Prof. Antonino Maltese Profile

Prof. Antonino Maltese

at Univ degli Studi di Palermo

SPIE Involvement:

Conference Chair | Author | Editor

Area of Expertise:

Remote Sensing , Hydrology , Ecosystems , Geomatics

Websites:

Personal Website

Publications (44)

Proceedings Article | 20 November 2024 Presentation + Paper

Spectral discrimination and separability analysis of beach macroplatisc litter from high-resolution RPAS images

Alessandra Capolupo, Marco Lonero, Antonino Maltese, Eufemia Tarantino

Proceedings Volume 13191, 131910C (2024) https://doi.org/10.1117/12.3033835

KEYWORDS: Plastics, Vegetation, Orthophoto maps, RGB color model, Polystyrene, Sand, Matrices, Education and training, Visual inspection, Remote sensing

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Proceedings Article | 20 November 2024 Presentation + Paper

Comparison of bathymetry estimated from multibeam echosounder and optical data

Antonino Maltese, Valeria Lo Presti, Giovanni Andrea Nocera, Attilio Sulli

Proceedings Volume 13191, 1319105 (2024) https://doi.org/10.1117/12.3038063

KEYWORDS: Satellites, Reflectivity, Satellite imaging, Ocean optics, Interpolation, Earth observing sensors, Water, Radiative transfer, Image processing

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Proceedings Article | 20 November 2024 Presentation

Surface energy balance in olive groves affected by Xylella fastidiosa

Antonino Maltese, Annarita D'Addabbo, Raffaella Matarrese

Proceedings Volume PC13191, PC131910A (2024) https://doi.org/10.1117/12.3038092

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SPIE Journal Paper | 26 March 2024

On the shoreline positioning via remote sensing imagery: an isoradiometric approach

Antonino Maltese, Francesco Caldareri, Gino Dardanelli, Simona Todaro, Nicolò Parrino, Attilio Sulli

JARS, Vol. 18, Issue 01, 014529, (March 2024) https://doi.org/10.1117/12.10.1117/1.JRS.18.014529

KEYWORDS: Satellites, Spatial resolution, Thermography, Synthetic aperture radar, Near infrared, Remote sensing, Raster graphics, Image processing, Speckle, Satellite imaging

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Proceedings Article | 17 October 2023 Presentation + Paper

A radiometric contouring approach to map the shoreline

Antonino Maltese, Francesco Caldareri, Nicolò Parrino, Simona Todaro, Gino Dardanelli, Attilio Sulli

Proceedings Volume 12727, 127270B (2023) https://doi.org/10.1117/12.2690731

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Proceedings Article | 20 September 2020 Presentation + Paper

Integration of terrestrial laser scanning and UAV-SFM technique to generate a detailed 3D textured model of a heritage building

Alessandra Capolupo, Antonino Maltese, Mirko Saponaro, Domenica Costantino

Proceedings Volume 11534, 115340Z (2020) https://doi.org/10.1117/12.2574034

KEYWORDS: 3D modeling, Laser scanners, Data modeling, Cultural heritage, Data acquisition, Clouds, Unmanned aerial vehicles, Gold, 3D imaging standards, Atomic force microscopy

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Proceedings Article | 20 September 2020 Presentation

Opening Remarks by Conference Chairs

Christopher M. Neale, Antonino Maltese

Proceedings Volume 11528, 1152803 (2020) https://doi.org/10.1117/12.2584221

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SPIE Journal Paper | 8 December 2018 Open Access

Special Section Guest Editorial: Advances in Agro-Hydrological Remote Sensing for Water Resources Conservation

Antonino Maltese, Christopher M. Neale

JARS, Vol. 12, Issue 04, 042801, (December 2018) https://doi.org/10.1117/12.10.1117/1.JRS.12.042801

KEYWORDS: Remote sensing, Agriculture, Ecosystems, Aerospace engineering, Satellites, Vegetation, Soil science, Environmental monitoring, Environmental sensing, Sensor technology

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Proceedings Article | 10 October 2018 Paper

Evaluation of different InSAR multi-baseline construction methods over a dam in southern Italy

Claudia Pipitone, Antonino Maltese, Gino Dardanelli, Fulvio Capodici, Jan-Peter Muller, Goffredo La Loggia

