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.
In this work we address the synergy of optical, SAR (Synthetic Aperture Radar) and topographic data in soil moisture retrieval over an Alpine area. As estimation technique, we consider Gaussian Process Regression (GPR). The test area is located in South Tyrol, Italy where the main land types are meadows and pastures. Time series of ASAR Wide Swath - SAR, optical, topographic and ancillary data (meteorological information and snow cover maps) acquired repetitively in 2010 were examined. Regarding optical data, we used both, daily MODIS reflectances, and daily NDVI, interpolated from the 16-day MODIS composite. Slope, elevation and aspect were extracted from a 2.5 m DEM (Digital Elevation Model) and resampled to 10 m. Daily soil moisture measurements were collected in the three fixed stations (two located in meadows and one located in pasture). The snow maps were used to mask the points covered by snow. The best performance was obtained by adding MODIS band 6 at
1640 nm to SAR and DEM features. The corresponding coefficient of determination, R2, was equal to 0.848, and the root mean square error, RMSE, to 5.4 % Vol. Compared to the case when no optical data were considered, there was an increase of ca. 0.05 in R2 and a decrease in RMSE of ca. 0.7 % Vol. This work showed that the joint use of NDVI or water absorption reflectance with SAR and topographic data can improve the estimation of soil moisture in specific Alpine area and that GPR is an effective method for estimation.
The goal of this study was to assess the applicability of medium resolution SAR time-series, in combination with in-situ
point measurements and machine learning, for the estimation of soil moisture content (SMC). One of the main
challenges was the combination of SMC point measurements and satellite data. Due to the high spatial variability of soil
moisture a direct linkage can be inappropriate. Data used in this study were a combination of in-situ data, satellite data
and modelled SMC from the hydrological model GEOtop. To relate the point measurements with the satellite pixel
footprint resolution, a spatial upscaling method was developed. It was found that both temporal and spatial SMC patterns
obtained from various data sources (ASAR WS, GEOtop and meteorological stations) show similar behaviors.
Furthermore, it was possible to increase the absolute accuracy of the estimated SMC through spatial upscaling of the
obtained in-situ data. Introducing information on the temporal behavior of the SAR signal proves to be a promising
method to increase the confidence and accuracy of SMC estimations. Following steps were identified as critical for the
retrieval process: the topographic correction and geocoding of SAR data, the calibration of the meteorological stations
and the spatial upscaling.