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Journal of Mountain Science

, Volume 16, Issue 6, pp 1435–1451 | Cite as

Dry/wet snow mapping based on the synergistic use of dual polarimetric SAR and multispectral data

  • Divyesh VaradeEmail author
  • Onkar Dikshit
  • Surendar Manickam
Article
  • 9 Downloads

Abstract

We propose a multi-sensor multi-spectral and bi-temporal dual-polarimetric Synthetic Aperture Radar (SAR) data integration scheme for dry/wet snow mapping using Sentinel-2 and Sentinel-1 data which are freely available to the research community. The integration is carried out by incorporating the information retrieved from ratio images of the conventional method for wet snow mapping and the multispectral data in two different frameworks. Firstly, a simple differencing scheme is employed for dry/wet snow mapping, where the snow cover area is derived using the Normalized Differenced Snow Index (NDSI). In the second framework, the ratio images are stacked with the multispectral bands and this stack is used for supervised and unsupervised classification using support vector machines for dry/wet snow mapping. We also investigate the potential of a state of the art backscatter model for the identification of dry/wet snow using Sentinel-1 data. The results are validated using a reference map derived from RADARSAT-2 full polarimetric SAR data. A good agreement was observed between the results and the reference data with an overall accuracy greater than 0.78 for the different blending techniques examined. For all the proposed frameworks, the wet snow was better identified. The coefficient of determination between the snow wetness derived from the backscatter model and the reference based on RADARSAT-2 data was observed to be 0.58 with a significantly higher root mean square error of 1.03% by volume.

Keywords

Snow mapping Ratio method Normalized Differenced Snow Index Classification Polarimetric synthetic-aperture radar 

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Notes

Acknowledgments

This work is partly supported by Project number DST-2016056, funded by the Department of Science and Technology, Government of India. The authors acknowledge the MacDonald, Dettwiler and Associates Ltd. (2018) and the Canadian Space Agency (CSA) for the RADARSAT-2 product. The raw test datasets can be downloaded freely for non-commercial research purposes through the Copernicus open access hub https://doi.org/scihub.copernicus.eu/. The processed test datasets are available on request.

