Comparison Between Surface Melt Estimation From Sentinel-1 Synthetic Aperture Radar and a Regional Climate Model. Case Study Over the Roi Baudouin Ice Shelf, East Antarctica
[en] Antarctica is the largest potential contributor to sea-level rise and needs to be monitored. It is also one of the first victims of global warming. However, it is often difficult to obtain high-resolution data on this vast and distant continent. Thanks to the Copernicus space program providing free and open access to high-quality data, this paper aims to show the complementarity between Sentinel-1 images and Modèle Atmosphérique régional (MAR) data over Antarctica. This study is conducted over Roi Baudouin Ice Shelf. The complementarity between the two datasets is established by a quantitative, temporal, and spatial comparison of the amplitude information of the radar signal and several variables modelled by MAR. Comparisons show strong spatial correlations between MAR variables representing melt and the backscatter coefficient recorded by the satellite. While temporal and quantitative analyses also give impressive results, further investigations are required to explain contrasting behaviors in other different areas of the ice shelf.
Disciplines :
Earth sciences & physical geography
Author, co-author :
Dethinne, Thomas ; Université de Liège - ULiège > Département de géographie > Climatologie et Topoclimatologie
Glaude, Quentin ; Université de Liège - ULiège > CSL (Centre Spatial de Liège)
Amory, Charles ; Université de Liège - ULiège > Département de géographie > Climatologie et Topoclimatologie
Kittel, Christoph ; Université de Liège - ULiège > Département de géographie > Climatologie et Topoclimatologie
Fettweis, Xavier ; Université de Liège - ULiège > Département de géographie > Climatologie et Topoclimatologie
Language :
English
Title :
Comparison Between Surface Melt Estimation From Sentinel-1 Synthetic Aperture Radar and a Regional Climate Model. Case Study Over the Roi Baudouin Ice Shelf, East Antarctica
Publication date :
2022
Journal title :
Bulletin de la Société Géographique de Liège
ISSN :
0770-7576
eISSN :
2507-0711
Publisher :
Société Geographique de Liege, Belgium
Special issue title :
De la géomorphologie à la géomatique, hommage à Marc BINARD et Yves CORNET
Volume :
78
Issue :
2022
Pages :
113-122
Peer reviewed :
Peer Reviewed verified by ORBi
Commentary :
L'Antarctique est le plus grand contributeur potentiel à l'élévation du niveau de la mer et doit être surveillé. C'est aussi l'une des premières victimes du réchauffement climatique. Or, il est souvent difficile d'obtenir des données à haute résolution sur ce vaste et lointain continent qu’est l’Antarctique. Grâce au programme Copernicus qui donne un accès libre et gratuit à des images satellite de hautes qualités, le but de ce travail est de montrer la complémentarité entre les images radar Sentinel-1 et les données du Modèle Atmosphérique Régional (MAR) au niveau de l'Antarctique. Cette étude est menée au niveau de la plateforme de glace du Roi Baudouin. La complémentarité entre les données est établie par comparaisons quantitative, temporelle et spatiale entre l'information d'amplitude du signal radar et des variables MAR. Les résultats obtenus sont prometteurs. Les comparaisons montrent de fortes corrélations spatiales entre les variables MAR représentant la fonte et la rétrodiffusion enregistrée par le satellite. Si les analyses temporelles et quantitatives donnent également de bons résultats, des investigations plus profondes sont nécessaires pour expliquer les comportements différents sur d’autres régions de la plateforme de glace.
