Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloCombining Machine Learning and Compact Polarimetry for Estimating Soil Moisture from C-Band SAR Data
Anno di pubblicazione2019
FormatoElettronico
Autore/iSanti, Emanuele; Dabboor, Mohammed; Pettinato, Simone; Paloscia, Simonetta
Affiliazioni autoriCNR IFAC; Govt Canada
Autori CNR e affiliazioni
  • EMANUELE SANTI
  • SIMONE PETTINATO
  • SIMONETTA PALOSCIA
Lingua/e
  • inglese
AbstractThis research aimed at exploiting the joint use of machine learning and polarimetry for improving the retrieval of surface soil moisture content (SMC) from synthetic aperture radar (SAR) acquisitions at C-band. The study was conducted on two agricultural areas in Canada, for which a series of RADARSAT-2 (RS2) images were available along with direct measurements of SMC from in situ stations. The analysis confirmed the sensitivity of RS2 backscattering (sigma degrees) to SMC. The comparison of SMC with the compact polarimetry (CP) parameters, computed from the RS2 acquisitions by the CP data simulator, pointed out that some CP parameters had a sensitivity to SMC equal or better than sigma degrees, with correlation coefficients up to R similar or equal to 0.4. Based on these results, the potential of machine learning (ML) for SMC retrieval was exploited by implementing and testing on the available data an artificial neural network (ANN) algorithm. The algorithm was implemented using several combinations of sigma degrees and CP parameters. Validation results of the algorithm with in situ observations confirmed the promising capabilities of the ML techniques for SMC monitoring. Furthermore, results pointed out the potential of CP in improving the SMC retrieval accuracy, especially when used in combination with linearly polarized sigma degrees. Depending on the considered input combination, the ANN algorithm was able to estimate SMC with Root Mean Square Error (RMSE) between 3% and 7% of SMC and R between 0.7 and 0.9.
Lingua abstractinglese
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Pagine totali18
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 rivista11
Fascicolo della rivista20
DOI10.3390/rs11202451
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000498395800119)
Parole chiaveRADARSAT-2, agricultural areas, soil moisture retrieval, machine learning algorithms, compact polarimetry
Link (URL, URI)https://www.mdpi.com/2072-4292/11/20/2451
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Strutture CNR
  • IFAC — Istituto di fisica applicata "Nello Carrara"
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
Allegati
Combining Machine Learning and Compact Polarimetry for Estimating Soil Moisture from C-Band SAR Data (documento privato )
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