This paper aims to highlight the benefits from the integration of wireless sensor network / meteorological data and remote sensing for monitoring and determine irrigation demand in Cyprus. Estimating evapotranspiration in Cyprus will help, in taking measures for an effective irrigation water management in the future in the island. For this purpose both multi-spectral satellite images (Landsat 7 ETM+ and ASTER) and hydro-meteorological data from wireless sensors and automatic meteorological stations have been used. The wireless sensor network, which consist approximately twenty wireless nodes, was placed in our case study. The wireless sensor network acts as a wide area distributed data collection system deployed to collect and reliably transmit soil and air environmental data to a remote base-station hosted at Cyprus University of Technology. Furthermore auxiliary meteorological field data, from an automatic meteorological station, nearby our case study, where used such as solar radiation, air temperature, air humidity and wind speed. These data were used in conjunction with remote sensing results. Satellite images where used in ERDAS Imagine Software after the necessary processing: geometric rectification, radiometric calibration and atmospheric corrections. The satellite images were atmospheric corrected and calibrated using spectro-radiometers and sun-photometers measurements taken in situ, in an agricultural area, south-west of the island of Cyprus. Evapotranspiration is difficult to determine since it combines various meteorological and field parameters while in literature quite many different models for estimating ET are indicated. For estimating evapotranspiration from satellite images and the hydro-meteorological data different methods have been evaluated such as FAO Penman-Monteith, Carlson-Buffum and Granger methods. These results have been compared with E-pan methods. Finally a water management irrigation schedule has been applied. The final results are presented and compared with some conclusion remarks.

Date Published: 18 September 2009
PDF: 8 pages
Proc. SPIE 7472, Remote Sensing for Agriculture, Ecosystems, and Hydrology XI, 74720F (18 September 2009); doi: 10.1117/12.830554

Published in SPIE Proceedings Vol. 7472:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XI
Christopher M. U. Neale; Antonino Maltese, Editor(s)