This study presents a preliminary assessment of the potentialities of the COSMO-SkyMed® (CSK®) satellite constellation to accurately classify different crops. The experiment is focused on the main crops grown in the agricultural region of Marchfeld (Austria) namely carrot, corn, potato, soybean and sugar beet. A Support Vector Machine (SVM) classifier was fed with temporally dense series of backscattering coefficients extracted from a stack of CSK® GTC products. In particular, twenty one CSK® dual polarization (11 HH, 10 VH) images were acquired over the site for the growing season (early April – mid October) in Stripmap Himage mode, with a nominal incidence angle at scene center of 40°. A comparison of the classifications obtained at the two different polarizations are reported and the result are analyzed in terms of the achieved accuracies. The SVM method was able to classify all five crop types with an overall accuracy of 81.6% (Kappa 0.77) at VH polarization and of 84.5% (Kappa 0.80) at HH polarization. Sugar beet, potato and carrot were accurately identified with OA never less than 83% at both polarizations, whereas corn and soybean showed remarkably differences in terms of producer’s and user’s accuracies, probably due to particular agricultural practices adopted for these two crop species. These first results show that the CSK® capability of acquiring temporally dense data sets can accurately identify several crop types.
The commercial market offers several software packages for the registration of remotely sensed data through standard
one-to-one image matching. Although very rapid and simple, this strategy does not take into consideration all the
interconnections among the images of a multi-temporal data set. This paper presents a new scientific software, called
Satellite Automatic Multi-Image Registration (SAMIR), able to extend the traditional registration approach towards
multi-image global processing. Tests carried out with high-resolution optical (IKONOS) and high-resolution radar
(COSMO-SkyMed) data showed that SAMIR can improve the registration phase with a more rigorous and robust
workflow without initial approximations, user’s interaction or limitation in spatial/spectral data size. The validation
highlighted a sub-pixel accuracy in image co-registration for the considered imaging technologies, including optical and
This work aims at investigating the capability of COSMO-SkyMed® (CSK®) constellation of Synthetic Aperture Radar
(SAR) system to monitor the Leaf Area Index (LAI) of different crops. The experiment was conducted in the Marchfeld
Region, an agricultural Austrian area, and focused on five crop species: sugar beet, soybean, potato, pea and corn. A
linear regression analysis was carried out to assess the sensitivity of CSK® backscattering coefficients to crops changes
base on LAI values. CSK® backscattering coefficients were averaged at a field scale (<σ°dB>) and were compared to the
DEIMOS-1 derived values of estimated LAI. LAI were as well averaged over the corresponding fields (<LAIest>). CSK®
data acquired at three polarizations (HH, VV and VH), four incidence angles (23°, 33°, 40° and 57°) and at different
pixel spacings (2.5 m and 10 m) were tested to assess whether spatial resolution may influence results at a field scale and
to find the best combination of polarizations and CSK® acquisition beams which indicate the highest sensitivity to crop
LAI values. The preliminary results show that sugar beet can be well monitored (r = 0.72 - 0.80) by CSK® by using any
of the polarization acquisition modes, at moderate to shallow incidence angles (33° - 57°). Slightly weaker correlations
were found, at VH polarization only, between CSK® < σ°dB> and <LAIest> for potato (r = 0.65), pea (r = 0.65) and
soybean (r = -0.83). Shallower view incidence angles seem to be preferable to steep ones in most cases. CSK®
backscattering coefficients were no sensitive at all to LAI changes for already developed corn fields.
Spatial and temporal information of soil water content is of essential importance for modelling of land surface processes
in hydrological studies and applications for operative systems of irrigation management. In the last decades, several
remote sensing domains have been considered in the context of soil water content monitoring, ranging from active and
passive microwave to optical and thermal spectral bands.
In the framework of an experimental campaign in Southern Italy in 2007, two innovative methodologies to retrieve soil
water content information from airborne earth observation (E.O.) data were exploited: a) analyses of the dependence of
surface temperature of vegetation with soil water content using thermal infrared radiometer (TIR), and b) estimation of
superficial soil moisture content using reflectance in the visible and near infrared regions acquired from optical sensors.
The first method (a) is applicable especially at surfaces completely covered with vegetation, whereas the second method
is preferably applicable at surfaces without or with sparse vegetation. The synergy of both methods allows the
establishment of maps of spatially distributed soil water content.
Results of the analyses are presented and discussed, in particular in view of an operative context in irrigation studies.
Earth Observation (E.O.) technologies provide a valuable data base for the monitoring of crop and soil characteristics on
a large scale, in a rapid, accurate and cost-effective way. The present work aims at evaluating different methods and
models for the estimation of the Leaf Area Index (LAI) by means of hyperspectral data acquired by the optical airborne
instrument CASI during the ESA AgriSAR 2006 campaign. Inversion of a physical model using an iterative optimization
technique (SQP) and a fast look-up-table (LUT) approach is performed and results are compared with an empirical
model based on the relationship between LAI and WDVI. Furthermore, the analyses carried out on the inversion of the
physical models provide the opportunity to test the spectral bands proposed for the upcoming E.O. satellite Sentinel-2
developed by ESA in the framework of GMES (Global Monitoring for Environment and Security). The Sentinel-2
spectral sampling is compared with the one proposed by an independent study determining the wavebands best
characterizing vegetation and crops. Accuracy of LAI estimation, evaluated with the AgriSAR 2006 field measurements,
is discussed in the context of operational agricultural monitoring.
In the context of vegetation studies Earth Observation (E.O.) data have been extensively used to retrieve biophysical
parameters of land surface. In some cases, thanks to the availability of near-real-time data, tools and applications have
been developed and implemented in the fields of precision agriculture, water resources monitoring and management. So
far, empirical approaches based on vegetation indices (VIs) have been successfully applied. They may provide a
satisfactory level of accuracy in the estimation of important vegetation biophysical parameters (e.g. LAI, fractional
ground cover, biomass, etc). Such methods, however, require a reliable reference data-set to calibrate empirical formulas
on different vegetation types; furthermore, they are generally based on a few spectral bands, with a consistent under-exploitation
of the full spectral range available in new generation sensors. Alternative approaches based on inversion of
radiative transfer models of vegetation represent a challenging opportunity for the estimation of vegetation parameters
from data with high dimensionality.
The Leaf Area Index is a key parameter that is indispensable for many biophysical and climatic models. LAI is required for modeling crop water requirements for precision farming and agricultural resource management. The objective of this study was to investigate different approaches for estimating LAI from EO data. To this aim multiangular CHRIS/PROBA data, from SPARC 2003 and 2004, were used in the inversion of PROSPECT-SAILH models using a numerical optimization technique based on Marquardt-Levenberg algorithm. The optimal spectral sampling to estimate LAI was investigated using a sensitivity analysis. From the same data set, the reflectance in the red and near-infrared bands, from the closer to nadir image, was considered in order to estimate the LAI using an empirical approach based on the CLAIR model. The LAI obtained from the empirical approach was finally employed as prior information in the physical based model. LAI values retrieved with the combined approaches were realistically estimated with a good accuracy (RMSE is 0.51 m2m-2).