Articles | Volume 10, issue 1
14 Jan 2010
 | 14 Jan 2010

Atmospheric correction for satellite remotely sensed data intended for agricultural applications: impact on vegetation indices

D. G. Hadjimitsis, G. Papadavid, A. Agapiou, K. Themistocleous, M. G. Hadjimitsis, A. Retalis, S. Michaelides, N. Chrysoulakis, L. Toulios, and C. R. I. Clayton

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 hydro-meteorological 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 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.