Vegetation moisture content model based on principal component analysis

doi:10.6046/gtzyyg.2013.03.07

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Abstract

In this paper, hyperspectral remote sensing technology was applied to the quantitative study of the relationship between the reflectance spectra of vegetation and vegetation moisture content, and reliable data were obtained for the study of vegetation water content as one of the "Eco-water" information parameters. Sensitive bands were extracted by relevance analysis and stepwise regression of the reflectance spectra and the moisture content of palm leaves collected in the sampling points. In avoidance of the interaction of sensitive bands, the relationship between principal constituents and moisture content was identified as a transition in the first place by extracting principal constituents using principal component analysis, the regression equation of every principal component and standard variables was established, the equation of regression between every standard variable and original variables was also established and, finally, the model of the relationship between vegetation moisture content and reflectance spectra was obtained from translating the transition model. The results showed that the reflectance spectra of palm leaves had significant correlation with vegetation water content at 454 nm, 668 nm,1 466 nm,1 664 nm and 1 924 nm, and that the relative correlation between the predicted values obtained in the niche model and the monitoring values was 0.92, with the root mean square error being 0.06.

Keywords coal mine dust pollution Wansheng hyperspectral

:	TP 75
	Q149

Issue Date: 03 July 2013

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	TAN Dejun
	XIE Jutian
	JIAN Ji
	XIE Hongbin
	LUO Zhenfu
	HU Yunhai

Cite this article:

TAN Dejun,XIE Jutian,JIAN Ji, et al. Vegetation moisture content model based on principal component analysis[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(3): 38-42.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2013.03.07 OR https://www.gtzyyg.com/EN/Y2013/V25/I3/38

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