中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Retrieval of Soil Dispersion Using Hyperspectral Remote Sensing

文献类型:期刊论文

作者Chen, Jia-ge1; Chen, Jun1; Wang, Qin-jun1; Zhang, Yue1; Ding, Hai-feng1; Huang, Zhang1
刊名JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING
出版日期2016
卷号44期号:4页码:563-572
关键词RANDOM SAMPLE CONSENSUS REGRESSION
通讯作者Chen, JG (reprint author), Beijing Normal Univ, Beijing, Peoples R China. ; Chen, JG (reprint author), Natl Geomat Ctr China, Beijing, Peoples R China.
英文摘要The aim of this study was to study the main determinants of soil dispersion, such as Na+, pH, kaolinite, illite and montmorillonite clay content, by grey relational analysis. Multiple linear regression and partial least squares regression were applied to establish correlations between soil dispersion and its main determinants. Raw spectra (or raw reflectance; RR) of 50 soil samples was measured between 350 and 2500 nm. Continuum-removal (CR) spectral reflectance was calculated. Relationships of Na+, pH, kaolinite, illite and montmorillonite clay content with spectral reflectance were constructed to find sensitive spectral bands and the best spectral indices. Multiple stepwise regression was used to calculate five determinants from hyperspectral reflectance. Models were evaluated by the determinant (R-2), root mean square error, and relative root mean square error. Sensitive wavelengths were chosen based on the highest positive and negative correlations between five determinants and RR or CR reflectance. Results indicated that with the highest R-2 (0.686), the multiple linear regression model was the best for the prediction of soil dispersion using spectral reflectance. According to R-2 values, CR reflectance was the best predictor of Na+ (0.725), pH (0.852), kaolinite (0.909), illite (0.923) and montmorillonite (0.876). In conclusion, the methods examined here offered quick and novel means of predicting soil dispersion using spectral reflectance data.
学科主题Environmental Sciences & Ecology; Remote Sensing
类目[WOS]Environmental Sciences ; Remote Sensing
收录类别SCI
语种英语
WOS记录号WOS:000379742400008
源URL[http://ir.radi.ac.cn/handle/183411/39512]  
专题遥感与数字地球研究所_SCI/EI期刊论文_期刊论文
作者单位1.Beijing Normal Univ, Beijing, Peoples R China
2.Natl Geomat Ctr China, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing, Peoples R China
推荐引用方式
GB/T 7714
Chen, Jia-ge,Chen, Jun,Wang, Qin-jun,et al. Retrieval of Soil Dispersion Using Hyperspectral Remote Sensing[J]. JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING,2016,44(4):563-572.
APA Chen, Jia-ge,Chen, Jun,Wang, Qin-jun,Zhang, Yue,Ding, Hai-feng,&Huang, Zhang.(2016).Retrieval of Soil Dispersion Using Hyperspectral Remote Sensing.JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING,44(4),563-572.
MLA Chen, Jia-ge,et al."Retrieval of Soil Dispersion Using Hyperspectral Remote Sensing".JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 44.4(2016):563-572.

入库方式: OAI收割

来源:遥感与数字地球研究所

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