土壤质地分类的近红外光谱分析方法研究  

NIR Spectral Analysis for Soil Textural Classification

作　　者：曾庆猛[1] 孙宇瑞[1] 严红兵 

机构地区：[1]中国农业大学信息与电气工程学院,北京100083 [2]北京英贤仪器有限公司,北京100070

出　　处：《光谱学与光谱分析》2009年第7期1759-1763,共5页Spectroscopy and Spectral Analysis

基　　金：国家高技术发展研究计划"863"项目(2006AA10Z205;2006AA10A301)资助

摘　　要：依据所收集的25种土样,采用两种不同精度仪器、三种光谱方法以及3个不同谱区,在四种分类标准条件下进行土壤质地分类分析方法研究。结果表明:(1)土壤化学组成的信息主要体现在近红外光谱的谱峰特征,而质地等物理信息主要反映在光谱的斜率、截距参数上,且二者在不同谱区的相对强度不同;(2)近红外光谱方法对土壤质地的分辨能力较低,随着分类粗化而有所提高;(3)在4组分类标准中土壤质地最高预测准确度为72%,其中在砂粒<70%和粘粒<40%条件下,预测准确度达到85%;(4)样本顶部漫反射光谱方法与扩展谱区范围均可有效提高质地预测准确度,而高精度仪器并不具备明显优势。Using 25 soil samples with known textural compositions, 2 types of NIR instruments, 3 spectral methods associated with 3 spectrum ranges and 3 sampling intervals, the approach to soil textural classification was investigated. From the results obtained, the following conclusions can be drawn： （1） The chemical information could be identified from the peak of the spectral curves, whereas the slope and intercept of spectral curves concerning soil texture resulted from the physical properties of soil samples. Moreover, the intensity of chemical and physical properties varied in different spectral （2） The distinguishing ability of NIR was limited, depending on the classification criterion proposed; （3） Being tested with four classifaction criterions, the maximal predicting probability was 72%. In the case of sand 〈70% and clay %40%, the maximum was up to 85%; （4） Either acquiring scatter information from the surface of soil samples or extending spectral bands could improve the predicting probability.

关 键 词：土壤质地 分类 近红外光谱 

分 类 号：S152.3[农业科学—土壤学] O657.3[农业科学—农业基础科学]