bechor N, Bevan S, Bignami C, Gourmelen N, Joughin I, Jung H S, Luckman A, Mouginot J, Neelmeijer J, Rignot E, Scharrer K, Nagler T, Scheuchl B and Strozzi T. 2018. Intercomparison and validation of SAR-Based ice velocity measurement techniques within the Greenland ice sheet CCI project. Remote Sensing, 10(6): 929
  3. 3.
    Casu F, Manconi A, Pepe A and Lanari R. 2011. Deformation Time-Series generation in areas characterized by large displacement dynamics: The SAR amplitude Pixel-Offset SBAS technique. IEEE Transactions on Geoscience and Remote Sensing, 49(7): 2752-2763
  4. 4.
    Chen X，Li G, Chen Z Q, Ju Q, Zheng L and Cheng X. 2023. A backscatter coefficient normalization method to incidence angle based on ascending and descending Sentinel-1 SAR imagery at the Greenland. National Remote Sensing Bulletin, 27(9): 2072-2084
  5. 5.
    Fettweis X, Box J E, Agosta C, Amory C, Kittel C, Lang C, Van As D, Machguth H and Gallée H. 2017. Reconstructions of the 1900-2015 Greenland ice sheet surface mass balance using the regional climate MAR model. The Cryosphere, 11(2): 1015-1033
  6. 6.
    Gao J and Liu Y S. 2001. Applications of remote sensing, GIS and GPS in glaciology: A review. Progress in Physical Geography: Earth and Environment, 25(4): 520-540
  7. 7.
    Giles A B, Massom R A and Warner R C. 2009. A method for sub-pixel scale feature-tracking using Radarsat images applied to the Mertz Glacier Tongue, East Antarctica. Remote Sensing of Environment, 113(8): 1691-1699
  8. 8.
    Goldstein R M, Engelhardt H, Kamb B and Frolich R M. 1993. Satellite radar interferometry for monitoring ice sheet motion: Application to an Antarctic ice stream. Science, 262(5139): 1525-1530
  9. 9.
    Gray A L, Mattar K E, Vachon P W, Bindschadler R, Jezek K C, Forster R and Crawford J P. 1998. InSAR results from the RADARSAT Antarctic Mapping Mission data: Estimation of glacier motion using a simple registration procedure//IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174). Seattle: IEEE: 1638-1640
  10. 10.
    Heid T and Kääb A. 2012. Evaluation of existing image matching methods for deriving glacier surface displacements globally from optical satellite imagery. Remote Sensing of Environment, 118: 339-355
  11. 11.
    Höcker A and Kartvelishvili V. 1996. SVD approach to data unfolding. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 372(3): 469-481
  12. 12.
    Hogg A E, Shepherd A, Gourmelen N and Engdahl M. 2016. Grounding line migration from 1992 to 2011 on Petermann Glacier, North-West Greenland. Journal of Glaciology, 62(236): 1104-1114
  13. 13.
    Howat I. 2017. MEaSUREs Greenland Ice Mapping Project (GIMP) land ice and ocean classification mask, Version 1. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center.
  14. 14.
    Howat I M, Negrete A and Smith B E. 2014. The Greenland Ice Mapping Project (GIMP) land classification and surface elevation data sets. The Cryosphere, 8(4): 1509-1518
  15. 15.
    Hu J, Li Z W, Ding X L, Zhu J J, Zhang L and Sun Q. 2014. Resolving three-dimensional surface displacements from InSAR measurements: A review. Earth-Science Reviews, 133: 1-17
  16. 16.
    Huang L, Li Z, Zhou J M and Tian B S. 2014. Glacier change monitoring using SAR: An overview. Advances in Earth Science, 29(9): 985-994
  17. 17.
    The IMBIE Team. 2020. Mass balance of the Greenland Ice Sheet from 1992 to 2018. Nature, 579(7798): 233-239
  18. 18.
    Johannessen O M, Babiker M and Miles M W. 2013. Unprecedented retreat in a 50-Year observational record for Petermann glacier, North Greenland. Atmospheric and Oceanic Science Letters, 6(5): 259-265
  19. 19.
    Joughin I, Howat I, Alley R B, Ekstrom G, Fahnestock M, Moon T, Nettles M, Truffer M and Tsai V C. 2008. Ice-front variation and tidewater behavior on Helheim and Kangerdlugssuaq Glaciers, Greenland. Journal of Geophysical Research: Earth Surface, 113(F1): F01004
  20. 20.
    Joughin I, Smith B E and Howat I. 2018a. Greenland Ice Mapping Project: Ice flow velocity variation at sub-monthly to decadal timescales. The Cryosphere, 12(7): 2211-2227
  21. 21.
    Joughin I, Smith B E and Howat I M. 2018b. A complete map of Greenland ice velocity derived from satellite data collected over 20 years. Journal of Glaciology, 64(243): 1-11
  22. 22.
