利用红边面积形状参数估测水稻叶层氮浓度

doi:10.3773/j.issn.1005-264x.2009.04.018

摘要/Abstract

摘要：

研究红边面积参数与叶层氮素状况的定量关系, 有助于水稻(Oryza sativa)生长信息的实时无损获取及精确追氮管理。该研究基于多年不同施氮水平和不同水稻品种的冠层高光谱数据, 系统分析了水稻的红边区域光谱、面积形状特征及其与叶层氮浓度的定量关系。结果表明, 水稻冠层红边区域微分光谱随不同氮素水平变化出现“三峰”现象, 峰值分别出现在700、720和730 nm附近, 且3个波段的峰值高低发生交替变化; 同时, 以3个峰值波段为中心与x坐标轴组成的微分光谱面积和形状相应发生变化。发现基于两两峰值波段划分所得红边子面积所构成的比值(双峰对称度)、归一化差值(归一化对称度)参数与叶层氮浓度具有密切的定量关系, 可作为估测水稻叶层氮浓度的红边面积形状参数。经曲线拟合和模型检验的结果显示, 双峰对称度DPS (A_675-700, A_675-755), 即由675~700 nm区域面积与675~755 nm区域面积的比值, 和DPS (A_730-755,A_675-700) (由730~755 nm区域面积和675~700 nm区域面积的比值)对水稻叶层氮浓度的估测效果最好, 可用于不同水稻品种和生长条件下的叶层氮浓度估测。

关键词: 水稻, 叶层, 红边区域面积, 双峰对称度, 归一化对称度, 氮浓度

Abstract:

Aims Quantifying the relationship between red edge area parameter and canopy leaf nitrogen status is the foundation for real-time and non-destructive monitoring of crop growth status and precision nitrogen fertilization in rice (Oryza sativa). Our objectives were to analyze 1) characteristics of the first-derivative reflectance spectra in red edge area and 2) the quantitative relationship of red edge area shape parameters to canopy leaf nitrogen concentrations, using different nitrogen levels and rice varieties and based on canopy hyper-spectral reflectance of field-grown rice in different years.
Methods Spectrum in the red edge area was significantly affected by different nitrogen levels and different rice varieties, and a “three-peak” feature could be observed with the first derivative spectrum at about 700, 720 and 730 nm. The maximum heights of the three peak bands changed with different nitrogen levels, so sub-areas surrounded by the first derivative spectra curve and x coordinate were formed by dividing the red edge area with the “three-peak band line”. Two random sub-areas were selected to calculate ratio (double peak symmetry, DPS) and normalization (normalized double peak symmetry, NDPS), which were related to canopy leaf nitrogen concentrations.
Important findings DPS based on the ratio of two different red edge sub-areas, and NDPS with normalization of the two different red edge sub-areas were significantly related to leaf canopy nitrogen concentrations in rice. Results of model calibration and validation indicated that DPS (A_675-700, A_675-755) and NDPS (A_675-700, A_675-755), ratio and normalized difference of areas in 675-700 to 675-755 nm red edge region, respectively, performed the best in estimating leaf canopy nitrogen concentration. Thus, these two spectral indices were suitable red edge area shape parameters for monitoring leaf canopy nitrogen concentrations in rice.

Key words: Oryza sativa, leaf canopy, red edge area, double peak symmetry, normalized double peak symmetry, nitrogen concentration

田永超, 杨杰, 姚霞, 朱艳, 曹卫星. 利用红边面积形状参数估测水稻叶层氮浓度. 植物生态学报, 2009, 33(4): 791-801. DOI: 10.3773/j.issn.1005-264x.2009.04.018

TIAN Yong-Chao, YANG Jie, YAO Xia, ZHU Yan, CAO Wei-Xing. ESTIMATION OF LEAF CANOPY NITROGEN CONCENTRATION WITH RED EDGE AREA SHAPE PARAMETER IN RICE. Chinese Journal of Plant Ecology, 2009, 33(4): 791-801. DOI: 10.3773/j.issn.1005-264x.2009.04.018

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https://www.plant-ecology.com/CN/Y2009/V33/I4/791

