A study has been carried out to test which one of two different approaches: use of L-band active and passive data or use of L-C-X-bands passive data, is more effective to retrieve soil moisture of bare soils. Simulated and measured data were used. Simulated data were generated implementing IEM model for active and L-C passive data, and GO model for X band passive data. Measured data derive from the Soil Moisture Experiment "SMEX-02".
As a preliminary investigation, retrieval was solved by the application of artificial feed forward backpropagation neural networks. Three different input configurations were considered:
1a) L-band: emissivity H and V polarizations-backscattering coefficient HH polarization ;
1b) L-band: emissivity H and V polarizations-backscattering coefficient VV polarization ;
2) L-band--C-band--X-band emissivity H polarization.
For all three input configurations the requested outputs were root mean square of heights s, correlation length l and dielectric constant er. To test the methodology, the best performing nets were chosen to simulate first a retrieval with an artificial dataset with noise added. All chosen configurations permit an excellent retrieval of the real part of the dielectric constant on every soil type (smooth, medium and rough), while roughness parameters, especially autocorrelation length, are not well retrieved.
Active-passive approach proved to be more efficient, as a consequence only active-passive configurations were used with real data. The algorithm confirmed to be efficient when neural networks have been trained with "noisy data". However, there is always an underestimation, probably due to vegetation. Further investigations need to be carried out in order to understand the cause of this underestimation.
This work intends to test the use of remotely sensed data, as a mean to identify degraded lands with a high environmental hazard. The approach uses data from the sensor Thematic Mapper on Landsat 5 in synergy with digital ortho-photos (1:10000) and land cover map Corine 1990 to create a methodology useful to identify areas with dumps. The analyzed scene is relative to an area located in the Apulia Region in Southern Italy, where it is known the presence of a dump near the Margherita di Savoia "saline" (salt evaporation pool).
As this dump is in its early phase, it is impossible to use thermal anomaly as a characteristic sign of its presence. So its identification proceeds through the extraction of the spectral signatures of the dump area and of the neighborhood zones.
The analysis is developed in three steps: (1) Monitoring the change in the zone nearby the pools, especially if abandoned; (2) Pointing out the dump presence by the spectral signature specificity;
(3) Individuating areas characterized by the same spectral properties. A pre-processing analysis is carried out by the Principal Component Transformation in order to minimize spectral noise and redundancy. Subsequently, the images are classified by the unsupervised algorithm ISODATA aiming at automatically individuating radiometric classes. The regions of interest are identified by help of the land cover map and then characterized by their spectral signatures. The identification of the dump is a feasible objective because of the temporal stability of its spectral signature, with respect to those of the other areas.