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124 results on '"Pignatti, Stefano"'

3. Early-Season Crop Mapping by PRISMA Images Using Machine/Deep Learning Approaches: Italy and Iran Test Cases.

Author

Mirzaei, Saham, Pascucci, Simone, Carfora, Maria Francesca, Casa, Raffaele, Rossi, Francesco, Santini, Federico, Palombo, Angelo, Laneve, Giovanni, and Pignatti, Stefano

Subjects
DEEP learning, MACHINE learning, CLASSIFICATION algorithms, CROPS, REMOTE-sensing images, CROP yields
Abstract

Despite its high importance for crop yield prediction and monitoring, early-season crop mapping is severely hampered by the absence of timely ground truth. To cope with this issue, this study aims at evaluating the capability of PRISMA hyperspectral satellite images compared with Sentinel-2 multispectral imagery to produce early- and in-season crop maps using consolidated machine and deep learning algorithms. Results show that the accuracy of crop type classification using Sentinel-2 images is meaningfully poor compared with PRISMA (14% in overall accuracy (OA)). The 1D-CNN algorithm, with 89%, 91%, and 92% OA for winter, summer, and perennial cultivations, respectively, shows for the PRISMA images the highest accuracy in the in-season crop mapping and the fastest algorithm that achieves acceptable accuracy (OA 80%) for the winter, summer, and perennial cultivations early-season mapping using PRISMA images. Moreover, the 1D-CNN algorithm shows a limited reduction (6%) in performance, appearing to be the best algorithm for crop mapping within operational use in cross-farm applications. Machine/deep learning classification algorithms applied on the test fields cross-scene demonstrate that PRISMA hyperspectral time series images can provide good results for early- and in-season crop mapping. [ABSTRACT FROM AUTHOR]

Published
2024
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7. Evaluation of Airborne HySpex and Spaceborne PRISMA Hyperspectral Remote Sensing Data for Soil Organic Matter and Carbonates Estimation

Author

Angelopoulou, Theodora, Chabrillat, Sabine, Pignatti, Stefano, Milewski, Robert, Karyotis, Konstantinos, Brell, Maximilian, Ruhtz, Thomas, Bochtis, Dionysis, Zalidis, George, Angelopoulou, Theodora, Chabrillat, Sabine, Pignatti, Stefano, Milewski, Robert, Karyotis, Konstantinos, Brell, Maximilian, Ruhtz, Thomas, Bochtis, Dionysis, and Zalidis, George

Abstract

Remote sensing and soil spectroscopy applications are valuable techniques for soil property estimation. Soil organic matter (SOM) and calcium carbonate are important factors in soil quality, and although organic matter is well studied, calcium carbonates require more investigation. In this study, we validated the performance of laboratory soil spectroscopy for estimating the aforementioned properties with referenced in situ data. We also examined the performance of imaging spectroscopy sensors, such as the airborne HySpex and the spaceborne PRISMA. For this purpose, we applied four commonly used machine learning algorithms and six preprocessing methods for the evaluation of the best fitting algorithm.. The study took place over crop areas of Amyntaio in Northern Greece, where extensive soil sampling was conducted. This is an area with a very variable mineralogical environment (from lignite mine to mountainous area). The SOM results were very good at the laboratory scale and for both remote sensing sensors with R2 = 0.79 for HySpex and R2 = 0.76 for PRISMA. Regarding the calcium carbonate estimations, the remote sensing accuracy was R2 = 0.82 for HySpex and R2 = 0.36 for PRISMA. PRISMA was still in the commissioning phase at the time of the study, and therefore, the acquired image did not cover the whole study area. Accuracies for calcium carbonates may be lower due to the smaller sample size used for the modeling procedure. The results show the potential for using quantitative predictions of SOM and the carbonate content based on soil and imaging spectroscopy at the air and spaceborne scales and for future applications using larger datasets.

