基于Vis/NIR光谱不同粒径下土壤碳氮的预测研究

Vis/NIR Spectral Prediction of Soil Carbon and Nitrogen with Different Particle Size

作者: 专业:资源环境信息工程 导师:蔡崇法 年度:2010 学位:硕士  院校: 华中农业大学

Keywords

Soil spectroscopy, particle-size, Vis/NIR, PLSR, VIP

        利用可见近红夕卜h(Vis/NIR)光谱测定技术快速无损地对土壤进行定量分析,是一种被广泛应用的行之有效的方法。大量研究表明土壤有机碳、水分、全氮等属性与光谱之间存在着较强的线性相关性。而野外原位测量受各种环境因素的影响,在一定程度上削弱了这种线性相关性。因此通常在实验室内对经过干燥、磨样处理的土壤进行光谱测量,以消除水分、质地和土壤类型等因素的影响。对于不同粒径导致预测模型精度的影响,目前的研究成果只给出了定性的结果。但是为何会导致差异以及对差异原因的定量解释,还未见诸于相关研究文献。本研究利用偏最小二乘回归(PLSR)考察了粒径对土壤全氮、有机碳含量Vis/NIR光谱预测模型的影响。将采自湖北潜江市后湖农场流塘分场的32个土壤样品制备成0.85~0.25mm、0.25~0.18mm、0.18~0.15mm和<0.15mm四种粒径土样,使用USB4000和NIR256-2.5地物光谱仪测量土样的Vis/NIR光谱,对原始光谱进行截取、重采样、SG平滑、一阶导等预处理后,建立了不同粒径下土壤全氮、有机碳的PLSR光谱预测模型,并对模型进行检验。通过VIP图考察了不同粒径下各波段对所建校正模型的影响程度,来挖掘不同粒径导致的具体差异。研究得出以下结论:(1)粒径对土壤光谱反射率有明显影响。随着粒径的减小同时土壤光谱反射率逐渐增大,通过去包络处理发现<0.15mm粒径土样反射光谱的特征吸收深度较之其他粒径最低,而其余粒径间未有显著差异。(2)土壤光谱反射率与全氮、有机碳含量之间存在良好的相关性。粒径范围0.18-0.15mm的土壤样品对于全氮的预测效果较佳,粒径范围0.85-0.25mm的土壤样品对于有机碳的预测效果较佳,预测值与实际值之间的验证决定系数Rvcl2,分别达到0.806和0.803。(3)粒径对土壤全氮和有机碳的定量预测有明显影响。原因是不同粒径下的光谱反射率各波段对于模型的重要性不完全相同,导致对土壤属性预测的特征波段位置不同,所以有针对地制样可以获得更准确的预测结果。
    Vis/NIR spectral technology is a quick soil quantitative analysis without loss, which is a widely used and effective method. Numerous studies show that there is a strong linear correlation between the soil OC, water, N, other properties and spectroscopy. However the field measurements are affected by various environmental factors and weaken this linear relationship to some extent. Generally the spectral measurements are on dried and ground samples in the lab to remove the impacts of moisture, soil type, and texture and so on. The current researches are given only qualitative results about the accuracy of the prediction model affected by different particle sizes.But the reason why led to differences and the quantitative interpretation of the differences are not found yet.In this study, the relationships among the soil TN, OC, and Vis-NIR reflectance of soil sample of different particle sizes were analyzed using PLSR.32 samples were obtained from Hou Lake Farm in Qianjiang, Hubei province, of the particle sizes varied from 0.85~0.25mm,0.25~0.18mm,0.18-0.15mm, and<0.15mm which were separated by nylon sieves.The spectra data processed by intercepting, re-sampling, SG smoothing and 1st derivative were used to establish the PLSR models of TN and OC. The importance of each wavelength in the calibration models was also determined by VIP Graph.(1)Smaller the particle sizes and brighter the samples always present higher the spectra. The continuum removed method shows that particles<0.15mm had the least absorption depth than others which have no linearity correlation with their reflectance.(2) The concentrations of TN and OC have significant correlations with spectral response. An accurate TN prediction was obtained when the particle sizes varied from 0.18~0.15mm with Rval2 of 0.806. And an accurate OC prediction was obtained when the particle sizes varied from 0.85-0.25mm with Rval2 of 0.803.(3)The reason is that soil spectroscopy in different particle sizes have different VIP, which lead the detecting bands in different regions. So exactitude prediction expects proper samples processing.
        

基于Vis/NIR光谱不同粒径下土壤碳氮的预测研究

摘要6-7
Abstract7
1 绪论8-12
    1.1 研究背景和意义8-9
    1.2 土壤光谱分析的研究现状9-10
        1.2.1 国外相关研究进展9
        1.2.2 国内相关研究进展9-10
        1.2.3 建模方法的进展10
    1.3 研究内容和技术路线10-12
        1.3.1 研究内容10-11
        1.3.2 主要技术路线11-12
2 土壤Vis/NIR光谱定量分析的理论与方法12-21
    2.1 土壤的光谱特征12-13
    2.2 光谱数据预处理方法13-16
        2.2.1 移动平均平滑法14
        2.2.2 Savitzky-Golay卷积平滑法14-15
        2.2.3 小波变换15-16
        2.2.4 导数算法16
    2.3 光谱定量校正方法16-21
        2.3.1 偏最小二乘回归16-18
        2.3.2 模型质量的评价指标18-19
        2.3.3 交叉验证19-21
3 数据的获取21-27
    3.1 土壤样品采集21-24
        3.1.1 地理位置21-22
        3.1.2 土壤类型分布22
        3.1.3 农业耕作概况22-23
        3.1.4 样品的采集与制备23-24
    3.2 数据获取24-27
        3.2.1 土壤属性测定24-25
        3.2.2 光谱测定25-27
4 基于Vis/NIR光谱的土壤TN及OC含量预测模型的建立27-42
    4.1 土壤属性数据的预处理27-28
    4.2 光谱数据的预处理28-35
        4.2.1 剔除噪声较大的波段28-29
        4.2.2 重采样29
        4.2.3 平滑滤波29-31
        4.2.4 求导数31-33
        4.2.5 去包络线33-35
    4.3 粒径对光谱的影响分析35-36
    4.4 PLSR模型的建立与分析36-38
    4.5 粒径对预测结果的影响分析38-41
    4.6 小结41-42
5 结论与展望42-44
    5.1 结论42
    5.2 讨论与展望42-44
参考文献44-48
致谢48
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