References

  1. Baghdadi N, Choker M, Zribi M, et al. (2016) A new empirical model for radar scattering from bare soil surfaces. Remote Sensing 8(11): 920.  https://doi.org/10.3390/rs8110920 CrossRefGoogle Scholar
  2. Ballesteros-Cánovas JA, Trappmann D, Madrigal-González J, et al. (2018) Climate warming enhances snow avalanche risk in the Western Himalayas. Proceedings of the National Academy of Sciences 115(13): 3410–3415.  https://doi.org/10.1073/pnas.1716913115 CrossRefGoogle Scholar
  3. Bezdek JC, Ehrlich R, Full W (1984) FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences 10(2–3): 191–203.  https://doi.org/10.1016/0098-3004(84)90020-7 CrossRefGoogle Scholar
  4. Buchroithner MFE (2001) A decade of Trans-European remote sensing cooperation. Proceedings of the EARSeL symposium, 20th, CRC Press/Taylor & Francis. pp 127–128.Google Scholar
  5. Choi H (2004) Cloud detection in Landsat imagery of ice sheets using shadow matching technique and automatic normalized difference snow index threshold value decision. Remote Sensing of Environment 91(2): 237–242.  https://doi.org/10.1016/j.rse.2004.03.007 CrossRefGoogle Scholar
  6. Colbeck SC (1983) Theory of metamorphism of dry snow. Journal of Geophysical Research: Oceans 88(C9): 5475–5482.  https://doi.org/10.1029/jc088ic09p05475 CrossRefGoogle Scholar
  7. Colbeck SC (1987) Snow metamorphism and classification. Seasonal Snowcovers: Physics, Chemistry, Hydrology. Dordrecht: Springer Netherlands. pp 1–35.  https://doi.org/10.1007/978-94-009-3947-9_1 Google Scholar
  8. Congalton RG, Green K (2009) Assessing the accuracy of remotely sensed data: Principles and practices. 2nd ed. CRC Press/Taylor & Francis, Boca Raton, London.Google Scholar
  9. Denoth A (1994) An electronic device for long-term snow wetness recording. Annals of Glaciology 19: 104–106.  https://doi.org/10.3189/s0260305500011058 CrossRefGoogle Scholar
  10. Dozier J (1989) Spectral signature of alpine snow cover from the landsat thematic mapper. Remote Sensing of Environment 28: 9–22.  https://doi.org/10.1016/0034-4257(89)90101-6 CrossRefGoogle Scholar
  11. Dozier J, Shi J (2000) Estimation of snow water equivalence using SIR-C/X-SAR. II. Inferring snow depth and particle size. IEEE Transactions on Geoscience and Remote Sensing 38(6): 2475–2488.  https://doi.org/10.1109/36.885196 CrossRefGoogle Scholar
  12. Dozier J, Green RO, Nolin AW, et al. (2009) Interpretation of snow properties from imaging spectrometry. Remote Sensing of Environment 113: S25–S37.  https://doi.org/10.1016/j.rse.2007.07.029 CrossRefGoogle Scholar
  13. Dubois PC, van Zyl J, Engman T (1995) Measuring soil moisture with imaging radars. IEEE Transactions on Geoscience and Remote Sensing 33(4): 915–926.  https://doi.org/10.1109/36.406677 CrossRefGoogle Scholar
  14. Duffy G, Bennartz R (2018) The role of melting snow in the ocean surface heat budget. Geophysical Research Letters 45(18): 9782–9789.  https://doi.org/10.1029/2018g1079182 CrossRefGoogle Scholar
  15. Eppanapelli LK, Lintzén N, Casselgren J, et al. (2018) Estimation of liquid water content of snow surface by spectral reflectance. Journal of Cold Regions Engineering 32(1): 05018001.  https://doi.org/10.1061/(asce)cr.1943-5495.0000158 CrossRefGoogle Scholar
  16. Ferraris V, Yokoya N, Dobigeon N, et al. (2018) A Comparative Study of Fusion-Based Change Detection Methods for Multi-Band Images with Different Spectral and Spatial Resolutions IGARSS 2018 — 2018 IEEE International Geoscience and Remote Sensing Symposium. Valencia. New York, USA: IEEE.  https://doi.org/10.1109/igarss.2018.8517712 CrossRefGoogle Scholar
  17. Gorodetskaya IV, Cane MA, Tremblay LB, et al. (2006) The effects of sea-ice and land-snow concentrations on planetary albedo from the earth radiation budget experiment. Atmosphere-Ocean 44(2): 195–205.  https://doi.org/10.3137/ao.440206 CrossRefGoogle Scholar
