摘要
This study focuses on the generation of reliable data for improving land and water use in Central Asia. An object-based remote sensing classification is applied and combined with the CropWat model developed by the Food and Agriculture Organization (FAO) to determine crop distribution and water requirements for irrigation of cotton and winter-wheat in Fergana Valley, Uzbekistan. The crop classification is conducted on RapidEye and Landsat data acquired before the onset of the main summer irrigation phases in July using a random forest algorithm. The ClimWat database of FAO is utilized for calculating crop water requirements (CWR) and crop irrigation requirements (CIR).Classification reveals an overall accuracy of 86.2% and exceeds a producer's (user's) accuracy of 95% (89%) for both, cotton and wheat. In 2010, cotton and winter-wheat are planted on 66.7% of the agricultural area under investigation, whereas orchard areas amount to 15.5%. The CWR modelled for winter-wheat and cotton cultivation revealed 5443 m3 ha鈭?#xA0;1 and 9278 m3 ha鈭?#xA0;1, respectively. Subtracting effective precipitation leads to CIR of 4133 m3 ha鈭?#xA0;1 and 8813 m3 ha鈭?#xA0;1. Comparisons of CWR and CIR for the area dominating crops with the total of water officially allocated for irrigation underline the pressure on the water resources in the entire Syr Darya catchment and suggest modifications of the cropping system towards more winter crops. The early season crop maps can be used for water saving as they enable modifications of water allocation plans within the different irrigation subsystems of the valley. The method for mapping spatially distributed CWR and CIR can be transferred to other irrigated areas in Central Asia and beyond.