Dr. Claudia Notarnicola Profile

Dr. Claudia Notarnicola

Senior Researcher at Eurac Research

SPIE Involvement:

Conference Chair | Editor | Author

Area of Expertise:

SAR processing , inversion methods , Biophysical parameter estimation

Websites:

Company Website | Personal Website

Publications (51)

Proceedings Article | 20 October 2023 Presentation

Presentation

Alpine rock glacier deformation estimation by using SAR multifrequency data

Giovanni Cuozzo, Melisa Soledad Heredia, Mattia Callegari, Fabio Bovenga, Ludovica De Gregorio, Abraham Mejia-Aguilar, Aldo Bertone, Ilienia Argentiero, Antonella Belmonte, Alberto Refice, Claudia Notarnicola

Proceedings Volume PC12732, PC1273205 (2023) https://doi.org/10.1117/12.2683972

KEYWORDS: Synthetic aperture radar, Glaciers, Deformation, Temporal resolution, Global Positioning System, Spatial resolution, Objectives, Image analysis, Correlation coefficients

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Proceedings Article | 28 October 2022 Poster + Paper

Poster + Paper

Two-source energy balance modeling of evapotranspiration with thermal remote sensing at different spatial resolutions: a case study of the European Alps

Paulina Bartkowiak, Mariapina Castelli, Roberto Colombo, Claudia Notarnicola

Proceedings Volume 12262, 122621E (2022) https://doi.org/10.1117/12.2646185

KEYWORDS: Infrared thermal wave imaging, MODIS, Data modeling

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Proceedings Article | 12 September 2021 Presentation

Presentation

Towards daily snowline observations on glaciers using multi-source and multi-resolution satellite data

Martina Barandun, Mattia Callegari, Ulrich Strasser, Claudia Notarnicola

Proceedings Volume 11861, 1186108 (2021) https://doi.org/10.1117/12.2601682

KEYWORDS: Satellites, Snow cover, MODIS, Earth observing sensors, Synthetic aperture radar, Satellite imaging, Image classification, Spatial resolution, Sensors, Image resolution

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Glacier melt is an important fresh water source. Seasonal changes can have impacting consequences on downstream water resources management. Today’s glacier monitoring lacks an observation-based tool for regional, sub-seasonal observation of glacier mass balance and a quantification of associated meltwater release at high temporal resolution. The snowline on a glacier marks the transition between the ice and snow surface, and is, at the end of the summer, a proxy for the annual glacier mass balance. It was shown that glacier mass balance model simulations closely tied to sub-seasonal snowline observations on optical satellite sensors are robust for the observation date. Recent advances in remote sensing permit efficient and extensive snowline mapping. Different methods automatically discriminate snow over ice on high- to medium-resolution optical satellite images. Other studies rely on lower ground resolution optical imagery to retrieve snow cover fraction at pixel level and produce regional maps of snow cover extent. However, state-of-the-art methods using optical sensors still have important shortcomings, such as cloud-cover related issues. Images acquired by Synthetic Aperture Radar (SAR), which are almost insensitive to cloud coverage, have proofed suitable for transient snowline delineation. The combination of SAR and optical data in a complementary way carries a unique potential for a better monitoring of snow depletion on high temporal and spatial resolution. The aim of this work is to map snow cover over glaciers by combining Sentinel-1 SAR, Sentinel-2 multispectral and lower resolution MODIS images. Consecutively, we developed an approach that can automatically handle classification of multi-source and multi-resolution satellite image stacks. This provides a unique solution for continuous snowline mapping since the beginning of the century. With the provided close-to-daily transient snow cover fractions on glacier level, we provide the basis for a new strategy to directly integrate multi-source satellite image classification into glacier mass balance monitoring.

Proceedings Article | 12 September 2021 Presentation

Presentation

Welcome and Introduction to Conference 11861

Fabio Bovenga, Claudia Notarnicola, Nazzareno Pierdicca, Emanuele Santi

Proceedings Volume 11861, 1186102 (2021) https://doi.org/10.1117/12.2613763

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Proceedings Article | 8 October 2019 Paper

Paper

SWE retrieval by exploiting COSMO-SkyMed X-band SAR imagery and ground data through a machine learning approach

Ludovica De Gregorio, Francesca Cigna, Giovanni Cuozzo, Alexander Jacob, Simonetta Paloscia, Simone Pettinato, Emanuele Santi, Deodato Tapete, Lorenzo Bruzzone, Claudia Notarnicola