    Kaushik S, Joshi P K and Singh T. 2019. Development of glacier mapping in Indian Himalaya: A review of approaches. International Journal of Remote Sensing, 40(17): 6607-6634
  23. 23.
    Lemos A, Shepherd A, McMillan M, Hogg A E, Hatton E and Joughin I. 2018. Ice velocity of Jakobshavn Isbræ, Petermann Glacier, Nioghalvfjerdsfjorden, and Zachariæ Isstrøm, 2015-2017, Sentinelfrom 1-a/b SAR imagery. The Cryosphere, 12(6): 2087-2097
  24. 24.
    Li F, Wang Z L, Zhang Y, Zhang S K and Zhu T T. 2018. Amery ice shelf frontal position automatic detection from sentinel-1 SAR imagery. Geomatics and Information Science of Wuhan University, 43(12): 2012-2022.
  25. 25.
    Li J, Li Z W, Wu L X, Xu B, Hu J, Zhou Y S and Miao Z L. 2018. Deriving a time series of 3D glacier motion to investigate interactions of a large mountain glacial system with its glacial lake: use of synthetic aperture radar pixel offset-small baseline subset technique. Journal of Hydrology, 559: 596-608
  26. 26.
    Li X, Che T and Li X W. 2020. Remote Sensing of Cryosphere. Beijing: Science Press: 38-40
  27. 27.
    Luckman A, Quincey D and Bevan S. 2007. The potential of satellite radar interferometry and feature tracking for monitoring flow rates of Himalayan glaciers. Remote Sensing of Environment, 111(2/3): 172-181
  28. 28.
    Nick F M, Luckman A, Vieli A, Van Der Veen C J, Van As D, Van De Wal R S W, Pattyn F, Hubbard A L and Floricioiu D. 2012. The response of Petermann Glacier, Greenland, to large calving events, and its future stability in the context of atmospheric and oceanic warming. Journal of Glaciology, 58(208): 229-239
  29. 29.
    Ouchi K. 2013. Recent trend and advance of synthetic aperture radar with selected topics. Remote Sensing, 5(2): 716-807
  30. 30.
    Pearce W, Holmberg K, Hellsten I and Nerlich B. 2014. Climate change on twitter: topics, communities and conversations about the 2013 IPCC Working Group 1 Report. PLoS One, 9(4): e94785
  31. 31.
    Rückamp M, Neckel N, Berger S, Humbert A and Helm V. 2019. Calving induced speedup of petermann glacier. Journal of Geophysical Research: Earth Surface, 124(1): 216-228
  32. 32.
    Rignot E and Kanagaratnam P. 2006. Changes in the velocity structure of the Greenland ice sheet. Science, 311(5763): 986-990
  33. 33.
    Rignot E, Velicogna I, Van Den Broeke M R, Monaghan A and Lenaerts J T M. 2011. Acceleration of the contribution of the Greenland and Antarctic ice sheets to sea level rise. Geophysical Research Letters, 38(5): L05503
  34. 34.
    Rott H. 2009. Advances in interferometric synthetic aperture radar (InSAR) in earth system science. Progress in Physical Geography: Earth and Environment, 33(6): 769-791
  35. 35.
    Scambos T A, Dutkiewicz M J, Wilson J C and Bindschadler R A. 1992. Application of image cross-correlation to the measurement of glacier velocity using satellite image data. Remote Sensing of Environment, 42(3): 177-186
  36. 36.
    Solgaard A, Kusk A, Boncori J P M, Dall J, Mankoff K D, Ahlstrøm A P, Andersen S B, Citterio M, Karlsson N B, Kjeldsen K K, Korsgaard N J, Larsen S H and Fausto R S. 2021. Greenland ice velocity maps from the PROMICE project. Earth System Science Data, 13(7): 3491-3512
  37. 37.
    Strozzi T, Luckman A, Murray T, Wegmuller U and Werner C L. 2002. Glacier motion estimation using SAR offset-tracking procedures. IEEE Transactions on Geoscience and Remote Sensing, 40(11): 2384-2391
  38. 38.
    Trusel L D, Das S B, Osman M B, Evans M J, Smith B E, Fettweis X, McConnell J R, Noël B P Y and Van Den Broeke M R. 2018. Nonlinear rise in Greenland runoff in response to post-industrial Arctic warming. Nature, 564(7734): 104-108
  39. 39.
    Whillans I M and Tseng Y H. 1995. Automatic tracking of crevasses on satellite images. Cold Regions Science and Technology, 23(2): 201-214
  40. 40.
    Wu S S, Yao Z J, Jiang L G and Liu Z F. 2015. Method review of modern glacier volume change. Advances in Earth Science, 30(2): 237-246.
  41. 41.
    Zhou J M, Zhang X, Liu Z P and Li Z. 2021. Extraction and analysis of mountain glacier movement from GF-1 satellite data. National Remote Sensing Bulletin, 25(2): 530-538

Читать полностью

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website