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Journal of Applied Remote Sensing

Estimation of chlorophyll content in the typical steppe vegetation of Inner Mongolia, China, using various red-edge position extraction techniques
Author(s): Dandan Wei; Xiaobing Li; Hong Wang; Ying Li
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Paper Abstract

The red-edge position (REP) was extracted from reflectance spectral data at canopy and leaf scale using six different methods in the temperate typical steppe of Inner Mongolia with relatively high species richness. The results suggested that the REPs varied with the extraction methods, sampling sites, plant species, and estimation scales. At the canopy scale, chlorophyll content (CC) was estimated with the linear extrapolation method, and the polynomial fitting technique had coefficients of determination (R2 <0.4 ). The chlorophyll estimates at Leymus chinensis- and Stipa grandis-dominated sites were slightly better than those from the large sampling sites with multiple dominant plant species. At the leaf scale, the linear extrapolation method and the polynomial fitting technique presented high coefficients of determination (R2 <0.6 ). CC estimated at L. chinensis-dominated sites was substantially higher than at S. grandis-dominated sites as well as the large sampling site. The results using the maximum first-derivative method and Lagrangian interpolation techniques revealed a discontinuity, whereas the REPs, as extracted by the linear interpolation method, were shifted toward longer wavelengths. The linear interpolation and inverted Gaussian method were easily saturated. The results obtained with the polynomial fitting technique and the linear extrapolation method had higher sensitivity and accuracy for estimation of CC.

Paper Details

Date Published: 5 December 2013
PDF: 20 pages
J. Appl. Rem. Sens. 7(1) 073471 doi: 10.1117/1.JRS.7.073471
Published in: Journal of Applied Remote Sensing Volume 7, Issue 1
Show Author Affiliations
Dandan Wei, Beijing Normal Univ. (China)
Xiaobing Li, Beijing Normal Univ. (China)
Hong Wang, Beijing Normal Univ. (China)
Ying Li, Beijing Normal Univ. (China)

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