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Prof. Guido D'Urso
Department of Agricultural Sciences, University of Naples Federico II, Corso Umberto I, 40, 80138 Napoli NA, Italy

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Research Keywords & Expertise

0 Remote Sensing
0 Evapotranspiration
0 irrigation scheduling
0 soil water balance
0 Digital and precision agriculture

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Remote Sensing
Evapotranspiration
irrigation scheduling
soil water balance

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Journal article
Published: 28 June 2021 in Journal of Agricultural Engineering
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The improvement of performance of irrigation systems plays a fundamental role in increasing their efficiency in order to reach a sound use of irrigation water. The COPAM (Combined Optimization and Performance Analysis Model) has proven its usefulness in performance evaluation of on-demand irrigation systems; however, in many cases, input data, such as water volumes delivered by hydrants, is not readily available. To support a wider application of the COPAM, we tested the possibility of using irrigation volumes estimated by means of space-borne remote sensing. The Sentinel-2 (S2) constellation provides high spatial resolution images with a frequency between 2 and 5 days, which is compatible with COPAM input requirements. In the present work, an irrigation sector in the Capitanata irrigation network (Foggia Province, no. 6 of District 10) in Italy was chosen to assess its performance by using COPAM with volumes estimated from Sentinel-2 data. As an input of COPAM, the upstream discharge was determined after a proper transformation of the estimated irrigation water requirement volumes and the recorded volumes into flowrates. The estimation of the irrigation water requirement volumes was accomplished through the estimation of crop evapotranspiration, Etcrop, and effective precipitation, Pn, by combining crop parameters (leaf area index - LAI, fractional vegetation cover - fc, and Albedo) derived from S2 images and the meteorological data from the ERA5 single levels reanalysis dataset collected for the whole study period, from June 1st to September 30th, 2019. The study comprised a comparison of the estimated irrigation water volumes and the corresponding recorded volumes. The results showed a good agreement between the estimated and the registered volumes in a large time scale for 10 days and a one-month period, while a large difference was observed in a daily time scale. The performance analysis was carried out for the overall system and at hydrant level. The estimated discharge was lower than the registered discharge, indicating better performance. Last but not least, some recommendations were proposed for improving performance in critical zones.

ACS Style

Meriem Er-Rami; Guido D'Urso; Nicola Lamaddalena; Daniela D'Agostino; Oscar Rosario Belfiore. Analysis of irrigation system performance based on an integrated approach with Sentinel-2 satellite images. Journal of Agricultural Engineering 2021, 52, 1 .

AMA Style

Meriem Er-Rami, Guido D'Urso, Nicola Lamaddalena, Daniela D'Agostino, Oscar Rosario Belfiore. Analysis of irrigation system performance based on an integrated approach with Sentinel-2 satellite images. Journal of Agricultural Engineering. 2021; 52 (2):1.

Chicago/Turabian Style

Meriem Er-Rami; Guido D'Urso; Nicola Lamaddalena; Daniela D'Agostino; Oscar Rosario Belfiore. 2021. "Analysis of irrigation system performance based on an integrated approach with Sentinel-2 satellite images." Journal of Agricultural Engineering 52, no. 2: 1.

Preprint content
Published: 04 March 2021
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COALA is a project funded by the Horizon 2020 program of the European Union with the aim of developing Copernicus Earth Observation-based information services for irrigation and nutrient management in Australia, building on consolidated experience of past EU projects and existing operational irrigation advisory services. Earth Observation-based services can provide “diagnostic” data and information relevant for integrated input management of irrigation water and nutrients, from subplot level to irrigation scheme or river basin levels.

COALA, started on January 2020, is developing Copernicus-based information service for the Australian agricultural systems, based on strong collaboration with Academic Australian institutions and business players. COALA services will provide to farmers, irrigation organisation and basin authorities information about crops development, water and nutrient status, irrigated areas by means of innovative algorithms based on Sentinel Earth Observation data, which will be accessed by means of the new cloud platforms (DIAS) of Copernicus. In-situ and other source of data, such as ground soil moisture probes, meteorological stations and Numerical Weather Prediction models, will be used to improve the information provided to the final users.

The advancements beyond the state of art of COALA methodologies for managing irrigation are:

COALA will demonstrate that Copernicus data and new DIAS infrastructure can greatly improve the availability of a multi-scale information product shared by the different levels of users. The innovative approach achieves a "converging loop procedure" between water authority, irrigation infrastructure operation and farmers, enabling transparency in all the decision taken at all levels and improving the accuracy of estimation of actual water use.

https://www.coalaproject.eu/

ACS Style

Guido D Urso; Carlo De Michele; Vuolo Francesco; Calera Alfonso; Osann Anna; Dongryeol Ryu; Metternicht Graciela. Copernicus satellites for supporting irrigation and water management in Australia: the COALA H2020 Project. 2021, 1 .

AMA Style

Guido D Urso, Carlo De Michele, Vuolo Francesco, Calera Alfonso, Osann Anna, Dongryeol Ryu, Metternicht Graciela. Copernicus satellites for supporting irrigation and water management in Australia: the COALA H2020 Project. . 2021; ():1.

Chicago/Turabian Style

Guido D Urso; Carlo De Michele; Vuolo Francesco; Calera Alfonso; Osann Anna; Dongryeol Ryu; Metternicht Graciela. 2021. "Copernicus satellites for supporting irrigation and water management in Australia: the COALA H2020 Project." , no. : 1.