Published
2023

12. Updates On PRISMA: Scientific Calibration/Validation Activities and Supporting Studies

Author

Genesio, Lorenzo, primary, Braga, Federica, additional, Bresciani, Mariano, additional, Boschetti, Mirco, additional, Carotenuto, Federico, additional, Cogliati, Sergio, additional, Colella, Simone, additional, Colombo, Roberto, additional, Giardino, Claudia, additional, Gioli, Beniamino, additional, Lopinto, Ettore, additional, Meloni, Daniela, additional, Pepe, Monica, additional, Pascucci, Simone, additional, Pignatti, Stefano, additional, Pompilio, Loredana, additional, Sacco, Patrizia, additional, Satalino, Giuseppe, additional, and Miglietta, Franco, additional

Published
2022
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13. Prisma Noise Coefficients Estimation

Author

Carfora, Maria Francesca, primary, Casa, Raffaele, additional, Laneve, Giovanni, additional, Mzid, Nada, additional, Pascucci, Simone, additional, and Pignatti, Stefano, additional

Published
2022
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17. Sino–EU Earth Observation Data to Support the Monitoring and Management of Agricultural Resources

Author

Pignatti, Stefano, primary, Casa, Raffaele, additional, Laneve, Giovanni, additional, Li, Zhenhai, additional, Liu, Linyi, additional, Marzialetti, Pablo, additional, Mzid, Nada, additional, Pascucci, Simone, additional, Silvestro, Paolo Cosmo, additional, Tolomio, Massimo, additional, Upreti, Deepak, additional, Yang, Hao, additional, Yang, Guijun, additional, and Huang, Wenjiang, additional

Published
2021
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19. Hyperspectral remote sensing of agriculture and vegetation

Author

Pascucci, Simone, Pignatti, Stefano, Casa, Raffaele, Darvishzadeh, R., Huang, Wenjiang, Department of Natural Resources, UT-I-ITC-FORAGES, and Faculty of Geo-Information Science and Earth Observation

Subjects
ITC-ISI-JOURNAL-ARTICLE, ITC-GOLD
Abstract

The advent of up-to-date hyperspectral technologies, and their increasing performance both spectrally and spatially, allows for new and exciting studies and practical applications in agriculture (soils and crops) and vegetation mapping and monitoring atregional (satellite platforms) andwithin-field (airplanes, drones and ground-based platforms) scales. Within this context, the special issue has included eleven international research studies using different hyperspectral datasets (from the Visible to the Shortwave Infrared spectral region) for agricultural soil, crop and vegetation modelling, mapping, and monitoring. Different classification methods (Support Vector Machine, Random Forest, Artificial Neural Network, Decision Tree) and crop canopy/leaf biophysical parameters (e.g., chlorophyll content) estimation methods (partial least squares and multiple linear regressions) have been evaluated. Further, drone-based hyperspectral mapping by combining bidirectional reflectance distribution function (BRDF) model for multi-angle remote sensing and object-oriented classification methods are also examined. A review article on the recent advances of hyperspectral imaging technology and applications in agriculture is also included in this issue. The special issue is intended to help researchers and farmers involved in precision agriculture technology and practices to a better comprehension of strengths and limitations of the application of hyperspectral measurements for agriculture and vegetation monitoring. The studies published herein can be used by the agriculture and vegetation research and management communities to improve the characterization and evaluation of biophysical variables and processes, as well as for a more accurate prediction of plant nutrient using existing and forthcoming hyperspectral remote sensing technologies.

Published
2020

20. Dynamics of sun-induced chlorophyll fluorescence and reflectance to detect stress-induced variations in canopy photosynthesis