Proceedings Volume 10783, 107831A (2018) https://doi.org/10.1117/12.2326909

KEYWORDS: Interferometric synthetic aperture radar, Picosecond phenomena, Satellite navigation systems, Synthetic aperture radar, Stars, Reflectivity, Image processing, Interferometry, Remote sensing, Satellites

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Proceedings Article | 2 November 2017 Presentation + Paper

Assessing actual evapotranspiration via surface energy balance aiming to optimize water and energy consumption in large scale pressurized irrigation systems

H. Awada, G. Ciraolo, A. Maltese, M. A. Moreno Hidalgo, G. Provenzano, J. I. Còrcoles

Proceedings Volume 10421, 104210G (2017) https://doi.org/10.1117/12.2279964

KEYWORDS: Earth observing sensors, Satellites, Satellite imaging, Wind energy, Landsat, Meteorology, Heat flux, Remote sensing, Agriculture, Systems modeling

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Proceedings Article | 2 November 2017 Paper

Detection of a reservoir water level using shape similarity metrics

Claudia Pipitone, Antonino Maltese, Gino Dardanelli, Fulvio Capodici, Mauro Lo Brutto, Goffredo La Loggia

Proceedings Volume 10421, 104211L (2017) https://doi.org/10.1117/12.2279268

KEYWORDS: Earth observing sensors, Image segmentation, Image processing, Image classification, Landsat, Geographic information systems, Spatial resolution, Satellite imaging, Satellites, Clouds

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Proceedings Article | 25 October 2016 Paper

Soil water content assessment: seasonal effects on the triangle method

A. Maltese, F. Capodici, G. Ciraolo, G. La Loggia, C. Cammalleri

Proceedings Volume 9998, 99980F (2016) https://doi.org/10.1117/12.2246801

KEYWORDS: Climatology, Thermography, Meteorology, Temperature metrology, Humidity, Solar radiation, MODIS, Vegetation, Solar radiation models, Resistance

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Proceedings Article | 14 October 2015 Paper

Testing two temporal upscaling schemes for the estimation of the time variability of the actual evapotranspiration

A. Maltese, F. Capodici, G. Ciraolo, G. La Loggia

Proceedings Volume 9637, 96370S (2015) https://doi.org/10.1117/12.2196750

KEYWORDS: MODIS, Data acquisition, Spatial resolution, Data modeling, Atmospheric modeling, Agriculture, Temporal resolution, Remote sensing, Lawrencium, Fourier transforms

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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 | 21 October 2014 Paper

The synergy of water quality and sea surface currents data in determining the spatio-temporal evolution of large-scale circulation features

Fulvio Capodici, Giuseppe Ciraolo, Simone Cosoli, Antonino Maltese, Giuseppe Mallandrino

Proceedings Volume 9239, 923927 (2014) https://doi.org/10.1117/12.2068457

KEYWORDS: Radar, Satellites, Artificial intelligence, Satellite imaging, Principal component analysis, MODIS, Spatial resolution, Temporal resolution, Remote sensing, Motion measurement

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Proceedings Article | 21 October 2014 Paper

Monitoring displacements of an earthen dam using GNSS and remote sensing

Gino Dardanelli, Goffredo La Loggia, Nicola Perfetti, Fulvio Capodici, Luigi Puccio, Antonino Maltese

Proceedings Volume 9239, 923928 (2014) https://doi.org/10.1117/12.2071222

KEYWORDS: Satellite navigation systems, Global Positioning System, Remote sensing, Geodesy, Landsat, Receivers, Polonium, Spatial resolution, Satellites, Earth observing sensors