Journal article
Published: 11 June 2020 in Water
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Reanalysis data are being increasingly used as gridded weather data sources for assessing crop-reference evapotranspiration (ET0) in irrigation water-budget analyses at regional scales. This study assesses the performances of ET0 estimates based on weather data, respectively produced by two high-resolution reanalysis datasets: UERRA MESCAN-SURFEX (UMS) and ERA5-Land (E5L). The study is conducted in Campania Region (Southern Italy), with reference to the irrigation seasons (April–September) of years 2008–2018. Temperature, wind speed, vapor pressure deficit, solar radiation and ET0 derived from reanalysis datasets, were compared with the corresponding estimates obtained by spatially interpolating data observed by a network of 18 automatic weather stations (AWSs). Statistical performances of the spatial interpolations were evaluated with a cross-validation procedure, by recursively applying universal kriging or ordinary kriging to the observed weather data. ERA5-Land outperformed UMS both in weather data and ET0 estimates. Averaging over all 18 AWSs sites in the region, the normalized BIAS (nBIAS) was found less than 5% for all the databases. The normalized RMSE (nRMSE) for ET0 computed with E5L data was 17%, while it was 22% with UMS data. Both performances were not far from those obtained by kriging interpolation, which presented an average nRMSE of 14%. Overall, this study confirms that reanalysis can successfully surrogate the unavailability of observed weather data for the regional assessment of ET0.

ACS Style

Anna Pelosi; Fabio Terribile; Guido D’Urso; Giovanni Battista Chirico. Comparison of ERA5-Land and UERRA MESCAN-SURFEX Reanalysis Data with Spatially Interpolated Weather Observations for the Regional Assessment of Reference Evapotranspiration. Water 2020, 12, 1669 .

AMA Style

Anna Pelosi, Fabio Terribile, Guido D’Urso, Giovanni Battista Chirico. Comparison of ERA5-Land and UERRA MESCAN-SURFEX Reanalysis Data with Spatially Interpolated Weather Observations for the Regional Assessment of Reference Evapotranspiration. Water. 2020; 12 (6):1669.

Chicago/Turabian Style

Anna Pelosi; Fabio Terribile; Guido D’Urso; Giovanni Battista Chirico. 2020. "Comparison of ERA5-Land and UERRA MESCAN-SURFEX Reanalysis Data with Spatially Interpolated Weather Observations for the Regional Assessment of Reference Evapotranspiration." Water 12, no. 6: 1669.

Review
Published: 02 June 2020 in Sustainability
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Biological invasions represent some of the most severe threats to local communities and ecosystems. Among invasive species, the vector-borne pathogen Xylella fastidiosa is responsible for a wide variety of plant diseases and has profound environmental, social and economic impacts. Once restricted to the Americas, it has recently invaded Europe, where multiple dramatic outbreaks have highlighted critical challenges for its management. Here, we review the most recent advances on the identification, distribution and management of X. fastidiosa and its insect vectors in Europe through genetic and spatial ecology methodologies. We underline the most important theoretical and technological gaps that remain to be bridged. Challenges and future research directions are discussed in the light of improving our understanding of this invasive species, its vectors and host–pathogen interactions. We highlight the need of including different, complimentary outlooks in integrated frameworks to substantially improve our knowledge on invasive processes and optimize resources allocation. We provide an overview of genetic, spatial ecology and integrated approaches that will aid successful and sustainable management of one of the most dangerous threats to European agriculture and ecosystems.

ACS Style

Francesca Raffini; Giorgio Bertorelle; Roberto Biello; Guido D’Urso; Danilo Russo; Luciano Bosso. From Nucleotides to Satellite Imagery: Approaches to Identify and Manage the Invasive Pathogen Xylella fastidiosa and Its Insect Vectors in Europe. Sustainability 2020, 12, 4508 .

AMA Style

Francesca Raffini, Giorgio Bertorelle, Roberto Biello, Guido D’Urso, Danilo Russo, Luciano Bosso. From Nucleotides to Satellite Imagery: Approaches to Identify and Manage the Invasive Pathogen Xylella fastidiosa and Its Insect Vectors in Europe. Sustainability. 2020; 12 (11):4508.

Chicago/Turabian Style

Francesca Raffini; Giorgio Bertorelle; Roberto Biello; Guido D’Urso; Danilo Russo; Luciano Bosso. 2020. "From Nucleotides to Satellite Imagery: Approaches to Identify and Manage the Invasive Pathogen Xylella fastidiosa and Its Insect Vectors in Europe." Sustainability 12, no. 11: 4508.

Journal article
Published: 17 April 2020 in Remote Sensing
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Lack of accurate and up-to-date data associated with irrigated areas and related irrigation amounts is hampering the full implementation and compliance of the Water Framework Directive (WFD). In this paper, we describe the framework that we developed and implemented within the DIANA project to map the actual extent of irrigated areas in the Campania region (Southern Italy) during the 2018 irrigation season. For this purpose, we considered 202 images from the Harmonized Landsat Sentinel-2 (HLS) products (57 images from Landsat 8 and 145 images from Sentinel-2). Such data were preprocessed in order to extract a multitemporal Normalized Difference Vegetation Index (NDVI) map, which was then smoothed through a gap-filling algorithm. We further integrated data coming from high-resolution (4 km) global satellite precipitation Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS) products. We collected an extensive ground truth in the field represented by 2992 data points coming from three main thematic classes: bare soil and rainfed (class 0), herbaceous (class 1), and tree crop (class 2). This information was exploited to generate irrigated area maps by adopting a machine learning classification approach. We compared six different types of classifiers through a cross-validation approach and found that, in general, random forests, support vector machines, and boosted decision trees exhibited the best performances in terms of classification accuracy and robustness to different tested scenarios. We found an overall accuracy close to 90% in discriminating among the three thematic classes, which highlighted promising capabilities in the detection of irrigated areas from HLS products.

ACS Style

Salvatore Falanga Bolognesi; Edoardo Pasolli; Oscar Belfiore; Carlo De Michele; Guido D’Urso. Harmonized Landsat 8 and Sentinel-2 Time Series Data to Detect Irrigated Areas: An Application in Southern Italy. Remote Sensing 2020, 12, 1275 .

