引用本文:熊静玲,高华光,朱西存,于瑞阳,温 新.基于MSC与SVM的夯土齐长城土壤含水率高光谱估测[J].土壤学报,2018,55(6):1336-1344.
XIONG Jingling,GAO Huaguang,ZHU Xicun,YU Ruiyang,WEN Xin.Hyperspectral Estimation of Soil Moisture Content in Rammed Soil of Qi-Dynasty Great Wall Based on MSC and SVM[J].Acta Pedologica Sinica,2018,55(6):1336-1344
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基于MSC与SVM的夯土齐长城土壤含水率高光谱估测
熊静玲,高华光,朱西存,于瑞阳,温新
山东农业大学北校区,中国国家博物馆,山东农业大学,山东农业大学,山东农业大学
摘要:
采用近地面高光谱技术,建立基于支持向量机(support vector machines, SVM)的土壤含水率的高光谱估测模型,为快速、无损估测土壤含水率提供科学依据。以青岛市黄岛区夯土齐长城为研究区,沿垂直于齐长城采集样品,并进行光谱反射率和含水率测定;对土壤原始光谱反射率进行对数的一阶微分(Lg(R)′ )处理以及多元散射校正(Multiple scatter correction, MSC)和对数的一阶微分(Lg(R)′ )处理,分别与土壤含水率进行相关分析,筛选敏感波长;利用Lg(R)′ 和MSC +Lg(R)′ 处理后的光谱数据分别建立基于SVM的土壤含水率高光谱估测模型。结果表明,多元散射校正可以增强光谱与土壤含水率之间的相关吸收信息,提高土壤光谱反射率与土壤含水率的相关性,筛选的敏感波长为1 861 nm、1 866 nm、1 549 nm、1 885 nm、1 871 nm、1 895 nm和2 095 nm;基于MSC+Lg(R)′ 预处理建立的SVM回归模型精度较高,其Rc2为0.811,Rv2为0.764,RPD为2.671。利用MSC方法对光谱数据进行预处理,可以更加准确地筛选出敏感波长,建立的SVM估测模型更加精准。
关键词:  土壤含水率  多元散射校正  SVM回归分析  夯土齐长城
DOI:10.11766/trxb201803070044
分类号:
基金项目:中国国家博物馆综合考古专项
Hyperspectral Estimation of Soil Moisture Content in Rammed Soil of Qi-Dynasty Great Wall Based on MSC and SVM
xiongjingling,gaohuaguang,zhuxicun,yuruiyang and wenxin
Shandong Agricultural University,National Museum of China,Shandong Agricultural University,Shandong Agricultural University,Shandong Agricultural University
Abstract:
【Objective】The Qi-Dynasty Great Wall is made up of rammed soil, in which soil moisture plays an important role. Excessive moisture in the soil will lead to partial collapse of the wall. Therefore, it is of great significance to estimate soil water content in the rammed earth of the Great Wall so as to protect the relics of Great Wall. Although the traditional soil moisture measuring method is quite high in precision, it is rather labor- and time-consuming and rigorous in measuring environment. The use of hyperspectral technology has the characteristics of rich data/information, high-efficiency and non-destructiveness, which makes up for the shortages of the traditional measuring methods. In recent years, scholars at home and abroad have found that the multiple scattering correction method can eliminate the scattering effect caused by particle size variation of the samples, and then the difference in physical scattering information between different spectra. However, so far little has been reported about researches on whether the spectra corrected with the MSC method can make wavelength optimization more accurate. 【Method】 In this paper, the Qi-Dynasty Great Wall in Huangdao District of Qingdao City was cited as object of the study, and samples were collected vertically along the Wall. Initial soil moisture content of the samples were determined with the oven-drying method, and soil hyperspectral data was obtained using the US ASD FieldSpec4 portable spectrometer. In order to study effect of soil moisture content on soil spectral characteristics, soil samples 6.16%, 8.94%, 10.27%, 14.10%, 18.03%, and 24.29% in moisture content were selected for acquisition of hyperspectral reflectivity curves. In order to verify the effect of MSC on the preferred sensitive wavelengths, the primary spectral reflectance of the soil was pretreated with Lg(R)' and MSC+Lg(R)', separately, and then correlation analysis was done between primary spectral reflectance and soil water content for screening sensitive wavelengths; based on the spectral data that had been pre-processed separately with Lg(R)' and MSC+Lg(R)', support vector machines (SVM)-based soil moisture content hyperspectral estimation models were constructed.【Results】Results show that spectral curves of the soil samples, regardless of soil moisture content, varied on the whole quite similarly, declining gradually with rising soil moisture content. For a specific band, the response of spectra to soil water content varied in characteristic with band region; when the soil moisture content was low, with rising soil moisture content, the reflectivity in the shortwave and infrared bands varied sharply. The sensitive bands of the spectral reflectance of the rammed Wall soils pre-treated with Lg(R)' and MSC+Lg(R)' were mainly concentrated in the range of 1 450~1 500nm, 1 850~1 900nm and 2 050~2100nm. After the logarithmic first order differential treatment of the original spectral data, only four wavelengths relatively high in correlativity were obtained, whereas after the pretreatment with MSC+Lg (R)' seven were, that is, 1 861nm, 1 866nm, 1 549nm, 1 885nm, 1 871nm, 1 895nm and 2 095nm, with significantly higher correlation coefficients, i.e. -0.72, -0.71, 0.7, -0.7, 0.69, 0.69 and 0.69, which indicates that the multi-dimensional scattering correction method can enhance the correlative absorption information between spectra and soil moisture content, thus increasing the correlation between soil spectral reflectance and soil moisture content; The model based on the spectral data pre-treated with Lg(R)' was verified with determination coefficient, Rv2 = 0.679, RE = 0.143, RMSEP = 0.431, and RPD = 1.765, while the model based on the data pre-treated with MSC+Lg(R)′ was found to have Rv2= 0.764,RE = 0.062, RMSEP = 0.159, and RPD = 2.671. Obviously, the former is better than the latter in prediction. All demonstrate that SVM regression models based on the sensitive bands screened out through pretreatment vary somewhat in prediction effect with pretreatment method. 【Conclusion】 All the findings in this study demonstrate that the use of the MSC method to preprocess spectral data can enhance the absorption information related to spectrum and soil moisture content, screen sensitive wavelengths more accurately and have the established SVM estimation model more accurate in prediction.
Key words:  Soil moisture content  Multivariate scatter correction  SVM regression analysis  Qi-Dynasty Great Wall