Author

Pinto, F, Celesti, M, Acebron, K, Alberti, G, Cogliati, S, Colombo, R, Juszczak, R, Matsubara, S, Miglietta, F, Palombo, A, Panigada, C, Pignatti, S, Rossini, M, Sakowska, K, Schickling, A, Schüttemeyer, D, Stróżecki, M, Tudoroiu, M, Rascher, U, Pinto, Francisco, Celesti, Marco, Acebron, Kelvin, Alberti, Giorgio, Cogliati, Sergio, Colombo, Roberto, Juszczak, Radosław, Matsubara, Shizue, Miglietta, Franco, Palombo, Angelo, Panigada, Cinzia, Pignatti, Stefano, Rossini, Micol, Sakowska, Karolina, Schickling, Anke, Schüttemeyer, Dirk, StróŻecki, Marcin, Tudoroiu, Marin, Rascher, Uwe, Pinto, F, Celesti, M, Acebron, K, Alberti, G, Cogliati, S, Colombo, R, Juszczak, R, Matsubara, S, Miglietta, F, Palombo, A, Panigada, C, Pignatti, S, Rossini, M, Sakowska, K, Schickling, A, Schüttemeyer, D, Stróżecki, M, Tudoroiu, M, Rascher, U, Pinto, Francisco, Celesti, Marco, Acebron, Kelvin, Alberti, Giorgio, Cogliati, Sergio, Colombo, Roberto, Juszczak, Radosław, Matsubara, Shizue, Miglietta, Franco, Palombo, Angelo, Panigada, Cinzia, Pignatti, Stefano, Rossini, Micol, Sakowska, Karolina, Schickling, Anke, Schüttemeyer, Dirk, StróŻecki, Marcin, Tudoroiu, Marin, and Rascher, Uwe

Abstract

Passive measurement of sun-induced chlorophyll fluorescence (F) represents the most promising tool to quantify changes in photosynthetic functioning on a large scale. However, the complex relationship between this signal and other photosynthesis-related processes restricts its interpretation under stress conditions. To address this issue, we conducted a field campaign by combining daily airborne and ground-based measurements of F (normalized to photosynthetically active radiation), reflectance and surface temperature and related the observed changes to stress-induced variations in photosynthesis. A lawn carpet was sprayed with different doses of the herbicide Dicuran. Canopy-level measurements of gross primary productivity indicated dosage-dependent inhibition of photosynthesis by the herbicide. Dosage-dependent changes in normalized F were also detected. After spraying, we first observed a rapid increase in normalized F and in the Photochemical Reflectance Index, possibly due to the blockage of electron transport by Dicuran and the resultant impairment of xanthophyll-mediated non-photochemical quenching. This initial increase was followed by a gradual decrease in both signals, which coincided with a decline in pigment-related reflectance indices. In parallel, we also detected a canopy temperature increase after the treatment. These results demonstrate the potential of using F coupled with relevant reflectance indices to estimate stress-induced changes in canopy photosynthesis.

Published
2020

26. Modeling and Multi-Temporal Characterization of Total Suspended Matter by the Combined Use of Sentinel 2-MSI and Landsat 8-OLI Data: The Pertusillo Lake Case Study (Italy)

Author

Ciancia, Emanuele, primary, Campanelli, Alessandra, additional, Lacava, Teodosio, additional, Palombo, Angelo, additional, Pascucci, Simone, additional, Pergola, Nicola, additional, Pignatti, Stefano, additional, Satriano, Valeria, additional, and Tramutoli, Valerio, additional

Published
2020
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27. Dynamics of sun‐induced chlorophyll fluorescence and reflectance to detect stress‐induced variations in canopy photosynthesis

Author

Pinto, Francisco, primary, Celesti, Marco, additional, Acebron, Kelvin, additional, Alberti, Giorgio, additional, Cogliati, Sergio, additional, Colombo, Roberto, additional, Juszczak, Radosław, additional, Matsubara, Shizue, additional, Miglietta, Franco, additional, Palombo, Angelo, additional, Panigada, Cinzia, additional, Pignatti, Stefano, additional, Rossini, Micol, additional, Sakowska, Karolina, additional, Schickling, Anke, additional, Schüttemeyer, Dirk, additional, Stróżecki, Marcin, additional, Tudoroiu, Marin, additional, and Rascher, Uwe, additional

Published
2020
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31. EnMAP airborne soil Greece campaign 2019

Author

Chabrillat, Sabine, primary, Ruhtz, Thomas, additional, Zalidis, Georges, additional, Ben-Dor, Eyal, additional, Brell, Maximilian, additional, Tziolas, Nikos, additional, Milewski, Robert, additional, Berger, Daniel, additional, Foerster, Saskia, additional, Kuester, Theres, additional, Tsakiridis, Nikos, additional, Liakopoulos, Vasilis, additional, Angelopoulou, Theodora, additional, Samarinas, Nikiforos, additional, Francos, Nicolas, additional, Cawse-Nicholson, Kerry, additional, and Pignatti, Stefano, additional

Published
2020
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32. Ophiolites mapping in the Taro Valley (Central Italy) by using an LWIR airborne TASI-600 survey: preliminary results on the Roccamurata complex

Author

Pignatti, Stefano, primary, Bogliolo, Maria Paola, additional, Buongiorno, Fabrizia, additional, Despini, Francesca, additional, Ionca, Victoria, additional, Musacchio, Massimo, additional, Palombo, Angelo, additional, Panigada, Cinzia, additional, Pascucci, Simone, additional, Santini, Federico, additional, and Teggi, Sergio, additional

Published
2020
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35. A Comparison between Standard and Functional Clustering Methodologies: Application to Agricultural Fields for Yield Pattern Assessment

Author

Pascucci, Simone, Carfora, Maria Francesca, Palombo, Angelo, Pignatti, Stefano, and Casa, Raffaele

Subjects
clustering methods, Landsat time series, high-resolution maps, agricultural fields
Abstract

This is the published version of the paper: Pascucci, S., Carfora, M.F., Palombo, A., Pignatti, S., Casa, R., Pepe, M., Castaldi, F. 2018. A comparison between standard and functional clustering methodologies: Application to agricultural fields for yield pattern assessment. Remote Sensing, 10(4),585 Abstract: The recognition of spatial patterns within agricultural fields, presenting similar yield potential areas, stable through time, is very important for optimizing agricultural practices. This study proposes the evaluation of different clustering methodologies applied to multispectral satellite time series for retrieving temporally stable (constant) patterns in agricultural fields, related to within-field yield spatial distribution. The ability of different clustering procedures for the recognition and mapping of constant patterns in fields of cereal crops was assessed. Crop vigor patterns, considered to be related to soils characteristics, and possibly indicative of yield potential, were derived by applying the different clustering algorithms to time series of Landsat images acquired on 94 agricultural fields near Rome (Italy). Two different approaches were applied and validated using Landsat 7 and 8 archived imagery. The first approach automatically extracts and calculates for each field of interest (FOI) the Normalized Difference Vegetation Index (NDVI), then exploits the standard K-means clustering algorithm to derive constant patterns at the field level. The second approach applies novel clustering procedures directly to spectral reflectance time series, in particular: (1) standard K-means; (2) functional K-means; (3) multivariate functional principal components clustering analysis; (4) hierarchical clustering. The different approaches were validated through cluster accuracy estimates on a reference set of FOIs for which yield maps were available for some years. Results show that multivariate functional principal components clustering, with an a priori determination of the optimal number of classes for each FOI, provides a better accuracy than those of standard clustering algorithms. The proposed novel functional clustering methodologies are effective and efficient for constant pattern retrieval and can be used for a sustainable management of agricultural fields, depending on farming systems and environmental conditions in different regions.

Published
2018

38. Earth observation in support of science and applications development in the field 'land and Environment': Synthesis results from the ESA-most dragon cooperation Programme

Author

European Space Agency, Pignatti, Stefano [0000-0002-0587-8926], Cartalis, C., Asimakopoulos, D. N., Ban, Y., Bao, Y., Bi, Y., Defourny, Pierre, Barrio, G. del, Fan, J., Gao, Z., Gong, H., Gong, J., Gong, P., Li, C., Pignatti, Stefano, Sarris, A., Yang, Guang, European Space Agency, Pignatti, Stefano [0000-0002-0587-8926], Cartalis, C., Asimakopoulos, D. N., Ban, Y., Bao, Y., Bi, Y., Defourny, Pierre, Barrio, G. del, Fan, J., Gao, Z., Gong, H., Gong, J., Gong, P., Li, C., Pignatti, Stefano, Sarris, A., and Yang, Guang

