LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
H.-R. Hannula; J. Lemmetyinen; A. Kontu; C. Derksen; J. Pulliainen (2016)
Publisher: Copernicus Publications
Journal: Geoscientific Instrumentation
Languages: English
Types: Article
Subjects: Geophysics. Cosmic physics, QC801-809
An extensive in situ data set of snow depth, snow water equivalent (SWE), and snow density collected in support of the European Space Agency (ESA) SnowSAR-2 airborne campaigns in northern Finland during the winter of 2011–2012 is presented (ESA Earth Observation Campaigns data 2000–2016). The suitability of the in situ measurement protocol to provide an accurate reference for the simultaneous airborne SAR (synthetic aperture radar) data products over different land cover types was analysed in the context of spatial scale, sample spacing, and uncertainty. The analysis was executed by applying autocorrelation analysis and root mean square difference (RMSD) error estimations. The results showed overall higher variability for all the three bulk snow parameters over tundra, open bogs and lakes (due to wind processes); however, snow depth tended to vary over shorter distances in forests (due to snow–vegetation interactions). Sample spacing/sample size had a statistically significant effect on the mean snow depth over all land cover types. Analysis executed for 50, 100, and 200 m transects revealed that in most cases less than five samples were adequate to describe the snow depth mean with RMSD < 5 %, but for land cover with high overall variability an indication of increased sample size of 1.5–3 times larger was gained depending on the scale and the desired maximum RMSD. Errors for most of the land cover types reached  ∼ 10 % if only three measurements were considered.

The collected measurements, which are available via the ESA website upon registration, compose an exceptionally large manually collected snow data set in Scandinavian taiga and tundra environments. This information represents a valuable contribution to the snow research community and can be applied to various snow studies.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Atkinson, P. M. and Tate, N. J.: Spatial scale problems and geostatistical solutions: A review, Prof. Geogr., 52, 607-623, 2000.
    • Beckie, R.: Sampling scale, network sampling scale, and groundwater model parameters, Water Resour. Res., 32, 65-76, 1996.
    • Blöschl, G. and Sivapalan, M.: Scale issues in hydrologival modelling - a review, Hydrol. Process., 9, 251-290, 1995.
    • Blöschl, G. and Kirnbauer, R.: An analysis of snow cover patterns in a small alpine catchment. Hydrol. Process., 6, 99-109, 1992.
    • Blöschl, G.: Scaling issues in snow hydrology, Hydrol. Process., 13, 2149-2175, 1999.
    • Bormann, K. L., Westra, S., Evans, J. P., and McCabe, M. F.: Spatial and temporal variability in seasonal snow density, J. Hydrol., 484, 63-73, 2013.
    • Chang, A. T. C., Kelly, R. E. J., Josberger, E. G., Armstrong, R. L., Foster, J. L., and Mognard, N. M.: Analysis of ground-measured and passive-microwave-derived snow depth variations in midwinter across the Northern Great Plains, J. Hydrometeorol., 6, 20-33, 2005.
    • Christakos, G.: Modern spatiotemporal goestatistics, e-book, http: //fmi.eblib.com/patron/FullRecord.aspx?p=430733 (last access: 29 April 2016), 2000.
    • Clark, M. P., Hendrikx, J., Slater, A. G., Kavetski, D., Anderson, B., Cullen, N. J., Kerr, T., Hreinsson, E.Ö., and Woods, R. A.: Representing spatial variability of snow water equivalent in hydrologic and land-surface models: A review, Water. Resour. Res., 47, W07539, doi:10.1029/2011WR010745, 2011.
    • Cohen, J., Lemmetyinen, J., Pulliainen, J., Heinilä, K., Montomoli, F., Seppänen, J., and Hallikainen, M. T.: The effect of boreal forest canopy in satellite snow mapping - a multisensor analysis, IEEE Trans. Geosci. Remote Sens., 52, 3275-3288, 2015.