AMA Style

Salvatore Falanga Bolognesi, Edoardo Pasolli, Oscar Belfiore, Carlo De Michele, Guido D’Urso. Harmonized Landsat 8 and Sentinel-2 Time Series Data to Detect Irrigated Areas: An Application in Southern Italy. Remote Sensing. 2020; 12 (8):1275.

Chicago/Turabian Style

Salvatore Falanga Bolognesi; Edoardo Pasolli; Oscar Belfiore; Carlo De Michele; Guido D’Urso. 2020. "Harmonized Landsat 8 and Sentinel-2 Time Series Data to Detect Irrigated Areas: An Application in Southern Italy." Remote Sensing 12, no. 8: 1275.

Journal article
Published: 20 March 2020 in Sensors
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Water use efficiency in agriculture can be improved by implementing advisory systems that support on-farm irrigation scheduling, with reliable forecasts of the actual crop water requirements, where crop evapotranspiration (ETc) is the main component. The development of such advisory systems is highly dependent upon the availability of timely updated crop canopy parameters and weather forecasts several days in advance, at low operational costs. This study presents a methodology for forecasting ETc, based on crop parameters retrieved from multispectral images, data from ground weather sensors, and air temperature forecasts. Crop multispectral images are freely provided by recent satellite missions, with high spatial and temporal resolutions. Meteorological services broadcast air temperature forecasts with lead times of several days, at no subscription costs, and with high accuracy. The performance of the proposed methodology was applied at 18 sites of the Campania region in Italy, by exploiting the data of intensive field campaigns in the years 2014–2015. ETc measurements were forecast with a median bias of 0.2 mm, and a median root mean square error (RMSE) of 0.75 mm at the first day of forecast. At the 5th day of accumulated forecast, the median bias and RMSE become 1 mm and 2.75 mm, respectively. The forecast performances were proved to be as accurate and as precise as those provided with a complete set of forecasted weather variables.

ACS Style

Anna Pelosi; Paolo Villani; Salvatore Falanga Bolognesi; Giovanni Battista Chirico; Guido D'urso. Predicting Crop Evapotranspiration by Integrating Ground and Remote Sensors with Air Temperature Forecasts. Sensors 2020, 20, 1740 .

AMA Style

Anna Pelosi, Paolo Villani, Salvatore Falanga Bolognesi, Giovanni Battista Chirico, Guido D'urso. Predicting Crop Evapotranspiration by Integrating Ground and Remote Sensors with Air Temperature Forecasts. Sensors. 2020; 20 (6):1740.

Chicago/Turabian Style

Anna Pelosi; Paolo Villani; Salvatore Falanga Bolognesi; Giovanni Battista Chirico; Guido D'urso. 2020. "Predicting Crop Evapotranspiration by Integrating Ground and Remote Sensors with Air Temperature Forecasts." Sensors 20, no. 6: 1740.

Journal article
Published: 22 October 2019 in Agronomy
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Remote sensing evapotranspiration estimation over agricultural areas is increasingly used for irrigation management during the crop growing cycle. Different methodologies based on remote sensing have emerged for the leaf area index (LAI) and the canopy chlorophyll content (CCC) estimation, essential biophysical parameters for crop evapotranspiration monitoring. Using Sentinel-2 (S2) spectral information, this study performed a comparative analysis of empirical (vegetation indices), semi-empirical (CLAIR model with fixed and calibrated extinction coefficient) and artificial neural network S2 products derived from the Sentinel Application Platform Software (SNAP) biophysical processor (ANN S2 products) approaches for the estimation of LAI and CCC. Four independent in situ collected datasets of LAI and CCC, obtained with standard instruments (LAI-2000, SPAD) and a smartphone application (PocketLAI), were used. The ANN S2 products present good statistics for LAI (R2 > 0.70, root mean square error (RMSE) < 0.86) and CCC (R2 > 0.75, RMSE < 0.68 g/m2) retrievals. The normalized Sentinel-2 LAI index (SeLI) is the index that presents good statistics in each dataset (R2 > 0.71, RMSE < 0.78) and for the CCC, the ratio red-edge chlorophyll index (CIred-edge) (R2 > 0.67, RMSE < 0.62 g/m2). Both indices use bands located in the red-edge zone, highlighting the importance of this region. The LAI CLAIR model with a fixed extinction coefficient value produces a R2 > 0.63 and a RMSE < 1.47 and calibrating this coefficient for each study area only improves the statistics in two areas (RMSE ≈ 0.70). Finally, this study analyzed the influence of the LAI parameter estimated with the different methodologies in the calculation of crop potential evapotranspiration (ETc) with the adapted Penman–Monteith (FAO-56 PM), using a multi-temporal dataset. The results were compared with ETc estimated as the product of the reference evapotranspiration (ETo) and on the crop coefficient (Kc) derived from FAO table values. In the absence of independent reference ET data, the estimated ETc with the LAI in situ values were considered as the proxy of the ground-truth. ETc estimated with the ANN S2 LAI product is the closest to the ETc values calculated with the LAI in situ (R2 > 0.90, RMSE < 0.41 mm/d). Our findings indicate the good validation of ANN S2 LAI and CCC products and their further suitability for the implementation in evapotranspiration retrieval of agricultural areas.

ACS Style

Nieves Pasqualotto; Guido D’Urso; Salvatore Falanga Bolognesi; Oscar Rosario Belfiore; Shari Van Wittenberghe; Jesús Delegido; Alejandro Pezzola; Cristina Winschel; José Moreno. Retrieval of Evapotranspiration from Sentinel-2: Comparison of Vegetation Indices, Semi-Empirical Models and SNAP Biophysical Processor Approach. Agronomy 2019, 9, 663 .

AMA Style

Nieves Pasqualotto, Guido D’Urso, Salvatore Falanga Bolognesi, Oscar Rosario Belfiore, Shari Van Wittenberghe, Jesús Delegido, Alejandro Pezzola, Cristina Winschel, José Moreno. Retrieval of Evapotranspiration from Sentinel-2: Comparison of Vegetation Indices, Semi-Empirical Models and SNAP Biophysical Processor Approach. Agronomy. 2019; 9 (10):663.

Chicago/Turabian Style

Nieves Pasqualotto; Guido D’Urso; Salvatore Falanga Bolognesi; Oscar Rosario Belfiore; Shari Van Wittenberghe; Jesús Delegido; Alejandro Pezzola; Cristina Winschel; José Moreno. 2019. "Retrieval of Evapotranspiration from Sentinel-2: Comparison of Vegetation Indices, Semi-Empirical Models and SNAP Biophysical Processor Approach." Agronomy 9, no. 10: 663.

Journal article
Published: 21 July 2019 in Agronomy
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A research study was conducted in an open field tomato crop in order to: (i) Evaluate the capability of Sentinel-2 imagery to assess tomato canopy growth and its crop water requirements; and (ii) explore the possibility to predict crop water requirements by assimilating the canopy cover estimated by Sentinel-2 imagery into AquaCrop model. The pilot area was in Campania, a region in the south west of Italy, characterized by a typical Mediterranean climate, where field campaigns were conducted in seasons 2017 and 2018 on processing tomato. Crop water use and irrigation requirement were estimated by means of three different methods: (i) The AquaCrop model; (ii) an irrigation advisory service based on Sentinel-2 imagery known as IRRISAT and (iii) assimilating the canopy cover estimated by Sentinel-2 imagery into AquaCrop model Sentinel-2 imagery proved to be effective for monitoring canopy growth and for predicting irrigation water requirements during mid-season stage of the crop, when the canopy is fully developed. Conversely, the integration of the Sentinel-2 imagery with a crop growth model can contribute to improve the irrigation water requirement predictions in the early and development stage of the crop, when the soil evaporation is not negligible with respect to the total evapotranspiration.

ACS Style

Anna Dalla Marta; Giovanni Battista Chirico; Salvatore Falanga Bolognesi; Marco Mancini; Guido D'Urso; Simone Orlandini; Carlo De Michele; Filiberto Altobelli. Integrating Sentinel-2 Imagery with AquaCrop for Dynamic Assessment of Tomato Water Requirements in Southern Italy. Agronomy 2019, 9, 404 .

AMA Style

Anna Dalla Marta, Giovanni Battista Chirico, Salvatore Falanga Bolognesi, Marco Mancini, Guido D'Urso, Simone Orlandini, Carlo De Michele, Filiberto Altobelli. Integrating Sentinel-2 Imagery with AquaCrop for Dynamic Assessment of Tomato Water Requirements in Southern Italy. Agronomy. 2019; 9 (7):404.

Chicago/Turabian Style

Anna Dalla Marta; Giovanni Battista Chirico; Salvatore Falanga Bolognesi; Marco Mancini; Guido D'Urso; Simone Orlandini; Carlo De Michele; Filiberto Altobelli. 2019. "Integrating Sentinel-2 Imagery with AquaCrop for Dynamic Assessment of Tomato Water Requirements in Southern Italy." Agronomy 9, no. 7: 404.

Journal article
Published: 01 April 2019 in Remote Sensing of Environment
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Late spring frost plays a major role in the structure and function of forest ecosystems with potential consequences on species distribution at both local and regional scales. Paradoxically, in a warmer world the incidence and impact of frost is increasing because of earlier leaf unfolding and flowering as a response to warmer temperatures. In this regard, European Beech (Fagus sylvatica L.), a native tree species widely distributed in European forests, is considered particularly sensitive to changes in spring temperature regimes associated with climate change and thus especially subject to the risk of frost damage. Although several studies concerning F. sylvatica frost damage have been conducted in northern and central Europe, no extensive studies are available for the southern part of its range, i.e. central and southern Italy as well as Greece. In this paper the effect of a late spring frost occurring at the end of April 2016 is extensively documented with high spatial detail all along the Apennine Chain through satellite image data. Three different remote-sensing greenness indexes, namely the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and the greenness index (GI) derived from Landsat-8 satellite images acquired from May to July in the years 2014, 2015, and 2016 at a spatial resolution of 30 m, were used to gauge the spatial response of common beech forests to this late frost event with relation to latitude, altitude and slope exposure. Frost damage was evaluated as a difference (Δ) of NDVI, GI and EVI between the mean of years 2014 and 2015 (i.e. MRY, mean of reference years), and 2016 (i.e. FEY, frost reference year). The three satellite remote-sensing indexes were efficient at detecting leaf damage with detailed spatial resolution and proved consistent with one another. The greatest damage occurred in the middle altitudinal range between 1500 and 1700 m a.s.l. with a decreasing trend toward both lower and higher elevations due to warmer temperatures below, and delayed phenology above. Exposure also influenced frost injury, with south-facing slopes of the mountain more damaged than the north. This difference was due to earlier spring leaf phenology of southern beech trees in response to a greater heat sum in the warm weeks preceding. Less damage in the northern Apennines is consistent with the spatial extent of minimum freezing temperatures. To sum up, frost damage is strongly related to site-specific conditions, i.e. on the one hand to minimum temperatures, and on the other to the phenological stage of the trees involving both altitude and exposure. Hence focusing on detailed sub-regional studies can be helpful for predicting future consequences of climate change on forests.

ACS Style

Emilia Allevato; Luigi Saulino; Gaspare Cesarano; Giovanni Battista Chirico; Guido D'Urso; Salvatore Falanga Bolognesi; Angelo Rita; Sergio Rossi; Antonio Saracino; Giuliano Bonanomi. Canopy damage by spring frost in European beech along the Apennines: effect of latitude, altitude and aspect. Remote Sensing of Environment 2019, 225, 431 -440.

AMA Style

Emilia Allevato, Luigi Saulino, Gaspare Cesarano, Giovanni Battista Chirico, Guido D'Urso, Salvatore Falanga Bolognesi, Angelo Rita, Sergio Rossi, Antonio Saracino, Giuliano Bonanomi. Canopy damage by spring frost in European beech along the Apennines: effect of latitude, altitude and aspect. Remote Sensing of Environment. 2019; 225 ():431-440.

