Advertisement

Cluster Computing

, Volume 22, Supplement 4, pp 8301–8308 | Cite as

Near-surface snowmelt detection on the Greenland ice sheet from FengYun-3 MWRI data

  • Xingdong WangEmail author
  • Zhankai Wu
  • Xinwu Li
Article
First Online:
  • 79 Downloads

Abstract

The melt extent on the Greenland ice sheet plays an important role in energy balance, and the Arctic and global climates. The micro-wave radiation imager (MWRI) is one of the major payloads of Chinese second-generation polar-orbiting meteorological satellite, FengYun-3 (FY-3), and it is similar to the special sensor microwave/image (SSM/I). The cross-polarized gradient ratio (XPGR) is mainly applied in the scanning multichannel microwave radiometer (SMMR) (18 GHz horizontal polarization (18 H) and 37 GHz vertical polarization (37 V)), the advanced microwave scanning radiometer-earth observing system (AMSR-E) (18.7 GHz horizontal polarization (18 H) and 36.5 GHz vertical polarization (36 V)) and SSM/I (19.3 GHz horizontal polarization (19 H) and 37 GHz vertical polarization (37 V)), which increases the differences between dry and wet snow. The hyperplane of support vector machine (SVM) is used to detect the melt information based on the XPGR data on the Greenland ice sheet, which has higher detection accuracy comparing with the existing threshold methods in theory. The results were compared with the SSM/I data (threshold = − 0.0154), and the results show that the proposed method (That is XPGR combining with SVM) for MWRI data is feasible for the detection of the near-surface snowmelt information on the Greenland ice sheet.

Keywords

FengYun-3 satellite Snowmelt detection Greenland ice sheet XPGR algorithm SVM algorithm 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgements

This research was supported by the National Key Research and Development Program of China (Grant No. 2016YFA0600302), and supported by Natural Science Foundation of China (No. 41606209), and supported by the Fujian Provincial Key Laboratory of Photonics Technology, Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, China (JYG1707), and supported by the Fundamental Research Funds for the Henan Provincial Colleges and Universities (2015QNJH16). We thank the Chief Editor of the Journal and the anonymous reviewers for their time and effort, which will significantly improves the manuscript.

