Dr. Bartolomeo Ventura Profile

Dr. Bartolomeo Ventura

Senior Researcher at EURAC research

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Author

Publications (9)

Proceedings Article | 27 October 2011 Paper

Analysis of snow changes in alpine regions with X-band data: electromagnetic analysis and snow cover mapping

B. Ventura, T. Schellenberger, C. Notarnicola, M. Zebisch, V. Maddalena, R. Ratti, L. Tampellini, Jinyang Du

Proceedings Volume 8179, 817908 (2011) https://doi.org/10.1117/12.897742

KEYWORDS: Backscatter, Snow cover, Scattering, Synthetic aperture radar, Electromagnetism, Data modeling, Landsat, Dielectrics, X band, Earth observing sensors

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Proceedings Article | 26 October 2010 Paper

Exploitation of C- and X-band SAR images for soil moisture change detection estimation in agricultural areas (Po Valley, Italy)

C. Notarnicola, B. Ventura, L. Pasolli, F. Di Giuseppe, M. Petitta, G. Bonafè, L. Caporaso, A. Spisni, M. Bitelli

Proceedings Volume 7829, 78290G (2010) https://doi.org/10.1117/12.870495

KEYWORDS: Soil science, X band, Vegetation, Backscatter, Synthetic aperture radar, Agriculture, Dielectrics, Databases, Algorithm development, Polonium

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Proceedings Article | 25 October 2010 Paper

Neural network adaptive algorithm applied to high resolution C-band SAR images for soil moisture retrieval in bare and vegetated areas

C. Notarnicola, E. Santi, M. Brogioni, S. Paloscia, S. Pettinato, G. Preziosa, B. Ventura

Proceedings Volume 7829, 78290F (2010) https://doi.org/10.1117/12.865988

KEYWORDS: Soil science, Vegetation, Synthetic aperture radar, Backscatter, Neural networks, Data modeling, Algorithm development, Evolutionary algorithms, Image resolution, Polarization

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Proceedings Article | 29 September 2009 Paper

Multisensor analysis of Titan surface: SAR and radiometric data synergy for estimating wind speed and liquid optical thickness of hydrocarbon lakes

B. Ventura, C. Notarnicola, D. Casarano, F. Posa, M. Janssen

Proceedings Volume 7477, 74771T (2009) https://doi.org/10.1117/12.834973

KEYWORDS: Data modeling, Synthetic aperture radar, Liquids, Radar, Scattering, Backscatter, Dielectrics, Radio optics, Radiometry, Electromagnetic scattering

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Proceedings Article | 29 September 2009 Paper

Spatial and temporal soil moisture monitoring in semi-arid and humid areas with high resolution ASAR images

C. Notarnicola, S. Paloscia, S. Pettinato, G. Preziosa, E. Santi, B. Ventura

Proceedings Volume 7477, 74771S (2009) https://doi.org/10.1117/12.830696

KEYWORDS: Soil science, Vegetation, Backscatter, Synthetic aperture radar, Sensors, Calibration, Neural networks, Data acquisition, Polarization, Neurons

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Proceedings Article | 29 September 2009 Paper

Soil moisture retrieval from SAR images as a calibration tool for soil moisture index derived from thermal inertia with MODIS images

C. Notarnicola, B. Ventura, S. Pettinato, E. Santi, M. Zebisch

Proceedings Volume 7477, 74771U (2009) https://doi.org/10.1117/12.834922

KEYWORDS: Soil science, Synthetic aperture radar, MODIS, Calibration, Vegetation, Image analysis, Radar, Sensors, Image retrieval, Temperature metrology

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Proceedings Article | 10 October 2008 Paper

Multi-sensor analysis of Titan surface: SAR and radiometric data synergy for estimating wind speed and liquid optical thickness of hydrocarbon lakes

B. Ventura, D. Casarano, C. Notarnicola, F. Posa

Proceedings Volume 7109, 71091D (2008) https://doi.org/10.1117/12.802765

KEYWORDS: Liquids, Dielectrics, Radar, Data modeling, Synthetic aperture radar, Radiometry, Radio optics, Backscatter, Electromagnetism, Scattering

