Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloEstimating Wheat Yield in China at the Field and District Scale from the Assimilation of Satellite Data into the Aquacrop and Simple Algorithm for Yield (SAFY) Models
Anno di pubblicazione2017
Formato
  • Elettronico
  • Cartaceo
Autore/iPaolo Cosmo Silvestro 1, Stefano Pignatti 2, Simone Pascucci 2, Hao Yang 3, Zhenhai Li 3, Guijun Yang 3, Wenjiang Huang 4 and Raffaele Casa
Affiliazioni autori1.DAFNE, Università della Tuscia, Via San Camillo de Lellis, 01100 Viterbo, Italy 2.Consiglio Nazionale delle Ricerche--Institute of Methodologies for Environmental Analysis (C.N.R.--IMAA), C.da S.Loja 85050 Tito Scalo (PZ), Italy 3.Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China 4.Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
Autori CNR e affiliazioni
  • STEFANO PIGNATTI MORANO DI CUSTOZA
  • SIMONE PASCUCCI
Lingua/e
  • inglese
AbstractAccurate yield estimation at the field scale is essential for the development of precision agriculture management, whereas at the district level it can provide valuable information for supply chain management. In this paper, Huan Jing (HJ) satellite HJ1A/B and Landsat 8 Operational Land Imager (OLI) images were employed to retrieve leaf area index (LAI) and canopy cover (CC) in the Yangling area (Central China). These variables were then assimilated into two crop models, Aquacrop and simple algorithm for yield (SAFY), in order to compare their performances and practicalities. Due to the models' specificities and computational constraints, different assimilation methods were used. For SAFY, the ensemble Kalman filter (EnKF) was applied using LAI as the observed variable, while for Aquacrop, particle swarm optimization (PSO) was used, using canopy cover (CC). These techniques were applied and validated both at the field and at the district scale. In the field application, the lowest relative root-mean-square error (RRMSE) value of 18% was obtained using EnKF with SAFY. On a district scale, both methods were able to provide production estimates in agreement with data provided by the official statistical offices. From an operational point of view, SAFY with the EnKF method was more suitable than Aquacrop with PSO, in a data assimilation context.
Lingua abstractinglese
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Pagine totali24
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 rivista9
Fascicolo della rivista5
DOI10.3390/rs9050509
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000402573700112)
  • Scopus (Codice:2-s2.0-85019892562)
Parole chiaveleaf area index (LAI), canopy cover (CC), Landsat 8, HJ1A/B, artificial neural network (ANN), ensemble Kalman filter (EnKF), particle swarm optimization (PSO)
Link (URL, URI)http://www.mdpi.com/2072-4292/9/5/509
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Strutture CNR
  • IMAA — Istituto di metodologie per l'analisi ambientale
Moduli/Attività/Sottoprogetti CNR
  • TA.P06.015.002 : Infrastruttura integrata per l'Osservazione della Terra da piattaforma aerea
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