Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloIntegration of microwave data from SMAP and AMSR2 for soil moisture monitoring in Italy
Anno di pubblicazione2018
FormatoElettronico
Autore/iSanti E.; Paloscia S.; Pettinato S.; Brocca L.; Ciabatta L.; Entekhabi D.
Affiliazioni autoriInstitute of Applied Physics, National Research Council, Florence, , Italy; Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, , Italy; Massachusetts Institute of Technology, Parsons Laboratory, Cambridge, , United States
Autori CNR e affiliazioni
  • EMANUELE SANTI
  • LUCA BROCCA
  • SIMONE PETTINATO
  • SIMONETTA PALOSCIA
  • LUCA CIABATTA
Lingua/e
  • inglese
AbstractIn this study, an integration of microwave data obtained from the SMAP and AMSR2 satellite radiometers has been attempted, to achieve an accurate estimation of the Soil Moisture Content (SMC). This research aimed to overcome the failure of radar sensor in SMAP satellite as well as the failure to generate the radar/radiometer combined SMC product at a spatial resolution of 9 km×9 km. A disaggregation technique, based on the Smoothing Filter based Intensity Modulation (SFIM), enabled us to obtain co-located SMAP and AMSR2 brightness measurements at L, C, X, Ku and Ka bands at approximately 10 km×10 km on the selected test area, which corresponds to the entire Italian territory. These disaggregated microwave data were used as inputs of the "HydroAlgo" retrieval algorithm based on Artificial Neural Networks (ANN), which were able to exploit the synergy between radiometric acquisitions from these two sensors. The algorithm was defined, implemented and tested using all the overlapping orbits of SMAP and AMSR2 over Italy throughout the 9_month period between April and December 2015. Distributed SMC reference values for implementing and validating the algorithm were obtained from the Soil Water Balance hydrological model, SWBM. Through HydroAlgo, an SMC product at a resolution of approximately 10 km×10 km was obtained. This result is close to the original Radar/Radiometer SMC product from SMAP, with an average correlation coefficient R > 0.75 and RMSE ? 0.03m3/m3, in both ascending and descending orbits.In this study, an integration of microwave data obtained from the SMAP and AMSR2 satellite radiometers has been attempted, to achieve an accurate estimation of the Soil Moisture Content (SMC). This research aimed to overcome the failure of radar sensor in SMAP satellite as well as the failure to generate the radar/radiometer combined SMC product at a spatial resolution of 9 km×9 km. A disaggregation technique, based on the Smoothing Filter based Intensity Modulation (SFIM), enabled us to obtain co-located SMAP and AMSR2 brightness measurements at L, C, X, Ku and Ka bands at approximately 10 km×10 km on the selected test area, which corresponds to the entire Italian territory. These disaggregated microwave data were used as inputs of the "HydroAlgo" retrieval algorithm based on Artificial Neural Networks (ANN), which were able to exploit the synergy between radiometric acquisitions from these two sensors. The algorithm was defined, implemented and tested using all the overlapping orbits of SMAP and AMSR2 over Italy throughout the 9_month period between April and December 2015. Distributed SMC reference values for implementing and validating the algorithm were obtained from the Soil Water Balance hydrological model, SWBM. Through HydroAlgo, an SMC product at a resolution of approximately 10 km×10 km was obtained. This result is close to the original Radar/Radiometer SMC product from SMAP, with an average correlation coefficient R > 0.75 and RMSE ? 0.03m3/m3, in both ascending and descending orbits.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da21
Pagine a30
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 rivista212
Fascicolo della rivista-
DOI10.1016/j.rse.2018.04.039
Verificato da referee-
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-85046148572)
Parole chiaveSoil Moisture Content, SMAP, AMSR2, Retrieval Algorithm, Artificial Neural Network
Link (URL, URI)http://www.scopus.com/record/display.url?eid=2-s2.0-85046148572&origin=inward
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • IFAC — Istituto di fisica applicata "Nello Carrara"
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
Allegati
Integration of microwave data from SMAP and AMSR2 for soil moisturemonitoring in Italy (documento privato )
Descrizione: PDF of published paper
Tipo documento: application/pdf