Hostname: page-component-848d4c4894-2xdlg Total loading time: 0 Render date: 2024-06-16T17:38:31.508Z Has data issue: false hasContentIssue false
  • English
  • Français

Spatio-temporal monitoring of the iceberg D28 using SCATSAT-1 data

Published online by Cambridge University Press:  19 April 2023

Khoisnam Nanaoba Singh ,
Mamata Maisnam ,
Rajkumar Kamaljit Singh Open the ORCID record for Rajkumar Kamaljit Singh [Opens in a new window] ,
Jayaprasad Pallipad ,
Arundhati Misra  and
Saroj Maity
Khoisnam Nanaoba Singh
Affiliation:
National Institute of Technology Manipur, Imphal-795004, India
Mamata Maisnam
Affiliation:
National Institute of Technology Manipur, Imphal-795004, India
Rajkumar Kamaljit Singh*
Affiliation:
Manipur Technical University, Imphal-795004, India
Jayaprasad Pallipad
Affiliation:
Space Applications Centre – ISRO, Ahmedabad-380015, India
Arundhati Misra
Affiliation:
Space Applications Centre – ISRO, Ahmedabad-380015, India
Saroj Maity
Affiliation:
Space Applications Centre – ISRO, Ahmedabad-380015, India
*
Author for correspondence: Rajkumar Kamaljit Singh, Email: kamaljit.rajkumar@gmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

The study of the icebergs and their movements is one of many applications of scatterometer data in the study of the ecosystems of polar regions. SCATSAT-1 is the Indian Space Research Organisation’s (ISRO’s) Ku-band (13.515625 GHz) scatterometer. Using enhanced resolution Gamma0H (horizontally polarised incidence angle normalised backscattering coefficient) data of SCATSAT-1, we observed the movement of iceberg D28 and its interaction with wind, ocean currents and sea ice for one and a half years of its journey (JD 269, 2019 to JD 051, 2021). The data sets used are as follows: (1) SCATSAT-1 level-4 Gamma0H; (2) OSCAR (Ocean Surface Current Analysis Real-time) third-degree resolution ocean surface currents; (3) hourly wind speed data of ERA5; 4) NSIDC (National Snow and Ice Data Center) sea ice concentration data; and (5) NSIDC Polar Path-finder Daily EASE-Grid Sea Ice Motion Vectors, Version-3. For this study, we divide the continent into five different regions/sectors. It is found that the trajectory of the iceberg is influenced by the resultant of the wind and ocean current, at different scales in these regions. Moreover, sea ice motion can also change the course of iceberg. From the on-screen digitisation of the iceberg, the average area of the iceberg is found to be approximately 1509.82 km2 with approximate dimensions of 27 km × 55.5 km. We conclude that spatial and temporal behaviours of the iceberg can be ascertained from the scatterometer data.