References

  1. 1.
    Ashcraft, I.S., Long, D.G.: Comparison of methods for melt detection over Greenland using active and passive microwave measurements. Int. J. Remote Sens. 27(12), 2469–2488 (2006)CrossRefGoogle Scholar
  2. 2.
    Waske, B., Benediktsson, J.A.: Fusion of support vector machines for classification of multisensor data. IEEE Trans. Geosci. Remote Sens. 45(12), 3858–3866 (2007)CrossRefGoogle Scholar
  3. 3.
    Tedesco, M., Fettweis, X., van den Broeke, M.R., et al.: The role of albedo and accumulation in the 2010 melting record in Greenland. Environ. Res. Lett. 6(1), 014005 (2011)CrossRefGoogle Scholar
  4. 4.
    Tedesco, M.: Snowmelt detection over the Greenland ice sheet from SSM/I brightness temperature daily variations. Geophys. Res. Lett. 34(2), 155–164 (2007)CrossRefGoogle Scholar
  5. 5.
    Liang, L., Guo, H.D., Li, X.W., et al.: Automated ice-sheet snowmelt detection using microwave radiometer measurements. Polar Res. 32, 19746 (2013)CrossRefGoogle Scholar
  6. 6.
    Abdalati, W., Steffen, K., Otto, C., et al.: Comparison of brightness temperatures from SSMI instruments on the DMSP F8 and F11 satellites for Antarctica and the Greenland Ice Sheet. Int. J. Remote Sens. 16(7), 1223–1229 (1995)CrossRefGoogle Scholar
  7. 7.
    Liu, J.P., Chen, Z.Q., Francis, J., et al.: Has Arctic Sea Ice Loss contributed to increased surface melting of the Greenland Ice Sheet? J. Clim. 29(9), 3373–3386 (2016)CrossRefGoogle Scholar
  8. 8.
    Abdalati, W., Steffen, K.: Passive microwave-derived snow melt regions on the Greenland Ice-Sheet. Geophys. Res. Lett. 22(7), 787–790 (1995)CrossRefGoogle Scholar
  9. 9.
    Abdalati, W., Steffen, K.: Snowmelt on the Greenland ice sheet as derived from passive microwave satellite data. J. Clim. 10, 165–175 (1997)CrossRefGoogle Scholar
  10. 10.
    Joshi, M.D., Bolzan, J.F., Jezek, K.C., et al.: Classification of snow facies on the Greenland ice sheet using passive microwave and SAR imagery. IEEE Int. Geosci. Remote Sens. Symp. 4, 1852–1854 (1998)Google Scholar
  11. 11.
    Fettweis, X., Gallee, H., Lefebre, F., et al.: The 1979–2005 Greenland ice sheet melt extent from passive microwave data using an improved version of the melt retrieval XPGR algorithm. Clim. Dyn. 27, 531–541 (2006)CrossRefGoogle Scholar
  12. 12.
    Fettweis, X., van Ypersele, J.P., Gallee, H., et al.: The 1979–2005 Greenland ice sheet melt extent from passive microwave data using an improved version of the melt retrieval XPGR algorithm. Geophys. Res. Lett. 2007(34), L05502 (2007)Google Scholar
  13. 13.
    McCabe, M.F., Chylek, P., Dubey, M.K.: Detecting ice-sheet melt area over western Greenland using MODIS and AMSR-E data for the summer periods of 2002–2006. Remote Sens. Lett. 2(2), 117–126 (2011)CrossRefGoogle Scholar
  14. 14.
    Lampkin, D.J., Wade, U.: Evaluation of a novel inversion model for surface melt magnitude over the Greenland ice sheet during the 2002 ablation season. Int. J. Remote Sens. 34(19), 6931–6946 (2013)CrossRefGoogle Scholar
  15. 15.
    Mantero, P., Moser, G., Serpico, S.B.: Partially supervised classification of remote sensing images through SVM-based probability density estimation. IEEE Trans. Geosci. Remote Sens. 43(3), 559–570 (2005)CrossRefGoogle Scholar
  16. 16.
    Chi, M., Feng, R., Bruzzone, L.: Classification of hyperspectral remote-sensing data with primal SVM for small-sized training dataset problem. Adv. Space Res. 41(11), 1793–1799 (2008)CrossRefGoogle Scholar
  17. 17.
    Mountrakis, G., Im, J., Ogole, C.: Support vector machines in remote sensing: a review. ISPRS J. Photogramm. Remote Sens. 66, 247–259 (2011)CrossRefGoogle Scholar
  18. 18.
    Roy, A., Singha, J., Devi, S.S., et al.: Impulse noise removal using SVM classification based fuzzy filter from gray scale images. Signal Process. 128, 262–273 (2016)CrossRefGoogle Scholar
  19. 19.
    Steger, C.R., Reijmer, C.H., van den Broeke, M.R.: The modelled liquid water balance of the Greenland Ice Sheet. Cryosphere 11(6), 2507–2526 (2017)CrossRefGoogle Scholar
  20. 20.
    Moustafa, S.E., Rennermalm, A.K., Roman, M.O., et al.: Evaluation of satellite remote sensing albedo retrievals over the ablation area of the southwestern Greenland ice sheet. Remote Sens. Environ. 198, 115–125 (2017)CrossRefGoogle Scholar
  21. 21.
    Wang, D.D., Liang, S.L., He, T., et al.: Surface shortwave net radiation estimation from FengYun-3 MERSI Data. Remote Sens. 7, 6224–6239 (2015)CrossRefGoogle Scholar
  22. 22.
    Weng, F.Z., Zou, X.L., Turk, F.J.: Introduction to the special issue on the Chinese FengYun-3 satellite instrument calibration and applications. IEEE Transactions on Geoscience & Remote Sensing 50(12), 4843–4844 (2012)CrossRefGoogle Scholar
  23. 23.
    Song, H.H., Wang, G.J., Cao, A.J., et al.: Improving the spatial resolution of FY-3 microwave radiation imager via fusion with FY-3/MERSI. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10(7), 3055–3063 (2017)CrossRefGoogle Scholar
  24. 24.
    Jaramillo, F., Orchard, M., Munoz, C., et al.: On-line estimation of the aerobic phase length for partial nitrification processes in SBR based on features extraction and SVM classification. Chem. Eng. J. 331, 114–123 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Information Science and EngineeringHenan University of TechnologyZhengzhouChina
  2. 2.Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingChina
  3. 3.Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics TechnologyFujian Normal UniversityFuzhouChina

Personalised recommendations

Cite article

Buy options