Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloRemote Sensing of Forest Biomass Using GNSS Reflectometry
Anno di pubblicazione2020
FormatoElettronico
Autore/iSanti, Emanuele; Paloscia, Simonetta; Pettinato, Simone; Fontanelli, Giacomo; Clarizia, Maria Paola; Comite, Davide; Dente, Laura; Guerriero, Leila; Pierdicca, Nazzareno; Floury, Nicolas
Affiliazioni autoriCNR; Deimos Space UK; Univ Roma La Sapienza; Univ Roma Tor Vergata; European Space Agcy
Autori CNR e affiliazioni
  • EMANUELE SANTI
  • SIMONE PETTINATO
  • SIMONETTA PALOSCIA
  • GIACOMO FONTANELLI
Lingua/e
  • inglese
AbstractIn this study, the capability of Global Navigation Satellite System Reflectometry in evaluating forest biomass from space has been investigated by using data coming from the TechDemoSat-1 (TDS-1) mission of Surrey Satellite Technology Ltd. and from the Cyclone Satellite System (CyGNSS) mission of NASA. The analysis has been first conducted using TDS-1 data on a local scale, by selecting five test areas located in different parts of the Earth's surface. The areas were chosen as examples of various forest coverages, including equatorial and boreal forests. Then, the analysis has been extended by using CyGNSS to a global scale, including any type of forest coverage. The peak of the Delay Doppler Map calibrated to retrieve an "equivalent" reflectivity has been exploited for this investigation and its sensitivity to forest parameters has been evaluated by a direct comparison with vegetation optical depth (VOD) derived from the Soil Moisture Active Passive L-band radiometer, with a pantropical aboveground biomass (AGB) map and then with a tree height (H) global map derived from the Geoscience Laser Altimeter System installed on-board the ICEsat satellite. The sensitivity analysis confirmed the decreasing trend of the observed equivalent reflectivity for increasing biomass, with correlation coefficients 0.31 <= R <= 0.54 depending on the target parameter (VOD, AGB, or H) and on the considered dataset (local or global). These correlations were not sufficient to retrieve the target parameters by simple inversion of the direct relationships. The retrieval has been therefore based on Artificial Neural Networks making it possible to add other inputs (e.g., the incidence angle, the signal to noise ratio, and the lat/lon information in case of global maps) to the algorithm. Although not directly correlated to the biomass, these inputs helped in improving the retrieval accuracy. The algorithm was tested on both the selected areas and globally, showing a promising ability to retrieve the target parameter, either AGB or H, with correlation coefficients R similar or equal to 0.8.
Lingua abstractinglese
Altro abstract-
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Pagine da2351
Pagine a2368
Pagine totali18
RivistaIEEE journal of selected topics in applied earth observations and remote sensing (Print)
Attiva dal 2008
Editore: IEEE, - Piscataway, N.J.
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 1939-1404
Titolo chiave: IEEE journal of selected topics in applied earth observations and remote sensing (Print)
Titolo proprio: IEEE journal of selected topics in applied earth observations and remote sensing. (Print)
Titolo abbreviato: IEEE j. sel. top. appl. earth obs. remote sens. (Print)
Titoli alternativi:
  • Institute of Electrical and Electronic Engineers journal of selected topics in applied earth observations and remote sensing (Print)
  • Journal of selected topics in applied earth observations and remote sensing (Print)
  • J-STARS (Print)
Numero volume della rivista13
Fascicolo della rivista-
DOI10.1109/JSTARS.2020.2982993
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000542949600017)
Parole chiaveBiomass, Forestry, Vegetation mapping, Sensitivity, Global navigation satellite system, Soil, Reflectivity, Artificial neural networks (ANNs), Cyclone Satellite System (CyGNSS), forest biomass, Global Navigation Satellite System (GNSS) Reflectometry, Soil Moisture Active Passive (SMAP), TechDemoSat-1 (TDS-1)
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Licenza-
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Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • IFAC — Istituto di fisica applicata "Nello Carrara"
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
Allegati