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Journal of Applied Remote Sensing • Open Access

Predictability of leaf area index using vegetation indices from multiangular CHRIS/PROBA data over eastern China
Author(s): Zhujun J. Gu; G. Arturo Sanchez-Azofeifa; Jilu Feng; Sen Cao

Paper Abstract

This study analyzed the predictability of leaf area index (LAI) to the variation of vegetation type, observation angle, and vegetation index (VI). The analysis was conducted by using the R2 of the LAI-VI models between in situ measured LAIs and VIs derived from CHRIS/PROBA data. The results show that the discrepancy of vegetation type mostly influences the LAI-VI models. The predictability of LAI to the variation of both vegetation type and index demonstrates the differences of oblique/vertical and backward/forward observations, and backward series are greater than the forward. The predictabilities of LAI to the variation of observation angle are greatest for the soil-adjusted VIs and least for the traditional ratio-based indices. Multivariable linear modeling with all VIs from all five angles yields acceptable accuracy except for the sparse shrub. The backward less-oblique observation (−36  deg) is the only angle chosen in the modeling for grass, shrub, and broad leaf forest, while the nadir view performs best for forests with coniferous trees. These results provide a reference to multiangular LAI estimation for different vegetation communities. VIs accounting for angular soil effects require further investigation in the future.

Paper Details

Date Published: 9 February 2015
PDF: 15 pages
J. Appl. Remote Sens. 9(1) 096085 doi: 10.1117/1.JRS.9.096085
Published in: Journal of Applied Remote Sensing Volume 9, Issue 1
Show Author Affiliations
Zhujun J. Gu, Nanjing Xiaozhuang Univ. (China)
G. Arturo Sanchez-Azofeifa, Univ. of Alberta (Canada)
Jilu Feng, Univ. of Alberta (Canada)
Sen Cao, Beijing Normal Univ. (China)


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