Comparison of local scale measured and modelled brightness temperatures and snow parameters from the CLPX 2003 by means of a dense medium radiative transfer theory model



Some 27 years of passive microwave satellite observations have demonstrated the value of remote sensing for cryospheric applications, albeit at regional to hemispheric scales due to the relatively large sensor footprints (e.g. ∼25 km × 25 km for the advanced microwave scanning radiometer). At the other extreme there is the primarily local-scale in situ experience of the snow community. Until higher-resolution sensors become available, understanding the effects of sub-pixel heterogeneity and the downscaling of observations remains an important topic.

In order to provide a baseline for performing scaling studies and for future radiance-based hydrological assimilation schemes, an evaluation of snowpack forward radiance modelling at the plot scale (least heterogeneous scale) is carried out. Snow microwave radiance models based on dense medium radiative transfer (DMRT) theory incorporate a high degree of physical fidelity, yet dense medium models are particularly sensitive to snowpack structural parameters such as grain size, density, and depth (parameters that may vary substantially within a snowpack). The DMRT performance is evaluated through the use of a fitting technique with respect to snow grain size, a hydrologically important parameter related to the total amount of melt water stored in a snowpack, snowpack metamorphism, vertical energy transport and melt dynamics, as well as various quantities useful in surface energy balance studies. All remaining snow input parameters to the model are derived from snow pit measurements. Model-predicted radiances are evaluated using microwave brightness measurements collected during the NASA Cold Land Process Experiment (CLPX) by the University of Tokyo's Ground-Based Microwave Radiometer-7 (GBMR-7) system at 18·7, 36·5, and 89 GHz, with incidence angles ranging from 30 to 70 °. The detailed CLPX microwave and snow data provide an excellent opportunity to gain insight into the strengths and weaknesses of this approach. Copyright © 2006 John Wiley & Sons, Ltd.