Proceedings Volume 11154, 111540M (2019) https://doi.org/10.1117/12.2550824

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Proceedings Article | 7 October 2019 Presentation + Paper

Presentation + Paper

Integration of hydro-climatological model and remote sensing for glacier mass balance estimation

Iwona Podsiadlo, Claudia Paris, Francesca Bovolo, Mattia Callegari, Ludovica De Gregorio, Daniel Günther, Carlo Marin, Thomas Marke, Milad Niroumand-Jadidi, Claudia Notarnicola, Ulrich Strasser, Marc Zebisch, Lorenzo Bruzzone

Proceedings Volume 11155, 1115513 (2019) https://doi.org/10.1117/12.2533232

KEYWORDS: Remote sensing, Data modeling, Numerical simulations, Multispectral imaging, Climatology, Meteorology, Statistical modeling, Statistical analysis

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SPIE Journal Paper | 6 March 2019

Fractal algorithm for surface roughness parameters retrieval using multiband/polarization AIRSAR data

Mohammad Maleki, Jalal Amini, Claudia Notarnicola

JARS, Vol. 13, Issue 01, 014525, (March 2019) https://doi.org/10.1117/12.10.1117/1.JRS.13.014525

KEYWORDS: Fractal analysis, Surface roughness, Soil science, Polarization, Synthetic aperture radar, Backscatter, L band, Neural networks, Radar, 3D image processing

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Proceedings Article | 11 October 2018 Presentation

Presentation

Machine learning algorithms for estimating forest biomass in the framework of ESA BRIX exercise (Conference Presentation)

Emanuele Santi, Simonetta Paloscia, Simone Pettinato, Claudia Notarnicola, Antonio Padovano

Proceedings Volume 10788, 1078808 (2018) https://doi.org/10.1117/12.2500046

KEYWORDS: Biological research, Machine learning, Synthetic aperture radar, Soil science, Artificial neural networks, Sensors, Radar, Algorithm development, Remote sensing, Analytical research

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In this study, two different machine learning approaches, namely Artificial Neural Network (ANN) [1-2], and supported Vector Regressions (SVR) [3-4] have been implemented and tested for estimating the forest biomass (t/ha) from the ESA airborne SAR missions. The study was carried out in the framework of the BRIX exercise, aimed at intercomparing biomass retrieval algorithms for P-band full-polarimetric SAR sensors in view of the upcoming ESA BIOMASS mission (a P-band synthetic aperture polarimetric radar). Several strategies have been exploited, by developing “general” algorithms trained with data derived from the whole dataset and “specific” algorithms, trained with data derived from a single campaign, among Afrisar, Biosar and Tropisar. In all cases, the algorithms have been trained on a subset of the available data and validated on the remaining, obtaining correlation coefficients between R=0.82 and R= 0.94, with a RMSE between 15 t/ha and 70 t/ha, depending on the algorithm and on the dataset. In the case of ANN, the validation of the “general” and “specific” algorithms resulted in a correlation coefficient between R=0.78 and R=0.94, depending on the dataset, with a RMSE between 15 and 60 t/ha and negligible BIAS. The validation of the SVR algorithms resulted in a correlation coefficient between R=0.27 and R=0.90, depending on the dataset, with a corresponding RMSE between 25 and 77 t/ha and BIAS negligible in this case too. After validation, both ANN and SVR algorithms have been applied to the whole SAR images available for generating the corresponding biomass maps. References [1]. Santi E., S. Paloscia, S. Pettinato and G. Fontanelli. “Application of artificial neural networks for the soil moisture retrieval from active and passive microwave spaceborne sensors,” Int. J. Appl. Earth Observ. Geoinf., vol 48, pp. 61–73, Jun. 2016. [2]. Santi E., S. Paloscia, S. Pettinato, G. Fontanelli, M. Mura, C. Zolli, F. Maselli, M. Chiesi, L. Bottai, G. Chirici, 2017, The potential of multifrequency SAR images for estimating forest biomass in Mediterranean areas, Remote Sensing of Environment 200 (2017), pp. 63–73. [3]. Pasolli, L., Notarnicola, C., Bruzzone, L., Bertoldi, G., Della Chiesa, S., Hell, V., Niedrist, G., Tappeiner, U., Zebisch, M., Del Frate, F., Vaglio Laurin, G. 2011. Estimation of Soil Moisture in an Alpine Catchment with RADARSAT2 Images. Hindawi Publishing Corporation, Applied and Environmental Soil Science, Article ID 175473, 12 pages, doi:10.1155/2011/175473 [4]. Pasolli, L., Notarnicola, C., Bertoldi, G., Bruzzone, L., Remelgado R., Greifeneder, F., Niedrist, G., Della Chiesa, Tappeiner, U., Zebisch, M. 2015. Estimation of Soil Moisture in Mountain Areas Using SVR Technique Applied to Multiscale Active Radar Images at C-Band. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 8, NO. 1, JANUARY 2015, doi: 10.1109/JSTARS.2014.2378795.

