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  

Using Orthogonal Signal Correction Algorithm Removing the Effects of Soil Moisture on Hyperspectral Reflectance to Estimate Soil Organic Matter

作　　者：洪永胜[1] 于雷[1] 朱亚星[1] 李思缔 郭力 刘家胜 聂艳[1] 周勇[1] 

机构地区：[1]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,武汉430079

出　　处：《中国农业科学》2017年第19期3766-3777,共12页Scientia Agricultura Sinica

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)

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�【Objective】 Rapid and accurate quantitative analysis of soil organic matter is essential for sustainable development of precision agriculture. Visible and near-infrared（Vis-NIR） reflectance spectroscopy has been widely used for soil properties estimation and digital soil mapping. However, it is less exact in monitoring soil organic matter（SOM） in the field when compared to laboratory-based spectroscopic measurement mainly due to some factors, such as soil moisture, temperature, and soil surface texture. Among these three factors, soil moisture（SM） has the most pronounced effects on spectral reflectance. Therefore, it is urgently significant that a method for removing SM effects from spectral reflectance and improving the accuracy of quantitative prediction of SOM should be proposed. 【Method】 A total of 217 soil samples used in this study were collected at 0-20 cm depth from Gong＇an County and Qianjiang City in Jianghan Plain. These soil samples were air-dried, ground, and sieved（less than 2 mm） in the laboratory, and the SOM of each soil sample was analyzed based on potassium dichromate external heating method. These 217 samples were further divided into three non-overlapping data-sets： The model calibration set（S^0）, this set consisted of 122 samples to develop multivariate models for SOM; The orthogonal signal correction（OSC） development set（S^1）, this set consisted of 60 samples for OSC development; The validation set（S^2）, this set consisted of 35 samples for independent OSC validation. Then, sample rewetting（S^1 and S^2 set） was carried out： each soil sample was weighed 150 g oven-dried soil in a cylindrical black box, and then they were rewetted by 4% SM increment for each level in the laboratory. Total 9 treatments were obtained, corresponding to the following SM levels i.e. 0%, 4%, 8%, 12%, 16%, 20%, 24%, 28%, and 32%. Soil hyperspectral reflectance was measured in the laboratory with an ASD Fieldspec-Pro spectroradiometer for the three data-sets（S^0, S^

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 

分 类 号：S153.621[农业科学—土壤学]