Skip to Main Content (Press Enter)

Logo CNR
  • ×
  • Home
  • Persone
  • Pubblicazioni
  • Strutture
  • Competenze

UNI-FIND
Logo CNR

|

UNI-FIND

cnr.it
  • ×
  • Home
  • Persone
  • Pubblicazioni
  • Strutture
  • Competenze
  1. Pubblicazioni

COSMO-SkyMed Image Investigation of Snow Features in Alpine Environment

Articolo
Data di Pubblicazione:
2017
Abstract:
In this work, X band images acquired by COSMO-SkyMed (CSK) on alpine environment have been analyzed for investigating snow characteristics and their effect on backscattering variations. Preliminary results confirmed the capability of simultaneous optical and Synthetic Aperture Radar (SAR) images (Landsat-8 and CSK) in separating snow/no-snow areas and in detecting wet snow.
The sensitivity of backscattering to snow depth has not always been confirmed, depending on snow characteristics related to the season. A model based on Dense Media Radiative Transfer theory (DMRT-QMS) was applied for simulating the backscattering response on the X band from snow cover in different conditions of grain size, snow density and depth. By using DMRT-QMS and snow in-situ data collected on Cordevole basin in Italian Alps, the effect of grain size and snow density, beside snow depth and snow water equivalent, was pointed out, showing that the snow features affect the backscatter in different and sometimes opposite ways. Experimental values of backscattering were correctly simulated by using this model and selected intervals of ground parameters. The relationship between simulated and measured backscattering for the entire dataset shows slope >0.9, determination coefficient, R2 = 0.77, and root mean square error, RMSE = 1.1 dB, with p-value <0.05.
Tipologia CRIS:
01.01 Articolo in rivista
Keywords:
COSMO-SkyMed; Snow Depth; backscattering; electromagnetic model; DMRT-QMS
Elenco autori:
Santi, Emanuele; Pettinato, Simone; Paloscia, Simonetta
Autori di Ateneo:
PALOSCIA SIMONETTA
PETTINATO SIMONE
SANTI EMANUELE
Link alla scheda completa:
https://iris.cnr.it/handle/20.500.14243/331178
Pubblicato in:
SENSORS (BASEL)
Journal
  • Dati Generali

Dati Generali

URL

http://www.mdpi.com/1424-8220/17/1/84/html
  • Utilizzo dei cookie

Realizzato con VIVO | Designed by Cineca | 26.5.2.0 | Sorgente dati: PREPROD (Ribaltamento disabilitato)