Abstract. Solar radiation reflected by the Earth's surface to satellite sensors is modified by its interaction with the atmosphere. The objective of applying an atmospheric correction is to determine true surface reflectance values and to retrieve physical parameters of the Earth's surface, including surface reflectance, by removing atmospheric effects from satellite images. Atmospheric correction is arguably the most important part of the pre-processing of satellite remotely sensed data. Such a correction is especially important in cases where multi-temporal images are to be compared and analyzed. For agricultural applications, in which several vegetation indices are applied for monitoring purposes, multi-temporal images are used. The integration of vegetation indices from remotely sensed images with other hydrometeorological data is widely used for monitoring natural hazards such as droughts. Indeed, the most important task is to retrieve the true values of the vegetation status from the satellite-remotely sensed data. Any omission of considering the effects of the atmosphere when vegetation indices from satellite images are used, may lead to major discrepancies in the final outcomes. This paper highlights the importance of considering atmospheric effects when vegetation indices, such as DVI, NDVI, SAVI, MSAVI and SARVI, are used (or considered) and presents the results obtained by applying the darkest-pixel atmospheric correction method on ten Landsat TM/ETM+ images of Cyprus acquired from July to December 2008. Finally, in this analysis, an attempt is Correspondence to: D. G. Hadjimitsis (d.hadjimitsis@cut.ac.cy) made to determine evapotranspiration and to examine its dependence on the consideration of atmospheric effects when multi-temporal image data are used. It was found that, without applying any atmospheric correction, the real daily evapotranspiration was less than the one found after applying the darkest pixel atmospheric correction method.

Plastic litter floating in the ocean is a significant problem on a global scale. This study examines whether Sentinel-2 satellite images can be used to identify plastic litter on the sea surface for monitoring, collection and disposal. A pilot study was conducted to determine if plastic targets on the sea surface can be detected using remote sensing techniques with Sentinel-2 data. A target made up of plastic water bottles with a surface measuring 3 m × 10 m was created, which was subsequently placed in the sea near the Old Port in Limassol, Cyprus. An unmanned aerial vehicle (UAV) was used to acquire multispectral aerial images of the area of interest during the same time as the Sentinel-2 satellite overpass. Spectral signatures of the water and the plastic litter after it was placed in the water were taken with an SVC HR1024 spectroradiometer. The study found that the plastic litter target was easiest to detect in the NIR wavelengths. Seven established indices for satellite image processing were examined to determine whether they can identify plastic litter in the water. Further, the authors examined two new indices, the Plastics Index (PI) and the Reversed Normalized Difference Vegetation Index (RNDVI) to be used in the processing of the satellite image. The newly developed Plastic Index (PI) was able to identify plastic objects floating on the water surface and was the most effective index in identifying the plastic litter target in the sea.

Abstract. The monitoring of aerosol concentrations comprises a high environmental priority, particularly in urban areas. Remote sensing of atmospheric aerosol optical thickness (AOT) could be used to assess particulate matter levels at the ground. However, such measurements often need further validation. In this study, aerosol data retrieved from satellite and sun-photometer, on the one hand, and visibility data at various locations in Cyprus, on the other hand, for the period from January to June 2009 are contrasted. The results obtained by the direct comparison between MODIS and handheld sun-photometer AOT data exhibited a significant correlation (r=0.83); these results are in agreement with those reported by the National Aeronautics and Space Administration (NASA). The correlation between sun-photometer AOT and that estimated from visibility measurements was also significant (r=0.76). A direct and significant relationship between MODIS AOT and AOT estimated from visibility values was also found for all the locations used (the correlation coefficient was found to vary from 0.80 to 0.84). It is concluded that MODIS AOT data provide accurate information on the aerosol content in Cyprus, while in the absence of such data, visibility measurements could be used as a secondary source of aerosol load information, in terms of aerosol optical thickness, and provide useful information on a near-real time basis, whenever data are available.

Determination of turbidity is a common component of water-quality assessments. In regions where there are a lot of inland waters such as dams, sampling even a small proportion of those dams for monitoring and assessing water quality is cost prohibitive. Satellite remote sensing has the potential to be a powerful tool for assessing water quality over large spatial scales. The overall objective of this study was to examine whether Landsat-5 TM (Thematic Mapper) and Landsat-7 ETM+ (Enhanced Thematic Mapper) could be used to measure turbidity across the Kourris Dam, which is the biggest dam in Cyprus. This paper presents the results obtained by applying the linear regression analysis in order to examine the relationship between the turbidity measurements measured in-situ during the satellite overpass against at-satellite atmospheric corrected reflectance values. It has been found that the reflectance, after atmospheric correction, at Landsat TM Bands 1 and 3 is strongly related with turbidity levels after linear regression analysis. The most significant correlation was occurred when reflectance in TM band 3 and logarithmic reflectance in TM band 3 were correlated with turbidity measurements. Indeed, the correlation coefficient (R) when atmospheric corrected reflectance (ρ) in the Landsat TM band 3 were correlated against turbidity, before atmospheric correction was R = 0.38 and after atmospheric correction was R = 1; and when atmospheric corrected logarithmic reflectance (Log ρ) in the Landsat TM band 3 were correlated against turbidity, before atmospheric correction was R = 0.46 and after atmospheric correction was R = 1.