Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloToward Global Soil Moisture Monitoring With Sentinel-1: Harnessing Assets and Overcoming Obstacles
Anno di pubblicazione2019
Formato-
Autore/iBauer-Marschallinger B.; Freeman V.; Cao S.; Paulik C.; Schaufler S.; Stachl T.; Modanesi S.; Massari C.; Ciabatta L.; Brocca L.; Wagner W.
Affiliazioni autoriDepartment of Geodesy and Geoinformation, Vienna University of Technology, 1040 Vienna, Austria (e-mail:bbm@geo.tuwien.ac.at)., ; Department of Geodesy and Geoinformation, Vienna University of Technology, 1040 Vienna, Austria., ; Research Institute for Geo-Hydrological Protection, National Research Council, 06128 Perugia, Italy.,
Autori CNR e affiliazioni
  • SARA MODANESI
  • LUCA BROCCA
  • CHRISTIAN MASSARI
  • LUCA CIABATTA
Lingua/e
  • inglese
AbstractSoil moisture is a key environmental variable, important to, e.g., farmers, meteorologists, and disaster management units. Here, we present a method to retrieve surface soil moisture (SSM) from the Sentinel-1 (S-1) satellites, which carry C-band Synthetic Aperture Radar (CSAR) sensors that provide the richest freely available SAR data source so far, unprecedented in accuracy and coverage. Our SSM retrieval method, adapting well-established change detection algorithms, builds the first globally deployable soil moisture observation data set with 1-km resolution. This paper provides an algorithm formulation to be operated in data cube architectures and high-performance computing environments. It includes the novel dynamic Gaussian upscaling method for spatial upscaling of SAR imagery, harnessing its field-scale information and successfully mitigating effects from the SAR's high signal complexity. Also, a new regression-based approach for estimating the radar slope is defined, coping with Sentinel-1's inhomogeneity in spatial coverage. We employ the S-1 SSM algorithm on a 3-year S-1 data cube over Italy, obtaining a consistent set of model parameters and product masks, unperturbed by coverage discontinuities. An evaluation of therefrom generated S-1 SSM data, involving a 1-km soil water balance model over Umbria, yields high agreement over plains and agricultural areas, with low agreement over forests and strong topography. While positive biases during the growing season are detected, the excellent capability to capture small-scale soil moisture changes as from rainfall or irrigation is evident. The S-1 SSM is currently in preparation toward operational product dissemination in the Copernicus Global Land Service.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da-
Pagine a-
Pagine totali-
RivistaIEEE transactions on geoscience and remote sensing
Attiva dal 1980
Editore: Institute of Electrical and Electronics Engineers, - New York, N.Y.
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 0196-2892
Titolo chiave: IEEE transactions on geoscience and remote sensing
Titolo proprio: IEEE transactions on geoscience and remote sensing
Titolo abbreviato: IEEE trans. geosci. remote sens.
Titoli alternativi:
  • Institute of Electrical and Electronics Engineers transactions on geoscience and remote sensing
  • I.E.E.E. transactions on geoscience and remote sensing
  • Transactions on geoscience and remote sensing
Numero volume della rivista-
Fascicolo della rivista-
DOI10.1109/TGRS.2018.2858004
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-85052707709)
Parole chiavesoil moisture
Link (URL, URI)http://www.scopus.com/record/display.url?eid=2-s2.0-85052707709&origin=inward
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • IRPI — Istituto di ricerca per la protezione idrogeologica
Moduli/Attività/Sottoprogetti CNR
  • DTA.AD003.076.001 : Modelli di processi geo-idrologici
Progetti Europei-
Allegati
documentoPDF (documento privato )
Tipo documento: application/pdf