Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloSoil moisture mapping using Sentinel-1 images: Algorithm and preliminary validation
Anno di pubblicazione2013
FormatoElettronico
Autore/iPaloscia S.; Pettinato S.; Santi E.; Notarnicola C.; Pasolli L.; Reppucci A.
Affiliazioni autoriInstitute of Applied Physics, National Research Council (IFAC-CNR), Via Madonna del Piano, 10, 50019 Florence, Italy; EURAC Research, Bolzano, Italy; Starlab., Barcelona, Spain
Autori CNR e affiliazioni
  • EMANUELE SANTI
  • SIMONE PETTINATO
  • SIMONETTA PALOSCIA
Lingua/e
  • inglese
AbstractThe main objective of this research is to develop, test and validate a soil moisture content (SMC) algorithm for GMES Sentinel-1 characteristics. The SMC product, which is to be generated from Sentinel-1 data, requires an algorithm capable of processing operationally in near-real-time and delivering the product to the GMES services within 3. h from observation. An approach based on an Artificial Neural Network (ANN) has been proposed that represents a good compromise between retrieval accuracy and processing time, thus enabling compliance with the timeliness requirements. The algorithm has been tested and subsequently validated in several test areas in Italy, Australia, and Spain.In all cases the validation results were very much in line with GMES requirements (with RMSE generally <. 4%SMC - between 1.67%SMC and 6.68%SMC - and very low bias), except for the case of the test area in Spain, where the validation results were penalized by the availability of only VV polarized SAR images and MODIS low-resolution NDVI. Nonetheless, the obtained RMSE was slightly higher than 4%SMC. © 2013 Elsevier Inc.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da234
Pagine a248
Pagine totali-
RivistaRemote sensing of environment
Attiva dal 1969
Editore: American Elsevier Pub. Co., - New York,
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 0034-4257
Titolo chiave: Remote sensing of environment
Titolo proprio: Remote sensing of environment.
Titolo abbreviato: Remote sens. environ.
Numero volume della rivista134
Fascicolo della rivista-
DOI10.1016/j.rse.2013.02.027
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-84876317031)
Parole chiaveArtificial Neural Network, Backscattering coefficient, Inversion algorithms, Sentinel-1, Soil moisture maps
Link (URL, URI)http://www.scopus.com/inward/record.url?eid=2-s2.0-84876317031&partnerID=q2rCbXpz
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Licenza-
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Strutture CNR
  • IFAC — Istituto di fisica applicata "Nello Carrara"
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
Allegati