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首页> 外文期刊>European Journal of Soil Science >The use of visible and near-infrared spectroscopy for the analysis of soil water repellency.
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The use of visible and near-infrared spectroscopy for the analysis of soil water repellency.

机译:可见光和近红外光谱法用于土壤疏水性分析。

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摘要

This study investigated the potential of visibleear-infrared reflectance spectroscopy (Vis-NIRS) to predict soil water repellency (SWR). The top 40 mm of soils (n=288) across 48 sites under pastoral land-use in the North Island of New Zealand, which represented 10 soil orders and covered five classes of drought proneness, were analysed by standard laboratory methods and Vis-NIRS. Soil WR was measured by using the molarity of ethanol droplet (MED) and the water drop penetration time (WDPT) tests. Soil organic carbon content (%C) was also measured to examine a possible relationship with SWR. A partial least squares regression (PLSR) model was developed by using Vis-NIRS spectral data and the reference laboratory data. In addition, we explored the power of discrimination based on WDPT classes using partial least squares discriminant analysis (PLS-DA). The PLSR of the processed spectra produced moderately accurate prediction for MED (R2val=0.61, RPDval=1.60, RMSEval=0.59) and good prediction for %C (R2val=0.82, RPDval=2.30, RMSEval=2.72). When the data from the 10 soil orders were considered separately and based on soil order rather than being grouped, the prediction of MED was further improved except for the Allophanic, Brown, Organic and Ultic soil orders. The PLS-DA was successful in classifying 60% of soil samples into the correct WDPT classes. Our results indicate clearly that Vis-NIRS has the potential to predict SWR. Further improvement in the prediction accuracy of SWR is envisaged by increasing the understanding of the relationship between Vis-NIRS and the SWR of all New Zealand soil orders as a function of their physical properties and chemical constituents such as hydrophobic compounds.
机译:这项研究调查了可见/近红外反射光谱(Vis-NIRS)预测土壤憎水性(SWR)的潜力。通过标准实验室方法和Vis-NIRS分析了新西兰北岛48个牧区土地利用耕地下40个土层的最上层40毫米土壤(n = 288),代表10个土阶,涵盖五类干旱倾向。 。通过使用乙醇滴的摩尔浓度(MED)和水滴渗透时间(WDPT)测试来测量土壤WR。还测量了土壤有机碳含量(%C),以检查与SWR的可能关系。利用Vis-NIRS光谱数据和参考实验室数据开发了偏最小二乘回归(PLSR)模型。此外,我们使用偏最小二乘判别分析(PLS-DA)探索了基于WDPT类的判别能力。处理后的光谱的PLSR对MED(R 2 val = 0.61,RPD val = 1.60,RMSE val = 0.59)和对%C的良好预测(R 2 val = 0.82,RPD val = 2.30,RMSE val = 2.72)。当分别考虑来自10个土壤阶的数据并基于土壤阶而不是将其分组时,除了同种异色阶,布朗阶,有机阶和Ultic阶土壤阶以外,对MED的预测得到了进一步改善。 PLS-DA成功地将60%的土壤样品分类为正确的WDPT类。我们的结果清楚地表明,Vis-NIRS具有预测SWR的潜力。通过增加对Vis-NIRS和所有新西兰土壤阶的SWR之间关系的理解,可以进一步提高SWR的预测精度,这些理解取决于它们的物理性质和化学成分(例如疏水性化合物)。

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