Proceedings Article | 9 December 2016 Presentation

Presentation

A novel multi-temporal approach to wet snow retrieval with Sentinel-1 images (Conference Presentation)

Carlo Marin, Mattia Callegari, Claudia Notarnicola

Proceedings Volume 10004, 100040X (2016) https://doi.org/10.1117/12.2242415

KEYWORDS: Synthetic aperture radar, Remote sensing, Earth sciences, Clouds, Backscatter, Image retrieval, Snow cover, Floods, Visible radiation, Short wave infrared radiation

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Snow is one of the most relevant natural water resources present in nature. It stores water in winter and releases it in spring during the melting season. Monitoring snow cover and its variability is thus of great importance for a proactive management of water-resources. Of particular interest is the identification of snowmelt processes, which could significantly support water administration, flood prediction and prevention. In the past years, remote sensing has demonstrated to be an essential tool for providing accurate inputs to hydrological models concerning the spatial and temporal variability of snow. Even though the analysis of snow pack can be conducted in the visible, near-infrared and short-wave infrared spectrum, the presence of clouds during the melting season, which may be pervasive in some parts of the World (e.g., polar regions), renders impossible the regular acquisition of information needed for the operational purposes. Therefore, the use of the microwave sensors, which signal can penetrate the clouds, can be an asset for the detection of snow proprieties. In particular, the SAR images have demonstrated to be effective and robust measurements to identify the wet snow. Among the several methods presented in the literature, the best results in wet snow mapping have been achieved by the bi-temporal change detection approach proposed by Nagler and Rott [1], or its slight improvements presented afterwards (e.g., [2]). Nonetheless, with the introduction of the Sentinel-1 by ESA, which provides free-of-charge SAR images every 6 days over the same geographical area with a resolution of 20m, the scientists have the opportunity to better investigate and improve the state-of-the-art methods for wet snow detection. In this work, we propose a novel method based on a supervised learning approach able to exploit both the experience of the state-of-the-art algorithms and the high multi-temporal information provided by the Sentinel-1 data. In detail, this is done by training the proposed method with examples extracted by [1] and refine this information by deriving additional training for the complex cases where the state-of-the-art algorithm fails. In addition, the multi-temporal information is fully exploited by modelling it as a series of statistical moments. Indeed, with a proper time sampling, statistical moments can describe the shape of the probability density function (pdf) of the backscattering time series ([3-4]). Given the description of the shape of the multi-temporal VV and VH backscattering pdfs, it is not necessary to explicitly identify which time instants in the time series are to be assigned to the reference image as done in the bi-temporal approach. This information is implicit in the shape of the pdf and it is used in the training procedure for solving the wet snow detection problem based on the available training samples. The proposed approach is designed to work in an alpine environment and it is validated considering ground truth measurements provided by automatic weather stations that record snow depth and snow temperature over 10 sites deployed in the South Tyrol region in northern Italy. References: [1] Nagler, T.; Rott, H., “Retrieval of wet snow by means of multitemporal SAR data,” in Geoscience and Remote Sensing, IEEE Transactions on , vol.38, no.2, pp.754-765, Mar 2000. [2] Storvold, R., Malnes, E., and Lauknes, I., “Using ENVISAT ASAR wideswath data to retrieve snow covered area in mountainous regions”, EARSeL eProceedings 4, 2/2006 [3] Inglada, J and Mercier, G., “A New Statistical Similarity Measure for Change Detection in Multitemporal SAR Images and Its Extension to Multiscale Change Analysis,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 5, pp. 1432-1445, May 2007. [4] Bujor, F., Trouve, E., Valet, L., Nicolas J. M., and Rudant, J. P., “Application of log-cumulants to the detection of spatiotemporal discontinuities in multitemporal SAR images,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 10, pp. 2073-2084, Oct. 2004.

Proceedings Article | 9 December 2016 Presentation

Presentation

A multitemporal probabilistic error correction approach to SVM classification of alpine glacier exploiting sentinel-1 images (Conference Presentation)

Mattia Callegari, Carlo Marin, Claudia Notarnicola, Luca Carturan, Federico Covi, Stephan Galos, Roberto Seppi

Proceedings Volume 10003, 100030F (2016) https://doi.org/10.1117/12.2242416

KEYWORDS: Synthetic aperture radar, Visual optics, Clouds, Image classification, Environmental sensing, Climatology, Climate change, Satellites, Landsat, Earth observing sensors

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In mountain regions and their forelands, glaciers are key source of melt water during the middle and late ablation season, when most of the winter snow has already melted. Furthermore, alpine glaciers are recognized as sensitive indicators of climatic fluctuations. Monitoring glacier extent changes and glacier surface characteristics (i.e. snow, firn and bare ice coverage) is therefore important for both hydrological applications and climate change studies. Satellite remote sensing data have been widely employed for glacier surface classification. Many approaches exploit optical data, such as from Landsat. Despite the intuitive visual interpretation of optical images and the demonstrated capability to discriminate glacial surface thanks to the combination of different bands, one of the main disadvantages of available high-resolution optical sensors is their dependence on cloud conditions and low revisit time frequency. Therefore, operational monitoring strategies relying only on optical data have serious limitations. Since SAR data are insensitive to clouds, they are potentially a valid alternative to optical data for glacier monitoring. Compared to past SAR missions, the new Sentinel-1 mission provides much higher revisit time frequency (two acquisitions each 12 days) over the entire European Alps, and this number will be doubled once the Sentinel1-b will be in orbit (April 2016). In this work we present a method for glacier surface classification by exploiting dual polarimetric Sentinel-1 data. The method consists of a supervised approach based on Support Vector Machine (SVM). In addition to the VV and VH signals, we tested the contribution of local incidence angle, extracted from a digital elevation model and orbital information, as auxiliary input feature in order to account for the topographic effects. By exploiting impossible temporal transition between different classes (e.g. if at a given date one pixel is classified as rock it cannot be classified as glacier ice in a following date) we here propose an innovative post classification correction based on SVM classification probabilities. Optical data, i.e. Landsat-8 and Sentinel-2, have been employed, when available, for training sample collection. Detailed field observations from two glaciers located in the Ortles-Cevedale massif (Eastern Italian Alps) have been employed for validation.

Proceedings Article | 15 October 2015 Paper

Paper

Cosmo-SkyMed and RADARSAT2 image investigation for the monitoring of agricultural areas

S. Paloscia, S. Pettinato, E. Santi, C. Notarnicola, F. Greifeneder, G. Cuozzo, I. Nicolini, B. Demir, L. Bruzzone

Proceedings Volume 9642, 964206 (2015) https://doi.org/10.1117/12.2196285

KEYWORDS: Vegetation, X band, Soil science, Agriculture, Synthetic aperture radar, Backscatter, Polarization, Data modeling, Sensors, RGB color model

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Proceedings Article | 21 October 2014 Paper

Paper

Estimation of surface soil moisture in alpine areas based on medium spatial resolution SAR time-series and upscaled in-situ measurements

F. Greifeneder, C. Notarnicola, G. Cuozzo, G. Bertoldi, S. Della Chiesa, G. Niedrist, J. Stamenkovic, W. Wagner

Proceedings Volume 9243, 92430G (2014) https://doi.org/10.1117/12.2071252

KEYWORDS: Soil science, Synthetic aperture radar, Spatial resolution, Satellites, Data modeling, Meteorology, Atmospheric modeling, Remote sensing, Data acquisition, Sensors

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Proceedings Article | 21 October 2014 Paper

Paper

COSMO-SkyMed potentiality to identify crop-specific behavior and monitor phenological parameters

Rocchina Guarini, Federica Segalini, Giovanni Mastronardi, Claudia Notarnicola, Francesco Vuolo, Luigi Dini

Proceedings Volume 9243, 92430P (2014) https://doi.org/10.1117/12.2067287

KEYWORDS: Synthetic aperture radar, Polarization, X band, Backscatter, Satellites, Vegetation, Data acquisition, Sensors, Microwave radiation, Meteorology

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Proceedings Article | 21 October 2014 Paper

Paper

Soil moisture estimation using synergy of optical, SAR, and topographic data with Gaussian Process Regression

J. Stamenkovic, C. Notarnicola, N. Spindler, G. Cuozzo, G. Bertoldi, S. Della Chiesa, G. Niedrist, F. Greifeneder, D. Tuia, M. Borgeaud, J.-Ph. Thiran