Anselin, L., Syabri, I. & Kho, Y. (2006). GeoDa: An introduction to spatial data analysis. Geographical Analysis, 38(1), 5-22. https://doi.org/10.1111/ j.0016-7363.2005.00671.x
ASF (2021). ASF Data Search. Alaska Satellite facility (ASF). Retrieved 26 May 2021, from https://search. asf.alaska.edu/#/
Baghdad, N. (2000). Potential and limitations of RADARSAT SAR data for wet snow monitoring. IEEE Transactions on Geoscience and Remote Sensing, 38 (1 I), 316-320. https://doi.org/10.1109/36.823925
Berger, S., Favier, L., Drews, R., Derwael, J.J. & Pattyn, F. (2016). The control of an uncharted pinning point on the flow of an Antarctic ice shelf. Journal of Glaciology, 62 (231), 37-45. https://doi.org/10.1017/ jog.2016.7
Brockmann Consult, Skywatch, Sensar & C-S (2020). SNAP - ESA Sentinel Application Platform v8.0.3 [Computer Software]. Retrieved from http://step. esa.int/
Callens, D., Drews, R., Witrant, E., Philippe, M. & Pattyn, F. (2016). Temporally stable surface mass balance asymmetry across an Ice rise derived from radar internal reflection horizons through inverse modeling. Journal of Glaciology, 62 (233), 525-534. https://doi.org/10.1017/jog.2016.41
Drews, R. (2015). Evolution of ice-shelf channels in Antarctic ice shelves. Cryosphere, 9 (3), 1169-1181. https://doi.org/10.5194/tc-9-1169-2015
Favier, L. & Pattyn, F. (2015). Antarctic ice rise formation, evolution, and stability. Geophysical Research Letters, 42 (11), 4456-4463. https://doi. org/10.1002/2015GL064195
Fettweis, X., Gallée, H., Lefebre, F. & van Ypersele, J.P. (2006). The 1988-2003 Greenland ice sheet melt extent using passive microwave satellite data and a regional climate model. Climate Dynamics, 27 (5), 531-541. https://doi.org/10.1007/s00382-006-0150-8
Fettweis, X., Tedesco, M., Van Den Broeke, M. & Ettema, J. (2011). Melting trends over the Greenland ice sheet (1958-2009) from spaceborne microwave data and regional climate models. Cryosphere, 5 (2), 359-375. https://doi.org/10.5194/tc-5-359-2011
Gilbert, E. & Kittel, C. (2021). Surface Melt and Runoff on Antarctic Ice Shelves at 1.5°C, 2°C, and 4°C of Future Warming. Geophysical Research Letters, 48 (8), 1-9. https://doi.org/10.1029/2020GL091733
Glaude, Q., Amory, C., Berger, S., Derauw, D., Pattyn, F., Barbier, C. & Orban, A. (2020). Empirical Removal of Tides and Inverse Barometer Effect on DInSAR from Double DInSAR and a Regional Climate Model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 4085-4094. https://doi.org/10.1109/JSTARS.2020.3008497
Goldberg, D., Holland, D.M. & Schoof, C. (2009). Grounding line movement and ice shelf buttressing in marine ice sheets. Journal of Geophysical Research: Earth Surface, 114 (4), 1-23. https://doi. org/10.1029/2008JF001227
Goodchild, M.F. (1986). Spatial Autocorrelation. Concepts and Techniques in Modern Geography (CATMOG), 47. Norwich: Geobooks, 57 p.
IMBIE team (2018). Mass balance of the Antarctic Ice Sheet from 1992 to 2017. Nature, 558, 219-222. https://doi.org/https://doi.org/10.1038/s41586-018-0179-y
Johnson, A., Fahnestock, M. & Hock, R. (2020). Evaluation of passive microwave melt detection methods on Antarctic Peninsula ice shelves using time series of Sentinel-1 SAR. Remote Sensing of Environment, 250 (2020), 9p. https://doi.org/10.1016/j. rse.2020.112044
Koskinen, J.T., Pulliainen, J.T. & Hallikainen, M.T. (1997). The use of ERS-1 SAR data in snow melt monitoring. IEEE Transactions on Geoscience and Remote Sensing, 35 (3), 601-610. https://doi. org/10.1109/36.581975
Liang, D., Guo, H., Zhang, L., Cheng, Y., Zhu, Q. & Liu, X. (2021). Time-series snowmelt detection over the Antarctic using Sentinel-1 SAR images on Google Earth Engine. Remote Sensing of Environment, 256, 112318. https://doi.org/10.1016/j. rse.2021.112318
Lievens, H., Demuzere, M., Marshall, H.P., Reichle, R.H., Brucker, L., Brangers, I., de Rosnay, P., Dumont, M., Girotto, M., Immerzeel, W.W., Jonas, T., Kim, E.J., Koch, I., Marty, C., Saloranta, T., Schöber, J. & De Lannoy, G.J.M. (2019). Snow depth variability in the Northern Hemisphere mountains observed from space. Nature Communications, 10 (1), 1-12. https://doi.org/10.1038/s41467-019-12566-y
