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机构地区:[1]浙江大学生物系统工程与食品科学学院,浙江杭州310029
出 处:《光谱学与光谱分析》2009年第3期675-677,共3页Spectroscopy and Spectral Analysis
基 金:国家科技支撑项目(2006BAD10A0902);浙江省重大科技攻关项目(2005C12029);宁波市重大科技攻关项目(2007C10034);浙江省自然科学基金项目(Y307119);浙江省三农五方项目(SN200504)资助
摘 要:应用美国ASD公司的FieldSpec HandHeld型可见/近红外光谱仪获得了52份不同含水量土壤的可见/近红外漫反射光谱数据,并通过实验测定了各土壤样本的含水量值,运用相关系数法寻找出了光谱对于土壤水分的敏感波段,然后利用单一敏感波段处的光谱数据建立了一元回归模型,并检测了土壤含水量。实验结果表明,该模型对土壤水分的检测效果比较好,模型的预测相关系数r为0.966 5,预测均方根误差RMSEP为0.012 1,为快速、准确检测土壤含水量提供了一条新的途径。Spectroscopy technique is one of the qualitative and quantitative analytical techniques developed quickly in recent years. The spectral analysis is a fast and non-destructive method and has been used in many fields such as oil industry, food industry and so on. In the present paper, the spectral band sensitive to soil moisture content was found from the visible/near infrared spectra and a monadic linear regression model based on the data of sensitive spectral band was applied to develop a method for rapid detection of soil moisture content. The spectral data of 52 soil samples were collected by using FieldSpec HandHeld spectroradiometer made by ASD (Analytical Spectral Device) company in the US, and the data of soil moisture content were obtained by experiment. The spectral band sensitive to soil moisture content was achieved by correlation coefficient method. Then, the data of sensitive spectral band were used to build monadic linear regression model of soil moisture content. Finally, the model was employed for the prediction of soil moisture content. Correlation coefficient (r) of prediction and root mean square error of prediction (RMSEP) were used as the evaluation standards. The results indicated that the r and RMSEP for the prediction of soil moisture content were 0. 966 5 and 0. 012 1 respectively. Thus, it is concluded that the method used in this paper is an available method for the rapid detection of soil moisture content based on the visible/near-infrared spectra.
关 键 词:可见/近红外光谱 土壤含水量 敏感波段 一元回归模型
分 类 号:S123[农业科学—农业基础科学] TH744.1[机械工程—光学工程]
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