威斯康星州土壤层和土壤等级的光谱特征

Malithi Weerasekara, Alfred E. Hartemink, Yakun Zhang, Annalisa Stevenson
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引用次数: 0

摘要

我们利用中红外(MIR)光谱(4000-600 cm-1)对五种土壤类型(Alfisols、Entisols、Mollisols、Spodosols 和 Histosols)102 块土壤的土壤类型和土壤层进行了识别和分类。对土壤的质地、总碳、pH 值和元素特性进行了分析。使用随机森林模型对主地层(O、A、E、B 和 C)、B 地层(Bs、Bt 和 Bw)以及五个土壤等级的光谱进行分组。主层和 B 层的预测准确度分别为 0.81 和 0.89。主层预测的 Kappa 系数为 0.71,B 层预测的 Kappa 系数为 0.73。土壤阶次的总体准确度为 0.73,Kappa 系数为 0.64。组织溶胶在 2930 和 2860 cm-1 处表现出与矿质土壤截然不同的独特吸收特征。近红外光谱可准确区分 O 层。Spodosols 表层土的光谱曲线与 O 层相当。森林下的 Spodosols 具有高有机质的 A 层,分类准确。Entisols(Psamments)显示出与沙有关的吸收峰,有助于将其与其他土壤等级区分开来。该模型难以辨别某些土壤类别之间的细微差别,如果土壤在干燥后发生不可逆的变化,则会影响识别。不过,我们的研究结果表明,近红外光谱可用于有效识别和划分土壤类别以及土壤层。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Spectral signatures of soil horizons and soil orders from Wisconsin

Spectral signatures of soil horizons and soil orders from Wisconsin

We used mid-infrared (MIR) spectra (4000–600 cm−1) to identify and classify soil orders and soil horizons from 102 pedons across five soil orders (Alfisols, Entisols, Mollisols, Spodosols, and Histosols). The soils were analyzed for texture, total carbon, pH, and elemental properties. Random forest models were used to group the spectra of master horizons (O, A, E, B, and C), B horizons (Bs, Bt, and Bw), and the five soil orders. The prediction accuracies for the master horizons and B horizons were 0.81 and 0.89, respectively. The Kappa coefficient was 0.71 for the prediction of master horizons and 0.73 for the prediction of B horizons. The soil orders had an overall accuracy of 0.73 and a Kappa coefficient of 0.64. Histosols exhibited unique absorption characteristics at 2930 and 2860 cm−1 that differed distinctly from mineral soils. The MIR spectra accurately distinguished the O horizons. The spectral curve of topsoil of Spodosols was comparable to the O horizons. Spodosols under forest had A horizons with high organic matter and were classified accurately. Entisols (Psamments) displayed absorption peaks associated with sand, facilitating their differentiation from the other soil orders. The model struggled to discern subtle differences among some soil orders, and identification is hampered if soils undergo irreversible changes upon drying. However, our results showed that MIR spectra can be used for effectively identifying and classifying soil orders as well as soil horizons.

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