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This paper shows the results of a scientific research in which a GNSS continuous monitoring system for earth-dam deformations has been developed, then, deformations have been related with reservoir water surface and level. The experiment was conducted near Bivona (Sicily, Italy), on the Castello dam (Magazzolo Lake). On the top of the dam three control points were placed and three GNSS permanent stations were installed. The three stations continuously transmitted data to the control centre of the University of Palermo. The former has been determined using freely available satellite data (specifically Landsat 7 SLC-Off) collected during the whole study period (DOYs 101 to 348 2011). Issues related with the un-scanned rows filling and to better distinguish water from land pixels on the shoreline. The aim of this work is various: first of all, we want to evaluate whether the GPS post processing techniques can provide static results comparable to other monitoring techniques, such as spirit levelling. The study could take a significant importance given that the Italian legislation until today does not provide for the use of this technology to manage or monitor dams displacements or other civil engineering constructions. The use of GPS data in structural monitoring could in fact reduce some management costs. Usually the conventional GPS monitoring methods, where a base station GPS receiver must be located near the dam, did not ensure that the accuracy of results have been independent from the displacement of the crown (top end of dam). In this paper, a new approach in the area of study of the GNSS permanent network has been engaged to solve these problems. Field-testing results show that the new GNSS approach has excellent performances, and the monitoring of different section of the dam could reveal important information on its deformation, that its not operationally possible to retrieve elsewhere. The post-processing accuracy positioning is around 1–5 mm for the deformations monitoring of the Castello dam. Displacements of different sections of the dam reveal different behaviour (in time and periodicity) that looks to be related with water surface (and level) retrieved from remote sensing.

Proceedings Article | 16 October 2013 Paper

Assessing daily actual evapotranspiration through energy balance: an experiment to evaluate the selfpreservation hypothesis with acquisition time

A. Maltese, F. Capodici, G. Ciraolo, G. La Loggia, G. Rallo

Proceedings Volume 8887, 888718 (2013) https://doi.org/10.1117/12.2029873

KEYWORDS: Spatial resolution, Remote sensing, Cameras, Wind energy, Heat flux, Radon, Sensors, Solar radiation models, Error analysis, Multispectral imaging

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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

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

A validation of a thermal inertia approach to map soil water content on soils characterized by low fractional cover

Antonino Maltese, Fulvio Capodici, Goffredo La Loggia, Chiara Corbari, Marco Mancini

Proceedings Volume 8887, 88871M (2013) https://doi.org/10.1117/12.2029794

KEYWORDS: Soil science, Remote sensing, Thermography, Vegetation, Solar radiation, Neodymium, Solar radiation models, Data acquisition, Infrared radiation, Spatial resolution

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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 | 16 October 2013 Paper

On the relationship between some production parameters and a vegetation index in viticulture

Tanino Santangelo, R. Di Lorenzo, G. La Loggia, A. Maltese

Proceedings Volume 8887, 888715 (2013) https://doi.org/10.1117/12.2029805

KEYWORDS: Vegetation, Remote sensing, Satellite imaging, Satellites, Earth observing sensors, Reflectivity, Magnesium, Meteorology, Analytical research, Spatial resolution

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SPIE Journal Paper | 17 June 2013 Open Access

Errata: Mapping soil water content under sparse vegetation and changeable sky conditions: comparison of two thermal inertia approaches

Antonino Maltese, Fulvio Capodici, Giuseppe Ciraolo, Goffredo La Loggia

JARS, Vol. 7, Issue 01, 079997, (June 2013) https://doi.org/10.1117/12.10.1117/1.JRS.7.079997

KEYWORDS: Vegetation, Lanthanum

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SPIE Journal Paper | 4 June 2013

Mapping soil water content under sparse vegetation and changeable sky conditions: comparison of two thermal inertia approaches

Antonino Maltese, Fulvio Capodici, Giuseppe Ciraolo, Goffredo La Loggia

JARS, Vol. 7, Issue 01, 073548, (June 2013) https://doi.org/10.1117/12.10.1117/1.JRS.7.073548

KEYWORDS: Vegetation, Soil science, Thermography, Remote sensing, Solar radiation, Shortwaves, Clouds, Thermal modeling, In situ remote sensing, Diffusion

