Italian experiments

Use of  SAR data for the evaluation
of vegetation roughness:
application to hydrological and meteorological modelling

  glacier1 glacier2
Courtesy of NASA/JPL 

Department of Civil Engineering, the University of Florence
research group coordinator Prof. Ignazio Becchi
DIC The Department of Civil Engineering of the University of Florence has done for many years research in subjects related to hydrogeological risk. The research includes the use of distributed hydrological models, both in space and in time, for which the use of remotely sensed data is particularly interesting.

Remote sensing, in fact, can provide large amounts of areal data, continuously updated and usually with an excellent spatial resolution, that would otherwise be very difficult to obtain.

Several research projects that make use of satellite data have been carried out, including a collaboration with EOSAT (USA) concerning the use of Landsat TM images for the evaluation of soil moisture, and a collaboration with Eurimage (Italy), regarding the study of flood events through the use of ERS SAR data.


In the present project, X-SAR/SRTM and satellite data in the visible-infrared wavelengths will be used to obtain a parametrization of the surface roughness and its characterization for the study of the dynamics of energy fluxes from earth to atmosphere.

Such researches are of relevant importance both for hydrological and meteorological modeling.

Experimental studies will investigate the effect of vegetation on the transport of mass and energy from the Earth to the atmosphere. Other activities will include the study of land surface temperature from satellite data.

After these preliminary studies, SAR data will be processed in order to investigate the relationship between radar backscatter and vegetation properties (height, foliage density).

The results obtained can be used in hydrological and meteorological modelling. In fact land surface parametrization is fundamental for the estimation of energy fluxes, for evapotranspiration and soil moisture evaluation. This is generally problematic due to the difficulties in finding appropriate data. Therefore, the use of remotely sensed data could greatly improve the reliability of such models.


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