    • D'Eon, R.G.: Snow depth as a function of canopy cover and other site attributes in a forested ungulate winter range in southeast British Columbia. BC J. Ecosys. Manag., 3, 1-9, 2004.
    • Deems, J. S., Fassnacht, S. R., and Elder, K. J.: Fractal distribution of snow depth from Lidar data, J. Hydrometeorol., 7, 285-297, 2006.
    • Derksen, C.: The contribution of AMSR-E 18.7 and 10.7 GHz measurements to improved boreal forest snow water equivalent retrievals, Remote Sens. Environ., 112, 2701-2710, 2008.
    • Derksen, C., Toose, P., Rees, A., Wang, L., English, M., Walker, A., and Sturm, M.: Development of a tundra-specific snow water equivalent retrieval algorithm for satellite passive microwave data, Remote Sens. Environ., 114, 1699-1709, 2010.
    • Derksen, C., Lemmetyinen, J., Toose, P., Silis, A., Pulliainen, J., and Sturm, M.: Physical properties of Arctic and subarctic snow: Implications for high latitude passive microwave snow water equivalent retrievals, J. Geophys. Res. Atmos., 119, 7254-7270, 2014.
    • Di Leo D., Coccia, A., and Meta, A.: Technical Assistance for the Development and Deployment of an X-and Ku-band MiniSAR Airborne System (SnowSAR), ESTEC No. 4000106761-CCN1, (https://earth.esa.int/web/guest/campaigns), 2015.
    • Dickinson, W. T. and Whitely, H. R.: A sampling scheme for shallow snowpacks, IASH Bull., 17, 247-258, 1972.
    • Dobre, M., Elliot, W. J., Wu, J., Link, T. E., Glaza, B., Jain, T. B., and Hudak, A. T.: Relationship of field and LiDAR estimates of forest canopy cover with snow accumulation and melt, Proc. of 80th Annual Western Snow Conference, 2012.
    • ESA, Report for Mission Selection: CoReH2O, ESA SP-1324/2 (3 volume series), European Space Agency, Noordwijk, The Netherlands, 2012.
    • ESA Earth Observation Campaigns Data 2000-2016, ESA Earth Online, https://earth.esa.int/web/guest/campaigns.
    • Essery, R. and Pomeroy, J.: Vegetation and topographic control of wind-blown snow distributions in distributed and aggregated simulations for an Arctic tundra basin, J. Hydrometeor., 5, 735- 744, 2004.
    • Foster, J. L., Sun, C., Walker, J. P., Kelly, R., Chang, A., Dong, J., and Powell, H.: Quantifying the uncertainty in passive microwave snow water equivalent observations, Remote Sens. Environ., 94, 187-203, 2005.
    • Freund, R. J., Wilson, W. J., and Mohr, D. L.: Statistical methods (3rd edition), Academic Press, Boston, 824 pp., 2010.
    • Gary, H. L.: Airflow patterns and snow accumulation in a forest clearing. In: Proceedings of the 43rd Western Snow Conference, Coronado, California, April 23-25, 106-113, 1975.
    • Gelfan, A. N., Pomeroy, J. W., and Kuchment, L. S.: Modeling forest cover influences on snow accumulation, sublimation, and melt, J. Hydrometeorol., 5, 758-803, 2004.
    • Ghasemi, A. and Zahediasl, S.: Normality tests for statistical analysis: A guide for non-statisticians, Int. J. Endocrinol. Metab., 10, 486-489, 2012.
    • Golding, D. L. and Swanson, R. H.: Snow accumulation and melt in small forest openings in Alberta, Can. J. For. Res., 8, 380-388, 1978.
    • Goodison, B. E.: Compatibility of Canadian snowfall and snow cover data, Water Resour. Res., 17, 893-900, 1981.
    • Harding, R. J. and Pomeroy, J. W.: The energy balance of the winter boreal landscape, J. Climate, 9, 2778-2787, 1996.