Abstract

Dragon is a cooperation Programme between the European Space Agency (ESA) and the Ministry of Science and Technology (MOST) of the P.R. China. The Programme, initiated in 2004, focuses on the exploitation of ESA, Third Party Missions (TPM) and Chinese Earth Observation (EO) data for geo-science and applications development in land, ocean and atmospheric applications. In particular, the Programme brings together joint SinoEuropean teams to investigate 50 thematic projects. In this paper, the results of the research projects1 in the thematic field “Land and Environment” will be briefly presented, whereas emphasis will be given in the assessment of the usefulness of the results for an integrated assessment of the state of the environment in the respective study areas. Furthermore new knowledge gained in such fields as desertification assessment, drought and epidemics’ monitoring, forest modeling, cropwatch monitoring, climate change vulnerability (including climate change adaptation and mitigation plans), urbanization monitoring and land use/cover change assessment and monitoring, will be presented. Such knowledge will be also linked to the capacities of Earth Observation systems (and of the respective EO data) to support the temporal, spatial and spectral requirements of the research studies. The potential of DRAGON to support such targets as “technology and knowledge transfer at the bilateral level”, “common EO database for exploitation” and “data sharing and open access data policy” will be also presented. Finally special consideration will be given in highlighting the replication potential of the techniques as developed in the course of the projects, as well as on the importance of the scientific results for environmental policy drafting and decision making

Published
2015

39. Downstream Services for Rice Crop Monitoring in Europe: From Regional to Local Scale

Author

Busetto, Lorenzo, Casteleyn, Sven, Granell, Carlos, Pepe, Monica, Barbieri, Massimo, Campos-Taberner, Manuel, CASA, Raffaele, Collivignarelli, Francesco, Confalonieri, Roberto, Crema, Alberto, García Haro, Francisco Javier, Luca Gatti, Gatti, Luca, Gitas, Ioannis, González-Pérez, Alberto, Grau Muedra, Gonçal Andreu, Guarneri, Tommaso, Holecz, Francesco, Katsantonis, Dimitrios, Minakou, Chara, Miralles, Ignacio, Movedi, Ermes, Nutini, Francesco, Pagani, Valentina, Palombo, Angelo, DI Paola, Francesco, Pascucci, Simone, Pignatti, Stefano, RAMPINI, ANNA, Ranghetti, Luigi, Ricciardelli, Elisabetta, Romano, Filomena, Stavrakoudis, Dimitris, STROPPIANA, DANIELA, VIGGIANO, MARIASSUNTA, and BOSCHETTI, MIRCO

Subjects
Atmospheric Science, food industry, Monitoring, 010504 meteorology & atmospheric sciences, Computer science, 0211 other engineering and technologies, Information Dissemination, 02 engineering and technology, 01 natural sciences, Electronic mail, Data modeling, Remote Sensing, ERMES, remote sensing, Food Industry, Computers in Earth Sciences, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Downstream (petroleum industry), agriculture, 2. Zero hunger, Data collection, End user, business.industry, Environmental resource management, Modeling, Agriculture, modeling, 15. Life on land, monitoring, business
Abstract

The ERMES agromonitoring system for rice cultivations integrates EO data at different resolutions, crop models, and user-provided in situ data in a unified system, which drives two operational downstream services for rice monitoring. The first is aimed at providing information concerning the behavior of the current season at regional/rice district scale, while the second is dedicated to provide farmers with field-scale data useful to support more efficient and environmentally friendly crop practices. In this contribution, we describe the main characteristics of the system, in terms of overall architecture, technological solutions adopted, characteristics of the developed products, and functionalities provided to end users. Peculiarities of the system reside in its ability to cope with the needs of different stakeholders within a common platform, and in a tight integration between EO data processing and information retrieval, crop modeling, in situ data collection, and information dissemination. The ERMES system has been operationally tested in three European rice-producing countries (Italy, Spain, and Greece) during growing seasons 2015 and 2016, providing a great amount of near-real-time information concerning rice crops. Highlights of significant results are provided, with particular focus on real-world applications of ERMES products and services. Although developed with focus on European rice cultivations, solutions implemented in the ERMES system can be, and are already being, adapted to other crops and/or areas of the world, thus making it a valuable testing bed for the development of advanced, integrated agricultural monitoring systems.