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A critical analysis of a thermal inertia approach to map surface soil water content on bare and sparsely vegetated soils by means of remotely sensed data is reported. The study area is an experimental field located in Barrax, Spain. In situ data were acquired within the Barrax 2011 research project. An advanced hyperspectral scanner airborne imager provides images in the visible/near-infrared and thermal infrared bands. Images were acquired both in day and night times by the Instituto Nacional de Técnica Aeroespacial between 12th and 13th of June 2011. The scene covers a corn irrigation pivot surrounded by bare soil, where a set of in situ data have been collected both previously and simultaneously to overpasses. To validate remotely sensed estimations, an ad hoc dataset has been produced by measuring spectra, radiometric temperatures, surface soil water content, and soil thermal properties. These data were collected on two transects covering bare and sparsely vegetated soils. This ground dataset was used (1) to verify if a thermal inertia method can be applied to map the water content on soil covered by sparse vegetation and (2) to quantify a correction factor accounting for solar radiation reduction due to sky cloudiness. The experiment intended to test a spatially constant and a spatially distributed approach to estimate the phase difference. Both methods were then applied to the airborne images collected during the following days to obtain the spatial distribution of surface soil water content. Results confirm that the thermal inertia method can be applied to sparsely vegetated soil characterized by low fractional cover if the solar radiation reaching the ground is accurately estimated. A spatially constant value of the phase difference allows a good assessment of thermal inertia, whereas the comparison with the three-temperature approach did not give conclusive responses. Results also show that clear sky, only at the time of the acquisition, does not provide a sufficient condition to obtain accurate estimates of soil water content. A corrective coefficient taking into account actual sky cloudiness throughout the day allows better estimates of thermal inertia and, thus, of soil water content.

Proceedings Article | 23 October 2012 Paper

An integrated information system for the acquisition, management and sharing of environmental data aimed to decision making

Goffredo La Loggia, Elisa Arnone, Giuseppe Ciraolo, Antonino Maltese, Leonardo Noto, Umberto Pernice

Proceedings Volume 8531, 853112 (2012) https://doi.org/10.1117/12.976300

KEYWORDS: Landslides, Data modeling, Data acquisition, Sensors, Decision support systems, Sensor networks, Printed circuit board testing, System integration, Environmental monitoring, Humidity

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Proceedings Article | 23 October 2012 Paper

Critical analysis of the thermal inertia approach to map soil water content under sparse vegetation and changeable sky conditions

Antonino Maltese, Fulvio Capodici, Chiara Corbari, Giuseppe Ciraolo, Goffredo La Loggia, José Antonio Sobrino

Proceedings Volume 8531, 85310T (2012) https://doi.org/10.1117/12.975676

KEYWORDS: Soil science, Vegetation, Remote sensing, In situ remote sensing, Shortwaves, Thermal modeling, Diffusion, Francium, Thermal effects, Atmospheric modeling

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The paper reports a critical analysis of the thermal inertia approach to map surface soil water content on bare and sparsely vegetated soils by means of remotely sensed data. The study area is an experimental area located in Barrax (Spain). Field data were acquired within the Barrax 2011 research project. AHS airborne images including VIS/NIR and TIR bands were acquired both day and night time by the INTA (Instituto Nacional de Técnica Aeroespacial) between the 11^th and 13^rd of June 2011. Images cover a corn pivot surrounded by bare soil, where a set of in situ data have been collected previously and simultaneously to overpasses. To validate remotely sensed estimations, a preliminary proximity sensing set up has been arranged, measuring spectra and surface temperatures on transects by means of ASD hand-held spectroradiometer and an Everest Interscience radiometric thermometer respectively. These data were collected on two transects: the first one on bare soil and the second from bare to sparsely vegetated soil; soil water content in both transects ranged approximately between field and saturation values. Furthermore thermal inertia was measured using a KD2Pro probe, and surface water content of soil was measured using FDR and TDR probes. This ground dataset was used: 1) to verify if the thermal inertia method can be applied to map water content also on soil covered by sparse vegetation, and 2) to quantify a correction factor of the downwelling shortwave radiation taking into account sky cloudiness effects on thermal inertia assessment. The experiment tests both Xue and Cracknell approximation to retrieve the thermal inertia from a dumped value of the phase difference and the three-temperature approach of Sobrino to estimate the phase difference spatial distribution. Both methods were then applied on the remotely sensed airborne images collected during the following days, in order to obtain the spatial distribution of the surface soil moisture on bare soils and sparse vegetation coverage. Results verify that the thermal inertia method can be applied on sparsely vegetated soil characterized by fractional cover up to ~0.25 (maximum value within this experiment); a lumped value of the phase difference allows a good estimate of the thermal inertia, whereas the comparison with the three-temperature approach did not give conclusive responses because ground radiometric temperatures were not acquired in optimal conditions. Results also show that clear sky only at the time of the remote sensing acquisitions is not a sufficient condition to apply the thermal inertia method. A corrective coefficient taking into account the actual sky cloudiness throughout the day allows accurate estimates of the spatial distribution of the thermal inertia (r² ~ 0.9) and soil water content (r² ~ 0.7).

