ABSTRACT Many factors could influence simultaneously soil spectra. We aimed to study the single effect of organic carbon and total iron in soil visible and short-wave near-infrared spectra and to quantify their contents. Two datasets of soil mixture samples were prepared by mixing, in various fractions, an organic carbon-rich material with a total iron-rich material and then with a total iron-poor material. For these two datasets, contents in organic carbon are quite similar but contents in total iron are significantly different. Results show that samples of the same dataset have the same overall spectral shape. Organic carbon has a decreasing effect that affects the whole spectral range without showing any specific absorption peaks. By contrast, total iron has specific absorption peaks. Spectra of the second dataset characterized by soil mixtures with higher total iron contents were more compact within the spectral bands 400–440 and 920–950 nm. Besides, continuum removal enables to exaggerate absorption peaks of wavelengths linked to total iron content. Partial Least Squares Regression (PLS R) models of both total organic carbon and total iron assign high coefficients to the wavelengths that are considered relevant and conversely low coefficients to those that are considered irrelevant. Both organic carbon content and total iron content were well predicted. For these models, coefficients of determination were superior to 0.9 and RMSE was closed to zero. The global models calibrated on all the samples demonstrated that PLS R was able to integrate sample heterogeneity.

中文翻译：

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有机碳和总铁对土壤 Vis-SWNIR 光谱的影响以及使用 PLS R 模型对其含量进行量化

摘要 许多因素会同时影响土壤光谱。我们旨在研究有机碳和总铁对土壤可见光和短波近红外光谱的单一影响，并量化它们的含量。土壤混合物样品的两个数据集是通过将富含有机碳的材料与总的富铁材料和总的贫铁材料以不同的部分混合来制备的。对于这两个数据集，有机碳中的含量非常相似，但总铁中的含量显着不同。结果表明，同一数据集的样本具有相同的整体光谱形状。有机碳具有影响整个光谱范围的递减效应，但不显示任何特定的吸收峰。相比之下，总铁具有特定的吸收峰。以总铁含量较高的土壤混合物为特征的第二个数据集的光谱在 400-440 和 920-950 nm 的光谱带内更紧凑。此外，连续去除能够夸大与总铁含量相关的波长的吸收峰。总有机碳和总铁的偏最小二乘回归 (PLS R) 模型将高系数分配给被认为相关的波长，相反地将低系数分配给那些被认为不相关的波长。有机碳含量和总铁含量都得到了很好的预测。对于这些模型，决定系数优于 0.9，RMSE 接近于零。在所有样本上校准的全局模型表明 PLS R 能够整合样本异质性。