In this paper, the potential of space-borne Synthetic Aperture Radar (SAR) sensors combined with optical ones has been exploited by analyzing datasets collected on two vegetated areas in Italy, by using COSMO-SkyMed X-band and Sentinel-1 C-band SAR, PRISMA hyperspectral and Sentinel-2 multispectral imagery, combined with field measurements acquired with spectroradiometers. On the mountain area in Alto Adige, a biomass estimation approach was developed by combining Sentinel-1 SAR and spectroradiometer hyperspectral data. On Val d'Elsa area in Tuscany, COSMO-SkyMed StripMap HIMAGE and Sentinel-1 Interferometric Wide swath mode SAR data have been integrated with Sentinel-2 imagery for improving the classification of agricultural crops. Convolutional Neural Networks (CNN) have been used for the classification of agricultural areas using these three sensors.
SAR multi-frequency observations of vegetation in agricultural and mountain areas
Paloscia S;Fontanelli G;Lapini A;Santi E;Pettinato S;Cigna F
2020
Abstract
In this paper, the potential of space-borne Synthetic Aperture Radar (SAR) sensors combined with optical ones has been exploited by analyzing datasets collected on two vegetated areas in Italy, by using COSMO-SkyMed X-band and Sentinel-1 C-band SAR, PRISMA hyperspectral and Sentinel-2 multispectral imagery, combined with field measurements acquired with spectroradiometers. On the mountain area in Alto Adige, a biomass estimation approach was developed by combining Sentinel-1 SAR and spectroradiometer hyperspectral data. On Val d'Elsa area in Tuscany, COSMO-SkyMed StripMap HIMAGE and Sentinel-1 Interferometric Wide swath mode SAR data have been integrated with Sentinel-2 imagery for improving the classification of agricultural crops. Convolutional Neural Networks (CNN) have been used for the classification of agricultural areas using these three sensors.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.