Chicago/Turabian Style

Emilia Allevato; Luigi Saulino; Gaspare Cesarano; Giovanni Battista Chirico; Guido D'Urso; Salvatore Falanga Bolognesi; Angelo Rita; Sergio Rossi; Antonio Saracino; Giuliano Bonanomi. 2019. "Canopy damage by spring frost in European beech along the Apennines: effect of latitude, altitude and aspect." Remote Sensing of Environment 225, no. : 431-440.

Research article
Published: 31 March 2019 in Outlook on Agriculture
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In recent years, the certification of environmental sustainability has been adopted by a large number of farms. A wide range of recent literature proved consumers’ preference and willingness to pay (WTP) for certification claiming for reduced environmental impact of food production, whereas the literature on farmers’ preference for a specific scheme design is scant. This study aims at investigating the possibilities of developing an environmental certification (EC) for agricultural products that is more tailored to farmers’ expectations. Data from an original survey among 116 producers from Italy, Croatia, and Greece were used to investigate the most preferred elements of a hypothetical EC for a general agricultural product, by means of a choice experiment. Although differences emerge in relation to the nationality of respondents, the results on average suggest a clear preference and a higher WTP by farmers for a certification that may guarantee an efficient use of water resources. Furthermore, farmers are found to be more inclined toward a public certifying body and the possibility to receive technical assistance for the scheme adoption.

ACS Style

F Altobelli; A Monteleone; O Cimino; A Dalla Marta; S Orlandini; S Trestini; L Toulios; P Nejedlik; V Vucetic; G Cicia; T Panico; C Cavallo; Guido D'Urso; T Del Giudice; E Giampietri. Farmers’ willingness to pay for an environmental certification scheme: Promising evidence for water saving. Outlook on Agriculture 2019, 48, 136 -142.

AMA Style

F Altobelli, A Monteleone, O Cimino, A Dalla Marta, S Orlandini, S Trestini, L Toulios, P Nejedlik, V Vucetic, G Cicia, T Panico, C Cavallo, Guido D'Urso, T Del Giudice, E Giampietri. Farmers’ willingness to pay for an environmental certification scheme: Promising evidence for water saving. Outlook on Agriculture. 2019; 48 (2):136-142.

Chicago/Turabian Style

F Altobelli; A Monteleone; O Cimino; A Dalla Marta; S Orlandini; S Trestini; L Toulios; P Nejedlik; V Vucetic; G Cicia; T Panico; C Cavallo; Guido D'Urso; T Del Giudice; E Giampietri. 2019. "Farmers’ willingness to pay for an environmental certification scheme: Promising evidence for water saving." Outlook on Agriculture 48, no. 2: 136-142.

Journal article
Published: 03 July 2018 in Remote Sensing of Environment
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The occurrence of water shortages ascribed to projected climate change, especially in the Mediterranean region, fosters the interest in remote sensing (RS) applications to optimize water use in agriculture. Remote sensing evapotranspiration and water demand estimation over large cultivated areas were used to manage irrigation to minimize losses during the crop growing cycle. The research aimed to explore the potential of the MultiSpectral Instrument (MSI) sensor on board Sentinel-2A to estimate crop parameters, mainly surface albedo (α) and Leaf Area Index (LAI) that influence the dynamics of potential evapotranspiration (ETp) and Irrigation Water Requirements (IWR) of processing tomato crop (Solanum lycopersicum L.). Maximum tomato ETp was calculated according to the FAO Penman-Monteith equation (FAO-56 PM) using appropriate values of canopy parameters derived by processing Sentinel-2A data in combination with daily weather information. For comparison, we used the actual crop evapotranspiration (ETa) derived from the soil water balance (SWB) module in the Environmental Policy Integrated Climate (EPIC) model and calibrated with in-situ Root Zone Soil Moisture (RZSM). The experiment was set up in a privately-owned farm located in the Tarquinia irrigation district (Central Italy) during two growing seasons, within the framework of the EU Project FATIMA (FArming Tools for external nutrient Inputs and water Management). The results showed that canopy growth, maximum evapotranspiration (ETp) and IWR were accurately inferred from satellite observations following seasonal rainfall and air temperature patterns. The net estimated IWR from satellite observations for the two-growing seasons was about 272 and 338 mm in 2016 and 2017, respectively. Such estimated requirement was lower compared with the actual amount supplied by the farmer with sprinkler and drip micro-irrigation system in both growing seasons resulting in 364 (276 mm drip micro-irrigation, and 88 mm sprinkler) and 662 (574 mm drip micro-irrigation, and 88 mm sprinkler) mm, respectively. Our findings indicated the suitability of Sentinel-2A to predict tomato water demand at field level, providing useful information for optimizing the irrigation over extended farmland.

ACS Style

Silvia Vanino; Pasquale Nino; Carlo De Michele; Salvatore Falanga Bolognesi; Guido D'Urso; Claudia Di Bene; Bruno Pennelli; Francesco Vuolo; Roberta Farina; Giuseppe Pulighe; Rosario Napoli. Capability of Sentinel-2 data for estimating maximum evapotranspiration and irrigation requirements for tomato crop in Central Italy. Remote Sensing of Environment 2018, 215, 452 -470.

AMA Style

Silvia Vanino, Pasquale Nino, Carlo De Michele, Salvatore Falanga Bolognesi, Guido D'Urso, Claudia Di Bene, Bruno Pennelli, Francesco Vuolo, Roberta Farina, Giuseppe Pulighe, Rosario Napoli. Capability of Sentinel-2 data for estimating maximum evapotranspiration and irrigation requirements for tomato crop in Central Italy. Remote Sensing of Environment. 2018; 215 ():452-470.

