The effects of variation in snow properties on passive microwave snow mass estimation
Davenport, I., Sandells, M. and Gurney, R. (2012) The effects of variation in snow properties on passive microwave snow mass estimation. Remote Sensing of the Environment, 118 (1). pp. 161-175. ISSN 0034-4257
To link to this item DOI: 10.1016/j.rse.2011.11.014
Estimating snow mass at continental scales is difficult, but important for understanding land-atmosphere interactions, biogeochemical cycles and the hydrology of the Northern latitudes. Remote sensing provides the only consistent global observations, butwith unknown errors. Wetest the theoretical performance of the Chang algorithm for estimating snow mass from passive microwave measurements using the Helsinki University of Technology (HUT) snow microwave emission model. The algorithm's dependence upon assumptions of fixed and uniform snow density and grainsize is determined, and measurements of these properties made at the Cold Land Processes Experiment (CLPX) Colorado field site in 2002–2003 used to quantify the retrieval errors caused by differences between the algorithm assumptions and measurements. Deviation from the Chang algorithm snow density and grainsize assumptions gives rise to an error of a factor of between two and three in calculating snow mass. The possibility that the algorithm performsmore accurately over large areas than at points is tested by simulating emission from a 25 km diameter area of snow with a distribution of properties derived from the snow pitmeasurements, using the Chang algorithm to calculate mean snow-mass from the simulated emission. The snowmass estimation froma site exhibiting the heterogeneity of the CLPX Colorado site proves onlymarginally different than that from a similarly-simulated homogeneous site. The estimation accuracy predictions are tested using the CLPX field measurements of snow mass, and simultaneous SSM/I and AMSR-E measurements.
Armstrong, R. L., & Brodzik, M. J. (2000). Validation of passive microwave snow algorithms. Proc. IGARSS 2000, 24–28 Jul 2000, Vol. 4. (pp. 1561–1563). Armstrong, R. L., & Brodzik, M. J. (2001). Recent Northern Hemisphere snow extent: A comparison of data derived fromvisible and microwave satellite sensors. Geophysical Research Letters, 28, 2676–3673. Bachmaier, M., & Backes, M. (2008). Variogram or semivariogram? Understanding the variances in a variogram. Precision Agriculture, 9(3), 173–175. Brodzik, M. J. (Ed.). (2003). CLPX-satellite: SSM/I brightness temperature grids. Boulder, CO: National Snow and Ice Data Center. Digital Media. Brodzik, M. J. (Ed.). (2003). CLPX-satellite: AMSR-E brightness temperature grids. Boulder, CO: National Snow and Ice Data Center. Digital Media. Butt, M. J. (2009). A comparative study of Chang and HUT models for UK snow depth retrieval. International Journal of Remote Sensing, 30(24), 6361–6379. Chang, A. T. C., Foster, J. L., & Hall, D. K. (1987). Nimbus-7 SMMR derived global snow cover parameters. Annals of Glaciology, 9, 39–44. Chang, A. T. C., Kelly, R. E., Josberger, E. G., Armstrong, R. L., Foster, J. L., & Mognard, N. M. (2005). Analysis of ground-measured and passive-microwave-derived snow depth variations in midwinter across the northern Great Plains. Journal of Hydrometeorology, 6(1), 20–33. Clifford, D. (2010). Global estimates of snow water equivalent from passive microwave instruments: History, challenges and future developments. International Journal of Remote Sensing, 31(14), 3707–3726. Cline, D., Armstrong, R., Davis, R., Elder, K., & Liston, G. (2002). Updated July 2004. CLPX LSOS Snow Pit Measurements. In M. Parsons, & M. J. Brodzik (Eds.), CLPX-Ground: Snow Measurements at the Local Scale Observation Site (LSOS). Hardy, J., Pomeroy, J., Link, T., Marks, D., Cline, D., Elder, K., Davis, R. (2003). Boulder, CO: National Snow and Ice Data Center. Digital Media. Cline, D., Armstrong, R., Davis, R., Elder, K., & Liston, G. (2002). Updated July 2004. CLPX-Ground: ISA snow pit measurements. In M. Parsons, & M. J. Brodzik (Eds.), Boulder, CO: National Snow and Ice Data Center. Digital Media. Cline, D., Elder, K., Davis, B. J., Liston, G. E., Imel, D., & Yueh, S. H. (2003). Overview of the NASA cold land processes field experiment (CLPX-2002). Proceedings of SPIE, 4894(361), doi:10.1117/12.467766. Elder, K., Cline, D., Liston, G. E., & Armstrong, R. (2009).NASA Cold Land Processes Experiment (CLPX 2002/03): Field Measurements of Snowpack Properties and SoilMoisture. Journal of Hydrometeorology, 10(1), 320–329. Foster, J. L., Chang, A. T. C., & Hall, D. K. (1997). Comparison of snow mass estimates from a prototype passive microwave algorithmand a snow depth climatology. Remote Sensing of Environment, 62, 132–142. Kelly, R. E., Chang, A. T., Tsang, L., & Foster, J. L. (2003). A prototype AMSR-E global snow area and snow depth algorithm. IEEE Transactions on Geoscience and Remote Sensing, 41(2), 230–242, doi:10.1109/TGRS.2003.809118. Lemmetyinen, J., Pulliainen, J., Rees, A., Kontu, A., Yubao Qiu, & Derksen, C. (2010). Multiplelayer adaptation of HUT snow emission model: Comparison with experimental data. IEEE Transactions on Geoscience and Remote Sensing, 48(7), 2781–2794, doi: 10.1109/TGRS.2010.2041357. Marks, D., Cooley, K. R., Robertson, D. C., & Winstral, A. (2000). ARS Technical Bulletin NWRC-2000-5, August 11, 2000. Snow monitoring at the Reynolds Creek Experimental Watershed, Idaho, USA. Nolin, A., & Dozier, J. (2000). A hyperspectralmethod for remotely sensing the grain diameter of snow. Remote Sensing of Environment, 74(2), 207–216. Pardé, M., Goïta, K., & Royer, A. (2007). Inversion of a passive microwave snow emission model for water equivalent estimation using airborne and satellite data. Remote Sensing of Environment, 111(2007), 346–356. Pulliainen, J. T., Grandell, J., & Hallikainen, M. T. (1999). HUT snow emission model and its applicability to snow water equivalent retrieval. IEEE Transactions on Geoscience and Remote Sensing, 37(3), 1378–1390, doi:10.1109/36.763302. Randall, D. A., & Wood, R. A. (2007). Climate models and their evaluation. In S. Solomon (Ed.), Climate Change 2007: The physical science basis. Contribution of working group 1 to the fourth assessment report of the intergovernmental panel on climate change. Cambridge, UK and New York, NY, USA: Cambridge University Press. Tedesco, M., & Kokhanovsky, A. A. (2007). The semi-analytical snow retrieval algorithm and its application to MODIS data. Remote Sensing of Environment, 111(2–3), 228–241.