Proceedings Volume 9243, 92430R (2014) https://doi.org/10.1117/12.2072828

KEYWORDS: Soil science, Synthetic aperture radar, MODIS, General packet radio service, Data processing, Data acquisition, Sensors, Composites, Control systems, Data modeling

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Proceedings Article | 17 October 2013 Paper

Paper

Comparison of three algorithms for the retrieval of soil moisture from ASCAT data in the framework of the round robin exercise

E. Santi, S. Paloscia, S. Pettinato, C. Notarnicola, L. Pasolli, M. Callegari

Proceedings Volume 8891, 88910F (2013) https://doi.org/10.1117/12.2029396

KEYWORDS: Soil science, Algorithm development, Vegetation, Climatology, Calibration, Backscatter, Microwave radiation, Neodymium, Remote sensing, Climate change

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Proceedings Article | 17 October 2013 Paper

Paper

How far can be SAR considered a tool for mountain hydrology?

Giacomo Bertoldi, Claudia Notarnicola, Stefano Della Chiesa, Georg Niedrist, Luca Pasolli, Ulrike Tappeiner

Proceedings Volume 8891, 88910G (2013) https://doi.org/10.1117/12.2031717

KEYWORDS: Synthetic aperture radar, Soil science, Vegetation, Sensors, Data modeling, Control systems, Remote sensing, Solar radiation models, Calibration, Ions

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Proceedings Article | 21 November 2012 Paper

Paper

Mapping spatial and temporal patterns of soil moisture with ASAR imagery in the Alps

L. Pasolli, C. Notarnicola, L. Bruzzone, G. Bertoldi, M. Zebisch

Proceedings Volume 8536, 85360M (2012) https://doi.org/10.1117/12.975687

KEYWORDS: Soil science, Synthetic aperture radar, Sensors, Satellites, Remote sensing, Spatial resolution, Image resolution, Aluminium phosphide, Temporal resolution, Data modeling

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Proceedings Article | 21 November 2012 Paper

Paper

Synergy of Cassini SAR and altimeter acquisitions for the retrieval of dune field characteristics on Titan

Valerio Poggiali, Marco Mastrogiuseppe, Mattia Callegari, Riccardo Martufi, Roberto Seu, Domenico Casarano, Luca Pasolli, Claudia Notarnicola

Proceedings Volume 8536, 853608 (2012) https://doi.org/10.1117/12.978185

KEYWORDS: Synthetic aperture radar, Backscatter, Radar, Image analysis, Data modeling, Scattering, Geometrical optics, Electromagnetism, Image resolution, Spatial frequencies

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Proceedings Article | 21 November 2012 Paper

Paper

COSMO SkyMed X-band SAR imagery for snowpack characterization in mountain areas

E. Santi, S. Paloscia, S. Pettinato, M. Callegari, L. Pasolli, C. Notarnicola

Proceedings Volume 8536, 85360L (2012) https://doi.org/10.1117/12.974828

KEYWORDS: Backscatter, X band, Synthetic aperture radar, Snow cover, Satellites, Meteorology, Crystals, Polarization, Data modeling, Particles

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Proceedings Article | 17 November 2011 Paper

Paper

Integration of X-SAR observations with data of other remote sensing techniques: preliminary results achieved with Cosmo/SkyMed announcement of opportunity projects

Francesco Vespe, Luca Baldini, Claudia Notarnicola, Claudio Prati, Susanna Zerbini, G. Celidonio

Proceedings Volume 8179, 817907 (2011) https://doi.org/10.1117/12.898302

KEYWORDS: Global Positioning System, Synthetic aperture radar, Radar, X band, Troposphere, Satellites, Signal attenuation, Snow cover, Atmospheric modeling, Picosecond phenomena

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Proceedings Article | 27 October 2011 Paper

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 | 27 October 2011 Paper

Paper

Comparison of L and C band polarimetric SAR data for the retrieval of soil moisture in the Alps

L. Pasolli, C. Notarnicola, L. Bruzzone, G. Bertoldi, G. Niedrist, U. Tappeiner, M. Zebisch, F. Del Frate, G. Laurin

Proceedings Volume 8179, 817903 (2011) https://doi.org/10.1117/12.898877

KEYWORDS: Synthetic aperture radar, Soil science, L band, Backscatter, Satellites, Vegetation, Polarimetry, Spatial resolution, Satellite imaging, Sensors

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Proceedings Article | 27 October 2011 Paper

