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Estimation of Water Contents from Vegetation Using Hyperspectral Indices

机译:高光谱指数估计植被的水含量

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This paper outlines the research objectives to investigate the approaches for assessment of vegetation water contents using hyperspectral remote sensing and moisture sensor. Water contents of crops monitor crop health for precision farming and monitoring. In the present research, spectral indices with some chemical extraction procedures were identified for estimation of water contents of crops. The investigated crop species, namely Vigna Radiata, Vigna Mungo, Pearl Millet, and Sorghum were collected from Aurangabad region of Maharashtra, India. Spectral reflectance curve of crop growth patterns was measured using ASD field Spec 4 Spectroradiometer and 150 Soil moisture sensor including healthy, diseased, and dry leaves with standard laboratory environment. It is found that there was a positive correlation between WI and Soil moisture sensor with 0.99,0.76, and 0.97 accuracy. The research work was implemented using Python open source software. In the conclusion, water estimation from crops may be useful in irrigation mapping and drought risk modeling.
机译:本文概述了研究目标,调查对于利用高光谱遥感和湿度传感器植被水分含量的评估方法。农作物含水量监测精耕细作和监测作物的健康。在目前的研究,具有一定的化学提取方法谱指数,确定了作物的水分内容推测。被调查的作物品种,即绿豆,豇豆蒙哥,珍珠米,高粱从印度马哈拉施特拉邦的奥兰加巴德地区收集。的作物生长模式的光谱反射曲线使用ASD字段规格4分光辐射和150土壤湿度传感器,包括健康的,患病的,和干叶标准实验室环境中测量。据发现,有WI和土壤湿度传感器之间的正相关性与0.99,0.76和0.97的精度。这项研究工作是用Python开源软件来实现。在总之,从作物水估计可能是在灌溉映射和干旱风险建模非常有用。

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