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.File | Dimensione | Formato | |
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