    • Hardy, J. P., Davis, R. E., Jordan, R., Li, X., Woodcock, C., Ni, W., and McKenzie, J. C.: Snow ablation modelling at the stand scale in a boreal jack pine forest, J Geophys. Res. Atmos., 102, 29397-29405, 1997.
    • Hedström, N. R. and Pomeroy, J. W.: Measurements and modelling of snow interception in the boreal forest, Hydrol. Process., 12, 1611-1625, 1998.
    • Heinilä, K., Salminen, M., Pulliainen, J., Cohen, J., Metsämäki, S., and Pellikka, P.: The effect of boreal forest canopy to reflectance of snow covered terrain based on airborne imaging spectrometer observations, Int. J. Appl. Earth Obs. Geoinf., 27, 31-41, 2014.
    • Hosang, J. and Dettwiler, K.: Evaluation of a water equivalent of snow cover map in a small catchment-area using geostatistical approach, Hydrol. Process., 5, 283-290, 1991.
    • Kanji, G. K.: 100 statistical tests (3rd edition), Sage Publications, London, 256 pp., 2006.
    • Kuchment, L. S. and Gelfan, A. N.: Statistical self-similarity of spatial variations of snow cover: verification of the hypotheses and application in the snowmelt runoff generation models, Hydrol. Process., 15, 3343-3355, 2001.
    • Lemmetyinen, J., Pulliainen, J., Kontu, A., Wiesmann, A., Mätzler, C., Rott, H., Voglmeier, K., Nagler, T., Meta, A., Coccia, A., Schneebeli, M., Proksch, M., Davidson, M., Schüttemeyer, D., Chung-Chi Lin, and Kern, M.: Observations of seasonal snow cover at X- and Ku bands during the NoSREx campaign, Proc. EUSAR 2014, 3-5 June, Berlin, 2014.
    • Lemmetyinen, J., Derksen, C., Toose, P., Proksch, M., Pulliainen, J., Kontu, A., Rautiainen, K., Seppänen, J., and Hallikainen, M.: Simulating seasonally and spatially varying snow cover brightness temperature using HUT emission model and retrieval of a microwave effective grain size, Remote Sens. Environ., 156, 71- 95, 2015.
    • Liston, G. E.: Interrelationships among snow distribution, snowmelt, and snow cover depletion: implications for atmospheric, hydrologic, and ecologic modelling, J. Appl. Meteorol., 38, 1474-1487, 1999.
    • Lloyd, C. D.: Exploring spatial scale in geography, WileyBlackwell, Chichester, West Sussex, UK, 5-6, 53-56, 2014.
    • López-Moreno, J. I., Fassnacht, S. R., Beguería, S., and Latron, J. B. P.: Variability of snow depth at the plot scale: Implications for mean depth estimation and sampling strategies, The Cryosphere, 5, 617-629, doi:10.5194/tc-5-617-2011, 2011.
    • McCreight, J. L., Slater, A. G., Marshall, H. P., and Rajagopalan, B.: Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effect of sample size, random sampling, predictor quality, and validation procedures, Hydrol. Process., 28, 933-957, 2014.
    • Metsämäki, S. J., Mattila, O. P., Pulliainen, J., Niemi, K., Luojus, K., and Böttcher, K.: An optical reflectance model-based method for fractional snow cover mapping applicable to continental scale, Remote Sens. Environ., 123, 508-521, 2012.
    • Molotch, N. P. and Bales, R. C.: Scaling snow observations from the point to the grid element: Implications for observation network design. Water Resour. Res., 41, W11421, doi:10.1029/2005WR004229, 2005.
    • Neumann, N. N., Derksen, C., Smith, C., and Goodison, B.: Characterizing local scale snow cover using point measurements during the winter season, Atmos. Ocean, 44, 257-269, 2006.