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

Planktothrix rubescens in freshwater reservoirs: remote sensing potentiality for mapping cell density

Antonino Maltese, Fulvio Capodici, Giuseppe Ciraolo, Goffredo La Loggia, Antonio Granata, Chiara Corbari

Proceedings Volume 8531, 85311X (2012) https://doi.org/10.1117/12.974550

KEYWORDS: Remote sensing, MODIS, Sensors, Landsat, Reflectivity, Solar radiation, Curium, Temperature metrology, Satellites, Earth observing sensors

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Planktothrix rubescens is sadly famous for producing microcystins (MCs), which are powerful hepatotoxins. During the winter 2005/06, P. rubescens has been found in the Pozzillo, Nicoletti, Prizzi and Garcia reservoirs, Sicily; in 2008 it was also detected in SS. Trinità di Delia and Castello reservoirs. Indeed, during periods of low shortwave irradiance such as winter, when light weakly penetrates water column and the water cools, P. rubescens filaments float up to the surface, forming red-colored blooms. Ancillary meteorological measurements highlighted low air temperatures between two frosts in December 2007 and February 2008, with a simultaneous reduction in the incoming total solar radiation, which probably triggered P. rubescens floating up to water surface. The manuscript reports empirical relationships to calculate the cell density of P. rubescens. A basic cell density relationship was set up using in situ data and satellite images (MODIS Terra) collected simultaneously. In particular, water samples were collected for cells counting and MCs detection, whereas Red and Near-Infrared bands of MODIS at 250 m provided a good dataset for modeling an empirical relationship, given also their high temporal resolution. Spectroradiometric campaigns were also carried out in February/March 2008 to characterize the spectral reflectance of water with and without P. rubescens bloom. A further cross calibration, using MODIS as reference data, allowed to setup the empirical relationships to be applied to other sensors such as Landsat ETM+, MERIS Envisat and Sentinel-2. Among those, Landsat ETM+ have an appropriate spatial resolution for monitoring Sicilian reservoirs (characterized by an average extension of few kilometers) although images are currently acquired in SLC-off mode, thus compromising the suitability of such data for an operational monitoring. MERIS had a sufficient spatial resolution (300 m) but it was lost with Envisat (on May the 9th, 2012). Sentinel-2, once operational, will provide a suitable dataset. Generally, the retrieved empirical equations tend to overestimate the actual values for low (or absent) surface filaments.

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

The Rhynchophorus ferruginous disease of Phoenix canariensis: early detection through proximity thermal sensing

Carmelo Cammalleri, Fulvio Capodici, Giuseppe Ciraolo, Giuseppe Filardo, Goffredo La Loggia, Antonino Maltese

Proceedings Volume 8174, 81741M (2011) https://doi.org/10.1117/12.899044

KEYWORDS: Thermography, Cameras, Neodymium, Sensors, Humidity, Atmospheric sensing, Thermal sensing, Ions, Spatial resolution, Remote sensing

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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 | 7 October 2011 Paper

A diachronic analysis of estuarine turbidity due to a flood following an extreme rainfall event

Antonino Maltese, Fulvio Capodici, Giuseppe Ciraolo, Antonino Granata, Goffredo La Loggia

Proceedings Volume 8174, 81741C (2011) https://doi.org/10.1117/12.899050

KEYWORDS: Floods, Mouth, Spatial resolution, Calibration, MODIS, Water, Bacteria, Remote sensing, Data acquisition, Agriculture

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Proceedings Article | 7 October 2011 Paper

Monitoring Mediterranean marine pollution using remote sensing and hydrodynamic modelling

Goffredo La Loggia, Fulvio Capodici, Giuseppe Ciraolo, Aldo Drago, Antonino Maltese

Proceedings Volume 8174, 817416 (2011) https://doi.org/10.1117/12.903761

KEYWORDS: Pollution, Ocean optics, Coastal modeling, Radar, Data modeling, Remote sensing, Synthetic aperture radar, Oceanography, Sensors, Environmental sensing

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Proceedings Article | 7 October 2011 Paper

Comparing actual evapotranspiration and plant water potential on a vineyard

Antonino Maltese, Carmelo Cammalleri, Fulvio Capodici, Giuseppe Ciraolo, Francesco Colletti, Goffredo La Loggia, Tanino Santangelo