Paper

Soil moisture mapping using Sentinel 1 images: the proposed approach and its preliminary validation carried out in view of an operational product

S. Paloscia, S. Pettinato, E. Santi, N. Pierdicca, L. Pulvirenti, C. Notarnicola, G. Pace, A. Reppucci

Proceedings Volume 8179, 817904 (2011) https://doi.org/10.1117/12.899523

KEYWORDS: Polarization, Soil science, Vegetation, Synthetic aperture radar, Algorithm development, Radar, Data modeling, Backscatter, Meteorology, Artificial neural networks

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Proceedings Article | 26 October 2010 Paper

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

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 | 25 October 2010 Paper

Paper

Analysis of polarimetric RADARSAT2 images for soil moisture retrieval in an alpine catchment

L. Pasolli, C. Notarnicola, L. Bruzzone, G. Bertoldi, G. Niedrist, U. Tappainer, M. Zebisch, F. Del Frate, G. Laurin

Proceedings Volume 7829, 78290C (2010) https://doi.org/10.1117/12.866266

KEYWORDS: Soil science, Synthetic aperture radar, Dielectrics, Vegetation, Backscatter, Microwave radiation, Sensors, Polarimetry, Satellites, Dielectric polarization

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SPIE Journal Paper | 1 December 2009

Use of historical orthophotos and digital elevation model to link watershed land use changes and storm flow response in a Karst environment

Claudia Notarnicola, Alessandro Mondini

JARS, Vol. 3, Issue 01, 033574, (December 2009) https://doi.org/10.1117/12.10.1117/1.3284717

KEYWORDS: Orthophoto maps, Vegetation, Agriculture, Floods, Image sensors, Statistical analysis, Image processing, Geographic information systems, Environmental sensing, Sensors

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Proceedings Article | 29 September 2009 Paper

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

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 | 29 September 2009 Paper

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 | 28 September 2009 Paper

Paper

Soil moisture estimation from microwave remote sensing data with nonlinear machine learning techniques

Luca Pasolli, Claudia Notarnicola, Lorenzo Bruzzone

Proceedings Volume 7477, 74771C (2009) https://doi.org/10.1117/12.830831

KEYWORDS: Soil science, Statistical analysis, Machine learning, Dielectrics, Data modeling, Biological research, Neural networks, Polarization, Statistical modeling, Backscatter

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Proceedings Article | 10 October 2008 Paper

Paper

Group inversion approach for soil moisture characteristics estimation from multi-sensor data

C. Notarnicola, F. Posa

Proceedings Volume 7109, 71091C (2008) https://doi.org/10.1117/12.802764

KEYWORDS: Soil science, L band, Vegetation, Sensors, Synthetic aperture radar, Data acquisition, Radar, Data analysis, Backscatter, Satellites

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Proceedings Article | 10 October 2008 Paper

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

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 | 3 October 2007 Paper

Paper

Combination of X, C, and L band SAR images for retrieval of surface parameters

C. Notarnicola, F. Posa

Proceedings Volume 6746, 67460E (2007) https://doi.org/10.1117/12.753797

KEYWORDS: L band, Vegetation, Soil science, Radar, Synthetic aperture radar, Sensors, Polarization, Data acquisition, Landsat, Earth observing sensors

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Proceedings Article | 20 October 2006 Paper

Paper

Environmental changes induced by dump pollution analyzed through historical orthophotos and multispectral images

C. Notarnicola

Proceedings Volume 6366, 63660K (2006) https://doi.org/10.1117/12.689723

KEYWORDS: Orthophoto maps, Agriculture, Vegetation, Thermography, Infrared imaging, Infrared radiation, Sensors, Atmospheric corrections, Multispectral imaging, Pollution

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Proceedings Article | 9 October 2006 Paper

Paper

On the comparison between soil moisture values retrieved from SAR images and ground truth point measurements

C. Notarnicola, F. Posa

Proceedings Volume 6363, 636308 (2006) https://doi.org/10.1117/12.690696

KEYWORDS: Soil science, Vegetation, L band, Satellites, Error analysis, Sensors, Backscatter, Synthetic aperture radar, Radar, Dielectrics

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Proceedings Article | 9 October 2006 Paper

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.