    • Pomeroy, J. W., Parviainen, J., Hedström, N., and Gray, D. M.: Coupled modelling of forest snow interception and sublimation, Hydrol. Process., 12, 2317-2337, 1998.
    • Rott, H., Yueh, S. H., Cline, D. W., Duguay, C., Essery, R., Haas, C., Heliere, F., Macelloni, G., Malnes, E., Nagler, T., Pulliainen, J., Rebhan, H., and Thompson, A.: Cold Regions Hydrology Highresolution Observatory for Snow and Cold Land Processes, Proc. IEEE, 98, 752-765, 2010.
    • Skøien, J. O. and Blöschl, G.: Sampling scale effects in random fields and implications for environmental monitoring, Environ. Monit. Assess., 114, 521-552, 2006.
    • Storck, P., Lettenmaier, D. P., and Bolton, S. M.: Measurement of snow interception and canopy effects on snow accumulation and melt in a mountainous maritime climate, Oregon, United States. Water Resour. Res., 39, 1-16, 2002.
    • Sturm, M. and Benson, C.: Scales of spatial heterogeneity for perennial and seasonal snow layers, Ann. Glaciol., 38, 253-260, 2004.
    • Sturm, M., Taras, B., Liston, G. E., Derksen, C., Jonas, T., and Lea, J.: Estimating snow water equivalent using snow depth data and climate classes, J. Hydrometeorol., 11, 1380-1394, 2010.
    • Trujillo, E. and Lehning, M.: Theoretical analysis of errors when estimating snow distribution through point measurements, The Cryosphere, 9, 1249-1264, doi:10.5194/tc-9-1249-2015, 2015.
    • Trujillo, E., Ramírez, J. A., and Elder, K. J.: Topographic, meteorologic, and canopy controls on the scaling characteristics of the spatial distribution of snow depth fields. Water. Resour. Res., 43, W07409, doi:10.1029/2006WR005317, 2007.
    • Trujillo, E., Ramírez, J. A., and Elder, K. J.: Scaling properties and spatial organization of snow depth fields in sub-alpine forest and alpine tundra, Hydrol. Process., 23, 1575-1590, 2009.
    • Varhola, A., Coops, N. C., Weiler, M., and Moore R. D.: Forest canopy effects on snow accumulation and ablation: An integrative review of empirical results, J. Hydrol., 392, 219-233, 2010.
    • Veatch, W., Brooks, P. D., Gustafson, J. R., and Molotch, N. P.: Quantifying the effects of forest canopy cover on net snow accumulation at a continental, mid-latitude site, Ecohydrology, 2, 115-128, 2009.
    • Watson, F. G. R., Anderson, T. N., Newman, W. B., Alexander, S. E., and Garrott, R. A.: Optimal sampling schemes for estimating mean snow water equivalents in stratified heterogeneous landscapes, J. Hydrol., 328, 432-452, 2006.
    • Wetlaufer, K., Hendrikx, J., and Marshall, L.: Spatial heterogeneity of snow density and its influence on snow water equivalence estimates in a large mountainous basin, Hydrology, 3, 1- 17, doi:10.3390/hydrology3010003, 2016.
    • Yang, D. and Woo, M.-K.: Representativeness of local snow data for large scale hydrological investigations. Hydrol. Process., 13, 1977-1988, 1999.
    • Zhang, Y., Suzuki, K., Kadota, T., Ohata, T.: Sublimation from snow surface in southern mountain taiga of eastern Siberia. J. Geophys. Res., 109, 115-128, 2004.
    • Zheng, Z., Kirchner, P. B., and Bales, R. C.: Topographic and vegetation effects on snow accumulation in the southern Sierra Nevada: a statistical summary from lidar data, The Cryosphere, 10, 257-269, doi:10.5194/tc-10-257-2016, 2016.
  • No related research data.
  • No similar publications.

Share - Bookmark

Funded by projects

  • EC | EURUCAS

Cite this article