Abstract
The aim of the “RedScale ID” research program which is funded by the CRPF is to develop an automated, energy-independent trap for continuous monitoring/identification of the RedScale population and in addition to record other relevant parameters related to the population. Such kind of parameter is temperature which is related to day-degrees growth calculation for estimating RedScale population. For this purpose MODIS satellite image data were used in order to estimate LST and then to correlate with air surface temperature. The use of satellite data has many advantages against traditional techniques such as local meteorological stations located in agricultural areas, since they can provide a synoptic coverage on a systematic daily basis. From the statistical regression analysis between the satellite images MODIS LST product for the year 2010 with data from 47 meteorological stations in Cyprus, it has been found that satellite images can accurate estimate air surface temperature. From the linear regression model, a high correlation coefficient R2 ≈ 0.77 was found. By categorizing observations acquired at an altitude of 100–200 m the correlation coefficient was improved up to 0.87. Seasonal regression analysis will be performed in the near future.
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Acknowledgments
“RedScale ID” project is funded by the Cyprus Promotion Research Foundation. Thanks are given to the Remote Sensing Laboratory of the Department of Civil Engineering & Geomatics at the Cyprus University of Technology for the support (http://www.cut.ac.cy/) and the Meteorological Service of Cyprus for providing climate data.
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Hadjimitsis, D.G., Agapiou, A., Papachristodoulou, A. (2013). MODIS Data for Monitoring RedScale (Aonidiella aurantii) Population: The Development of a Regression Model Using Temperature Measurements from Satellite and Meteorological Stations. In: Helmis, C., Nastos, P. (eds) Advances in Meteorology, Climatology and Atmospheric Physics. Springer Atmospheric Sciences. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29172-2_17
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DOI: https://doi.org/10.1007/978-3-642-29172-2_17
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