Proceedings Volume 8174, 81740O (2011) https://doi.org/10.1117/12.899070

KEYWORDS: Remote sensing, Soil science, Vegetation, Heat flux, Directed energy weapons, Carbon dioxide, Photosynthesis, Sensors, Data acquisition, Agriculture

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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 | 22 October 2010 Paper

Coupling a hydro-maritime model and remotely sensed techniques to assess the shoreline positioning uncertainty: the Marsala coast study case

Giorgio Manno, Carlo Lo Re, Giuseppe Ciraolo, Antonino Maltese

Proceedings Volume 7824, 78241Z (2010) https://doi.org/10.1117/12.865000

KEYWORDS: Wave propagation, Coastal modeling, Data acquisition, Remote sensing, Spatial resolution, Modeling, Statistical analysis, Ranging, Hassium, Visualization

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The severe erosion phenomena affecting the Mediterranean coasts are strictly related to geophysical characteristics and socio-economic pressures. This suggests the need of monitoring and modelling the phenomenon in order to quantify its strength. In fact, the shoreline position, as well as its temporal evolution, provides important information for designing defence structures and for the development of a coastal management plan. The shoreline has a dynamic nature as it changes both in the short and long period. Those changes are caused by geo-morphological (e.g. bars and barrier island development etc.) and hydrodynamic (wave motion, tides and flows) processes, as well as by sudden and fast events such as sea storms, earthquakes and tsunamis. The research examines the uncertainty in positioning the shoreline coupling remotely sensed images and a hydro-maritime model. Although the assessment accuracy strongly relies on data availability and consistency, the resulting assessment of the shoreline erosion and accretion is crucial for an overall understanding of the hydro-maritime geo-morphological interaction. The study case is the Marsala coastline (western coast of Sicily, Italy), named 12th island physiographic unit. It is characterized by a low coast with sandy sediments from Holocene age. These sediments are in continuity of sedimentation on whitish debris composed by organogenic limestone from Pleistocene age. The diachronic analysis was carried out on both emerged and submerged parts of the beach and involves two distinct phases. In the first phase, geo-morphological in situ data have been compared with maps and georeferenced remote sensing images referred to the period 1994-2006. It allowed the identification of shoreline indicators [2] such as the beach cross-section and the shoreline positioning including its spatial and temporal variations. It should be noted that the comparison between EO (Earth Observation) images and cartographic maps is subjected to several uncertainties, due to graphic error, geo-referencing accuracy and spatial resolution. Moreover tidal and climate waves data refer to an acquisition time different to that of the EO images. In the second phase, a maritime hydraulic modelling accounting for sea fluctuations has been performed. The run-up is related to wave's amplitude and phase, as well as to the composition and particle size of the beach sediments determining the beach slope [3]. Prior to run-up calculation, an investigation aiming to evaluate how the waves propagate from offshore to inshore (a third-generation spectral wave numerical model, SWAN - Simulating WAves Nearshore), has been carried out. Wave data have been acquired by a buoy belonging to the National Network Waves Data, located at the SW of the Mazara del Vallo harbour (Trapani), while tide data were recorded by the national marigraph of Porto Empedocle (Agrigento). The results allowed assessing the uncertainty and the consequent accuracy in the shoreline positioning for given slope, highlighting that it is not always possible to assess the shoreline rise and fall, for values lower than 10-15 m.

Proceedings Article | 22 October 2010 Paper

Surface soil humidity retrieval using remote sensing techniques: a triangle method validation

Antonino Maltese, Carmelo Cammalleri, Fulvio Capodici, Giuseppe Ciraolo, Goffredo La Loggia

Proceedings Volume 7824, 782425 (2010) https://doi.org/10.1117/12.865089

KEYWORDS: Humidity, Vegetation, Soil science, Temperature metrology, Remote sensing, In situ metrology, Spatial resolution, Francium, In situ remote sensing, Agriculture

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Proceedings Article | 22 October 2010 Paper

Spectroradiometric characteristics of inland water bodies infestated by Oscillatoria Rubescens algae

Giuseppe Ciraolo, Goffredo La Loggia, Antonino Maltese

Proceedings Volume 7824, 78241Y (2010) https://doi.org/10.1117/12.864674

KEYWORDS: Water, Signal attenuation, Visibility, Reflectivity, Optical properties, Remote sensing, Phosphorus, Liver cancer, Data acquisition, Agriculture