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In this paper, the Titan lake features detected by active and passive sensors during the Cassini flybys have been analyzed. The first part of the work is dedicated to the analysis of the SAR data. The main aim of this part is to find situations where it could be feasible to infer information about the likely ranges of lake parameters. The approach is mainly divided in the following steps: • Electromagnetic modeling of the lakes surface and comparison with measured radar data; • Introduction of the electromagnetic modeling in the inversion procedure based on a Bayesian framework. The inversion procedure itself is divided into training and test phase. • Analysis of the inferred information and determination of the likely ranges for lake optical thickness and wind speed. In the second part, the analysis have been extended to the radiometer data. The likely ranges of surface parameters e.g. dielectric constant, wind speed and optical thickness derived from the Bayesian inversion have been used as an input to a forward radiative transfer model calculation to obtain simulated brightness temperatures. Comparison of the observed and computed brightness temperatures will allow us to address the consistency of the observations from the two instruments and to determine the coarse characteristics of the surface parameters that lead to consistent results between the two instruments at a global scale. In processing radiometric data two main aspects have to be considered. The correlation between SAR and radiometry is limited by the low radiometry footprint resolution at closest approach, preventing detection and correlation of surface features smaller than 6 km azimuth resolution. In addition, there is a large-scale variability in calibration among the five radiometer beams caused by sidelobes that should be accounted for obtaining relative brightness variations. For these two reasons, the data have been compared only on areas with large extensions such as large liquid surfaces detected during T25 and T30 Cassini flybys and relative surrounding areas. Furthermore only relative brightness temperature, calculated as difference between various areas, have been considered.

Proceedings Article | 26 October 2007 Paper

Inferring Titan's surface features by means of Bayesian inversion algorithm applied to radar data

B. Ventura, D. Casarano, C. Notarnicola, M. Janssen, F. Posa

Proceedings Volume 6746, 67460A (2007) https://doi.org/10.1117/12.753798

KEYWORDS: Radar, Dielectrics, Liquids, Scattering, Backscatter, Synthetic aperture radar, Data modeling, Radio optics, Motion models, Tantalum

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Proceedings Article | 9 October 2006 Paper

Modeling the electromagnetic response of Titan's surface features observed by Cassini Radar

B. Ventura, D. Casarano, C. Notarnicola, D. Di Rosa, F. Posa

Proceedings Volume 6363, 63630D (2006) https://doi.org/10.1117/12.695373

KEYWORDS: Radar, Synthetic aperture radar, Scattering, Liquids, Backscatter, Electromagnetism, Methane, Image analysis, Dielectrics, Radiometry

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The Cassini spacecraft, launched in 1997, begun the exploration of the Saturnian system in July 2004. 44 flybies of the Saturn largest moon Titan are planned during the four years of the Cassini mission and 16 are Radar passes. A Radar, developed jointly by JPL, ASI and Alenia Spazio, is mounted on Cassini. The instrument operates at 13.8 GHz (Ku band) and has passive (radiometer) and active (scatterometer, altimeter, SAR imaging) capabilities. Until July 2006, six (Ta, T3, T7, T8, T13, T16) radar planned passes have been accomplished. The data are processed by the JPL and stored in the Basic Image Data Records (BIDR) files, thus obtaining SAR images and brightness temperature profiles of a significant fraction of Titan's surface. The Radar Cross Section, RCS, derived from the SAR imagery, reflects the complex Titan's surface morphology. The data show RCS variations in excess of 20 dB between the "brightest" and the "darkest" areas. On the basis of brightness, texture, and morphology eight different surface units were identified by Cassini Radar Science Team (CRST) scientists. The darkest features are good candidate to be lakes of hydrocarbons. Moreover, periodic structures ("sand dunes") have been observed: in this case the RCS variations can be described in terms of tilt angle effect, thus modifying the local incidence angle. In this paper, the RCS behaviour of the observed features is studied in detail by the means of the Integral Equation Method, IEM. The dependence of the backscattering coefficient on the surface physical properties, composition and roughness of different areas is analyzed resulting in some possible scenarios for the observed features. Surfaces are modeled as Gaussian stationary processes and volume scattering is also taken into account, due to the transparency of water ice at radar wavelength. The data of the IEM model are compared with numerical simulations based on the Kirchhoff approximation with auto similar surfaces performed as preparatory work in the preliminary stage of the Cassini mission. The RCS data simulated for some likely scenarios of Titan's surface are well consistent with the real radar data and can help their interpretation in terms of physical and morphological surface properties.

Showing 5 of 9 publications

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