Published
2017
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42. Downstream Services for Rice Crop Monitoring in Europe: From Regional to Local Scale

Author

Busetto, Lorenzo, primary, Casteleyn, Sven, additional, Granell, Carlos, additional, Pepe, Monica, additional, Barbieri, Massimo, additional, Campos-Taberner, Manuel, additional, Casa, Raffaele, additional, Collivignarelli, Francesco, additional, Confalonieri, Roberto, additional, Crema, Alberto, additional, Garcia-Haro, Francisco Javier, additional, Gatti, Luca, additional, Gitas, Ioannis Z., additional, Gonzalez-Perez, Alberto, additional, Grau-Muedra, Goncal, additional, Guarneri, Tommaso, additional, Holecz, Francesco, additional, Katsantonis, Dimitrios, additional, Minakou, Chara, additional, Miralles, Ignacio, additional, Movedi, Ermes, additional, Nutini, Francesco, additional, Pagani, Valentina, additional, Palombo, Angelo, additional, Paola, Francesco Di, additional, Pascucci, Simone, additional, Pignatti, Stefano, additional, Rampini, Anna, additional, Ranghetti, Luigi, additional, Ricciardelli, Elisabetta, additional, Romano, Filomena, additional, Stavrakoudis, Dimitris G., additional, Stroppiana, Daniela, additional, Viggiano, Mariassunta, additional, and Boschetti, Mirco, additional

Published
2017
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49. Reducing the Influence of Soil Moisture on the Estimation of Clay from Hyperspectral Data: A Case Study Using Simulated PRISMA Data.

Author

Castald, Fabio, Palombo, Angelo, Pascucci, Simone, Pignatti, Stefano, Santini, Federico, and Casa, Raffaele

Subjects
SOIL moisture, CLAY soils, HYPERSPECTRAL imaging systems, ARTIFICIAL satellites, PREDICTION models
Abstract

Soil moisture hampers the estimation of soil variables such as clay content from remote and proximal sensing data, reducing the strength of the relevant spectral absorption features. In the present study, two different strategies have been evaluated for their ability to minimize the influence of soil moisture on clay estimation by using soil spectra acquired in a laboratory and by simulating satellite hyperspectral data. Simulated satellite data were obtained according to the spectral characteristics of the forthcoming hyperspectral imager on board of the Italian PRISMA satellite mission. The soil datasets were split into four groups according to the water content. For each soil moisture level a prediction model was applied, using either spectral indices or partial least squares regression (PLSR). Prediction models were either specifically developed for the soil moisture level or calibrated using synthetically dry soil spectra, generated from wet soil data. Synthetically dry spectra were obtained using a new technique based on the effects caused by soil moisture on the optical spectrum from 400 to 2400 nm. The estimation of soil clay content, when using different prediction models according to soil moisture, was slightly more accurate as compared to the use of synthetically dry soil spectra, both employing clay indices and PLSR models. The results obtained in this study demonstrate that the a priori knowledge of the soil moisture class can reduce the error of clay estimation when using hyperspectral remote sensing data, such as those that will be provided by the PRISMA satellite mission in the near future. [ABSTRACT FROM AUTHOR]

Published
2015
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50. Advanced Anomalous Pixel Correction Algorithms for Hyperspectral Thermal Infrared Data: The TASI-600 Case Study.

Author

Santini, Federico, Palombo, Angelo, Dekker, Rob J., Pignatti, Stefano, Pascucci, Simone, and Schwering, Piet B. W.

Abstract

Anomalous pixel responses often seriously affect remote sensing applications, especially in the thermal spectral range. In this paper, a new method to identify and correct anomalous pixel responses is presented. The method was specifically developed to handle with hyperspectral data and is based on the statistical analysis of a gray scale RX detector (RXD) image applied on the focal plane space rather than on the image space. An iterative thresholding method to correct anomalous pixels in automatic modality was tuned. Moreover, a band depth-based method to properly restore the lost information was applied. The band depth method serves to prevent the creation of new artifacts during the anomalous pixel correction that could affect applications such as anomaly or change detection and classification for thermal infrared (TIR) hyperspectral imagery. In this paper, we take into consideration hyperspectral TASI-600 data acquired during recent airborne campaigns in Europe. Evidences of the benefits on remote sensing applications such as classification and change detection algorithms in urban areas are shown. [ABSTRACT FROM PUBLISHER]

Published
2014
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