Hyperspectral Remote Sensing of Leaf Chlorophyll Content

From Leaf, Canopy, to Landscape Scales

Authored by: Yongqin Zhang

Hyperspectral Indices and Image Classifications for Agriculture and Vegetation

Print publication date:  December  2018
Online publication date:  December  2018

Print ISBN: 9781138066038
eBook ISBN: 9781315159331
Adobe ISBN:

10.1201/9781315159331-9

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Abstract

Leaf optical properties are tightly related to the absorption features of biochemical compounds in leaves. Of all the biochemicals, leaf chlorophyll content stands out as being both sensitive to environmental conditions and having a very strong influence on leaf optical properties and canopy albedo. Quantitative estimates of leaf chlorophyll content from hyperspectral remote sensing are of great use for estimating plant primary production, modeling the terrestrial ecosystem carbon cycle, and managing plant stress. This chapter presents methods of estimating leaf chlorophyll content of forests at leaf, canopy, and landscape scales. For broadleaf species, chlorophyll content was estimated through empirical and semi-empirical methods at both leaf and canopy scales. Needleleaf species and open forest canopies present a challenge for chlorophyll content estimation. Process-based models and algorithms at leaf and canopy scales were developed, respectively, to estimate the chlorophyll content from leaf and canopy hyperspectral measurements. Leaf area index, an important plant structure parameter, is applied to scale leaf chlorophyll estimates to landscape scale.

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