A STUDY OF CORN FPAR ESTIMATION FROM HYPERSPECTRAL DATA BASED ON PCA APPROACH AND NEAR-INFRARED SHORTWAVE BANDS

doi:10.6046/gtzyyg.2008.04.03

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Abstract

Fraction of Photosynthetically Active Radiation (FPAR) is a key parameter in the study of such topics

as ecological system function and global changes, and hence it is important to estimate FPAR accurately. Based on

an analysis of hyperspectral and photosynthetical active radiation data of the corn, this paper studied the

feasibility of Principal Component Analysis (PCA) for hyperspectral information extraction and corn canopy FPAR

estimation, and analyzed the potential of near-shortwave infrared hyperspectral data for FPAR estimation. The

results show that the PCA method can be used effectively to compress hyperpsectral information, and will give a

better performance than vegetation indices for FPAR estimation. Near-infrared and shortwave band hyperspectral

reflectance has a great potential for estimating FPAR and hence can help improve the precision of FPAR estimation.

Keywords Hyperspectral remote sensing Identify and extract directly SUM and MGM Texture information Tibet Plateau

Issue Date: 23 June 2009

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Cite this article:

YANG Fei, ZHANG Bai, LIU Zhi-Meng, LIU Dian-Wei, WANG Zong-Meng, SONG Kai-Shan. A STUDY OF CORN FPAR ESTIMATION FROM HYPERSPECTRAL DATA BASED ON PCA APPROACH AND NEAR-INFRARED SHORTWAVE BANDS[J]. REMOTE SENSING FOR LAND & RESOURCES,2008, 20(4): 9-13.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2008.04.03 OR https://www.gtzyyg.com/EN/Y2008/V20/I4/9

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