Pitfalls in the use of middle-infrared spectroscopy: representativeness and ranking criteria for the estimation of soil properties
Pitfalls in the use of middle-infrared spectroscopy: representativeness and ranking criteria for the estimation of soil properties
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DOI：
10.1016/j.geoderma.2016.01.010
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发表时间：
2016-04-15
期刊：
影响因子：
6.1
通讯作者：
Vohland, Michael
中科院分区：
文献类型：
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作者：
Ludwig, Bernard;Linsler, Deborah;Vohland, Michael
中红外光谱法是一种较为成熟的土壤有机碳和全氮含量的测定方法。然而，获得的估计精度不同的研究和只有少数研究是可用的，处理C和N馏分。目的是确定估计精度的SOC，微生物生物量C（C-MIC）和C和N组分的含量为两个样品的表层土壤使用不同的软件包（具有不同的数据处理），并讨论MIRS的有用性和局限性的土壤性质的定量评估。从德国8个州的耕地采集了84种表层土壤，记录了它们的中红外光谱，并测定了它们的物理、化学和生物学特性。在交叉验证中，使用WinISI软件获得SOC含量的估计值，去除和不去除光谱（H>10）离群值和测量值与估计值之间存在较大偏差（T>2.5）的单位。样本I（所有84种土壤）由来自不同土层的土壤组成（部分具有相当大比例的根系缠结），并由伪重复（不同的管理，但相同的矿物学）组成;对于这个定义不清的样本，WinISI在删除可疑异常值时达到了明显出色的估计精度。我们建议，在土壤红外研究中，除了初步评估外，不应从样品中去除T离群值。相反，对于一致定义的子集样本II（即，土壤取自51个德国耕地点的Ap和M-Ap层，具有典型的SOC含量，没有伪重复），没有离群值，只有很好的估计精度。这表明，除了寻找最佳的估计精度，应同样注意样本的代表性为特定的人口，一个可疑的离群值和MIRS结果的普遍性的适当处理。关于准确度，我们获得了良好的结果，近似的定量结果或准确度，具有区分所有C和N组分（轻组分N除外）以及C-mic的高值和低值的潜力。在没有红外数据的情况下，使用SOC、N、pH值、砂、粉砂和粘土的含量进行多元线性回归估计这些属性一般比使用OPUS的MIRS估计略不成功。然而，当我们仅基于测量的pH值、SOC和N含量以及纹理数据创建人工光谱，而没有任何真实的基础红外数据时，然后将其用于OPUS中的PLS回归，性能与MIRS估计相似，对于被动C和N略有差异。总体而言，我们的研究表明，MIRS是一个有用的方法，估计光谱活性的主要成分SOC和N，并可能为被动C和N。然而，使用MIRS获得这些属性的光谱评估没有太大的好处，其中没有红外数据的方法（使用pH值，SOC，N和纹理数据的多重线性或PLS回归）给出了类似准确度的估计，这是C-MIC，轻馏分C和N，矿物相关C和N或中间C和N的情况下，这里研究的数据集。(C)2016爱思唯尔B. V.保留所有权利。
Middle-infrared spectroscopy (MIRS) is an established method for estimating the contents of soil organic carbon (SOC) and total soil nitrogen (N). However, obtained estimation accuracies vary between studies and only few studies are available that deal with C and N fractions. Objectives were to determine estimation accuracies for contents of SOC, microbial biomass C (C-mic) and C and N fractions for two samples of surface soils using different software packages (with different data treatments) and to discuss the usefulness and limitations of MIRS for a quantitative assessment of soil properties. Eighty-four surface soils were collected from arable sites from eight German states; their middle infrared spectra were recorded and their physical, chemical and biological properties determined. Estimates of SOC contents were obtained with WinISI software in cross-validations with and without removal of spectral (H>10) outliers and units with large deviations between measured and estimated values (T>2.5). Sample I (all 84 soils) consisted of soils from different horizons (partly with a substantial fraction of tangle of roots) and comprised of pseudo-replicates (different managements, but same mineralogy); for this ill-defined sample WinISI achieved an apparently excellent estimation accuracy when suspected outliers were removed. We suggest that T outliers should not be removed from samples in soil infrared studies except for preliminary evaluations. In contrast, for the consistently defined subset sample II (i.e., soils were taken from Ap and M-Ap horizons from 51 German arable sites with typical SOC contents and without pseudo-replicates) without outliers only a good estimation accuracy was reached. This indicates that besides a search for optimum estimation accuracies, equal attention should be paid to the representativeness of the sample for a specific population, an appropriate handling of suspected outliers and the generalizability of the MIRS results. With respect to accuracies, we obtained good results, approximative quantitative results or accuracies with the potential to discriminate between high and low values for all C and N fractions (except for light-fraction N) and also C-mic. An estimation of these properties without infrared data using the contents of SOC, N, pH, sand, silt and clay in multiple linear regressions was generally slightly less successful than the MIRS estimates using OPUS. However, when we created artificial spectra based solely on the measured pH, contents of SOC and N and texture data without any real underlying infrared data - and then used them for a PLS regression in OPUS, the performance was similar to the MIRS estimates, with a slight difference for passive C and N. Overall, our study indicates that MIRS is a useful method for an estimation of the spectrally active main constituents SOC and N and possibly for passive C and N. However, there is not much benefit of using MIRS to obtain a spectral assessment for those properties, where approaches without infrared data (either multiple linear or PLS regressions using pH, SOC, N and texture data) give estimates of similar accuracy, which was the case for of C-mic, light-fraction C and N, mineral-associated C and N or intermediate C and N for the dataset investigated here. (C) 2016 Elsevier B.V. All rights reserved.