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.
The temperature of the sea surface has been identified as an important parameter of the natural environment, governing processes that occur in the upper ocean. This paper focuses on the analysis of the Sea Surface Temperature (SST) anomalies at the greater area of Cyprus. For that, SST data derived from MODerate-resolution Imaging Spectroradiometer (MODIS) instrument on board both Aqua and Terra sun synchronous satellites were used. A four year period was chosen as a first approach to address and describe this phenomenon. Geographical Information Systems (GIS) has been used as an integrated platform of analysis and presentation in addition of the support of MATLAB®. The methodology consists of five steps: (i) Collection of MODIS SST imagery, (ii) Development of the digital geo-database; (iii) Model and run the methodology in GIS as a script; (iv) Calculation of SST anomalies; and (v) Visualization of the results.
The SST anomaly values have presented a symmetric distribution over the study area with an increase trend through the years of analysis. The calculated monthly and annual average SST anomalies (ASST) make more obvious this trend, with negative and positive SST changes to be distributed over the study area. In terms of seasons, the same increase trend presented during spring, summer, autumn and winter with 2013 to be the year with maximum ASST observed values. Innovative aspects comprise of straightforward integration and modeling of available tools, providing a versatile platform of analysis and semi-automation of the operation. In addition, the fine resolution maps that extracted from the analysis with a wide spatial coverage, allows the detail representation of SST and ASST respectively in the region.
In this study, the framework of an integrated method for the estimation and analysis of potential wind energy resources in Cyprus is presented and applied, at three selected sites at the western coast-line of the island. Firstly, a statistical analysis of wind speed and direction data for selected meteorological stations at the Cyprus coast is carried out. Daily, monthly and annual variations of wind speed are established. The Weibull distributions of the sites are also determined and examined. The wind statistics obtained serve as the basis in order to estimate corrected statistical distributions over the extended areas of application through Wind Atlas Analysis and Application Program (WAsP) which modifies the wind flow due to local topographic effects. As a result, a geo-data and wind resource base is formulated around each station. Aggregation of the data with statistical weighting methods, allows the extrapolation of the results and the visualization over the western part of the island. The application indicates the strong influence of the sea-breeze on the wind potential of the island and recovers interesting points with higher wind energy potential, suitable for wind resource exploitation. The particular methodological framework applied and the results obtained can be utilized by potential investors and wind energy developers.
Land Surface Temperature (LST) is an extremely important parameter that controls the exchange of long wave radiation between surface and atmosphere. It is a good indicator of the energy balance at the Earth’s surface and it is one of the key parameters in the physics of land-surface processes on regional as well as global scale. This paper utilizes monthly night and day averaged LST MODIS imagery over Cyprus for a 9 year period. Fourier analysis and Least squares estimation fitting are implemented to analyze mean daily data over Cyprus in an attempt to investigate possible temperature tenancy over these years and possible differences among areas with different land cover and land use, such as Troodos Mountain and Nicosia, the main city in the center of the island. The analysis of data over a long time period, allows questions such as whether there is a tenancy to temperature increase, to be answered in a statistically better way, provided that ‘noise’ is removed correctly. Dealing with a lot of data, always provides a more accurate estimation, but on the other hand, more noise in implemented on the data, especially when dealing with temperature which is subject to daily and annual cycles. A brief description over semi-automated data acquisition and standardization using object-oriented programming and GIS-based techniques, will be presented. The paper fully describes the time series analysis implemented, the Fourier method and how it was used to analyze and filter mean daily data with high frequency. Comparison of mean monthly daily LST against day and night LSTs is also performed over the 9 year period in order to investigate whether use of the extended data series provide significant advantage over short.