An automatic soil moisture retrieval algorithm from Synthetic Aperture Radar (SAR) over agricultural bare and vegetated fields is investigated. The work is carried out in the framework of the CLEXIDRA project funded by the Italian Space Agency (ASI).Soil moisture retrieval is based on (i) multi-frequency and polarimetric SAR data in L- (SAOCOM), X- (COSMO-SkyMed both First and Second generation) and C-band (Sentinel-1) integration (ii) bare and vegetated soil scattering models inversion; (iii) Bayesian minimization and machine learning techniques; (iv) biomass estimation from hyperspectral and multi-spectral electro-optical data; (v) ground-truth data collected over crop fields located in Argentina (Monte Buey) and in Northern Italy (Jolanda di Savoia). The proposed soil moisture detection algorithm is implemented for precision agricultural applications, thus enabling SAR products of soil moisture in agricultural irrigation management systems, as well as in global weather models for environmental monitoring purposes.

CLEXIDRA: soil moisture retrieval on crop fields by integration of multi-source Earth observation data and modeling / Gentile, V.; Pieroni, N.; Frezzotti, M.; Tricomi, A.; Anconitano, G.; Siad, S. M.; Pierdicca, N.; Comite, D.; Vittucci, C.; Papale, L. G.; Guerriero, L.; Casa, R.; Marrone, L.; Cillis, D.; Campi, M.; Lenti, F.; Sacco, P.; Virelli, M.; Tapete, D.. - (2024), pp. 342-346. (Intervento presentato al convegno 2024 IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS) tenutosi a Oran; Algeria) [10.1109/m2garss57310.2024.10537555].

CLEXIDRA: soil moisture retrieval on crop fields by integration of multi-source Earth observation data and modeling

Anconitano, G.;Siad, S. M.;Pierdicca, N.;Comite, D.;
2024

Abstract

An automatic soil moisture retrieval algorithm from Synthetic Aperture Radar (SAR) over agricultural bare and vegetated fields is investigated. The work is carried out in the framework of the CLEXIDRA project funded by the Italian Space Agency (ASI).Soil moisture retrieval is based on (i) multi-frequency and polarimetric SAR data in L- (SAOCOM), X- (COSMO-SkyMed both First and Second generation) and C-band (Sentinel-1) integration (ii) bare and vegetated soil scattering models inversion; (iii) Bayesian minimization and machine learning techniques; (iv) biomass estimation from hyperspectral and multi-spectral electro-optical data; (v) ground-truth data collected over crop fields located in Argentina (Monte Buey) and in Northern Italy (Jolanda di Savoia). The proposed soil moisture detection algorithm is implemented for precision agricultural applications, thus enabling SAR products of soil moisture in agricultural irrigation management systems, as well as in global weather models for environmental monitoring purposes.
2024
2024 IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS)
SAR; hyperspectral; backscattering model; machine learning; ANN; soil moisture
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
CLEXIDRA: soil moisture retrieval on crop fields by integration of multi-source Earth observation data and modeling / Gentile, V.; Pieroni, N.; Frezzotti, M.; Tricomi, A.; Anconitano, G.; Siad, S. M.; Pierdicca, N.; Comite, D.; Vittucci, C.; Papale, L. G.; Guerriero, L.; Casa, R.; Marrone, L.; Cillis, D.; Campi, M.; Lenti, F.; Sacco, P.; Virelli, M.; Tapete, D.. - (2024), pp. 342-346. (Intervento presentato al convegno 2024 IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS) tenutosi a Oran; Algeria) [10.1109/m2garss57310.2024.10537555].
File allegati a questo prodotto
File Dimensione Formato  
Gentile_CLEXIDRA_2024.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 1.41 MB
Formato Adobe PDF
1.41 MB Adobe PDF   Contatta l'autore
Gentile_CLEXIDRA_Indice_2024.pdf

solo gestori archivio

Note: Indice
Tipologia: Altro materiale allegato
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 1.03 MB
Formato Adobe PDF
1.03 MB Adobe PDF   Contatta l'autore
Gentile_CLEXIDRA_Frontespizio_2024.pdf

solo gestori archivio

Note: Frontespizio
Tipologia: Altro materiale allegato
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 1.05 MB
Formato Adobe PDF
1.05 MB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1710736
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact