Fadi Karam, Nabil Amacha, Wassim Katerji, Weicheng Wu, Alfonso Domnguez, Safa Baydoun
Abstract: To understand the cropped areas and assess seasonal water supply for irrigation, remote sensing-based crop classification was conducted on satellite imagery data for a pilot area in the Bekaa Valley, Lebanon, during the 2011-2012 growing years. The crop classification was achieved using three sets of RapidEye and Landsat7 ETM+ (Enhanced Thematic Mapper Plus) images acquired in early (May), mid (July) and late (September) of 2011 and 2012 growing years, respectively. Field crop data were obtained throughout the growing seasons in well-defined farmers plots before the images acquisitions using a hand-held GPS (Global Positioning System) Unit. Ten crop classification profiles and three non-crop profiles were derived for each year from the different class signatures in the pre-selected bands of the two satellite data. Then, image-derived results were checked for accuracy and used to produce cropping maps within GIS (Geographic Information System).These maps enabled us to define different cropping calendars and determine seasonal irrigation water requirements (IWRs) at the pilot area level. IWRs were calculated for the surveyed crops as the product of the produced cropping maps and net irrigation requirements (NIR) calculated by means of MOPECO (Economic Optimization Model for Irrigation Water Management). The results were compared with the Litani River Authority Database (LRAD) and found a good agreement. The classification results of RapidEye images (2011) compared quite well in the whole test area with Landsat derived crop maps (2012). The overall accuracy of the classification against the field data ranges from 84 % to 95 %. In addition, crop classification profiles appeared consistent with field crop observations, even though a slight variation was noted. The examination of the crop maps showed decreases of as much as 7 %, 30 % and 5 %inbareland, woodland and fallow areas, respectively, in 2012 when compared to 2011. Data showed that these decreases were reported as increases in wheat (15 %), fruit trees (11 %), olive (6 %), and vineyard (3 %). The increased cropland that was observed in 2012 was accompanied by an increase in the amount of water allocated from the Canal 900 irrigation conveyor in comparison with that of 2011. This study presented an example of remote sensing application for water allocation in agriculture. It was concluded that satellite imagery was essential for the definition of the existing cropping patterns in the pilot area and helped better estimate seasonal irrigation needs at the scheme level. The proposed methodology may help irrigation deciders to better assess water resources with respect to the surveyed cropped areas.
Keywords: Satellite Imagery, Remote Sensing, Crop Signature, Cropping Pattern, Irrigation Water Requirements, Water Allocation