气候变化领域集成服务门户(CCPortal): Quantitative Model Based on Field-Derived Spectral Characteristics to Estimate Soil Salinity in Minqin County, China

CCPortal

DOI	10.2136/sssaj2013.06.0241
	Quantitative Model Based on Field-Derived Spectral Characteristics to Estimate Soil Salinity in Minqin County, China
	Pang, Guojin; Wang, Tao; Liao, Jie; Li, Sen
发表日期	2014
ISSN	0361-5995
EISSN	1435-0661
卷号	78 期号:2
英文摘要	Soil salinization, which is one of the most important land degradation problems in arid and semiarid regions, has a significant impact on ecological equilibrium. Hyperspectral remote sensing, with a large number of measured wavelength bands and a high resolution, has gradually become a popular technology to investigate soil salinization. In this study, a model based on field-derived spectra was developed for soil salinity and the quantitative relationships between the soil spectrum and vegetation spectrum with the soil salt content (SSC) and soil electrical conductivity (EC). A field study was performed in Minqin County, China. A genetic algorithm (GA), partial least squares regression (PLS), and back-propagation neural network (BPNN) were used for modeling. The results showed that GA has a relatively strong ability for band selection. After the selection, the predictive ability of the GA-PLS model was better than the PLS model based on the full spectra. The BPNN model built by selected bands (GA-BP) was superior to the GA-PLS linear model. The models built using the soil spectrum after band selection have a high predictive ability. The R-2 and ratio of prediction to deviation (RPD) for SSC were 0.68 and 1.76 for GA-PLS and 0.72 and 1.89 for GA-BP, respectively. There were no significant correlational relationships between the normalized difference vegetation index and SSC or EC. The GA-BP model fitted using the vegetation spectrum was superior to a single vegetation index model, whereas the predictive ability of SSC (0.56 for R-2 and 1.47 for RPD) was not high due to influences such as plant species differences and vegetation coverage.
关键词	ANN artificial neural network BPNN back-propagation neural network EC electrical conductivity FDR first derivative reflectance GA genetic algorithm NDVI normalized difference vegetation index PLS partial least squares regression RMSECV root mean square error of cross-validation RPD ratio of prediction to deviation SSC soil salt content
学科领域	Agriculture
语种	英语
WOS研究方向	Soil Science
来源期刊	SOIL SCIENCE SOCIETY OF AMERICA JOURNAL
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/111735
作者单位	Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Key Lab Desert & Desertificat, Lanzhou 730000, Peoples R China
推荐引用方式 GB/T 7714	Pang, Guojin,Wang, Tao,Liao, Jie,et al. Quantitative Model Based on Field-Derived Spectral Characteristics to Estimate Soil Salinity in Minqin County, China[J]. 中国科学院西北生态环境资源研究院,2014,78(2).
APA	Pang, Guojin,Wang, Tao,Liao, Jie,&Li, Sen.(2014).Quantitative Model Based on Field-Derived Spectral Characteristics to Estimate Soil Salinity in Minqin County, China.SOIL SCIENCE SOCIETY OF AMERICA JOURNAL,78(2).
MLA	Pang, Guojin,et al."Quantitative Model Based on Field-Derived Spectral Characteristics to Estimate Soil Salinity in Minqin County, China".SOIL SCIENCE SOCIETY OF AMERICA JOURNAL 78.2(2014).