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Free Iron Oxide Determination in Mediterranean Soils using Diffuse Reflectance Spectroscopy

机译:漫反射光谱法测定地中海土壤中的游离氧化铁

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Soil Fe oxides occur in almost all soils and reflect different environmental conditions by the high variability of their mineralogy and concentration. Quantitatively determining this important pedogenic indicator enables diffuse reflectance spectroscopy (DRS) based on material-specific absorption characteristics. This paper presents a methodology that directly links free Fe oxide content (Fed, citrate-dithionite extractable Fe) with the diagnostic Fe absorption band near 900 nm (Fe-NIR). In addition, we investigated the influence of soil texture on the spectral characteristics and prediction accuracy. We showed that the Fe absorption bands of clay-dominated soil samples were, in general, deeper than sand-dominated samples with comparable Fed content. Based on the Fe-NIR absorption depth, we created two texture-dependent Fed prediction models, retrieving the best Fed estimates for the sand calibrated model (R2v = 0.87, rel. MSEv = 13.9%). Due to the high texture variability in sand, silt, and clay fractions of the clay-silt dominated samples, the clay-silt calibrated model produced good predictions (R2v = 0.70, rel. RMSEv = 19.0%). The soil texture appeared to have no significant influence on model stability but did affect the prediction accuracy. Constant Fed contents were over- and underestimated when applying the texture-dependent models to other texture groups. The texture-independent model was stable and performed well (R2v = 0.76, rel. RMSEv = 18.1%). These results are highly relevant to the subsequent spatial assessment of free Fe oxide content as an indicator for soil development from hyperspectral remote sensing data.
机译:土壤中的铁氧化物几乎存在于所有土壤中,并因其矿物学和浓度的高度变化而反映出不同的环境条件。定量确定这一重要的成岩指示剂可以根据特定于材料的吸收特性进行漫反射光谱(DRS)。本文提出了一种方法,该方法将游离的氧化铁含量(可摄取的柠檬酸盐-连二亚硫酸盐提取的铁)与诊断性的900 nm附近的铁吸收带(Fe-NIR)直接联系起来。此外,我们研究了土壤质地对光谱特征和预测精度的影响。我们发现,以粘土为主的土壤样品中的铁吸收带通常比具有相同Fed含量的以沙子为主的样品深。基于Fe-NIR的吸收深度,我们创建了两个与纹理有关的Fed预测模型,以获取砂校准模型的最佳Fed估计值(R2v = 0.87,相对MSEv = 13.9%)。由于以粘土-粉砂为主的样品的沙子,粉砂和粘土级分的质地变化很大,因此通过粘土-粉砂校准的模型可以得出良好的预测(R2v = 0.70,相对RMSEv = 19.0%)。土壤质地似乎对模型稳定性没有明显影响,但确实影响了预测准确性。当将纹理相关模型应用于其他纹理组时,恒定美联储的含量被高估或低估了。独立于纹理的模型稳定且运行良好(R2v = 0.76,相对RMSEv = 18.1%)。这些结果与随后的自由氧化铁含量的空间评估高度相关,自由氧化铁含量可作为根据高光谱遥感数据进行土壤发育的指标。

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