Chicago/Turabian Style

Silvia Vanino; Pasquale Nino; Carlo De Michele; Salvatore Falanga Bolognesi; Guido D'Urso; Claudia Di Bene; Bruno Pennelli; Francesco Vuolo; Roberta Farina; Giuseppe Pulighe; Rosario Napoli. 2018. "Capability of Sentinel-2 data for estimating maximum evapotranspiration and irrigation requirements for tomato crop in Central Italy." Remote Sensing of Environment 215, no. : 452-470.

Journal article
Published: 01 July 2018 in The Journal of Agricultural Science
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The aim of the current study was to define, optimize and customize IRRISAT, a fully operative satellite-based irrigation advisory service (IAS) provided in Campania Region, Italy, using a choice experiment to determine the preferences of farmers regarding the main characteristics and attributes of IRRISAT. Furthermore, willingness to pay for the main attributes of the services provided was estimated. The information on the amount of water required for irrigation provided by the IAS is sent out to farmers via SMS and email, as well as via the IRRISAT webpage. The study was related to the 2013–2014 irrigation season, when the service provided support to 669 farmers over an area of 55 ha. The study considered four attributes and levels of IRRISAT service: land management unit (scale of service); different levels of water saving (5, 10 and 30%) that could be achieved at different prices; annual fee paid by farmers (ranging between €6 and €10/ha/year); length of contract for the service supply, ranging from 1 to 3 years. Results showed that farmers’ preferences are influenced positively by scale (entire area of the farm instead of single fields) and duration of the service delivering contract. Concerning the duration of the contract, the most preferred option was the 3-year service. Finally, water saving was shown to affect farmers’ choices very little and thus it is probably less attractive for farmers probably due to the low price and to a relatively large availability of water for irrigation.

ACS Style

F. Altobelli; U. Lall; A. Dalla Marta; F. Caracciolo; G. Cicia; Guido D'Urso; T. Del Giudice. Willingness of farmers to pay for satellite-based irrigation advisory services: a southern Italy experience. The Journal of Agricultural Science 2018, 156, 723 -730.

AMA Style

F. Altobelli, U. Lall, A. Dalla Marta, F. Caracciolo, G. Cicia, Guido D'Urso, T. Del Giudice. Willingness of farmers to pay for satellite-based irrigation advisory services: a southern Italy experience. The Journal of Agricultural Science. 2018; 156 (5):723-730.

Chicago/Turabian Style

F. Altobelli; U. Lall; A. Dalla Marta; F. Caracciolo; G. Cicia; Guido D'Urso; T. Del Giudice. 2018. "Willingness of farmers to pay for satellite-based irrigation advisory services: a southern Italy experience." The Journal of Agricultural Science 156, no. 5: 723-730.

Journal article
Published: 01 May 2018 in Future Generation Computer Systems
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Gerardo Severino; Guido D’Urso; Maddalena Scarfato; Gerardo Toraldo. The IoT as a tool to combine the scheduling of the irrigation with the geostatistics of the soils. Future Generation Computer Systems 2018, 82, 268 -273.

AMA Style

Gerardo Severino, Guido D’Urso, Maddalena Scarfato, Gerardo Toraldo. The IoT as a tool to combine the scheduling of the irrigation with the geostatistics of the soils. Future Generation Computer Systems. 2018; 82 ():268-273.

Chicago/Turabian Style

Gerardo Severino; Guido D’Urso; Maddalena Scarfato; Gerardo Toraldo. 2018. "The IoT as a tool to combine the scheduling of the irrigation with the geostatistics of the soils." Future Generation Computer Systems 82, no. : 268-273.

Journal article
Published: 28 February 2018 in The Journal of Agricultural Science
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Irrigation according to reliable estimates of crop water requirements (CWR) is one of the key strategies to ensure long-term sustainability of irrigated agriculture. In southern Mediterranean regions, during the irrigation season, CWR is almost totally controlled by the potential evapotranspiration of the irrigated crop. An innovative system for forecasting crop potential evapotranspiration (ETp) has been implemented recently in the Campania region (southern Italy). The system produces ETp forecasts with a lead time of up to 5 days, by coupling the visible and near-infrared crop imagery with numerical weather prediction outputs of a limited area model. The forecasts are delivered to farmers with a simple and intuitive web app interface, which makes daily real-time ETp maps accessible from desktop computers, tablets and smartphones. Forecast performances were evaluated for maize fields of two farms in two irrigation seasons (2014–2015). The mean absolute bias of the forecasted ETp was

ACS Style

G. B. Chirico; Anna Pelosi; Carlo De Michele; Salvatore Falanga Bolognesi; Guido D'Urso. Forecasting potential evapotranspiration by combining numerical weather predictions and visible and near-infrared satellite images: an application in southern Italy. The Journal of Agricultural Science 2018, 156, 702 -710.

AMA Style

G. B. Chirico, Anna Pelosi, Carlo De Michele, Salvatore Falanga Bolognesi, Guido D'Urso. Forecasting potential evapotranspiration by combining numerical weather predictions and visible and near-infrared satellite images: an application in southern Italy. The Journal of Agricultural Science. 2018; 156 (5):702-710.

Chicago/Turabian Style

G. B. Chirico; Anna Pelosi; Carlo De Michele; Salvatore Falanga Bolognesi; Guido D'Urso. 2018. "Forecasting potential evapotranspiration by combining numerical weather predictions and visible and near-infrared satellite images: an application in southern Italy." The Journal of Agricultural Science 156, no. 5: 702-710.

