基于高光谱分析的草地叶绿素含量估算研究进展

doi:10.18306/dlkxjz.2016.01.004

Abstract

Abstract:

As the key pigment for photosynthesis, chlorophyll is closely related to nitrogen, protein, moisture, and other biochemical parameters of vegetation. Chlorophyll is a good indicator for photosynthesis activity and physiological state of grasslands. Hyperspectral analysis is a powerful means for accurately retrieving grassland chlorophyll contents in large areas. Based on the existing methods for monitoring grassland chlorophyll contents, this article summarizes and groups such methods into two categories: empirical models of red edge position (REP) / spectral indices, and radiative transfer models. With statistical analysis of the relationships between chlorophyll contents and REP/ spectral indices, empirical models were extensively employed for estimating grassland chlorophyll contents. These models use simple parameters and the REP that is closely related to grassland chlorophyll contents can be conveniently calculated. Due to the diverse structural forms of the spectral indices and the complicated relationships between spectral indices and chlorophyll contents, accuracy of chlorophyll content estimation is still limited, although novel algorithms of REP calculation and, to a lesser extent, new spectral indices, have significantly improved the estimation accuracy of grassland chlorophyll contents. Based on the relationships of chlorophyll contents and radiation energy, radiative transfer models were founded. Multiple parameters are required in the models, which are highly sensitive to the scale of estimation, and these models need to be further improved. Currently there exist few studies on grassland chlorophyll content estimation and only very few models have been developed for grassland chlorophyll content estimation. Future research should focus on developing new indices or improving existing indices and determining appropriate parameters of radiative transfer models to achieve better results for grassland application. In addition, current studies are limited to leaf and canopy scale analyses, and therefore expanding grassland chlorophyll content estimation to the landscape scale will be an important and challenging task.

Key words: hyperspectral analysis, grassland chlorophyll, red edge position, spectral index, radiative transfer model

Wenyong MA, Xunming WANG. Progress on grassland chlorophyll content estimation by hyperspectral analysis[J].PROGRESS IN GEOGRAPHY, 2016, 35(1): 25-34.

Figures/Tables 4

Tab.1

Comparison of six red edge position (REP) techniques"

红边位置技术	算法	执行难易及光谱类型	参考文献
最大一阶微分	$D λ (i) = （ R λ (i + 1) - R λ (i) ） ? λ$ $REP 为 Max (D λ (i))$ 对应的λ	容易反射率光谱	Horler et al, 1983
拉格朗日插值	$A = D λ (i - 1) (λ i - 1 - λ i) (λ i - 1 - λ i + 1)$ $B = D λ (i) (λ i - λ i - 1) (λ i - λ i + 1)$ $C = D λ (i + 1) (λ i + 1 - λ i - 1) (λ i + 1 - λ i)$ $REP = A λ i + λ i + 1 + B λ i - 1 + λ i + 1 + C (λ i - 1 + λ i) 2 A + B + C$	适中导数光谱	Dawson et al, 1998
线性外推法	远红色区直线： $D λ = m 1 λ + c 1$ 近红外区直线： $D λ = m 2 λ + c 2$ REP= $-$ ( $c 1 - c 2$ )/( $m 1 - m 2$ )	适中导数光谱	Cho et al, 2006
线性插值	$R rep$ =(R₆₇₀+R₇₈₀)/2 $REP = 700 + 40 R rep - R 700 R 740 - R 700$	容易反射率光谱	Guyot et al,1988
倒高斯模型	$R λ = R s - (R s - R 0) exp - (λ 0 - λ) 2 2 δ 2$ $REP = λ 0 + δ$	困难反射率光谱	Bonham-Carter, 1988
多项式拟合	$R λ (i) = a 0 + ∑ j = 1 5 a j λ j$ $REP 为 Max (D λ (i))$ 对应的λ	较难反射率光谱	Pu et al, 2003

Tab.1

Fig.1

Tab.2

Spectral indices developed as chlorophyll indicators"

指数类型	指数缩写	计算公式	参考文献
单波谱及其变换形式	RR	$R 700 - 1$	Gitelson et al, 1999
单波谱及其变换形式	LRR	$Log R 737 - 1$	Yoder et al, 1995
归一化型光谱指数	NDI	$(R 750 - R 705) (R 750 + R 705)$ $(R 800 - R 670) (R 800 + R 670)$ $(R 800 - R 550) (R 800 + R 550)$	Richardson et al, 2002; Tucker, 1979; Gitelson , Merzlyak, 1996
	mNDI	$R 750 - R 705 (R 750 + R 705 - 2 R 445)$	Sims et al, 2002
	OSAVI	$NIR - R NIR + R + X$	Rondeaux et al, 1996
比值型光谱指数	SR	$R 750 R 700$ $R 800 R 680$ $F 735 F 700$ $D 720 D 700$	Gitelson, Merzlyak, Lichtenthale,1996, 1999; Sims et al, 2006
	CI_red-edge	$(R 780 R 710)$ -1	Clevers et al, 2011
	mSR	$R 750 - R 445 R 705 - R 445$	Sims et al, 2002
	PSSR	PSSRa= $R 800 R 675$ PSSRb= $R 800 R 650$	Blackburn, 1998, 1999; Sims et al, 2002
	RARS	RARSa= $R 675 R 700$ $RARSb = R 675 R 650 × R 700$	Chappelle et al, 1992; Blackburn, 1999
多波段光谱指数	MTCI	$(R 753.75 - R 708.75) (R 708.75 - R 681.25)$	Dash et al, 2007
	GCI	$(R 780 - R 735) (R 715 - R 670)$	肖汉等, 2014
	DCNI	$(R 720 - R 700) (R 700 - R 670) (R 720 - R 670 + 0.03)$	Chen et al, 2010
	CARI	$a × 670 + R 670 + b / a 2 + 1 0.5 R 700 / R 670$ a=( $R 700 - R 550$ )/150; b= $R 550 -$ (a×550)	Kim et al, 1994
	MCARI/OSAVI	$R 750 - R 705 - 0.2 (R 750 - R 550) R 750 / R 705 1 + 0.16 R 750 - R 705 R 750 + R 705 + 0.16$	Wu et al, 2008
	RII	$∫ 705750 R λ R 705 - 1 d λ$	Richardson et al, 2002
	NAOC	$1 - ∫ 643795 R λ dλ 152 R 795$	Delegido et al, 2010

Tab.2

Fig.2

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