Research

Remote sensing of snow for land surface modeling

Snow is a geophysical variable that controls climate and water cycles/patterns at various temporal and spatial scales. Among other land surface properties and states, surface albedo and water contents affect on the exchange of energy and water between land surface and atmosphere. Because of high albedo and latent heat of fusion, snow is important in regulating the land surface temperature. Apart from its role in land-atmosphere heat and water exchange, snow is also a vital source for fresh water. Estimates by Gleick (1996) show ice (including ice caps, glaciers, permanent snow, ground ice, and permafrost) constitutes almost 70% of fresh water (Figure 1). Many regions of the Earth especially in the mid-latitudes (30° – 60°) rely on snow melt for irrigation, municipal uses and for hydro-power generation.

Figure 1: Distribution of Earth's water.

Snow albedo, Snow Cover Extents (SCE) and Snow Water Equivalent (SWE) are the most important variables of terrestrial seasonal snow cover for modeling and forecasting water and energy fluxes. The accuracy of these simulations depends on model physics, parameterizations, forcings and initial conditions. Neither of these four is perfect, which introduces uncertainties in the modeling results. These uncertainties cause biases in the simulation of important variables like discharge, timing of peak flows, accumulation and melting of snow cover (Feng et al., 2008; Pan et al., 2003; Sheffield et al., 2003). Many studies (Andreadis and Lettenmaier, 2006; Huang et al., 2008; Rodell and Houser, 2004; Sun et al., 2004; Zaitchik and Rodell, 2009) show potentials of assimilation of remote sensing (RS) retrieved states for improved simulations. RS observations in the various part of the electromagnetic (EM) spectrum are affected by land surface states. In this context, various investigations (Kelly et al., 2003; Koskinen et al., 1997; Lampkin and Yool, 2004) have studied the potential of using Synthetic Aperture Radar (SAR), optical/thermal and passive microwave observations for the retrieval of snow properties (e.g. Snow coverage area (SCA), SWE).  

This research aims at developing methods for the retrieval of snow properties from optical/thermal and SAR-based RS observations and using this information for improving the simulations of energy and water fluxes. 

Pictures: Snow bi-directional reflectance measurements

Figure 2: Preparing setup for measuring spectral distribution of down willing irradiance.

Figure 3: Setup for measuring the BRDF of snow.

References

Andreadis, K.M. and Lettenmaier, D.P., 2006. Assimilating remotely sensed snow observations into a macroscale hydrology model. Advances in Water Resources, 29(6): 872-886.

Feng, X., Sahoo, A., Arsenault, K., Houser, P., Luo, Y. and Troy, T.J., 2008. The Impact of Snow Model Complexity at Three CLPX Sites. Journal of Hydrometeorology, 9(6): 1464-1481.

Gleick, P.H., 1996. Water resources. In Encyclopedia of Climate and Weather, ed. by S. H. Schneider, Oxford University Press, New York, 2: 817-823.

Huang, C., Li, X. and Lu, L., 2008. Retrieving soil temperature profile by assimilating MODIS LST products with ensemble Kalman filter. Remote Sensing of Environment, 112(4): 1320-1336.

Kelly, R.E., Chang, A.T., Leung, T. and 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.

Koskinen, J.T., Pulliainen, J.T. and Hallikainen, M.T., 1997. The use of ERS-1 SAR data in snow melt monitoring. IEEE Transactions on Geoscience and Remote Sensing, 35(3): 601-610.

Lampkin, D.J. and Yool, S.R., 2004. Monitoring mountain snowpack evolution using near-surface optical and thermal properties. Hydrological Processes, 18(18): 3527-3542.

Pan, M., Sheffield, J., Wood, E.F., Mitchell, K.E., Houser, P.R., Schaake, J.C., Robock, A., Lohmann, D., Cosgrove, B., Duan, Q.Y., Luo, L., Higgins, R.W., Pinker, R.T. and Tarpley, J.D., 2003. Snow process modeling in the North American Land Data Assimilation System (NLDAS): 2. Evaluation of model simulated snow water equivalent. Journal of Geophysical Research-Atmospheres, 108(D22): 14.

Rodell, M. and Houser, P.R., 2004. Updating a Land Surface Model with MODIS-Derived Snow Cover. Journal of Hydrometeorology, 5(6): 1064-1075.

Sheffield, J., Pan, M., Wood, E.F., Mitchell, K.E., Houser, P.R., Schaake, J.C., Robock, A., Lohmann, D., Cosgrove, B., Duan, Q.Y., Luo, L.F., Higgins, R.W., Pinker, R.T., Tarpley, J.D. and Ramsay, B.H., 2003. Snow process modeling in the North American Land Data Assimilation System (NLDAS): 1. Evaluation of model-simulated snow cover extent. Journal of Geophysical Research-Atmospheres, 108(D22): 13.

Sun, C., Walker, J.P. and Houser, P.R., 2004. A methodology for snow data assimilation in a land surface model. J. Geophys. Res., 109.

Zaitchik, B.F. and Rodell, M., 2009. Forward-looking assimilation of MODIS-derived snow-covered area into a land surface model. Journal of Hydrometeorology, 10(1): 130-148.