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    Estimating soil moisture based on image processing technologies

    机译:基于图像处理技术的土壤湿度估算

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    Soil moisture is a critical factor to crop growth. Due to the facts of drought and less rain in northern China, it is necessary to introduce water controlled irrigating. Therefore, estimating soil moisture distribution rapidly and accurately is very important for decision making of water saving irrigating. This study took a farmland in Beijing as the experiment field. The aerial image at each experimental spot was taken from a balloon at the height of 100m above the land surface, the hyperspectral data of each test site was measured by a handheld spectroradiometer in the meantime, and the soil moisture of each sample was obtained in laboratory. With the obtained aerial images of the experiment field, the characteristics of each image were calculated by image processing technologies. And then the correlation analysis between soil moisture and each image characteristics was executed. Firstly, the coefficients of correlation between soil moisture and RGB values, as well as between soil moisture and HSV values were calculated respectively, and corresponding estimation models were established with R~2 of 0.887 and 0.706 respectively. Secondly, using the combination of RGB and HSV values, another estimation model was established, and its R~2 reached to 0.900. Finally, using the combination of the RGB values, HSV values and spectral reflectance data at 835 nm, a multiple linear regression model was also explored, which R~2 reached to 0.905. The result showed that the estimation of soil moisture content by using aerial images and hyperspectral data was rapid and accurate. Further more, high resolution image can be obtained conveniently now, thus it should be more practicable for forecasting the soil moisture accurately and timely by image processing technologies.
    机译:土壤水分是影响作物生长的关键因素。由于中国北方干旱少雨的事实,有必要引入水控灌溉。因此,快速准确地估算土壤水分分布对于节水灌溉决策至关重要。本研究以北京的一块农田为试验场。每个实验点的航空影像取自地面以上100m高处的气球,同时用手持式光谱仪测量每个测试点的高光谱数据,并在实验室获得每个样品的土壤湿度。利用获得的实验场的航拍图像,通过图像处理技术来计算每个图像的特征。然后进行了土壤水分与各图像特征之间的相关性分析。首先,分别计算了土壤水分与RGB值之间的相关系数,以及土壤水分与HSV值之间的相关系数,并建立了相应的估计模型,R?2分别为0.887和0.706。其次,结合RGB和HSV值,建立了另一个估计模型,其R?2达到0.900。最后,结合RGB值,HSV值和835 nm处的光谱反射率数据,建立了多元线性回归模型,R?2达到0.905。结果表明,利用航空影像和高光谱数据估算土壤含水量是快速,准确的。此外,由于现在可以方便地获得高分辨率图像,因此通过图像处理技术准确,及时地预测土壤水分应该更为实用。

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