Monitoring flood evolution in agricultural areas using COSMO-SkyMed data: analysis of the Tuscany inundation of December 2009

被引:2
|
作者
Pulvirenti, Luca [1 ]
Pierdicca, Nazzareno [1 ]
Chini, Marco [2 ]
Guerriero, Leila [3 ]
机构
[1] Sapienza Univ Rome, Dept Informat Engn Elect & Telecommun, Via Eudossiana 18, I-00184 Rome, Italy
[2] Ist Nazl Geofis & Vulcanol, Rome, Italy
[3] TorVergata Univ Rome, Dept Comp Sci Syst & Prod, Rome, Italy
关键词
SAR; COSMO-SkyMed; floods; multitemporal analysis; SYNTHETIC-APERTURE RADAR; ACTIVE CONTOUR MODEL; SAR; DELINEATION; VEGETATION;
D O I
10.1117/12.898209
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Synthetic Aperture Radar (SAR) systems represent the most powerful tool to monitor flood events because of their all-weather capability that allows them to collect suitable images even in cloudy conditions. The quality of the flood monitoring using SAR is increasing thanks to the improved spatial resolution of the new generation of instruments and to the short revisit time of the present and future satellite constellations. To fully exploit these technological advances, the methods to interpret images and produce flood maps must be upgraded, so that an accurate interpretation of the multitemporal radar signature, accounting for system parameters (frequency, polarization, incidence angle) and land cover, becomes very important. The images collected by the COSMO-SkyMed constellation of X-band radars represent an example of the aforesaid technological advances. This paper presents a case study regarding a flood occurred in Tuscany (Central Italy) in 2009 monitored using COSMO-SkyMed data. It is shown that the interpretation of the radar data is not straightforward, especially in the presence of vegetation and should rely on the knowledge about the radar scattering mechanisms implemented into electromagnetic models. The paper discusses the multitemporal radar signatures observed during the event and describes the approach we have followed to account for the electromagnetic background into a semi-automatic data processing system.
引用
收藏
页数:12
相关论文
共 21 条
  • [21] SWE retrieval in Alpine areas with high-resolution COSMO-SkyMed X-band SAR data using Artificial Neural Networks and Support Vector Regression techniques
    Santi, Emanuele
    Paloscia, Simonetta
    Pettinato, Simone
    De Gregorio, Ludovica
    Cuozzo, Giovanni
    Jacob, Alexander
    Notarnicola, Claudia
    Cigna, Francesca
    Tapete, Deodato
    2020 XXXIIIRD GENERAL ASSEMBLY AND SCIENTIFIC SYMPOSIUM OF THE INTERNATIONAL UNION OF RADIO SCIENCE, 2020,