Prof. Guido D'Urso Profile

Prof. Guido D'Urso

at Univ degli Studi di Napoli Federico II

SPIE Involvement:

Author

Publications (20)

Proceedings Article | 20 November 2024 Presentation + Paper

Satellite-based monitoring of groundwater utilization for irrigation: a case study in Southern Italy

Oscar Rosario Belfiore, Stefania Cavallo, Alessandro Aquino, Salvatore Falanga Bolognesi, Carlo De Michele, Camilla Della Monica, Qotada Alali, Ileana Mula, Antonio Pizzolante, Guido D'Urso

Proceedings Volume 13191, 131910H (2024) https://doi.org/10.1117/12.3031728

KEYWORDS: Satellites, Vegetation, Machine learning, Rain, Clouds, Agriculture, Geographic information systems, Classification systems

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Proceedings Article | 11 November 2014 Paper

E.O.-based estimation of transpiration and crop water requirements for vineyards: a case study in southern Italy

Guido D'Urso, Antonino Maltese, Mario Palladino

Proceedings Volume 9239, 92390T (2014) https://doi.org/10.1117/12.2074180

KEYWORDS: Heat flux, Vegetation, In situ metrology, Data modeling, Remote sensing, Soil science, Temperature metrology, Fourier transforms, Sensors, Spatial resolution

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An efficient use of water for irrigation is a challenging task. From an agronomical point of view, it requires establishing the optimal amount of water to be supplied, at the correct time, based on phenological phase and water stress spatial distribution. Indeed, the knowledge of the actual water stress is essential for agronomic decisions, vineyards need to be managed to maintain a moderate water stress, thus allowing to optimize berries quality and quantity. Methods for quickly quantifying where, when and in what extent, vines begin to experience water stress are beneficial. Traditional point based methodologies, such those based on Scholander pressure chamber, even if well established are time expensive and do not give a comprehensive picture of the vineyard water deficit. Earth Observation (E.O.) based methodologies promise to achieve a synoptic overview of the water stress. Some E.O. data, indeed, sense the territory in the thermal part of the spectrum and, as it is well recognized, leaf radiometric temperature is related to the plant water status. However, current satellite sensors have not detailed enough spatial resolution to detect pure canopy pixels; thus, the pixel radiometric temperature characterizes the whole soil-vegetation system, and in variable proportions. On the other hand, due to limits in the actual crop dusters, there is no need to characterize the water stress distribution at plant scale, and a coarser spatial characterization would be sufficient. The research aims to assess to what extent: 1) E.O. based canopy radiometric temperature can be used, straightforwardly, to detected plant water status; 2) E.O. based canopy transpiration, would be more suitable (or not) to describe the spatial variability in plant water stress. To these aims: 1) radiometric canopy temperature measured in situ, and derived from a two-source energy balance model applied on airborne data, were compared with in situ leaf water potential from freshly cut leaves; 2) two source energy balance components were validated trough flux tower measures, then, the actual canopy latent heat flux is compared to in situ leaf water potential.

Proceedings Article | 29 October 2014 Paper

Surface soil water content estimation based on thermal inertia and Bayesian smoothing

Paolo Addesso, Antonino Maltese, Riccardo Garella, Fulvio Capodici, Guido D'Urso, Maurizio Longo, Rocco Restaino, Gemine Vivone

Proceedings Volume 9239, 923908 (2014) https://doi.org/10.1117/12.2067360

KEYWORDS: Clouds, MODIS, Satellites, Thermography, Spatial resolution, Data acquisition, Image fusion, Image processing, Antennas, Image analysis

