标题：Prediction of Soil Organic Matter by VIS-NIR Spectroscopy Using Normalized Soil Moisture Index as a Proxy of Soil Moisture
作者： Hong, YS (Hong, Yongsheng); Yu, L (Yu, Lei); Chen, YY (Chen, Yiyun); Liu, YF (Liu, Yanfang); Liu, YL (Liu, Yaolin); Liu, Y (Liu, Yi); Cheng, H (Cheng, Hang)
来源出版物： REMOTE SENSING 卷：10 期：1 文献编号：28 DOI：10.3390/rs10010028 出版年： JAN 2018
摘要：Soil organic matter (SOM) is an important parameter of soil fertility, and visible and near-infrared (VIS-NIR) spectroscopy combined with multivariate modeling techniques have provided new possibilities to estimate SOM. However, the spectral signal is strongly influenced by soil moisture (SM) in the field. Interest in using spectral classification to predict soils in the moist conditions to minimize the influence of SM is growing. The objective of this study was to investigate the transferability of two approaches, SM-based cluster method with known SM (classifying the VIS-NIR spectra into different SM clusters to develop models separately), the normalized soil moisture index (NSMI)-based cluster method with unknown SM (utilizing NSMI to indicate the SM and establish models separately), to predict SOM directly in moist soil spectra. One hundred and twenty one soil samples were collected from Central China, and eight SM levels were obtained for each sample through rewetting experiments. Their reflectance spectra and SOM concentrations were measured in the laboratory. Partial least square-support vector machine (PLS-SVM) was employed to construct SOM prediction models. Specifically, prediction models were developed for NSMI-based clusters with unknown SM data. The models were assessed through three statistics in the processes of calibration and validation: the coefficient of determination (R-2), root mean square error (RMSE) and the ratio of the performance to deviation (RPD). Results showed that the variable SM led to reduced VIS-NIR reflectance nonlinearly across the entire spectral range. NSMI was an effective spectral index to indicate the SM. Classifying the VIS-NIR spectra into different SM clusters in known SM states could improve the performance of PLS-SVM models to acceptable prediction accuracies (R-cv(2) = 0.69-0.77, RPD = 1.79-2.08). The estimation of SOM, when using the NSMI-based cluster method with unknown SM (RPD = 1.95-2.04), was similar to the use of the SM-based cluster method with known SM (RPD = 1.79-2.08). The predictive results (RPD = 1.87-2.06) demonstrated that the NSMI--based cluster method has potential for application outside the laboratory for SOM prediction without knowing the SM explicitly, and this method is also easy to carry out and only requires spectral information.
作者关键词：visible and near-infrared spectroscopy; soil moisture; soil organic matter; fuzzy k-means clustering; normalized soil moisture index; partial least square-support vector machine
扩展关键词： EXTERNAL PARAMETER ORTHOGONALIZATION; DIFFUSE-REFLECTANCE SPECTRA; NEAR-INFRARED SPECTROSCOPY; IN-SITU MEASUREMENTS; VIS/NIR SPECTROSCOPY; CLAY CONTENT; CARBON; LIBRARY; FIELD; MODELS
通讯作者地址：Chen, YY; Liu, YF (通讯作者),Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Hubei, Peoples R China.
Chen, YY (通讯作者),Chinese Acad Sci, Inst Soil Sci, State Key Lab Soil & Sustainable Agr, Nanjing 210008, Jiangsu, Peoples R China.
Chen, YY (通讯作者),Wuhan Univ, Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Hubei, Peoples R China.
Liu, YF (通讯作者),Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, Wuhan 430079, Hubei, Peoples R China.
电子邮件地址： hys@whu.edu.cn; yulei@mail.ccnu.edu.cn; chenyy@whu.edu.cn; yfliu610@sina.com; yaolin6100@sina.com; liuyi2010@whu.edu.cn; chh0530@foxmail.com
[Hong, Yongsheng; Chen, Yiyun; Liu, Yanfang; Liu, Yaolin; Liu, Yi; Cheng, Hang] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Hubei, Peoples R China.
[Hong, Yongsheng; Chen, Yiyun; Liu, Yi; Cheng, Hang] Chinese Acad Sci, Inst Soil Sci, State Key Lab Soil & Sustainable Agr, Nanjing 210008, Jiangsu, Peoples R China.
[Hong, Yongsheng; Chen, Yiyun; Liu, Yi; Cheng, Hang] Wuhan Univ, Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Hubei, Peoples R China.
[Yu, Lei] Cent China Normal Univ, Sch Urban & Environm Sci, Wuhan 430079, Hubei, Peoples R China.
[Yu, Lei] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Hubei, Peoples R China.
[Liu, Yanfang; Liu, Yaolin] Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, Wuhan 430079, Hubei, Peoples R China.
