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A Simple Algorithm to Identify Irrigated Croplands by Remote Sensing

机译:遥感识别农田的简单算法

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The identification of irrigated cropland is essential for crop monitoring, yield estimation and water management assessment in drylands. The standard approach is to use supervised classification on multispectral bands, vegetation indices or the Principal Components or, more recently, the combination of optical images with radar data. More complex methods, such as time-series analysis, sub-pixel calculation method and decision-tree based supervised classification have been proposed to differentiate the irrigated areas and identify the irrigation system. As an alternative approach to identify irrigated land, this paper introduces a simple and easily implemented algorithm, based on the logical operation and thresholding of a combination of thermal temperature (T_s) and vegetation indices (e.g. NDVI). This approach is illustrated through a case study in northern Syria using Landsat TM images. The results show a good consistence with the field observations (99%).
机译:对干旱地区的作物监测,产量估算和水管理评估,确定灌溉农田是必不可少的。标准方法是在多光谱波段,植被指数或主成分上使用监督分类,或者最近将光学图像与雷达数据结合使用。已经提出了更复杂的方法,例如时间序列分析,子像素计算方法和基于决策树的监督分类,以区分灌溉区域并确定灌溉系统。作为识别灌溉土地的另一种方法,本文介绍了一种简单且易于实现的算法,该算法基于逻辑运算以及热温度(T_s)和植被指数(例如NDVI)的组合的阈值确定。通过使用Landsat TM图像在叙利亚北部进行的案例研究说明了这种方法。结果显示与实地观察的一致性很好(99%)。

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