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Soil water content plays a critical role in agro-hydrology since it regulates the rainfall partition between surface runoff and infiltration and, the energy partition between sensible and latent heat fluxes. Current thermal inertia models characterize the spatial and temporal variability of water content by assuming a sinusoidal behavior of the land surface temperature between subsequent acquisitions. Such behavior implicitly supposes clear sky during the whole interval between the thermal acquisitions; but, since this assumption is not necessarily verified even if sky is clear at the exact epoch of acquisition, , the accuracy of the model may be questioned due to spatial and temporal variability of cloud coverage. During the irrigation season, cloud coverage exhibits a quite regular daily behavior, which, when rendered in probabilistic terms, allows for an a-priory evaluation of the most likely suitable pair of images to estimate thermal inertia, given the results of the satellite passes. In turn, the water content of soil is estimated through thermal inertia by coupling diurnal optical and nighttime thermal images, e.g. as acquired by MODIS sensor on board polar orbiting satellites AQUA and TERRA, which have spatial resolution high enough to cope with typical agricultural applications. The method relies on the availability of the shortwave albedo and, at least, two daily thermographs preferably acquired in specific epochs of the day: the first at sunset when latent and heat fluxes are negligible; the second just before sunrise, when surface soil temperature reaches its minimum. Unfortunately, high resolution thermal images are often not available in those specific epochs, so that the accuracy of estimate accuracy decays even severely. In this perspective the paper, following previous contributions by some of the authors of the present paper ^[1-4], proposes exploiting SEVIRI data, characterized by higher acquisition rate but coarser spatial resolution as available from geostationary platform, to supplement MODIS data in a twofold way: i) by allowing to verify, by means of cloud detection algorithms, the hypothesis of clear sky throughout the time; ii) by synthesizing a high spatial/high temporal resolution sequence of images, through fusion of MODIS and SEVIRI data via Bayesian smoothing. A first validation of the latter method is achieved by comparing the results with in situ micro-meteorological measurements.

Proceedings Article | 16 October 2013 Paper

Soil water content monitoring: a verification of thermal inertia approaches on low spatial, high temporal resolutions images

Antonino Maltese, Fulvio Capodici, Guido D'Urso, Paolo Addesso, Maurizio Longo, Rita Montone, Rocco Restaino, Gemine Vivone

Proceedings Volume 8887, 888711 (2013) https://doi.org/10.1117/12.2030586

KEYWORDS: Thermography, MODIS, Soil science, Vegetation, Fourier transforms, Picosecond phenomena, Remote sensing, Temporal resolution, Spatial resolution, Solar radiation models

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Proceedings Article | 16 October 2013 Paper

Coupling SAR X-band and optical data for NDVI retrieval: model calibration and validation on two test areas

Fulvio Capodici, Guido D'Urso, Antonino Maltese, Giuseppe Ciraolo

Proceedings Volume 8887, 88871C (2013) https://doi.org/10.1117/12.2029875

KEYWORDS: Data modeling, Synthetic aperture radar, Calibration, Vegetation, X band, Backscatter, Spatial resolution, Landsat, Agriculture, Earth observing sensors

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Sustainability of modern agro-hydrology requires the knowledge of spatial and temporal variability of vegetation biomass to optimize management of land and water resources. Diversely from optical imaging, temporal resolution of active sensors, such as SAR, is not limited by sky cloudiness; thus, they may be combined with optical imageries to provide a more continuous monitoring of land surfaces. Several new SAR missions (e.g., ALOS-PALSAR, COSMO-SkyMed 1 and 2, TerraSAR-X, TerraSAR-X2, Sentinel 1) acquiring at X-, C- and L-bands and dual polarization capability, are characterized by a short revisit time (from 12 h to ~10 days) and high spatial resolution (<20 m). These satellites could provide suitable data for operative crop monitoring. The new COSMO-SkyMed (owned by Italian Space Agency-ASI) represents a valuable source of SAR data in X-band, opening new opportunities to develop agro-hydrological applications. Although, X-band backscattering is not the most suitable to model agricultural and hydrological processes; it could help assessing the vegetation development if combined with optical vegetation indices (VIs). Recently, two models to infer a VI from σ° have been setup. One of these achieved accurate results in VI retrieval at a certain time using σ°(VISAR), once known the NDVI derived from optical images (VIopt) at a reference time. This paper aims to further validate this model through two independent datasets. The model was firstly implemented on COSMO-SkyMed and optical DEIMOS-1 data acquired over the Sele plain (Campania, Italy). It has been further validated using different optical images. To this aim, a dataset of 2 COSMOSkyMed images and 2 Landsat 7 SLC-off images were acquired in the southwestern part of Sicily (Italy) between 8 and 25 August 2011. Determination coefficients of the validation set were similar to those of the calibration set. Results confirm that VISAR obtained using the combined model is a suitable surrogate of VIopt if estimated at parcel scale.

