The structure of vegetation is paramount in regulating the exchange of mass and energy across the biosphereatmosphere
interface. In particular, changes in vegetation density affected the partitioning of incoming solar
energy into sensible and latent heat fluxes that may result in persistent drought through reductions in agricultural
productivity and in the water resources availability. Limited research with citrus orchards has shown
improvements to irrigation scheduling due to better water-use estimation and more appropriate timing of
irrigation when crop coefficient (Kc) estimate, derived from remotely sensed multispectral vegetation indices
(VIs), are incorporated into irrigation-scheduling algorithms.
The purpose of this article is the application of an empirical reflectance-based model for the estimation of Kc and
evapotranspiration fluxes (ET) using ground observations on climatic data and high-resolution VIs from ASTER
TERRA satellite imagery. The remote sensed Kc data were used in developing the relationship with the
normalized difference vegetation index (NDVI) for orange orchards during summer periods. Validation of remote
sensed data on ET, Kc and vegetation features was deal through ground data observations and the resolution of the
energy balance to derive latent heat flux density (λE), using measures of net radiation (Rn) and soil heat flux
density (G) and estimate of sensible heat flux density (H) from high frequency temperature measurements
(Surface Renewal technique).
The chosen case study is that of an irrigation area covered by orange orchards located in Eastern Sicily (Italy)
during the irrigation seasons 2005 and 2006.
With the aim to derive crop water requirements (ETp) for an irrigated area covered by orange orchard in Sicily, Quick Bird and ASTER TERRA high resolution satellites data were used and compared with reference to their different spatial and spectral resolution. Satellites data allowed to improve the monitoring of canopy development in the irrigated area by identifying biophysical vegetation variable (LAI, albedo, vegetation indicators, etc); this information was successively used for the evaluation of maximum crop water needs by means of the well known Penman-Monteith equation. The paper results evidence the importance of very-high resolution sensors such as QuickBird in areas characterised by strong spatial heterogeneity. The algorithms applied to estimate the canopy parameters and the crop water requirements were applied by considering different levels of radiometric calibration of the satellite data, which produced marked
differences in the final results.