Review
Published: 10 January 2018 in Remote Sensing
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Upcoming satellite hyperspectral sensors require powerful and robust methodologies for making optimum use of the rich spectral data. This paper reviews the widely applied coupled PROSPECT and SAIL radiative transfer models (PROSAIL), regarding their suitability for the retrieval of biophysical and biochemical variables in the context of agricultural crop monitoring. Evaluation was carried out using a systematic literature review of 281 scientific publications with regard to their (i) spectral exploitation, (ii) vegetation type analyzed, (iii) variables retrieved, and (iv) choice of retrieval methods. From the analysis, current trends were derived, and problems identified and discussed. Our analysis clearly shows that the PROSAIL model is well suited for the analysis of imaging spectrometer data from future satellite missions and that the model should be integrated in appropriate software tools that are being developed in this context for agricultural applications. The review supports the decision of potential users to employ PROSAIL for their specific data analysis and provides guidelines for choosing between the diverse retrieval techniques.

ACS Style

Katja Berger; Clement Atzberger; Martin Danner; Guido D’Urso; Wolfram Mauser; Francesco Vuolo; Tobias Hank. Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study. Remote Sensing 2018, 10, 85 .

AMA Style

Katja Berger, Clement Atzberger, Martin Danner, Guido D’Urso, Wolfram Mauser, Francesco Vuolo, Tobias Hank. Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study. Remote Sensing. 2018; 10 (2):85.

Chicago/Turabian Style

Katja Berger; Clement Atzberger; Martin Danner; Guido D’Urso; Wolfram Mauser; Francesco Vuolo; Tobias Hank. 2018. "Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study." Remote Sensing 10, no. 2: 85.

Journal article
Published: 11 October 2017 in Water Resources Research
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We consider transport of a conservative solute through an aquifer as determined: (i) by the advective velocity, which depends upon the hydraulic conductivity K and (ii) by the local spreading due to the pore-scale dispersion (PSD). The flow is steady, and it takes place in a porous formation where, owing to its erratic spatial variations, the hydraulic log conductivity Y?ln?K is modeled as a stationary Gaussian random field. The relative effect of the above mechanisms (i)–(ii) is quantified by the Peclet number (Pe) which, in most of the previous studies, was considered infinite (i.e., no PSD) due to the overtake of advective heterogeneities upon the PSD. Here we aim at generalizing such studies by accounting for the impact of finite Pe on conservative transport. Previous studies on the topic required extensive numerical computations. In the present note, we remove the computational burden by adopting the rational approximate expression of Dagan and Cvetkovic (1993) for the covariance of the velocity field. This allows one to obtain closed form expressions for the quantities characterizing the longitudinal plume's dispersion. Transport can be straightforwardly investigated by dealing with a modified Peclet number (Pe) incorporating both the PSD and the aquifer's anisotropy. The satisfactory match to Cape Cod field data suggests that the present theoretical results lend themselves as a useful tool to assess the impact of the PSD upon conservative transport through heterogeneous porous formations.

ACS Style

Gerardo Severino; Salvatore Cuomo; Angelo Sommella; Guido D'urso. On the Longitudinal Dispersion in Conservative Transport Through Heterogeneous Porous Formations at Finite Peclet Numbers. Water Resources Research 2017, 53, 8614 -8625.

AMA Style

Gerardo Severino, Salvatore Cuomo, Angelo Sommella, Guido D'urso. On the Longitudinal Dispersion in Conservative Transport Through Heterogeneous Porous Formations at Finite Peclet Numbers. Water Resources Research. 2017; 53 (10):8614-8625.

Chicago/Turabian Style

Gerardo Severino; Salvatore Cuomo; Angelo Sommella; Guido D'urso. 2017. "On the Longitudinal Dispersion in Conservative Transport Through Heterogeneous Porous Formations at Finite Peclet Numbers." Water Resources Research 53, no. 10: 8614-8625.

Preprint
Published: 26 September 2017
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Water managers need map of irrigated areas (defined as the identification of their location and their areal extent) to plan a rational use of water under limited availability and to prevent the unauthorized withdrawals. Many authors have shown that the Earth Observation techniques are an effective tool for mapping irrigated areas worldwide at different spatial scales (global/regional/and local). This study presents a methodology for mapping irrigated areas in semi-arid environment based on Earth Observation techniques and by fully exploiting datasets freely available processed by open source software and tools. Data acquired with the Landsat 8 Operational Land Imager (OLI) and the new Sentinel 2A MultiSpectral Instrument (MSI) sensors were integrated to obtain cloud free dense time series allowing to monitor the vegetation development throughout the growing seasons. Irrigated areas were identified by analysing the growing patterns under water deficit conditions from NDVI values under the assumption that, in arid and semi-arid environment (like the Mediterranean Region), high trend of vegetation growth are compatible only with irrigation. The method was applied inside the Cixerri Consortium Irrigation District located in South of Sardinia (Italy).

ACS Style

Pasquale Nino; Silvia Vanino; Flavio Lupia; Guido D'urso; Carlo De Michele; Giuseppe Pulighe; Guido Bonati. Mapping irrigated areas using multi-sensor remote sensing data in a Mediterranean environment. 2017, 1 .

AMA Style

Pasquale Nino, Silvia Vanino, Flavio Lupia, Guido D'urso, Carlo De Michele, Giuseppe Pulighe, Guido Bonati. Mapping irrigated areas using multi-sensor remote sensing data in a Mediterranean environment. . 2017; ():1.

Chicago/Turabian Style

Pasquale Nino; Silvia Vanino; Flavio Lupia; Guido D'urso; Carlo De Michele; Giuseppe Pulighe; Guido Bonati. 2017. "Mapping irrigated areas using multi-sensor remote sensing data in a Mediterranean environment." , no. : 1.

