Wspólnotowy Serwis Informacyjny Badan i Rozwoju - CORDIS

Algorithms and models for the retrieval of snow parameters from microwave data

Electromagnetic models simulating backscattering from natural media are fundamentals, for interpreting experimental data, performing sensitivity analysis and retrieving the unknown parameters. Two different codes have been implemented in Matlab.

In one approach propagation and scattering in snow are described by using the Dense Medium Radiative Transfer theory (DMRT) assuming that the particle size is small compared to the operational wavelength of sensor and that the effective propagation constant has a small imaginary part compared with its real part. In an alternative approach, based on the Strong Fluctuation Theory (SFT), the inhomogeneous layer of snow is modelled as a continuous medium with the scattering effects taken into account by making use of random fluctuations of permittivity. The latter ones can be described by a correlation function, with the variance characterizing the strength of the permittivity function of the medium and correlation length corresponding to the scales of the fluctuations. An effective permittivity is used to characterize the randomness and scattering effects.

Both models have been validated with experimental data. The sensitivity analysis carried out as a function of sensor and medium parameters has pointed out the following:
- Backscattering from dry snow increases as a function of observation frequency, snow depth, snow water equivalent and particle radius, and shows a maximum as a function of volume fraction.

- At the Envisat ASAR frequency (C-band) the contribution to total backscattering of a layer of dry snow 2 meter deep is very low and close to the threshold of SAR sensitivity.

- For a soil covered by wet snow the backscattering coefficient decreases as a function of snow wetness with a trend that is a function of frequency.

An Algorithm for the retrieval of dry snow water equivalent (SWE) and snow depth (SD) by using multi-frequency radiometric data from satellite and artificial neural networks (ANN¿s) has been developed and tested by using data at 19 and 37 GHz. The algorithms have been tested by using data from the Special Sensor Microwave Imager (SMM/I) collected for long periods of times (years) over Finland, and from the Advanced Microwave Scanning Radiometer (AMSR-E) acquired over a large in Norway) in the 2002/2004 period.

The results obtained have been compared with those obtained using other approaches on the basis of the root mean square error (RMSE) and the regression coefficient. It has been shown that the ANN based technique gives significantly better results than other approaches, such as those based on semi-empirical models or on iterative inversion. The developed technique, which is suitable for near real-time applications, can be very useful when periodical ground measurements are collected in a few stations only, and no information is available from any areas in between.

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