{"title":"利用湿润样品的可见和近红外光谱改进土壤有机质预测","authors":"Changkun Wang, Xianzhang Pan","doi":"10.1255/jnirs.1184","DOIUrl":null,"url":null,"abstract":"Soil moisture reduces the accuracy of in situ measurements of soil properties using visible and near infrared reflectance spectroscopy and limits the application scope of existing soil spectral libraries built from air-dried samples. The preprocessing method of external parameter orthogonalisation (EPO) has been successfully used to improve the prediction of soil organic matter (SOM) in moist samples. However, the traditional strategy of EPO development (EPOI) requires a complex experimental design. In this study, we proposed a new EPO strategy (EPOII) that only uses a single sample but containing various soil moisture content (SMC) levels. Reflectance spectra (350– 2500 nm) of 130 samples with different SMC levels were measured in the laboratory. We built calibration models using air-dried samples and partial least squares regression before and after EPOI and EPOII, which were validated using air-dried samples and moist samples, respectively. For the validation of moist samples, we first classified the SMC into four groups (A: all SMC levels; B: SMC < 0.1 g g−1; C: 0.1 < SMC < 0.2 g g−1; and D: SMC > 0.2 g g−1) and then used a Monte Carlo method to simulate SMC distributions in the field with a result of 500 cases for each SMC group. Before EPO, an apparent decrease in the accuracy of the SOM predictions for moist samples occurred in each SMC group when using the calibration model derived from air-dried samples, compared with that of air-dried samples. Both EPOI and EPOII improved the SOM prediction of moist samples for most scenarios of SMC variations (groups A–C), but did not apply to the scenario with both large SMC variation and high SMC (group D). When considering EPOII, it could be a feasible strategy of EPO implementation for removing moisture effects on SOM prediction using visible and near infrared spectroscopy, although the degree of improvement was less than that from EPOI. Also, EPOII will pave the way as a standard pretreatment especially for soil spectra measured in the field when projection matrices of EPOII have been derived and incorporated in spectroscopy-related software.","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":null,"pages":null},"PeriodicalIF":16.4000,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1255/jnirs.1184","citationCount":"13","resultStr":"{\"title\":\"Improving the Prediction of Soil Organic Matter Using Visible and near Infrared Spectroscopy of Moist Samples\",\"authors\":\"Changkun Wang, Xianzhang Pan\",\"doi\":\"10.1255/jnirs.1184\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Soil moisture reduces the accuracy of in situ measurements of soil properties using visible and near infrared reflectance spectroscopy and limits the application scope of existing soil spectral libraries built from air-dried samples. 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For the validation of moist samples, we first classified the SMC into four groups (A: all SMC levels; B: SMC < 0.1 g g−1; C: 0.1 < SMC < 0.2 g g−1; and D: SMC > 0.2 g g−1) and then used a Monte Carlo method to simulate SMC distributions in the field with a result of 500 cases for each SMC group. Before EPO, an apparent decrease in the accuracy of the SOM predictions for moist samples occurred in each SMC group when using the calibration model derived from air-dried samples, compared with that of air-dried samples. Both EPOI and EPOII improved the SOM prediction of moist samples for most scenarios of SMC variations (groups A–C), but did not apply to the scenario with both large SMC variation and high SMC (group D). When considering EPOII, it could be a feasible strategy of EPO implementation for removing moisture effects on SOM prediction using visible and near infrared spectroscopy, although the degree of improvement was less than that from EPOI. 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引用次数: 13
摘要
土壤湿度降低了使用可见光和近红外反射光谱原位测量土壤特性的准确性,并限制了现有的由风干样品建立的土壤光谱库的应用范围。将外部参数正交化(EPO)预处理方法成功地应用于湿样土壤有机质(SOM)的预测。然而,传统的EPO开发策略需要复杂的实验设计。在本研究中,我们提出了一种新的EPO策略(EPOII),该策略仅使用单个样品,但含有不同的土壤水分含量(SMC)水平。在实验室测量了130个不同SMC水平样品的反射光谱(350 ~ 2500 nm)。我们利用风干样品和偏最小二乘回归建立了EPOI和EPOII前后的校准模型,并分别用风干样品和湿样进行了验证。为了验证潮湿样品,我们首先将SMC分为四组(A:所有SMC水平;B: SMC < 0.1 g g−1;C: 0.1 < SMC < 0.2 g g−1;和D: SMC > 0.2 g g−1),然后使用蒙特卡罗方法模拟SMC在现场的分布,结果为每个SMC组500例。在EPO之前,与风干样品相比,使用风干样品衍生的校准模型时,每个SMC组中湿样品的SOM预测的准确性明显下降。在大多数SMC变化情景(a - c组)中,EPOI和EPOII都改善了潮湿样品的SOM预测,但在SMC变化大且SMC高的情景(D组)中不适用。当考虑EPOII时,EPO实施策略可以消除可见光和近红外光谱对SOM预测的湿度影响,尽管改善程度低于EPOI。此外,当推导出EPOII的投影矩阵并将其纳入光谱相关软件时,EPOII将为特别是在现场测量的土壤光谱的标准预处理铺平道路。
Improving the Prediction of Soil Organic Matter Using Visible and near Infrared Spectroscopy of Moist Samples
Soil moisture reduces the accuracy of in situ measurements of soil properties using visible and near infrared reflectance spectroscopy and limits the application scope of existing soil spectral libraries built from air-dried samples. The preprocessing method of external parameter orthogonalisation (EPO) has been successfully used to improve the prediction of soil organic matter (SOM) in moist samples. However, the traditional strategy of EPO development (EPOI) requires a complex experimental design. In this study, we proposed a new EPO strategy (EPOII) that only uses a single sample but containing various soil moisture content (SMC) levels. Reflectance spectra (350– 2500 nm) of 130 samples with different SMC levels were measured in the laboratory. We built calibration models using air-dried samples and partial least squares regression before and after EPOI and EPOII, which were validated using air-dried samples and moist samples, respectively. For the validation of moist samples, we first classified the SMC into four groups (A: all SMC levels; B: SMC < 0.1 g g−1; C: 0.1 < SMC < 0.2 g g−1; and D: SMC > 0.2 g g−1) and then used a Monte Carlo method to simulate SMC distributions in the field with a result of 500 cases for each SMC group. Before EPO, an apparent decrease in the accuracy of the SOM predictions for moist samples occurred in each SMC group when using the calibration model derived from air-dried samples, compared with that of air-dried samples. Both EPOI and EPOII improved the SOM prediction of moist samples for most scenarios of SMC variations (groups A–C), but did not apply to the scenario with both large SMC variation and high SMC (group D). When considering EPOII, it could be a feasible strategy of EPO implementation for removing moisture effects on SOM prediction using visible and near infrared spectroscopy, although the degree of improvement was less than that from EPOI. Also, EPOII will pave the way as a standard pretreatment especially for soil spectra measured in the field when projection matrices of EPOII have been derived and incorporated in spectroscopy-related software.
期刊介绍:
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