  18. Gupta RP, Haritashya UK, Singh P (2005) Mapping dry/wet snow cover in the Indian Himalayas using IRS multispectral imagery. Remote Sensing of Environment 97(4): 458–469.  https://doi.org/10.1016/j.rse.2005.05.010 CrossRefGoogle Scholar
  19. Hall DK, Riggs GA, Salomonson VV (1995) Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data. Remote Sensing of Environment 54(2): 127–140.  https://doi.org/10.1016/0034-4257(95)00137-p CrossRefGoogle Scholar
  20. Hasanzadeh M, Kasaei S (2010) A multispectral image segmentation method using size-weighted fuzzy clustering and membership connectedness. IEEE Geoscience and Remote Sensing Letters 7(3): 520–524.  https://doi.org/10.1109/lgrs.2010.2040800 CrossRefGoogle Scholar
  21. He GJ, Feng XZ, Xiao PF, et al. (2017) Dry and wet snow cover mapping in mountain areas using SAR and optical remote sensing data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10(6): 2575–2588.  https://doi.org/10.1109/jstars.2017.2673409 CrossRefGoogle Scholar
  22. Holden M, Schistad Solberg A, Solberg R (1998) Wet snow-cover mapping by C- and L-band polarimetric SARIGARSS’ 98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No. 98CH36174). Seattle, WA, USA. New York, USA: IEEE8.  https://doi.org/10.1109/igarss.1998.691574 Google Scholar
  23. Jamieson B (2006) Formation of refrozen snowpack layers and their role in slab avalanche release. Reviews of Geophysics 44(2): RG2001.  https://doi.org/10.1029/2005rg000176 CrossRefGoogle Scholar
  24. Jeelani G, Feddema JJ, van der Veen CJ, Stearns L (2012) Role of snow and glacier melt in controlling river hydrology in Liddar watershed (western Himalaya) under current and future climate. Water Resources Research 48:148.  https://doi.org/10.1029/2011WR011590 CrossRefGoogle Scholar
  25. Lee JS, Pottier E (2009) Polarimetric radar imaging: From basics to applications. Jong-Sen Lee, Eric Pottier. Optical science and engineering, vol 143. CRC; London: Taylor & Francis [distributor], Boca Raton, Fla.Google Scholar
  26. Li YJ, Chen J, Ma QM, et al. (2018) Evaluation of sentinel-2A surface reflectance derived using Sen2Cor in North America. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11(6): 1997–2021.  https://doi.org/10.1109/jstars.2018.2835823 CrossRefGoogle Scholar
  27. Lu DS, Li GY, Moran E (2014) Current situation and needs of change detection techniques. International Journal of Image and Data Fusion 5(1): 13–38.  https://doi.org/10.1080/19479832.2013.868372 CrossRefGoogle Scholar
  28. Marsh P (1987) Grain growth in a wet arctic snow cover. Cold Regions Science and Technology 14(1): 23–31.  https://doi.org/10.1016/0165-232x(87)90041-3 CrossRefGoogle Scholar
  29. Matzler C (1996) Microwave permittivity of dry snow. IEEE Transactions on Geoscience and Remote Sensing 34(2): 573–581.  https://doi.org/10.1109/36.485133 CrossRefGoogle Scholar
  30. Nagler T, Rott H (2000) Retrieval of wet snow by means of multitemporal SAR data. IEEE Transactions on Geoscience and Remote Sensing 38(2): 754–765.  https://doi.org/10.1109/36.842004 CrossRefGoogle Scholar
  31. Nagler T, Rott H, Ripper E, et al. (2016) Advancements for snowmelt monitoring by means of sentinel-1 SAR. Remote Sensing 8(4): 348.  https://doi.org/10.3390/rs8040348 CrossRefGoogle Scholar
  32. Navalgund RR, Senthil Kumar A, Nandy S (eds.) (2019) Remote Sensing of Northwest Himalayan Ecosystems. (1st ed). Springer, Singapore.Google Scholar
  33. Schaner N, Voisin N, Nijssen B, et al. (2012) The contribution of glacier melt to streamflow. Environmental Research Letters 7(3): 034029.  https://doi.org/10.1088/1748-9326/7/3/034029 CrossRefGoogle Scholar
  34. Schreier G (1993) SAR geocoding: Data and systems. Wichmann, Karlsruhe.Google Scholar
  35. Shi JC, Dozier J (1995) Inferring snow wetness using C-band data from SIR-C’s polarimetric synthetic aperture radar. IEEE Transactions on Geoscience and Remote Sensing 33(4): 905–914.  https://doi.org/10.1109/36.406676 CrossRefGoogle Scholar
