利用OSC算法消除土壤含水量变化对Vis-NIR光谱估算有机质的影响

发布时间：2018-02-22 20:54

本文关键词： Vis-NIR光谱土壤有机质土壤含水量正交信号校正偏最小二乘回归江汉平原　出处：《中国农业科学》2017年19期 　论文类型：期刊论文

【摘要】：【目的】快速、准确地监测土壤有机质对于精准农业的发展具有重要意义。可见光-近红外(visible and near-infrared,Vis-NIR)光谱技术在土壤属性估算、数字化土壤制图等方面应用较为广泛,然而,在田间进行光谱测量,易受土壤含水量(soil moisture,SM)、温度、土壤表面状况等因素的影响,导致光谱信息中包含大量干扰信息,其中,SM变化是影响光谱观测结果最为显著的因素之一。此研究的目的是探讨OSC算法消除其影响,提升Vis-NIR光谱定量估算土壤有机质(soil organic matter,SOM)的精度。【方法】以江汉平原公安县和潜江市为研究区域,采集217份耕层(0—20 cm)土壤样本,进行风干、研磨、过筛等处理,采用重铬酸钾-外加热法测定SOM;将总体样本划分为3个互不重叠的样本集:建模集S~0(122个样本)、训练集S~1(60个样本)、验证集S~2(35个样本);设计SM梯度试验(梯度间隔为4%),在实验室内获取S~1和S~2样本集的9个梯度SM(0%—32%)的土壤光谱数据;分析SM对土壤Vis-NIR光谱反射率的影响,采用外部参数正交化算法(external parameter orthogonalization,EPO)、正交信号校正算法(orthogonal signal correction,OSC)消除SM对土壤光谱的干扰;利用主成分分析(principal component analysis,PCA)的前两个主成分得分和光谱相关系数两种方法检验消除SM干扰前、后的效果;基于偏最小二乘回归(partial least squares regression,PLSR)方法建立EPO和OSC处理前、后的SOM估算模型,利用决定系数(coefficient of determination,R~2)、均方根误差(root mean square error,RMSE)和RPD(the ratio of prediction to deviation)3个指标比较PLSR、EPO-PLSR、OSC-PLSR模型的性能。【结果】土壤Vis-NIR光谱受SM的影响十分明显,随着SM的增加,土壤光谱反射率呈非线性降低趋势。OSC处理前的湿土光谱数据主成分得分散点相对分散,与干土光谱数据主成分得分空间的位置不重叠,不同SM梯度之间的光谱相关系数变化较大;OSC处理后的湿土光谱数据主成分得分空间的位置基本与干土光谱数据相重合,各样本光谱数据之间相似性很高,不同SM梯度之间的光谱相关系数变化较小。9个SM梯度的EPO-PLSR模型的验证平均R~2_(pre)、RPD分别为0.69、1.7。9个SM梯度的OSC-PLSR模型的验证平均R~2_(pre)、RPD分别为0.72、1.89,校正后的OSC-PLSR模型受SM的较小,有效提升SOM估算模型的精度和鲁棒性。【结论】OSC能够消除SM变化对土壤Vis-NIR光谱的影响,可为将来田间原位实时监测SOM信息提供一定的理论支撑。
[Abstract]:[objective] the rapid and accurate monitoring of soil organic matter is of great significance for the development of precision agriculture. The visible and near infrared visible and near-infrared Vis-NIR spectroscopy is widely used in soil attribute estimation and digital soil mapping. Spectral measurements in the field are easily affected by soil moisture content, soil temperature, soil surface conditions and other factors, which result in a large amount of interference information in the spectral information. The change of SM is one of the most significant factors affecting the spectral observation results. The purpose of this study is to study the OSC algorithm to eliminate its influence. To improve the accuracy of quantitative estimation of soil organic matter soil organic matterland (SOM) by Vis-NIR spectroscopy. [methods] 217 soil samples were collected from Gongan County and Qianjiang City in Jianghan Plain for air drying, grinding, sifting and so on. SOM was determined by potassium dichromate external heating method, and the total sample was divided into 3 non-overlapping sample sets: the modeling set S0 (122 samples), the training set S1 (60 samples), the validation set S0 (35 samples), and the design of SM gradient test (gradient interval was used as the gradient interval). (4) soil spectral data of 9 gradient SM0- and -32s of Sz1 and Sz2 samples were obtained in the laboratory. The effect of SM on soil Vis-NIR spectral reflectance was analyzed. The external parameter orthogonalization algorithm and orthogonal signal correction algorithm were used to eliminate the interference of SM to soil spectrum. The first two principal component scores and spectral correlation coefficients of principal component analysis (PCA) were used to test the effect of eliminating SM interference before and after SM interference, and the SOM estimation models before and after EPO and OSC treatment were established based on partial least squares regression (PLSRS) method. The performance of the ratio EPO-PLSR-OSC-PLSR model was compared by using the three indexes of determining coefficient of determination, root mean square error (RMSE) and RPD(the ratio of prediction to deviation. [results] the effect of SM on the soil Vis-NIR spectrum was obvious, and with the increase of SM, Soil spectral reflectance showed a nonlinear decreasing trend. The scatter points of principal component scores of wet soil spectral data before OSC treatment were relatively dispersed, and there was no overlap between the distribution of principal component score space of dry soil spectral data and that of dry soil spectral data. The position of principal component score space of wet soil spectral data after OSC treatment basically coincided with dry soil spectral data, and the similarity of spectral data among different samples was very high. The spectral correlation coefficient between different SM gradients is smaller. The validation of EPO-PLSR models with nine SM gradients is 0.69,1.7.9SM gradient OSC-PLSR models are 0.69,1.7.9SM gradient OSC-PLSR models are 0.72 / 1.89 respectively, and the corrected OSC-PLSR models are smaller by SM's. [conclusion] OSC can eliminate the influence of SM variation on soil Vis-NIR spectrum and provide some theoretical support for real-time monitoring SOM information in the field in the future.
【作者单位】：华中师范大学城市与环境科学学院/华中师范大学地理过程分析与模拟湖北省重点实验室;
【基金】：国家自然科学基金(41401232) 中央高校基本科研业务费专项资金(CCNU15A05006) 华中师范大学研究生教育创新资助项目(2016CXZZ15)
【分类号】：S153.621

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