Proceedings Article | 12 October 2005 Paper

Paper

Vegetation parameters retrieval from radar multipolarimetric, multifrequency data

C. Notarnicola, F. Posa

Proceedings Volume 5980, 598009 (2005) https://doi.org/10.1117/12.629196

KEYWORDS: Vegetation, Soil science, Radar, Backscatter, L band, Landsat, Sensors, Algorithm development, Data acquisition, Earth observing sensors

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Proceedings Article | 22 October 2004 Paper

Paper

Soil moisture retrieval by means of real and simulated microwave data to test L-band active-passive and L-C-X-bands passive approaches

Mariella Angiulli, Claudia Notarnicola, Francesco Posa

Proceedings Volume 5574, (2004) https://doi.org/10.1117/12.569016

KEYWORDS: Soil science, Neural networks, Dielectric polarization, Backscatter, Dielectrics, L band, Vegetation, Microwave radiation, Data modeling, Radar

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Proceedings Article | 22 October 2004 Paper

Paper

Feasibility of soil moisture and roughness retrieval using microwave data

Claudia Notarnicola, Francesco Posa

Proceedings Volume 5574, (2004) https://doi.org/10.1117/12.568185

KEYWORDS: Soil science, Dielectrics, Backscatter, L band, Vegetation, Polarization, Sensors, Data acquisition, Surface roughness, Dielectric polarization

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Proceedings Article | 16 February 2004 Paper

Paper

Automatic cloud detection from MODIS images

Gennaro Cappelluti, Alberto Morea, Claudia Notarnicola, Francesco Posa

Proceedings Volume 5235, (2004) https://doi.org/10.1117/12.511675

KEYWORDS: Clouds, MODIS, Reflectivity, Visible radiation, Sensors, RGB color model, Infrared radiation, Temperature metrology, Atmospheric particles, Atmospheric physics

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Proceedings Article | 13 February 2004 Paper

Paper

Southern Italy illegal dumps detection based on spectral analysis of remotely sensed data and land-cover maps

Claudia Notarnicola, Mariella Angiulli, Concetta Giasi

Proceedings Volume 5239, (2004) https://doi.org/10.1117/12.511668

KEYWORDS: Landsat, Sensors, Vegetation, Biogases, Remote sensing, Environmental hazards, Visualization, Soil contamination, Thermal effects, Earth observing sensors

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Proceedings Article | 12 January 2004 Paper

Paper

Bayesian iterative inversion algorithm applied to soil moisture mapping using ground-based and airborne remote sensing data

Claudia Notarnicola, Francesco Posa

Proceedings Volume 5236, (2004) https://doi.org/10.1117/12.514413

KEYWORDS: Soil science, Polarization, L band, Dielectrics, Sensors, Calibration, Backscatter, Algorithm development, Synthetic aperture radar, Associative arrays

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Proceedings Article | 7 March 2003 Paper

Paper

Effect of prior information on Bayesian estimates of dielectric constant from remotely sensed data

Claudia Notarnicola, Francesco Posa

Proceedings Volume 4883, (2003) https://doi.org/10.1117/12.463064

KEYWORDS: Dielectrics, Polarization, Soil science, Backscatter, Data modeling, Error analysis, Statistical analysis, Sensors, Radar, Microwave radiation

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Proceedings Article | 7 March 2003 Paper

Paper

C-band polarimetric scatterometer for soil studies

Angelo D'Alessio, Antonio Mongelli, Claudia Notarnicola, Giuseppina Paparella, Francesco Posa, Vincenzo Sabatelli

Proceedings Volume 4883, (2003) https://doi.org/10.1117/12.464222

KEYWORDS: Polarization, Calibration, Backscatter, Radar, Polarimetry, Reflectors, Antennas, Sensors, Electromagnetism, Scattering

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Proceedings Article | 28 January 2002 Paper

Paper

Temporal monitoring of soil surface state by means of a 5.3 GHz ground-based scatterometer

Claudia Notarnicola, Angelo D'Alessio, Domenico Casarano, Francesco Posa, Vincenzo Sabatelli

Proceedings Volume 4543, (2002) https://doi.org/10.1117/12.453974

KEYWORDS: Soil science, Backscatter, Radar, Polarization, Remote sensing, Data modeling, Data acquisition, Dielectrics, Error analysis, Surface roughness

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Proceedings Article | 12 December 2001 Paper

Paper

Microwave C-band fully polarimetric scatterometer and measurements of backscattering matrix

Angelo D'Alessio, Claudia Notarnicola, Francesco Posa, Giuseppe Calbi, Domenico Casarano, Antonio Mongelli, Vincenzo Sabatelli

Proceedings Volume 4540, (2001) https://doi.org/10.1117/12.450699

KEYWORDS: Backscatter, Microwave radiation, Soil science, Dielectric polarization, Polarimetry, Radar, Calibration, Antennas, Modulation, Scatter measurement