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In December 2006 blooms of Oscillatoria rubescens were found in the reservoir Prizzi in Sicily. Oscillatoria is a genus of filamentous alga comprising approximately 6 species, between these the O. rubescens is sadly famous since this organism produces microcystins which are powerful hepatotoxins. Firstly found in Europe in 1825 on Geneva lake, recently (2006) those algae has been find out in Pozzillo, Nicoletti e Ancipa reservoirs (Enna Province), as well as in Prizzi (Palermo Province) and Garcia reservoirs (Trapani Province). Toxins produced by those bacteria (usually called microcystine LR-1 and LR-2) are highly toxic since they can activate oncogenes cells causing cancer pathologies on liver and gastrointestinal tract. Even if water treatment plants should ensure the provision of safe drinking water from surface waters contaminated with those toxic algae blooms, the contamination of reservoirs used for civil and agricultural supply highlights human health risks. International literature suggests a threshold value of 0.01 μgl^-1 to avoid liver cancer using water coming from contaminated water bodies for a long period. Since O. rubescens activities is strongly related to phosphate and nitrogen compounds as well as to temperature and light transmission within water, the paper presents the comparison between optical properties of the water of an infested reservoir and those of a reservoir characterized by clear water. Field campaigns were carried out in February-March 2008 in order to quantify the spectral transparencies of two water bodies through the calculation of the diffuse attenuation coefficient, measuring underwater downwelling irradiance at different depths as well as water spectral reflectance. Results show that diffuse attenuation coefficient is reduced by approximately 15% reducing light penetration in the water column; coherently reflectance spectral signature generally decreases, exhibiting a characteristic peak around 703 nm not present in uncontaminated waters. Latter findings highlight the possibility to detect O. rubescens infestations using their spectral characteristics by means of multitemporal remote sensing techniques.

Proceedings Article | 22 October 2010 Paper

A thermal inertia model for soil water content retrieval using thermal and multispectral images

A. Maltese, M. Minacapilli, C. Cammalleri, G. Ciraolo, F. D'Asaro

Proceedings Volume 7824, 78241G (2010) https://doi.org/10.1117/12.864672

KEYWORDS: Soil science, Cameras, Remote sensing, Thermal modeling, Humidity, Multispectral imaging, Near infrared, Thermography, Forward looking infrared, Reflectometry

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Soil moisture is difficult to quantify because of its high spatial variability. Consequently, great efforts have been undertaken by the research community to develop practical remote sensing approaches to estimate the spatial distribution of surface soil moisture over large areas and with high spatial detail. Many methodologies have been developed using remote sensing data acquiring information in different parts of the electromagnetic spectrum. Conventional field measurement techniques (including gravimetric and time-domain reflectometry) are point-based, involve on-site operators, are time expensive and, in any case, do not provide exhaustive information on the spatial distribution of soil moisture because it strongly depends on pedology, soil roughness and vegetation cover. The technological development of imaging sensors acquiring in the visible (VIS), near infrared (NIR) and thermal infrared (TIR), renewed the research interest in setting up remote sensed based techniques aimed to retrieve soil water content variability in the soil-plant-atmosphere system (SPA). In this context different approaches have been widely applied at regional scale throughout synthetic indexes based on VIS, NIR and TIR spectral bands. A laboratory experiment has been carried out to verify a physically based model based on the remote estimation of the soil thermal inertia, P, to indirectly retrieve the soil surface water content, θ. The paper shows laboratory retrievals using simultaneously a FLIR A320G thermal camera, a six bands customized TETRACAM MCA II (Multiple Camera Array) multispectral camera working in the VIS/NIR part of the spectrum. Using these two type of sensors a set of VIS/NIR and TIR images were acquired as the main input dataset to retrieve the spatial variability of the thermal inertia values. Moreover, given that the accuracy of the proposed approach strongly depends on the accurate estimation of the soil thermal conductivity, a Decagon Device KD2 PRO thermal analyzer was used to verify the remotely estimate of thermal conductivity. Remotely estimated water contents were validated using the gravimetric method. The considered thermal inertia approach allowed prediction of the spatial distribution of the soil water with a satisfactory level of accuracy.

