Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloSpecial Issue "Hyperspectral Remote Sensing of Agriculture and Vegetation"
Anno di pubblicazione2020
Formato
  • Elettronico
  • Cartaceo
Autore/iPascucci, Simone; Pignatti, Stefano; Casa, Raffaele; Darvishzadeh, Roshanak; Huang, Wenjiang
Affiliazioni autoria.Institute of Methodologies for Environmental Analysis (CNR IMAA), National Research Council, Tito Scalo, PZ 85050, Italy b.Department of Agricultural and Forestry scieNcEs (DAFNE), Tuscia University, Via San Camillo de Lellis, Viterbo, 01100, Italy c.ITC--Faculty of Geo-Information Science and Earth Observation, Department of Natural Resources, University of Twente, PO Box 217, Enschede, 7500 AE, Netherlands d.Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China
Autori CNR e affiliazioni
  • STEFANO PIGNATTI MORANO DI CUSTOZA
  • SIMONE PASCUCCI
Lingua/e
  • inglese
AbstractThe 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.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine daArt.3665-1
Pagine aArt.3665-7
Pagine totali7
RivistaRemote sensing (Basel)
Attiva dal 2009
Editore: Molecular Diversity Preservation International - Basel
Lingua: inglese
ISSN: 2072-4292
Titolo chiave: Remote sensing (Basel)
Titolo proprio: Remote sensing. (Basel)
Titolo abbreviato: Remote sens. (Basel)
Numero volume della rivista12
Fascicolo della rivista21
DOI10.3390/rs12213665
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000589408300001)
  • Scopus (Codice:2-s2.0-85096078192)
Parole chiavehyperspectral remote sensing for soil and crops in agriculture, hyperspectral imaging for vegetation, plant traits, high-resolution spectroscopy for agricultural soils and vegetation, hyperspectral databases for agricultural soils and vegetation, hyperspectral data as input for modelling soil, crop, and vegetation, product validation, new hyperspectral technologies, future hyperspectral missions
Link (URL, URI)https://www.mdpi.com/2072-4292/12/21/3665
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • IMAA — Istituto di metodologie per l'analisi ambientale
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
Allegati
Special issue "hyperspectral remote sensing of agriculture and vegetation" (documento privato )
Tipo documento: application/pdf