Proceedings Article | 16 October 2013 Paper

Enhancing TIR image resolution via bayesian smoothing for IRRISAT irrigation management project

Paolo Addesso, Fulvio Capodici, Guido D'Urso, Maurizio Longo, Antonino Maltese, Rita Montone, Rocco Restaino, Gemine Vivone

Proceedings Volume 8887, 888710 (2013) https://doi.org/10.1117/12.2029273

KEYWORDS: Lawrencium, Filtering (signal processing), Clouds, MODIS, Image resolution, Spatial resolution, Data acquisition, Image fusion, Data fusion, Thermography

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Proceedings Article | 23 October 2012 Paper

Mapping evapotranspiration on vineyards: a comparison between Penman-Monteith and Energy Balance approaches for operational purposes

Giuseppe Ciraolo, Carmelo Cammalleri, Fulvio Capodici, Guido D'Urso, Antonino Maltese

Proceedings Volume 8531, 85310Q (2012) https://doi.org/10.1117/12.974967

KEYWORDS: Remote sensing, Fourier transforms, Spatial resolution, Cameras, Pixel resolution, Satellites, Heat flux, Phase modulation, Vegetation, Near infrared

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Estimation of evapotranspiration (ET) in Sicilian vineyard is an emerging issue since these agricultural systems are more and more converted from rainfed to irrigated conditions, with significant impacts on the management of the scarce water resources of the region. The choice of the most appropriate methodology for assessing water use in these systems is still an issue of debating, due to the complexity of canopy and root systems and for their high spatial fragmentation. In vineyards, quality and quantity of the final product are dependent on the controlled stress conditions to be set trough irrigation. This paper reports an application of the well-known Penman-Monteith approach, applied in a distributed way, using high resolution remote sensing data to map the potential evapotranspiration (ET_p). In 2008 a series of airborne multispectral images were acquired on "Tenute Rapitalà", a wine farm located in the northwest of Sicily. Five airborne remote sensing scenes were collected using a SKY ARROW 351 650 TC/TCNS aircraft, at a height of about 1000 m a.g.l.. The acquisitions were performed encompassing a whole phenological period, period between June and September 2008 (approximately every three weeks). The platform had on board a multi-spectral camera with 3 spectral bands at green (G, 530-570 nm), red (R, 650-690 nm) and near infrared (NIR, 767-832 nm) wavelengths, and a thermal camera with a broad band in the range 7.5-13 µm. The nominal pixel resolution was approximately 0.7 m for VIS/NIR acquisitions, and 1.7 m for the thermal-IR data. Field data were acquired simultaneously to airborne acquisitions. These data include spectral reflectances in VIS-NIR-SWIR (shortwave infrared), leaf area index (LAI), soil moisture at different depths (both in row and below plants). Moreover, meteo variables were measured by a standard weather station whereas fluxes were measured by means of an Eddy correlation tower located within the field. The VIS-NIR bands were atmospherically corrected and calibrated in order to calculate albedo, NDVI and LAI, which represented the distributed inputs of the Penman-Monteith algorithm. Moreover a sensitivity analysis has been carried out on input parameters (such as albedo). A sensitivity analysis was carried out to highlight the variability of outputs (such as ET_p) on the accuracy in the parameters assessment obtainable using high spatial resolution airborne images. Scale effects have been also investigated by means of an artificial degradation of images spatial resolution. Finally the relationship between stress factor evaluated as the ratio between actual and reference ET and pre-dawn leaf water potential has been also investigated.

