Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-30T20:17:19.719Z Has data issue: false hasContentIssue false
  • English
  • Français

Antarctic snow melt detection based on the synergy of SSM/I and QuikSCAT

Published online by Cambridge University Press:  25 July 2017

Xinwu Li ,
Xingdong Wang ,
Cheng Wang  and
Lu Zhang
Xinwu Li
Affiliation:
Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences, 9 Dengzhuang South Road, Haidian District, Beijing 100094, China
Xingdong Wang*
Affiliation:
Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences, 9 Dengzhuang South Road, Haidian District, Beijing 100094, China Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou 350007, Peoples Republic of China
Cheng Wang
Affiliation:
Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences, 9 Dengzhuang South Road, Haidian District, Beijing 100094, China
Lu Zhang
Affiliation:
Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences, 9 Dengzhuang South Road, Haidian District, Beijing 100094, China
*
*Corresponding author: zkywxd@163.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Microwave radiometer SSM/I (Special Sensor Microwave Imager) data and scatterometer QuikSCAT (Quick Scatterometer) data have been widely used for near-surface snow melt detection based on their sensitivity to liquid water present in snow. The SSM/I data have high reliability and the QuikSCAT data have high spatial resolution. In order to improve the accuracy of Antarctic near-surface snow melt detection, we propose a new method based on the synergy of SSM/I and QuikSCAT data, i.e. the snow melt physical model incorporates the complementary advantages of both datasets. Based on comparisons with temperature data from three automatic weather stations, the proposed algorithm improved the accuracy of snow melt detection. The algorithm could also be applied to other regions, which would provide further evidence to support its use and additional data to document changes in the Antarctic due to global climate change.

Keywords

microwave radiometermicrowave scatterometerphysical modelpolar regionssynergistic algorithm
Type
Physical Sciences
Information
Antarctic Science , Volume 29 , Issue 6 , December 2017 , pp. 561 - 568
Check for updates
Copyright
© Antarctic Science Ltd 2017 

