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15 October 2015 Cosmo-SkyMed and RADARSAT2 image investigation for the monitoring of agricultural areas
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This research aims at investigating the backscatter sensitivity at C and X band to the characteristics of agricultural surfaces and analyzing the integration of these data collected from Radarsat2 (RS2) and COSMO-SkyMed (CSK) systems on tree agricultural test areas in Italy (San Pietro Capofiume, in Emilia Romagna, Sesto Fiorentino, in Tuscany, and Mazia Valley, in South Tyrol).

A preliminary test of the sensitivity of SAR signal to the soil and vegetation characteristics was first carried out by also comparing data from previous experiments. From these results, it can be concluded that X-band data are mainly sensitive to vegetation structure and biomass, and to soil moisture of bare or slightly vegetate soils, whereas C-band images could provide valuable information for the retrieval of soil moisture, even in vegetation covered soils.

Two retrieval algorithms were implemented for estimating the main geophysical parameters, namely soil moisture content (SMC) and vegetation biomass (PWC) from these sensors. Over Sesto Fiorentino area, an algorithm based on Artificial Neural Network (ANN) technique was implemented for estimating both SMC of bare or scarcely vegetated soil and vegetation biomass of wheat crops at X band. On the South-Tyrol area, a SMC retrieval approach based on the Support Vector Regression methodology, which was already tested in this area using C-band data from ENVISAT/ASAR data, was adopted. This algorithm integrated data at both X and C bands showing encouraging results, even though further investigations shall be carried out on a larger time-series and larger set of samples.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
S. Paloscia, S. Pettinato, E. Santi, C. Notarnicola, F. Greifeneder, G. Cuozzo, I. Nicolini, B. Demir, and L. Bruzzone "Cosmo-SkyMed and RADARSAT2 image investigation for the monitoring of agricultural areas", Proc. SPIE 9642, SAR Image Analysis, Modeling, and Techniques XV, 964206 (15 October 2015);

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