Proceedings Article | 19 October 2012 Paper

Vegetation index retrieval by coupling optical and SAR images

Fulvio Capodici, Giuseppe Ciraolo, Guido D'Urso, Goffredo La Loggia, Antonino Maltese

Proceedings Volume 8531, 853118 (2012) https://doi.org/10.1117/12.974951

KEYWORDS: Synthetic aperture radar, Vegetation, Backscatter, Agriculture, Satellite imaging, Satellites, Temporal resolution, Polarization, Calibration, Neodymium

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Monitoring spatial and temporal variability of Vegetation Indices (VIs) is important to manage land and water resources, with significant impact on the sustainability of modern agriculture Although algorithms based on optical data give accurate products, cloud cover dramatically reduces the temporal resolution of these outputs. The launch of new Synthetic Aperture Radar (SAR) constellations such as COSMO-Skymed opened new opportunities to develop agro-hydrological applications. Indeed, these satellites may represent a suitable source of data for operational applications due to their high spatial and temporal resolutions (10 m in StripMap PingPong acquisition mode, best revisit time with 4 satellites: 4 images per day at equator; every 7 hours on average at 40° latitude). Although X band is not optimal for agricultural and hydrological applications, reliable continuous assessment of the VIs can be achieved combing optical and SAR images. To this aim, an algorithm was implemented and validated coupling a VI derived from optical DEIMOS images (VI_opt) and the crossed HV backscattering σ°_HV (PingPong in HV polarization). A correlation analysis has been performed between σ° and VIs measurements taken simultaneously to Cosmo-SkyMed acquisitions in several plots. The correlation analysis was based on: incidence angle, spatial resolution and polarization mode. Results have shown that temporal changes of σ°_HV (Δσ°_HV), acquired with high angles (off nadir angle, θ> 40°) is characterized by the best correlation with variations of VI (ΔVI). The correlation between ΔVI and Δσ°_HV is shown to be temporally robust. Based on this experimental evidence a model to infer VI_SAR at the time t_i+1 once known the VI_opt at a reference time, t_i and Δσ°_HV between times t_i+1 and t_i, was implemented and verified. The study is carried out over the SELE plain (Campania, Italy) mainly characterized by herbaceous crops. Five couples of COSMO-Skymed and optical DEIMOS images have been acquired between August and September 2011. Data have been collected within the COSMOLAND project (Use of COSMO-SkyMed SAR data for LAND cover classification and surface parameters retrieval over agricultural sites) funded by the Italian Space Agency (ASI). Results confirm that VI_SARobtained using the combined model is a satisfactory surrogate of the VI_opt.

Proceedings Article | 7 October 2011 Paper

On the influences of vegetation biomass on COSMO-Skymed X-band

Fulvio Capodici, Giuseppe Ciraolo, Guido D'Urso, Goffredo La Loggia, Antonino Maltese

Proceedings Volume 8174, 81741H (2011) https://doi.org/10.1117/12.898989

KEYWORDS: Vegetation, Synthetic aperture radar, X band, Calibration, Landsat, Polarization, Spatial resolution, Agriculture, Temporal resolution, Earth observing sensors

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Proceedings Article | 22 October 2010 Paper

Surface soil humidity retrieval by means of a semi-empirical coupled SAR model

F. Capodici, G. La Loggia, G. D'Urso, A. Maltese, G. Ciraolo

Proceedings Volume 7824, 782427 (2010) https://doi.org/10.1117/12.865096

KEYWORDS: Vegetation, Humidity, Soil science, Data modeling, Synthetic aperture radar, Dielectrics, Backscatter, Calibration, Data acquisition, Francium

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Proceedings Article | 18 September 2009 Paper

Sensitivity analysis on the relationship between vegetation growth and multi-polarized radar data

F. Capodici, G. La Loggia, G. D'Urso, A. Maltese, G. Ciraolo

Proceedings Volume 7472, 74720S (2009) https://doi.org/10.1117/12.830304

KEYWORDS: Vegetation, Soil science, Radar, Data modeling, Surface roughness, Backscatter, Dielectric polarization, Polarimetry, Data acquisition, Polarization