Keywords

IcebergD28SCATSAT-1WindOcean currentSea ice
Type
Note
Information
Polar Record , Volume 59 , 2023 , e15
Copyright
© The Author(s), 2023. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ballantyne, J., & Long, D. G. (2002). A multidecadal study of the number of Antarctic icebergs using scatterometer data. In IEEE international geoscience and remote sensing symposium, Toronto, ON, Canada, vol. 5 (pp. 3029–3031). https://doi.org/10.1109/IGARSS.2002.1026859.CrossRefGoogle Scholar
Bigg, G. (2015). The physics of icebergs. In Icebergs: Their Science and Links to Global Change (pp. 52–81). Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9781107589278.004.CrossRefGoogle Scholar
Bigg, G.R., Wadley, M. R., Stevens, D. P., & Johnson, J. A. (1996). Prediction of iceberg trajectories for the North Atlantic and Arctic Oceans. Geophysical Research Letters, 23(24), 3587–3590. https://doi.org/10.1029/96GL03369.CrossRefGoogle Scholar
Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(6), 679–698. https://doi.org10.1109/TPAMI.1986.4767851.CrossRefGoogle ScholarPubMed
Cavalieri, D. J., Parkinson, C. L., Gloersen, P., & Zwally, H. J. (1996). Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Version 1. Boulder, Colorado USA: NASA National Snow and Ice Data Center Distributed Active Archive Center. (Accessed on 04 December 2021). https:/doi.org/10.5067/8GQ8LZQVL0VL.Google Scholar
Crépon, M., Houssais, M.N., & Saint, G. B. (1988). The drift of icebergs under wind action. Journal of Geophysical Research, 93(C4), 3608–3612. https://doi.org/10.1029/JC093iC04p03608.CrossRefGoogle Scholar
ESR (2009). OSCAR third deg. Ver. 1. PO.DAAC, CA, USA. (Accessed on 06 December 2021). https://doi.org/10.5067/OSCAR-03D01.CrossRefGoogle Scholar
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., … Thépaut, J. N. (2018). ERA5 hourly data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS) (Accessed on 27 November 2021). https://doi.org/10.24381/cds.adbb2d47.CrossRefGoogle Scholar
Hunke, E. C., & Comeau, D. (2011). Sea ice and iceberg dynamic interaction, Journal of Geophysical Research, 116, C05008. https://doi.org/10.1029/2010JC006588.CrossRefGoogle Scholar
Lichey, C., & Hellmer, H.H. (2001). Modelling giant-iceberg drift under the influence of sea ice in the Weddell Sea, Antarctica. Journal of Glaciology, 47(158), 452–460. https://doi.org/10.3189/172756501781832133.CrossRefGoogle Scholar
Long, D.G. (2016). Polar applications of spaceborne scatterometers. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(5), 2307–2320. https://doi.org/10.1109/JSTARS.2016.2629418.CrossRefGoogle ScholarPubMed
McCarthy, D.D. (1998). The Julian and Modified Julian Dates. Journal for the History of Astronomy, 29(4), 327–330. https://doi.org/10.1177/002182869802900402.CrossRefGoogle Scholar
Misra, T., Chakraborty, P., Lad, C., Gupta, P., Rao, J., Upadhyay, G., … Tolani, H. (2019). SCATSAT-1 Scatterometer: An improved successor of OSCAT. Current Science, 117(6), 941–949. https://doi.org/10.18520/cs/v117/i6/941-949.CrossRefGoogle Scholar
Oza, S.R., Bothale, R.V., Rajak, D.R., Jayaprasad, P., Maity, S., Thakur, P.K., … Bahuguna, I.M. (2019). Assessment of cryospheric parameters over the Himalaya and Antarctic regions using SCATSAT-1 enhanced resolution data. Current Science, 117(6), 1002–1013. https://doi.org/10.18520/cs/v117/i6/1002-1013.CrossRefGoogle Scholar
Singh, K. N., Singh, R. K., Maisnam, M., Jayaprasad, P., Maity, S., Putrevu, D., & Misra, A. (2021). Detection of Two Recent Calving Events in Antarctica from SCATSAT-1. In IEEE international geoscience and remote sensing symposium, IGARSS 2021 (pp. 439–442), https://doi.org/10.1109/IGARSS47720.2021.9553306.CrossRefGoogle Scholar
Singh, R.K., Singh, K.N., Maisnam, M., Jayaprasad, P., & Maity, S. (2019). Observing Larsen C ice-shelf using ISRO’s SCATSAT-1 data. Polar Science, 19, 57–68. https://doi.org/10.1016/j.polar.2018.12.007.CrossRefGoogle Scholar
Singh, S., Tiwari, R.K., Gusain, H.S., & Sood, V. (2020). Potential applications of SCATSAT-1 satellite sensor: a systematic review. IEEE Sensors Journal, 20(21), 12459–12471. https://doi.org/10.1109/JSEN.2020.3002720.CrossRefGoogle Scholar
Singh, S., Tiwari, R.K., Sood, V., Kaur, R., & Prashar, S. (2022). The Legacy of Scatterometers: Review of applications and perspective. IEEE Geoscience and Remote Sensing Magazine, 10(2), 39–65, https://doi.org/10.1109/MGRS.2022.3145500.CrossRefGoogle Scholar
Smith, S. D., & Banke, E. G. (1983). The influence of winds, currents and towing forces on the drift of icebergs. Cold Regions Science and Technology, 6(3), 241–255, https://doi.org/10.1016/0165-232X(83)90045-9.CrossRefGoogle Scholar
Tschudi, M., Meier, W. N., Stewart, J. S., Fowler, C., & Maslanik, J. (2019). Polar Pathfinder Daily 25 km EASE-Grid Sea Ice Motion Vectors, Version 4. Boulder, Colorado USA: NASA National Snow and Ice Data Center Distributed Active Archive Center, (Accessed on 05 December 2021), https://doi.org/10.5067/INAWUWO7QH7B.CrossRefGoogle Scholar
Wagner, T. J. W., Dell, R. W., & Eisenman, A. I. (2017). An analytical model of iceberg drift. Journal of Physical Oceanography, 47(7), 1605–1616. https://doi.org/10.1175/JPO-D-16-0262.1.CrossRefGoogle Scholar
Wesche, C., & Dierking, W. (2016). Estimating iceberg paths using a wind driven model. Cold Regions Science and Technology, 125, 31–39. https://doi.org/10.1016/j.coldregions.2016.01.008.CrossRefGoogle Scholar
Yu, L., Zhong, S., & Sun, B. (2020). The climatology and trend of surface wind speed over Antarctica and the Southern Ocean and the implication to wind energy application. Atmosphere, 11(1), 108. https://doi.org/10.3390/atmos11010108.CrossRefGoogle Scholar
Supplementary material: File

Singh et al. supplementary material

Figures S1-S6 and Table S1

Download Singh et al. supplementary material(File)
File 3.1 MB