| 摘要: |
| 为实时、准确地获取原位土壤含水量信息,利用可见/近红外光谱技术,分别使用全局偏最小二乘(PLS)建模、局部PLS建模方法,对田间原位土壤含水量进行快速估测。结果表明:全局PLS模型中,其建模集的决定系数(R2)、交叉验证均方根误差(RMSECV)分别为0.943和1.750%,检验集的决定系数(R2)、预测均方根误差(RMSEP)分别为0.956和1.260%。局部PLS模型中,分别比较了选取定标子集的2种方法(欧氏距离法和马氏距离法),采用欧氏距离法和马氏距离法选取定标子集进行建模的R2值分别为0.974和0.979,RMSEP值分别为0.976%和0.943%。因此,将可见/近红外光谱技术应用到田间原位含水量测量是可行的,其中,使用局部建模方法的效果优于全局建模。 |
| 关键词: 土壤含水量 可见/近红外 偏最小二乘 田间原位 全局建模 局部建模 |
| DOI：10.11841/j.issn.1007-4333.2016.08.17 |
| 投稿时间：2015-06-12 |
| 基金项目:中国烟草总公司云南省公司资助项目(2013YN17) |
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| Estimation of soil water content in situ by using visible/near infrared spectrum modeling |
|
LIU Guang-lin1, GUO Yan1, LAO Cai-lian2, XU Xing-yang3, QIN Chun-li3
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| (1.College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China;2.College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China;3.Yunnan Tobacco Company Kunming Branch, Kunming 650051, China) |
| Abstract: |
| In order to collect the data of soil water content in situ real-timely and accurately, this study used visible/near infrared (Vis/NIR) spectroscopy technology to estimate soil water content in situ rapidly, by global partial least squares (PLS) modeling and local PLS modeling.The results showed that:For global PLS modeling, the decision coefficient (R2) and root mean square error of cross validation (RMSECV) of the modeling set were 0.943 and 1.750%, respectively.The decision coefficient (R2) and root mean square error of prediction (RMSEP) set were 0.956 and 1.260%, respectively.For local PLS modeling, two methods (i.e., Euclidean distance method and Mahalanobis distance method) for subset selection were used.The R2 of the two methods were 0.974 and 0.979, respectively.RMSEP were 0.976% and 0.943% respectively.This study suggests that it is feasible to measure soil water content in situ by using the Vis/NIR spectroscopy technology.And the result of local modeling was better than that of global modeling. |
| Key words: soil water content Vis/NIR partial least square in situ global modeling local modeling |