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Spatially distributed soil moisture is required for watershed applications such as drought and flood prediction, crop irrigation scheduling, etc. In particular, an accurate assessment of the spatial and temporal variation of soil moisture is necessary to improve the predictive capability of runoff models, and for improving and validating hydrological processes forecasting. In recent years, several models have been developed in order to retrieve soil moisture using RADAR data. However, these models need precise prior knowledge about surface roughness. Within this framework, the present research aims to investigate the capabilities of multi polarimetric RADAR images to overcome the use of in situ data for surface roughness assessment. The research is carried out on a 24 km² test-site of DEMMIN (Görmin farm), Mecklenburg Vorpommern, in the North-East of Germany approximately 150 km north from Berlin. Data were acquired within ESA-funded project AgriSAR 2006 between April and July 2006. Images used include L-band in HH, VV and HV polarizations acquired from the airborne sensor E-SAR system operated by the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt - DLR). Two models have been coupled in order to obtain a rms Surface Roughness Index (rSRI) that is related to terrain physical characteristics as well as vegetation surface properties. These are the PSEM (Polarimetric Semi-Empirical Model) published by Oh et al. in 2002 and a semi empirical model developed by Dubois in 1995. A finite difference iterative solution allowed rSRI retrieval without the use of in situ data. Results have been compared both with in situ rms roughness over bare soil and with Normalized Difference Vegetation Index (NDVI) obtained from Airborne Hyperspectral Scanner (AHS) optical images collected over the whole phenological cycle. They show a good agreement with bare soil in situ data, describing its whole range of variability well, and moreover the NDVI vs. rSRI relationship seems similar to that occurring between NDVI and Leaf Area Index (LAI) for most crop types meaning that rSRI can be considered as LAI look like.

Proceedings Article | 18 September 2009 Paper

Earth observation products for operational irrigation management: the PLEIADeS project

G. D'Urso, F. Vuolo, K. Richter, A. Calera Belmonte, M. A. Osann

Proceedings Volume 7472, 74720D (2009) https://doi.org/10.1117/12.830072

KEYWORDS: Agriculture, Vegetation, Data modeling, Sensors, Reflectivity, Satellites, Climatology, Soil science, Calibration, Earth observing sensors

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Proceedings Article | 18 September 2009 Paper

Spatial distribution of soil water content from airborne thermal and optical remote sensing data

Katja Richter, Mario Palladino, Francesco Vuolo, Luigi Dini, Guido D'Urso

Proceedings Volume 7472, 74720W (2009) https://doi.org/10.1117/12.829508

KEYWORDS: Thermography, Thermal modeling, Soil science, Data modeling, Vegetation, Reflectivity, Remote sensing, Data acquisition, Near infrared, Agriculture

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Proceedings Article | 2 October 2008 Paper

Exploiting optical E.O. data for soil moisture retrieval

Katja Richter, Guido D'Urso, Mario Palladino, Francesco Vuolo

Proceedings Volume 7104, 71040D (2008) https://doi.org/10.1117/12.798762

KEYWORDS: Soil science, Reflectivity, Data modeling, Vegetation, Agriculture, Solar radiation models, Sensors, Near infrared, Short wave infrared radiation, Sun

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Proceedings Article | 9 October 2007 Paper

Evaluation of different methods for the retrieval of LAI using high resolution airborne data

K. Richter, F. Vuolo, G. D'Urso, L. Dini

Proceedings Volume 6742, 67420E (2007) https://doi.org/10.1117/12.738167

KEYWORDS: Reflectivity, Vegetation, Sensors, Data acquisition, Data modeling, Agriculture, Optimization (mathematics), Calibration, Statistical modeling, Satellites

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Proceedings Article | 17 October 2006 Paper

Actual evapotranspiration estimation by means of airborne and satellite remote sensing data

Giuseppe Ciraolo, Guido D'Urso, Mario Minacapilli

Proceedings Volume 6359, 63590Y (2006) https://doi.org/10.1117/12.689419

KEYWORDS: Data modeling, Landsat, Vegetation, Spatial resolution, Resistance, Satellites, Heat flux, Aerodynamics, Solar radiation models, Earth observing sensors

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Proceedings Article | 17 October 2006 Paper

Cost effectiveness of vegetation biophysical parameters retrieval from remote sensing data

F. Vuolo, G. D'Urso, L. Dini

Proceedings Volume 6359, 63590N (2006) https://doi.org/10.1117/12.689839

KEYWORDS: Vegetation, Data modeling, Reflectivity, Remote sensing, Sensors, Agriculture, Radiative transfer, Satellites, Calibration, Systems modeling

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Proceedings Article | 24 February 2004 Paper

Remote sensing of crop water requirements in orange orchards using high-spatial-resolution sensors

