Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloComparison between SAR Soil Moisture Estimates and Hydrological Model Simulations over the Scrivia Test Site
Anno di pubblicazione2013
Autore/iSanti, Emanuele; Paloscia, Simonetta; Pettinato, Simone; Notarnicola, Claudia; Pasolli, Luca; Pistocchi, Alberto
Affiliazioni autoriNatl Res Council IFAC CNR; EURAC Res; Gecosistema
Autori CNR e affiliazioni
  • inglese
AbstractIn this paper, the results of a comparison between the soil moisture content (SMC) estimated from C-band SAR, the SMC simulated by a hydrological model, and the SMC measured on ground are presented. The study was carried out in an agricultural test site located in North-west Italy, in the Scrivia river basin. The hydrological model used for the simulations consists of a one-layer soil water balance model, which was found to be able to partially reproduce the soil moisture variability, retaining at the same time simplicity and effectiveness in describing the topsoil. SMC estimates were derived from the application of a retrieval algorithm, based on an Artificial Neural Network approach, to a time series of ENVISAT/ASAR images acquired over the Scrivia test site. The core of the algorithm was represented by a set of ANNs able to deal with the different SAR configurations in terms of polarizations and available ancillary data. In case of crop covered soils, the effect of vegetation was accounted for using NDVI information, or, if available, for the cross-polarized channel. The algorithm results showed some ability in retrieving SMC with RMSE generally <0.04 m(3)/m(3) and very low bias (i.e., <0.01 m(3)/m(3)), except for the case of VV polarized SAR images: in this case, the obtained RMSE was somewhat higher than 0.04 m(3)/m(3) (0.058 m(3)/m(3)). The algorithm was implemented within the framework of an ESA project concerning the development of an operative algorithm for the SMC retrieval from Sentinel-1 data. The algorithm should take into account the GMES requirements of SMC accuracy (5% in volume), spatial resolution (1 km) and timeliness (3 h from observation). The SMC estimated by the SAR algorithm, the SMC estimated by the hydrological model, and the SMC measured on ground were found to be in good agreement. The hydrological model simulations were performed at two soil depths: 30 and 5 cm and showed that the 30 cm simulations indicated, as expected, SMC values higher than the satellites estimates, with RMSE higher than 0.08 m(3)/m(3). In contrast, in the 5-cm simulations, the agreement between hydrological simulations, satellite estimates and ground measurements could be considered satisfactory, at least in this preliminary comparison, showing a RMSE ranging from 0.054 m(3)/m(3) to 0.051 m(3)/m(3) for comparison with ground measurements and SAR estimates, respectively.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da4961
Pagine a4976
Pagine totali16
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 rivista5
Fascicolo della rivista10
Verificato da refereeSì: Internazionale
Stato della pubblicazione-
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000328614900012)
Parole chiaveSAR data, soil moisture, hydrological model, Artificial Neural Networks, inversion algorithms
Link (URL, URI)-
Titolo parallelo-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • IFAC — Istituto di fisica applicata "Nello Carrara"
Moduli/Attività/Sottoprogetti CNR
  • TA.P06.007.001 : Telerilevamento a microonde con sensori attivi e passivi
Progetti Europei-