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

Abdalati, W. & Steffen, K. 1995. Passive microwave-derived snow melt regions on the Greenland ice sheet. Geophysical Research Letters, 22, 787–790.Google Scholar
Abdalati, W. & Steffen, K. 1997. Snow melt on the Greenland ice sheet as derived from passive microwave satellite data. Journal of Climate, 10, 165–175.Google Scholar
Ashcraft, I.S. & Long, D.G. 2006. Comparison of methods for melt detection over Greenland using active and passive microwave measurements. International Journal of Remote Sensing, 27, 2469–2488.CrossRefGoogle Scholar
Bartsch, A., Kidd, R.A., Wagner, W. & Bartalis, Z. 2007. Temporal and spatial variability of the beginning and end of daily spring freeze/thaw cycles derived from scatterometer data. Remote Sensing of Environment, 106, 360–374.CrossRefGoogle Scholar
Bothale, R.V., Rao, P.V.N., Dutt, C.B.S. & Dadhwal, V.K. 2015. Detection of snow melt and freezing in Himalaya using OSCAT data. Journal of Earth System Science, 124, 101–113.CrossRefGoogle Scholar
Brown, R., Derksen, C. & Wang, L. 2007. Assessment of spring snow cover duration variability over northern Canada from satellite datasets. Remote Sensing of Environment, 111, 367–381.Google Scholar
Dupont, F., Royer, A., Langlois, A., Gressent, A., Picard, G., Fily, M., Cliche, P. & Chum, M. 2012. Monitoring the melt season length of the Barnes ice cap over the 1979–2010 period using active and passive microwave remote sensing data. Hydrological Processes, 26, 2643–2652.CrossRefGoogle Scholar
Early, D.S. & Long, D.G. 2001. Image reconstruction and enhanced resolution imaging from irregular samples. IEEE Transactions on Geoscience and Remote Sensing, 39, 291–302.Google Scholar
Foster, J.L., Hall, D.K., Eylander, J.B., Riggs, G.A., Nghiem, S.V., Tedesco, M., Kim, E., Montesano, P.M., Kelly, R.E.J., Casey, K.A. & Choudhury, B. 2011. A blended global snow product using visible, passive microwave and scatterometer satellite data. International Journal of Remote Sensing, 32, 1371–1395.Google Scholar
Joshi, M., Merry, C.J., Jezek, K.C. & Bolzan, J.F. 2001. An edge detection technique to estimate melt duration, season and melt extent on the Greenland ice sheet using passive microwave data. Geophysical Research Letters, 28, 3497–3500.CrossRefGoogle Scholar
Kunz, L.B. & Long, D.G. 2006. Melt detection in Antarctic ice shelves using spaceborne scatterometers and radiometers. IEEE Transactions on Geoscience and Remote Sensing, 44, 2461–2469.Google Scholar
Lee, K.H. & Anagnostou, E.N. 2004. A combined passive/active microwave remote sensing approach for surface variable retrieval using tropical rainfall measuring mission observations. Remote Sensing of Environment, 92, 112–125.Google Scholar
Liu, H., Wang, L. & Jezek, K.C. 2005. Wavelet-transform based edge detection approach to derivation of snow melt onset, end and duration from satellite passive microwave measurements. International Journal of Remote Sensing, 26, 4639–4660.Google Scholar
Liu, H., Wang, L. & Jezek, K.C. 2006. Spatiotemporal variations of snow melt in Antarctica derived from satellite scanning multichannel microwave radiometer and Special Sensor Microwave Imager data (1978–2004). Journal of Geophysical Research - Earth Surface, 10.1029/2005JF000318.Google Scholar
Nghiem, S.V., Steffen, K., Kwok, R. & Tsai, W.Y. 2001. Detection of snow melt regions on the Greenland ice sheet using diurnal backscatter change. Journal of Glaciology, 47, 539–547.CrossRefGoogle Scholar
Nghiem, S.V., Hall, D.K., Mote, T.L., Tedesco, M., Albert, M.R., Keegan, K., Shuman, C.A., DiGirolamo, N.E. & Neumann, G. 2012. The extreme melt across the Greenland ice sheet in 2012. Geophysical Research Letters, 10.1029/2012GL053611.Google Scholar
Oza, S.R. 2015. Spatial-temporal patterns of surface melting observed over Antarctic ice shelves using scatterometer data. Antarctic Science, 27, 403–410.Google Scholar
Ramage, J.M. & Isacks, B.L. 2003. Interannual variations of snow melt and refreeze timing on southeast-Alaskan icefields, USA. Journal of Glaciology, 49, 102–116.Google Scholar
Steffen, K., Nghiem, S.V., Huff, R. & Neumann, G. 2004. The melt anomaly of 2002 on the Greenland ice sheet from active and passive microwave satellite observations. Geophysical Research Letters, 10.1029/2004GL020444.Google Scholar
Steiner, N. & Tedesco, M. 2014. A wavelet melt detection algorithm applied to enhanced-resolution scatterometer data over Antarctica (2000–2009). Cryosphere, 8, 25–40.Google Scholar
Torinesi, O., Fily, M. & Genthon, C. 2003. Variability and trends of the summer melt period of Antarctic ice margins since 1980 from microwave sensors. Journal of Climate, 16, 1047–1060.Google Scholar
Wang, L. 2012. Deriving spatially varying thresholds for real-time snow melt detection from space-borne passive microwave observations. Remote Sensing Letters, 3, 305–313.Google Scholar
Wismann, V. 2000. Monitoring of seasonal snow melt on Greenland with ERS scatterometer data. IEEE Transactions on Geoscience and Remote Sensing, 38, 1821–1826.CrossRefGoogle Scholar
Zwally, H.J. & Fiegles, S. 1994. Extent and duration of Antarctic surface melting. Journal of Glaciology, 40, 463–476.CrossRefGoogle Scholar