, Volume 54, Issue 1-4, pp 241-260

Passive microwave signatures of landscapes in winter

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The successful application of passive microwave sensors requires signatures for the unambiguous inversion of the remote sensing data. Due to the large number of object types and large variability of physical properties, the inversion of data from land surfaces is a delicate and often ambiguous task. The present paper is a contribution to the assessment of multi-frequency passive microwave signatures of typical objects on land in winter. We discuss the behaviour of measured emissivities at vertical and horizontal polarization over the frequency range of 5 to 100 GHz (incidence angle of 50 degrees) of water and bare soil surfaces, grass and snowcovers under various conditions. These data and their variabilities lead us toward a classificaion algorithm for some, but not all object classes. Most snowcovers can easily be discriminated from other surfaces, difficulties occur for fresh powder snow if 94 GHz data are not available. The problem of wet snow has found a solution by using a certain combination of observables.

In addition to snowcover types we find large differences between frozen and unfrozen bare soil. On the other hand the different situations of grasscovers show all very similar emissivities.

For the estimation of physical parameters we propose algorithms for certain object classes. The estimation of surface temperature, especially for snow-free land, seems to be feasible, also the estimation of the snow liquid water content at the surface. For estimating soil moisture lower frequencies (e.g. 1.4 GHz) should be used.

For the estimation of the Water Equivalent, WE, we cannot yet find a definitive solution. Certain correlations exist for dry winter snow between WE and observables at frequencies between 10 and 35 GHz. Especially the polarization difference at 10 GHz shows a monotonous increase with increasing WE. Algorithms using higher frequencies are more sensitive to WE, however, they are subject to ambiguities.

With 7 Figures