In situ measurements of soil colour, mineral composition and clay content by vis-NIR spectroscopy
In situ measurements of soil colour, mineral composition and clay content by vis-NIR spectroscopy
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DOI：
10.1016/j.geoderma.2009.01.025
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发表时间：
2009-05-15
期刊：
影响因子：
6.1
通讯作者：
Fouad, Y.
中科院分区：
文献类型：
--
作者：
Rossel, R. A. Viscarra;Cattle, S. R.;Fouad, Y.
使用便携式可见近红外（vis-NIR：400-2500 nm）分光光度计的近端土壤传感（PSS）可用于原位测量土壤特性。本研究的目的是：（i）将现场收集的光谱与实验室收集的光谱进行比较，（ii）根据光谱估计土壤颜色和矿物成分，以及（iii）使用光谱库预测粘土含量，该光谱库主要包含实验室收集的光谱，但也包含少量现场收集的现场光谱。使用来自不同母质的 10 个土壤剖面进行评估。在现场和实验室不同深度收集了光谱测量结果，一式三份。使用主成分分析和特定波长 t 检验对这些光谱进行多变量比较。除1400 nm和1900 nm附近的吸水区域以及不主要用于表征土壤矿物成分的区域外，现场采集的光谱与实验室采集的光谱没有显着差异。使用连续谱去除技术并针对特征吸收特征，根据光谱对土壤颜色和矿物成分进行了估计。土壤颜色的估计值是使用 Munsell HVC 和 CIELab 颜色模型从每个剖面的光谱得出的。将这些结果与现场对孟塞尔颜色的定性估计进行比较。土壤颜色的光谱估计与孟塞尔书上的估计相当一致，尽管可见近红外估计往往更暗、更黄。通过比较土壤光谱与纯矿物的光谱得出矿物成分的定量估计。使用定性 X 射线衍射 (XRD) 分析评估这些估计值。可见近红外对土壤矿物成分的表征是有效的，该方法的结果与 XRD 分析的结果具有良好的一致性。可见近红外技术比传统 XRD 更省力，不需要样品制备，并且更适合检测铁氧化物。包含 1287 个实验室收集的光谱和 74 个现场条件下原位收集的光谱的光谱库用于开发偏最小二乘回归 (PLSR) 模型，以预测 10 个土壤剖面中现场和实验室收集的光谱的粘土含量。根据现场收集的光谱 (RMSE = 7.9%) 对粘土含量的预测比根据实验室收集的光谱 (RMSE = 8.3%) 的预测稍微准确一些。通过使用 74 个场谱“尖峰”扩展 PLSR 校准的范围，提高了模型的泛化能力。带有 bootstrap 聚合的 PLSR 或 bagging-PLSR (bPLSR) 可对每个剖面的粘土含量进行预测，并测量其不确定性。 (C) 2009 Elsevier B.V. 保留所有权利。
Proximal soil sensing (PSS) using portable visible-near infrared (vis-NIR: 400-2500 nm) spectrophotometers can be used to measure soil properties in situ. The objectives of this research were: (i) to compare field spectra collected in situ to spectra collected in the laboratory, (ii) to estimate soil colour and mineral composition from the spectra, and (iii) to make predictions of clay content using a spectral library that contains mostly spectra collected in the laboratory but also a smaller number of field spectra that were collected in situ. The evaluation was conducted using 10 soil profiles derived from different parent materials. Spectroscopic measurements were collected both in the field and in the laboratory at different depths, in triplicate. These spectra were compared multivariately using principal component analysis and by using wavelength specific t-tests. Except in the water absorption regions around 1400 nm and 1900 nm and in regions that are not primarily used to characterise soil mineral composition, field-collected spectra were not significantly different to spectra collected in the laboratory. Estimates of soil colour and mineral composition were made from the spectra using a continuum-removal technique and by targeting characteristic absorption features. Estimates of soil colour were derived from the spectra of each profile using the Munsell HVC and CIELab colour models. These were compared to qualitative estimates of Munsell colour made in the field. Spectroscopic estimates of soil colour were in fair agreement with Munsell book estimates, although the vis-NIR estimates tended to be somewhat darker and more yellow. Quantitative estimates of mineral composition were derived by comparing soil spectra to the spectra of pure minerals. These estimates were assessed using qualitative X-ray diffraction (XRD) analysis. The characterisation of soil mineral composition by vis-NIR was effective, with good agreement between the results of this method and XRD analysis. The vis-NIR technique was less laborious than conventional XRD, did not require sample preparation and was better at detecting iron oxides. A spectral library containing 1287 laboratory-collected spectra and 74 spectra collected in situ at field conditions was used to develop partial least squares regression (PLSR) models to predict the clay content of both the field- and laboratory-collected spectra from the 10 soil profiles. Predictions of clay content from the field-collected spectra (RMSE = 7.9%) were slightly more accurate than those from the laboratory-collected spectra (RMSE = 8.3%). Extending the range of the PLSR calibrations by 'spiking' them with 74 field spectra improved the generalisation capacity of the models. PLSR with bootstrap aggregation, or bagging-PLSR (bPLSR), produced predictions of clay content for each profile with a measure of their uncertainty. (C) 2009 Elsevier B.V. All rights reserved.