Salvatore Barbagallo, Simona Consoli, Guido D'Urso, Rosaria Giorgio Gaggia, Attilio Toscano

Proceedings Volume 5232, (2004) https://doi.org/10.1117/12.511655

KEYWORDS: Reflectivity, Sensors, Vegetation, Satellites, Spatial resolution, Near infrared, Short wave infrared radiation, Atmospheric corrections, Spectral resolution, Calibration

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Proceedings Article | 24 November 1995 Paper

Mapping crop coefficients in irrigated areas from Landsat TM images

Guido D'Urso, Massimo Menenti

Proceedings Volume 2585, (1995) https://doi.org/10.1117/12.227167

KEYWORDS: Reflectivity, Vegetation, Associative arrays, Remote sensing, Landsat, Satellites, Surface roughness, Image classification, Agriculture, Earth observing sensors

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Proceedings Article | 31 January 1995 Paper

Remote sensing of surface soil moisture from a spaceborne SAR sensor over the Sele plain

Guido D'Urso, Andrea Giacomelli, Marco Mancini, Peter Troch

Proceedings Volume 2314, (1995) https://doi.org/10.1117/12.200729

KEYWORDS: Soil science, Synthetic aperture radar, Dielectrics, Sensors, Backscatter, Microwave radiation, Remote sensing, Vegetation, Radar, Speckle

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Showing 5 of 20 publications

Proceedings Volume Editor (10)

SPIE Conference Volume | 2 October 2008

Remote Sensing for Agriculture, Ecosystems, and Hydrology X

SPIE Conference Volume | 4 October 2007

Remote Sensing for Agriculture, Ecosystems, and Hydrology IX

SPIE Conference Volume | 29 September 2006

Remote Sensing for Agriculture, Ecosystems, and Hydrology VIII

SPIE Conference Volume | 17 October 2005

Remote Sensing for Agriculture, Ecosystems, and Hydrology VII

SPIE Conference Volume | 26 October 2004

Remote Sensing for Agriculture, Ecosystems, and Hydrology VI

Showing 5 of 10 publications

Conference Committee Involvement (19)

Remote Sensing for Agriculture, Ecosystems, and Hydrology XV

24 September 2013 | Dresden, Germany

Remote Sensing for Agriculture, Ecosystems, and Hydrology

24 September 2012 | Edinburgh, United Kingdom

Remote Sensing for Agriculture, Ecosystems, and Hydrology

19 September 2011 | Prague, Czech Republic

Remote Sensing for Agriculture, Ecosystems, and Hydrology

20 September 2010 | Toulouse, France

Remote Sensing for Agriculture, Ecosystems, and Hydrology XI

1 September 2009 | Berlin, Germany

Remote Sensing for Agriculture, Ecosystems, and Hydrology X

16 September 2008 | Cardiff, Wales, United Kingdom

SPIE Remote Sensing

15 September 2008 | Cardiff, United Kingdom

Remote Sensing for Agriculture, Ecosystems, and Hydrology

19 September 2007 | Florence, Italy

SPIE Remote Sensing

17 September 2007 | Florence, Italy

Remote Sensing for Agriculture, Ecosystems, and Hydrology VIII

11 September 2006 | Stockholm, Sweden

SPIE Remote Sensing

11 September 2006 | Stockholm, Sweden

Remote Sensing for Agriculture, Ecosystems, and Hydrology VII

20 September 2005 | Bruges, Belgium

SPIE Remote Sensing

19 September 2005 | Bruges, Belgium

Remote Sensing for Agriculture, Ecosystems, and Hydrology VI

15 September 2004 | Maspalomas, Canary Islands, Spain

Remote Sensing for Agriculture, Ecosystems, and Hydrology V

8 September 2003 | Barcelona, Spain

Remote Sensing for Agriculture, Ecosystems, and Hydrology IV

23 September 2002 | Crete, Greece

Remote Sensing for Agriculture, Ecosystems, and Hydrology III

17 September 2001 | Toulouse, France

Remote Sensing for Agriculture, Ecosystems and Hydrology II

25 September 2000 | Barcelona, Spain

Remote Sensing of Vegetation and Sea

23 September 1996 | Taormina, Italy

Showing 5 of 19 Conference Committees

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