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Water irrigation management using remote sensing techniques: a case study in Central Tunisia

机译:利用遥感技术进行水灌溉管理:以突尼斯中部为例

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The optimization of water resources management in arid region needs reliable information and knowledge on water resources and water requirements. Remote sensing and Geographic Information System techniques are used to estimate the Irrigation Water Requirements (IWR) in the Regueb-watershed for the summer season, when the peak of the water consumption is reached. The landsat 8 images were used to identify irrigated area and to estimate the IWR in dry season. Five methods were applied for the identification of irrigated area based on supervised classification and spectral indices. Two different approaches were applied to estimate IWR: K-c-NDVI method based on the relationship between the Normalized Difference Vegetation Index (NDVI) and the crop coefficient (K-c) and FAO approach based on the empirical equation of Penman-Monteith and the single Kc given by FAO-56. The maximum likelihood classifier (MLC) performed the highest overall accuracy (85%) and a kappa coefficient of 0.82. The IWR resulting values range from 10 to 14.5 Mm(3) in summer season. To evaluate the methodology, five test areas were selected based on the diversity of crops in the whole study area. The validation of results indicates that the IWR values calculated using FAO method were in good agreement with the IWR values derived from remote sensing approach.
机译:干旱地区水资源管理的优化需要可靠的水资源信息和知识。遥感和地理信息系统技术用于估计夏季用水量达到峰值时在雷格布(Regueb)流域的灌溉水需求量(IWR)。 Landsat 8图像用于识别灌溉区域并估算干旱季节的IWR。根据监督分类和光谱指标,采用了五种方法进行灌溉面积的识别。两种不同的方法被用于估算IWR:基于归一化植被指数(NDVI)与作物系数(Kc)之间关系的Kc-NDVI方法和基于Penman-Monteith经验方程和给定的单个Kc的FAO方法由粮农组织56。最大似然分类器(MLC)表现出最高的总体准确性(85%)和kappa系数为0.82。夏季的IWR结果值范围从10到14.5 Mm(3)。为了评估方法,根据整个研究区域的作物多样性选择了五个测试区域。结果的验证表明,使用粮农组织方法计算的IWR值与从遥感方法得出的IWR值非常吻合。

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