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Air surface temperature is an important parameter for a wide range of applications such as agriculture, hydrology and
climate change studies. Air temperature data is usually obtained from measurements made in meteorological stations,
providing only limited information about spatial patterns over wide areas. The use of remote sensing data can help
overcome this problem, particularly in areas with low station density, having the potential to improve the estimation of
air surface temperature at both regional and global scales. Land Surface (skin) Temperatures (LST) derived from
Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra and Aqua satellite platforms provide
spatial estimates of near-surface temperature values. In this study, LST values from MODIS are compared to groundbased
near surface air (Tair) measurements obtained from 14 observational stations during 2011 to 2015, covering
coastal, mountainous and urban areas over Cyprus. Combining Terra and Aqua LST-8 Day and Night acquisitions into a
mean monthly value, provide a large number of LST observations and a better overall agreement with Tair. Comparison
between mean monthly LSTs and mean monthly Tair for all sites and all seasons pooled together yields a very high
correlation and biases. In addition, the presented high standard deviation can be explained by the influence of surface
heterogeneity within MODIS 1km2 grid cells, the presence of undetected clouds and the inherent difference between
LST and Tair. However, MODIS LST data proved to be a reliable proxy for surface temperature and mostly for studies
requiring temperature reconstruction in areas with lack of observational stations.
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