Journal article
Published: 04 July 2017 in Remote Sensing
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The knowledge of spatial and temporal variability of soil water content and others soil-vegetation variables (leaf area index, fractional cover) assumes high importance in crop management. Where and when the cloudiness limits the use of optical and thermal remote sensing techniques, synthetic aperture radar (SAR) imagery has proven to have several advantages (cloud penetration, day/night acquisitions and high spatial resolution). However, measured backscattering is controlled by several factors including SAR configuration (acquisition geometry, frequency and polarization), and target dielectric and geometric properties. Thus, uncertainties arise about the more suitable configuration to be used. With the launch of the ALOS Palsar, Cosmo-Skymed and Sentinel 1 sensors, a dataset of multi-frequency (X, C, L) and multi-polarization (co- and cross-polarizations) images are now available from a virtual constellation; thus, significant issues concerning the retrieval of soil-vegetation variables using SAR are: (i) identifying the more suitable SAR configuration; (ii) understanding the affordability of a multi-frequency approach. In 2006, a vast dataset of both remotely sensed images (SAR and optical/thermal) and in situ data was collected in the framework of the AgriSAR 2006 project funded by ESA and DLR. Flights and sampling have taken place weekly from April to August. In situ data included soil water content, soil roughness, fractional coverage and Leaf Area Index (LAI). SAR airborne data consisted of multi-frequency and multi-polarized SAR images (X, C and L frequencies and HH, HV, VH and VV polarizations). By exploiting this very wide dataset, this paper, explores the capabilities of SAR in describing four of the main soil-vegetation variables (SVV). As a first attempt, backscattering and SVV temporal behaviors are compared (dynamic analysis) and single-channel regressions between backscattering and SVV are analyzed. Remarkably, no significant correlations were found between backscattering and soil roughness (over both bare and vegetated plots), whereas it has been noticed that the contributions of water content of soil underlying the vegetation often did not influence the backscattering (depending on canopy structure and SAR configuration). Most significant regressions were found between backscattering and SVV characterizing the vegetation biomass (fractional cover and LAI). Secondly, the effect of SVV changes on the spatial correlation among SAR channels (accounting for different polarization and/or frequencies) was explored. An inter-channel spatial/temporal correlation analysis is proposed by temporally correlating two-channel spatial correlation and SVV. This novel approach allowed a widening in the number of significant correlations and their strengths by also encompassing the use of SAR data acquired at two different frequencies.

ACS Style

Fulvio Capodici; Antonino Maltese; Giuseppe Ciraolo; Guido D’Urso; Goffredo La Loggia. Power Sensitivity Analysis of Multi-Frequency, Multi-Polarized, Multi-Temporal SAR Data for Soil-Vegetation System Variables Characterization. Remote Sensing 2017, 9, 677 .

AMA Style

Fulvio Capodici, Antonino Maltese, Giuseppe Ciraolo, Guido D’Urso, Goffredo La Loggia. Power Sensitivity Analysis of Multi-Frequency, Multi-Polarized, Multi-Temporal SAR Data for Soil-Vegetation System Variables Characterization. Remote Sensing. 2017; 9 (7):677.

Chicago/Turabian Style

Fulvio Capodici; Antonino Maltese; Giuseppe Ciraolo; Guido D’Urso; Goffredo La Loggia. 2017. "Power Sensitivity Analysis of Multi-Frequency, Multi-Polarized, Multi-Temporal SAR Data for Soil-Vegetation System Variables Characterization." Remote Sensing 9, no. 7: 677.

Review
Published: 11 May 2017 in Sensors
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The experiences gathered during the past 30 years support the operational use of irrigation scheduling based on frequent multi-spectral image data. Currently, the operational use of dense time series of multispectral imagery at high spatial resolution makes monitoring of crop biophysical parameters feasible, capturing crop water use across the growing season, with suitable temporal and spatial resolutions. These achievements, and the availability of accurate forecasting of meteorological data, allow for precise predictions of crop water requirements with unprecedented spatial resolution. This information is greatly appreciated by the end users, i.e., professional farmers or decision-makers, and can be provided in an easy-to-use manner and in near-real-time by using the improvements achieved in web-GIS methodologies (Geographic Information Systems based on web technologies). This paper reviews the most operational and explored methods based on optical remote sensing for the assessment of crop water requirements, identifying strengths and weaknesses and proposing alternatives to advance towards full operational application of this methodology. In addition, we provide a general overview of the tools, which facilitates co-creation and collaboration with stakeholders, paying special attention to these approaches based on web-GIS tools.

ACS Style

Alfonso Calera; Isidro Campos; Anna Osann; Guido D’Urso; Massimo Menenti. Remote Sensing for Crop Water Management: From ET Modelling to Services for the End Users. Sensors 2017, 17, 1104 .

AMA Style

Alfonso Calera, Isidro Campos, Anna Osann, Guido D’Urso, Massimo Menenti. Remote Sensing for Crop Water Management: From ET Modelling to Services for the End Users. Sensors. 2017; 17 (5):1104.

Chicago/Turabian Style

Alfonso Calera; Isidro Campos; Anna Osann; Guido D’Urso; Massimo Menenti. 2017. "Remote Sensing for Crop Water Management: From ET Modelling to Services for the End Users." Sensors 17, no. 5: 1104.

Journal article
Published: 01 April 2017 in Remote Sensing of Environment
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Maurizio Teobaldelli; Francesco Cona; Luigi Saulino; Antonello Migliozzi; Guido D'Urso; Giuliano Langella; Piero Manna; Antonio Saracino. Detection of diversity and stand parameters in Mediterranean forests using leaf-off discrete return LiDAR data. Remote Sensing of Environment 2017, 192, 126 -138.

AMA Style

Maurizio Teobaldelli, Francesco Cona, Luigi Saulino, Antonello Migliozzi, Guido D'Urso, Giuliano Langella, Piero Manna, Antonio Saracino. Detection of diversity and stand parameters in Mediterranean forests using leaf-off discrete return LiDAR data. Remote Sensing of Environment. 2017; 192 ():126-138.

Chicago/Turabian Style

Maurizio Teobaldelli; Francesco Cona; Luigi Saulino; Antonello Migliozzi; Guido D'Urso; Giuliano Langella; Piero Manna; Antonio Saracino. 2017. "Detection of diversity and stand parameters in Mediterranean forests using leaf-off discrete return LiDAR data." Remote Sensing of Environment 192, no. : 126-138.