Proceedings Article | 18 September 2009 Paper

Effects of rainfall events on the evapotranspiration retrieved by an energy balance model

Antonino Maltese, Carmelo Cammalleri, Giuseppe Ciraolo, Goffredo La Loggia

Proceedings Volume 7472, 747213 (2009) https://doi.org/10.1117/12.830372

KEYWORDS: MODIS, Heat flux, Soil science, Vegetation, Solar radiation models, Satellites, Data modeling, Meteorology, Humidity, Agriculture

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An alternative way to map the actual evapotranspiration (ET) spatial distribution at daily scale is the application of residual surface energy balance models to satellite images that are characterised by high temporal frequency and moderate spatial resolution, like those acquired by the MODIS sensors on board of TERRA and AQUA platforms. Within this research the well-known SEBAL model has been applied on an area located in the southern part of Sicily (Imera Meridionale catchment) using four images acquired between the 27th of March and the 11th of April 2007. The catchment extends for about 2000 km² and includes both mountains and hill areas: the first are located in the northern part (the Madonie Mountains), while the latter characterise the southern area. The altitude ranges between 0 and 1900 m a.s.l., with an average altitude of 500 m a.s.l.. In order to validate the outputs of the energy balance model, a flux tower has been installed within a homogeneous field of cereal located in the valley part of the basin (close to Licata town within the Quignones Farm), characterised by semi-arid conditions. The selected images have been acquired before and after a three-rainfall events period (5 mm during the night between the 2nd and the 3rd of April, 17 mm on the 3rd evening, 8 mm on the 4th thoroughly all the day). The study area is mostly cultivated with cereals that, during the study period, were characterised by flag and bolting phenological phases. In the first, the leaf at the stem apex assumes a flag shape, while during the latter, the terminal part of the stem a barrel shape enlargement containing the ear appears. The study aim is the evaluation of the energy partition between sensible and latent heat fluxes, due to the increased water availability in a period characterised by a significant vegetation growth. The comparison between model results and flux tower measurements shows that both the latent heat flux and the other energy balance components are modelled with high accuracy. Moreover, the model well represents the variation in time of energy partition, which increases from 0.5 up to 0.65 in terms of evaporative fraction (Λ). Therefore a greater percent of energy is used to evapotranspiration after the rainfall events.

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

Critical analysis of empirical ground heat flux equations on a cereal field using micrometeorological data

Carmelo Cammalleri, Goffredo La Loggia, Antonino Maltese

Proceedings Volume 7472, 747225 (2009) https://doi.org/10.1117/12.830289

KEYWORDS: Heat flux, Soil science, Vegetation, Humidity, Radon, Remote sensing, Calibration, Temperature metrology, Phase shifts, Climatology

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Proceedings Article | 2 October 2008 Paper

A sensitivity analysis of a surface energy balance model to LAI (Leaf Area Index)

A. Maltese, M. Cannarozzo, F. Capodici, G. La Loggia, T. Santangelo

Proceedings Volume 7104, 71040K (2008) https://doi.org/10.1117/12.800333

KEYWORDS: Vegetation, Heat flux, Remote sensing, Analytical research, Resistance, Data modeling, Data acquisition, Reflectivity, Ear, Aerodynamics

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Showing 5 of 44 publications

Proceedings Volume Editor (16)

SPIE Conference Volume | 7 November 2023

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV

SPIE Conference Volume | 31 October 2022

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIV

SPIE Conference Volume | 12 October 2021

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII

SPIE Conference Volume | 13 October 2020

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXII

SPIE Conference Volume | 11 December 2019

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI

Showing 5 of 16 publications

Conference Committee Involvement (26)

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXVI

16 September 2024 | Edinburgh, United Kingdom

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV

3 September 2023 | Amsterdam, Netherlands

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV

3 September 2023 | Amsterdam, Netherlands

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIV

5 September 2022 | Berlin, Germany

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIV

5 September 2022 | Berlin, Germany

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII

13 September 2021 | Online Only, Spain

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII

13 September 2021 | Online Only, Spain

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXII

22 September 2020 | Online Only, United Kingdom

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXII

22 September 2020 | Online Only, United Kingdom

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI

9 September 2019 | Strasbourg, France

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI

9 September 2019 | Strasbourg, France