Matsuoka, K., Skoglund, A., Roth, G., de Pomereu, J., Griffiths, H., Headland, R., Herried, B., Katsumata, K., Le Brocq, A., Licht, K., Morgan, F., Neff, P.D., Ritz, C., Scheinert, M., Tamura, T., Van de Putte, A., van den Broeke, M., von Deschwanden, A., Deschamps-Berger, C. & Melvær, Y. (2021). Quantarctica, an integrated mapping environment for Antarctica, the Southern Ocean, and sub-Antarctic islands. Environmental Modelling and Software, 140. https://doi.org/10.1016/j.envsoft.2021.105015
Moreira, A., Prats-iraola, P., Younis, M., Krieger, G., Hajnsek, I. & Papathanassiou, K.P. (2013). SAR-Tutorial-March-2013. IEEE Geoscience and Remote Sensing Magazine, 1 (1), 6-43. https://doi. org/10.1109/MGRS.2013.2248301
Nagler, T. & Rott, H. (2000). Retrieval of wet snow by means of multitemporal SAR data. IEEE Transactions on Geoscience and Remote Sensing, 38 (2 I), 754-765. https://doi.org/10.1109/36.842004
Nagler, T., Rott, H., Hetzenecker, M., Wuite, J. & Potin, P. (2015). The Sentinel-1 mission: New opportunities for ice sheet observations. Remote Sensing, 7 (7), 9371-9389. https://doi.org/10.3390/rs70709371
Nagler, T., Rott, H., Ripper, E., Bippus, G. & Hetzenecker, M. (2016). Advancements for snowmelt monitoring by means of Sentinel-1 SAR. Remote Sensing, 8 (4), 1-17. https://doi.org/10.3390/rs8040348
Navari, M., Margulis, S.A., Bateni, S.M., Tedesco, M., Alexander, P. & Fettweis, X. (2016). Feasibility of improving a priori regional climate model estimates of Greenland ice sheet surface mass loss through assimilation of measured ice surface temperatures. Cryosphere, 10 (1), 103-120. https://doi. org/10.5194/tc-10-103-2016
Navari, M., Margulis, S.A., Tedesco, M., Fettweis, X. & Alexander, P.M. (2018). Improving Greenland Surface Mass Balance Estimates Through the Assimilation of MODIS Albedo: A Case Study Along the K-Transect. Geophysical Research Letters, 45 (13), 6549-6556. https://doi. org/10.1029/2018GL078448
Paolo, F.S., Fricker, H.A. & Padman, L. (2015). Volume loss from Antarctic ice shelves is accelerating. Science, 348 (6232), 327-331. https://doi.org/10.1126/ science.aaa0940
Pattyn, F. (2018). The paradigm shift in Antarctic ice sheet modelling. Nature Communications, 9 (1), 10-12. https://doi.org/10.1038/s41467-018-05003-z
Payne, A.J., Vieli, A., Shepherd, A.P., Wingham, D.J. & Rignot, E. (2004). Recent dramatic thinning of largest West Antarctic ice stream triggered by oceans. Geophysical Research Letters, 31 (23), 1-4. https:// doi.org/10.1029/2004GL021284
Pollard, D., DeConto, R.M. & Alley, R.B. (2015). Potential Antarctic Ice Sheet retreat driven by hydrofracturing and ice cliff failure. Earth and Planetary Science Letters, 412, 112-121. https:// doi.org/10.1016/j.epsl.2014.12.035
Pritchard, H.D., Arthern, R.J., Vaughan, D.G. & Edwards, L.A. (2009). Extensive dynamic thinning on the margins of the Greenland and Antarctic ice sheets. Nature, 461 (7266), 971-975. https://doi. org/10.1038/nature08471
Pritchard, H.D., Ligtenberg, S.R.M., Fricker, H.A., Vaughan, D.G., Van Den Broeke, M.R. & Padman, L. (2012). Antarctic ice-sheet loss driven by basal melting of ice shelves. Nature, 484 (7395), 502-505. https://doi.org/10.1038/nature10968
Scambos, T., Hulbe, C. & Fahnestock, M. (2013). Climate-Induced Ice Shelf Disintegration in the Antarctic Peninsula. Antarctic Peninsula Climate Variability, 79, 79-92. https://doi.org/10.1029/ ar079p0079
Shah, E., Jayaprasad, P. & James, M.E. (2019). Image Fusion of SAR and Optical Images for Identifying Antarctic Ice Features. Journal of the Indian Society of Remote Sensing, 47 (12), 2113-2127. https://doi. org/10.1007/s12524-019-01040-3
Wingham, D.J., Shepherd, A., Muir, A. & Marshall, G.J. (2006). Mass balance of the Antarctic ice sheet. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 364 (1844), 1627-1635. https://doi.org/10.1098/ rsta.2006.1792
