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GOFFREDO LA LOGGIA

On the influences of vegetation biomass on COSMO-Skymed X-band

  • Autori: Capodici, F.; Ciraolo, G.; D'Urso, G.; LA LOGGIA, G.; Maltese, A.
  • Anno di pubblicazione: 2011
  • Tipologia: Proceedings (TIPOLOGIA NON ATTIVA)
  • OA Link: http://hdl.handle.net/10447/60767

Abstract

The knowledge of spatial and temporal variability of land cover is important to manage water resources for yield forecasting, water stress prediction, irrigation water management and flood protection. Cloud cover dramatically reduces the temporal resolution of optical data thus limiting their operational use; in addition, the spatial resolution is often inadequate for applications in heterogeneous areas. On the other hand, algorithms based on Synthetic Aperture Radar (SAR) implemented to retrieve vegetation parameters are not yet fully validated. New SAR missions (COSMO-Skymed and Terrasar-X) may represent a suitable source of data for operational uses due to the high spatial and temporal resolution, although X band is not optimal for agricultural and hydrological applications. This paper reports the influence of soil-vegetation variables (especially biomass indices) on X-band COSMO-Skymed data using Ping Pong products. The study is carried out over two different sites: the SELE plain (in the south-eastern part of Campania, Italy) that is mainly characterized by herbaceous plants and tree crops; and the Campobello-Castelvetrano area (in the south-western part of Sicily, Italy) mainly covered by olive trees, vineyards and woods. The sensitivity analysis is performed by comparing vegetation indices (NDVI or LAI) derived by Landsat TM 5 and ETM+ 7 with COSMO-Skymed (CSK) images acquired in May-June 2010 within the project COSMOLAND (Use of COSMO-SkyMed SAR data for LAND cover classification and surface parameters retrieval over agricultural sites) funded by the Italian Space Agency (ASI). Sensitivity analysis results address to develop algorithms to retrieve vegetation biomass maps from CSK X band characterized by high temporal and spatial resolution.