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Proceedings Article | 27 November 2001 Paper

Paper

Sensitivity to soil moisture and roughness by a 5.3 GHz ground-based scatterometer

Claudia Notarnicola, Angelo D'Alessio, Domenico Casarano, Francesco Posa, Vincenzo Sabatelli

Proceedings Volume 4491, (2001) https://doi.org/10.1117/12.450149

KEYWORDS: Soil science, Backscatter, Radar, Polarization, Surface roughness, Remote sensing, Microwave radiation, Sensors, Databases, Agriculture

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Proceedings Article | 27 November 2001 Paper

Paper

Microwave polarimetric scatterometer as a tool for monitoring topsoil moisture

Angelo D'Alessio, Claudia Notarnicola, Francesco Posa, Giuseppe Calbi, Domenico Casarano, Antonio Mongelli, Vincenzo Sabatelli

Proceedings Volume 4491, (2001) https://doi.org/10.1117/12.450174

KEYWORDS: Backscatter, Microwave radiation, Soil science, Polarimetry, Dielectric polarization, Radar, Calibration, Antennas, Modulation, Polarization

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Proceedings Article | 21 December 2000 Paper

Paper

Retrieval of soil moisture profile by combined C-band scatterometer data and a surface hydrological model

Claudia Notarnicola, Angelo D'Alessio, Francesco Posa, Vincenzo Sabatelli, Domenico Casarano

Proceedings Volume 4173, (2000) https://doi.org/10.1117/12.410648

KEYWORDS: Soil science, Data modeling, Backscatter, Polarization, Radar, Microwave radiation, Remote sensing, Agriculture, Antennas, Sensors

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Showing 5 of 51 publications

Proceedings Volume Editor (18)

SPIE Conference Volume | 7 November 2023

Microwave Remote Sensing: Data Processing and Applications II

SPIE Conference Volume | 4 November 2022

Image and Signal Processing for Remote Sensing XXVIII

SPIE Conference Volume | 18 October 2021

Microwave Remote Sensing: Data Processing and Applications

SPIE Conference Volume | 9 October 2020

Image and Signal Processing for Remote Sensing XXVI

SPIE Conference Volume | 19 November 2019

Active and Passive Microwave Remote Sensing for Environmental Monitoring III

Showing 5 of 18 publications

Conference Committee Involvement (19)

Microwave Remote Sensing: Data Processing and Applications III

16 September 2024 | Edinburgh, United Kingdom

Microwave Remote Sensing: Data Processing and Applications II

6 September 2023 | Amsterdam, Netherlands

Image and Signal Processing for Remote Sensing XXVIII

5 September 2022 | Berlin, Germany

Microwave Remote Sensing: Data Processing and Applications

13 September 2021 | Online Only, Spain

Image and Signal Processing for Remote Sensing XXVI

21 September 2020 | Online Only, United Kingdom

Active and Passive Microwave Remote Sensing for Environmental Monitoring III

11 September 2019 | Strasbourg, France

Active and Passive Microwave Remote Sensing for Environmental Monitoring

12 September 2018 | Berlin, Germany

Active and Passive Microwave Remote Sensing for Environmental Monitoring

13 September 2017 | Warsaw, Poland

Active and Passive Microwave Remote Sensing for Environmental Monitoring

28 September 2016 | Edinburgh, United Kingdom

SAR Image Analysis, Modeling, and Techniques

23 September 2015 | Toulouse, France

SAR Image Analysis, Modeling, and Techniques

24 September 2014 | Amsterdam, Netherlands

SAR Image Analysis, Modeling, and Techniques XIII

25 September 2013 | Dresden, Germany

SAR Image Analysis, Modeling, and Techniques

26 September 2012 | Edinburgh, United Kingdom

SAR Image Analysis, Modeling, and Techniques

21 September 2011 | Prague, Czech Republic

SAR Image Analysis, Modeling, and Techniques

22 September 2010 | Toulouse, France

SAR Image Analysis, Modeling, and Techniques

2 September 2009 | Berlin, Germany

SAR Image Analysis, Modeling, and Techniques

18 September 2008 | Cardiff, Wales, United Kingdom

SAR Image Analysis, Modeling, and Techniques

18 September 2007 | Florence, Italy

SAR Image Analysis, Modelling, and Techniques XI

14 September 2006 | Stockholm, Sweden

Showing 5 of 19 Conference Committees

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