  36. Singh G, Venkataraman G (2010) Snow permittivity retrieval inversion algorithm for estimating snow wetness. Geocarto International 25(3): 187–212.  https://doi.org/10.1080/10106040903486130 CrossRefGoogle Scholar
  37. Singh G, Yamaguchi Y, Park SE (2013) General four-component scattering power decomposition with unitary transformation of coherency matrix. IEEE Transactions on Geoscience and Remote Sensing 51(5): 3014–3022.  https://doi.org/10.1109/tgrs.2012.2212446 CrossRefGoogle Scholar
  38. Singh VP, Singh P, Haritashya UK (2011) Encyclopedia of snow, ice and glaciers. The encyclopedia of earth sciences series. Springer, Dordrecht.CrossRefGoogle Scholar
  39. Snapir B, Momblanch A, Jain SK, et al. (2019) A method for monthly mapping of wet and dry snow using Sentinel-1 and MODIS: Application to a Himalayan river basin. International Journal of Applied Earth Observation and Geoinformation 74: 222–230.  https://doi.org/10.1016/j.jag.2018.09.011 CrossRefGoogle Scholar
  40. Surendar M, Bhattacharya A, Singh G, et al. (2015) Development of a snow wetness inversion algorithm using polarimetric scattering power decomposition model. International Journal of Applied Earth Observation and Geoinformation 42: 65–75.  https://doi.org/10.1016/j.jag.2015.05.010 CrossRefGoogle Scholar
  41. Thakur P, Aggarwal S, Dhote PR, et al. (2019) Hydrometeorological Hazards Mapping, Monitoring and Modelling. In: Navalgund RR, Senthil Kumar A, Nandy S (eds.), Remote Sensing of Northwest Himalayan Ecosystems, 1st edn. Springer, Singapore. pp 143–148.  https://doi.org/10.1007/978-981-13-2128-3_7 Google Scholar
  42. Ulaby FT, Moore RK, Fung AK (1986) Microwave Remote Sensing Active and Passive-Volume III: From Theory to Applications. Artech House Inc, Norwood.Google Scholar
  43. Ulaby FT, Long DG, Blackwell WJ, et al. (2014) Microwave radar and radiometric remote sensing. The University of Michigan Press, Ann Arbor.CrossRefGoogle Scholar
  44. van Zyl J, Kim Y (2011) Synthetic aperture radar polarimetry (1st ed). JPL space science and technology series. Wiley, Hoboken, N.J.CrossRefGoogle Scholar
  45. Varade D, Dikshit O (2017) A novel linear physical model for remote sensing of snow wetness and snow density using the visible and infrared bands: Abstract C13C-0975 presented. AGU Fall Meeting, New Orleans, Lousiana.Google Scholar
  46. Varade D, Dikshit O (2018a) Assessment of winter season land surface temperature in the Himalayan regions around the Kullu area in India using landsat-8 data. Geocarto International: 1–22.  https://doi.org/10.1080/10106049.2018.1520928
  47. Varade D, Maurya AK, Sure A, et al. (2017) Supervised classification of snow cover using hyperspectral imagery. International Conference on Emerging Trends in Computing and Communication Technologies (ICETCCT). Dehradun, India. New York, USA: IEEE.  https://doi.org/10.1109/icetcct.2017.8280302 CrossRefGoogle Scholar
  48. Varade DM, Maurya AK, Dikshit O (2018) Development of spectral indexes in hyperspectral imagery for land cover assessment. IETE Technical Review 1–9.  https://doi.org/10.1080/02564602.2018.1503569
  49. Varade D, Dikshit O (2018b) estimation of surface snow wetness using sentinel-2 multispectral data. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences IV-5: 223–228.  https://doi.org/10.5194/isprs-annals-iv-5-223-2018 CrossRefGoogle Scholar
  50. Varade D, Dikshit O (2019) Improved assessment of atmospheric water vapor content in the Himalayan regions around the kullu valley in India using landsat-8 data. Water Resources Research 55(1): 462–475.  https://doi.org/10.1029/2018wr023806 CrossRefGoogle Scholar
  51. Viera AJ, Garrett JM (2005) Understanding interobserver agreement: the kappa statistic. Family Medicine 37(5): 360–3.Google Scholar

Copyright information

© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Geoinformatics, Department of Civil EngineeringIndian Institute of Technology KanpurKalyanpur, KanpurIndia
  2. 2.Department of Civil and Environmental EngineeringDuke UniversityDurhamUSA

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