Remotely-Sensed innovative approach for the cumulative meteorological effects on cotton production C. Domenikiotis *a , M. Spiliotopoulos b , E. Tsiros b , N. R. Dalezios a a Laboratory of Agrometeorology, Department of Agriculture, Animal Production and Aquatic Environment, School of Agricultural Sciences, University of Thessaly; b Department of Management of Rural Environment and Natural Resources, University of Thessaly ABSTRACT In this study an innovative approach for investigating the accumulated meteorological effects on cotton production during the growing season is presented. The quantification of the meteorological effects is based on the incorporation of the Bhalme and Mooley Drought Index (BMDI) methodology into the Vegetation Condition Index (VCI) extracted by NOAA/AVHRR data. The resulted Bhalme and Mooley Vegetation Condition Index (BMVCI) uses the same scale as the Z-Index of the Palmer Drought Severity Index (PDSI) for drought monitoring. The study area consists of the country of Greece. Eighteen years of NOAA/AVHRR data are examined and processed with the BMVCI to examine the unfavourable conditions for cotton production. For the validation of BMVCI an empirical relationship between the cotton production and the BMVCI values is derived. The method is developed based on the first sixteen years time series data and validated utilizing the following two years. The resultant high correlation coefficient and the approximation of the production for the validated years refer to very favourable results and confirms the usefulness of this integrated methodological approach as an effective tool to assess cotton production in Greece. Keywords: Cotton, production, VCI, PDSI, BMDI, BMVCI, Greece, satellite data, meteorology 1. INTRODUCTION In general, cotton productivity and/or production varies greatly from year to year, with the meteorological factors being one of the main sources of variation. On the other hand, cultivated areas seem to be rather stable on an annual basis. However, cotton productive areas in Greece may vary from year to year. Distribution of production/biomass is usually surveyed in the field, with the results being not very accurate, since it is very difficult to determine the sample locations and measure sufficient number of samples 1 . These in situ observations and extracted information are not only labor intensive, but also slow. At recent years satellite data can be an inexpensive and quick method to evaluate biological activity of green vegetation. One of the most prominent applications of remote sensing is the estimation of net primary agricultural production over time and space. It is well known that field locations change on an annual basis with crop rotation 2 . Over the past there has been progress in the development of the bio-physical models to estimate the ecological production using satellite spectral data 2 . Such applications focus on the assessment of the impacts of climate change and are conducted on global scale 3 . The relationship between cotton production and remotely sensed biomass production is not statistically significant on a monthly basis, because biomass production is related to antecedent growing conditions 4 . In order to mitigate the consequences of meteorological conditions on agricultural productivity or production, monitoring in low spectral resolution is required. Satellite data collected from NOAA’s Advanced Very High Resolution Radiometer (AVHRR) provide an efficient and powerful means for environmental monitoring 4,5,6 . Satellite data can be used to * cdomen@uth.gr ; phone: +302421093261; fax: +302421093253; www.uth.gr