图/表 11

参考文献 30

[1]	Belanger MJ, Miller JR, Boyer MG (1995). Comparative relationships between some red edge parameters and seasonal leaf chlorophyll concentrations. Canada Journal of Remote Sensing, 21, 16-21.
[2]	Boochs F, Kupfer G, Dockter K, Kuhbauch W (1990). Shape of the red edge as vitality indicator for plants. International Journal of Remote Sensing, 11, 1741-1753.
[3]	Bonham-Carter GF (1988). Numerical procedures and computer program for fitting an inverted Gaussian model to vegetation reflectance data. Computers and Geosciences, 14, 339-356.
[4]	Cho MA, Skidmore AK (2006). A new technique for extracting the red edge position from hyperspectral data: the linear extrapolation method. Remote Sensing of Environment, 101, 181-193.
[5]	Clevers JGPW, Kooistra L, Salas EAL (2004). Study of heavy metal contamination in river floodplains using the red-edge position in spectroscopic data. International Journal of Remote Sensing, 25, 3883-3895.
[6]	Curran PJ (1989). Remote sensing of foliar chemistry. Remote Sensing Environment, 30, 271-278.
[7]	Curran PJ, Dungan JL, Gholz HL (1990). Exploring the relationship between reflectance red edge and chlorophyll content in slash pine. Tree Physiology, 7, 33-38. DOI URL PMID
[8]	Dawson TP, Curran PJ (1998). A new technique for interpolating the reflectance red edge position. International Journal of Remote Sensing, 19, 2133-2139.
[9]	Elvidge CD, Chen ZK (1995). Comparison of broad-band and narrow-band red and near-Infrared vegetation indices. Remote Sensing of Environment, 54, 38-48.
[10]	Filella I, Peñuelas J (1994). The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. International Journal of Remote Sensing, 15, 1459-1470.
[11]	Gausman HW (1977). Reflectance of leaf components. Remote Sensing Environment, 6, 1-9.
[12]	Gitelson AA, Merzlyak MN (1994). Quantitative estimation of chlorophyll-a using reflectance spectra: experiments with autumn chestnut and maple leaves. Journal of Photochemistry and Photobioliology, 22, 247-252.
[13]	Horler DNH, Dockray M, Barber J (1983). The red edge of plant leaf reflectance. International Journal of Remote Sensing, 4, 273-288.
[14]	Jongschaap REE, Booij R (2004). Spectral measurements at different spatial scales in potato: relating leaf, plant and canopy nitrogen status. International Journal of Applied Earth Observation and Geoinformation, 5, 204-218.
[15]	Jordan CF (1969). Derivation of leaf area index from quality of light on the forest floor. Ecology, 50, 663-666.
[16]	Lamb DW, Steyn-ross M, Schaare P, Hanna MM, Silvester W, Steyn-Ross A (2002). Estimating leaf nitrogen concentration in ryegrass (Lolium spp.) pasture using the chlorophyll red-edge: theoretical modelling and experimental observations. International Journal of Remote Sensing, 23, 3619-3648.
[17]	Li Y, Demetriades-Shah TH, Kanemasu ET, Shultis KK, Kirkham MB (1993). Use of second derivatives of canopy reflectance for monitoring prairie vegetation over different soil backgrounds. Remote Sense of Environment, 44, 81-87.
[18]	Liu LY, Wang JH, Huang WJ, Zhao CJ, Zhang B, Tong QX (2004). Estimating winter wheat plant water content using red edge parameters. International Journal of Remote Sensing, 25, 3331-3342.
[19]	Maccioni A, Agati G, Mazzinghi P (2001). New vegetation indices for remote measurement of chlorophylls based on leaf directional reflectance spectra. Journal of Photochemistry and Photobiology, 61, 52-61.
[20]	Maire GL, Francois C, Dufrene E (2004). Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sensing of Environment, 89, 1-28.
[21]	Miller JR (1991). Season patterns in leaf reflectance red edge characteristics. International Journal of Remote Sensing, 12, 1509-1523.
[22]	Miller JR, Hare EW, Wu J (1990). Quantitative characterization of the red edge reflectance. An inverted- Gaussian reflectance model. International Journal of Remote Sensing, 11, 1755-1773.
[23]	Munden R, Curran PJ, Catt JA (1994). The relationship between red edge and chlorophyll concentration in broadbalk winter wheat experiment at rothamsted. International Journal of Remote Sensing, 15, 705-709.
[24]	Pu RL, Gong P, Biging GS, Larrieu MR (2003). Extraction of red edge optical parameters from Hyperion data for estimation of forest leaf area index. IEEE Transactions on Geoscience and Remote Sensing, 41, 916-921. DOI URL
[25]	Shibayama M, Akiyama T (1989). Seasonal visible, near-infrared and mid-infrared spectra of rice canopies in relation to LAI and above-ground dry phytomass. Remote Sensing of Environment, 27, 119-127. DOI URL
[26]	Sims DA, Gamon JA (2002). Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 81, 337-354. DOI URL
[27]	Sun L (孙莉), Chen X (陈曦), Wu JJ (武建军), Feng XW (冯先伟), Bao AM (包安明), Ma YQ (马亚琴), Wang DW (王登伟) (2006). Changes of canopy leaf hyper-spectral reflectance under water stress in cotton. Chinese Science Bulletin (科学通报), 51(Suppl.), 143-147. (in Chinese with English abstract)
[28]	Tsai F, Philpot W (1998). Derivative analysis of hyperspectral data. Remote Sensing of Environment, 66, 41-51. DOI URL
[29]	Wang YY (王圆圆), Chen YH (陈云浩), Li J (李京), Huang WJ (黄文江) (2007). Two new red edge indices as indicators for stripe rust disease severity of winter wheat. Journal of Remote Sensing (遥感学报), 11, 875-881. (in Chinese with English abstract) DOI URL
[30]	Wu CS (吴长山), Tong QX (童庆禧), Zhen LF (郑兰芬), Liu WD (刘伟东) (2000). Correlation analysis between spectral data and chlorophyll of rice and maze. Journal of Basic Science and Engineering (应用基础与工程科学学报), 8, 31-37. (in Chinese with English abstract)

试验和年份 Experiment & year		品种 Cultivar	施氮水平 Nitrogen rate (kg N·hm^-2)	小区面积 Plot area	采样日期 Sampling date (month / day)	数据用途 Data using
试验1 Exp.1	2004	‘武香粳9号’ WXJ 9	0, 105, 210, 315	18 m² (4 m×4.5 m)	9/2, 9/12, 9/23, 9/30,10/12	模型检验 Model validation
		‘华粳2号’ HJ 2			8/27, 9/2, 9/11, 9/23, 9/30
		‘日本晴’ NPB			8/27, 9/2, 9/11, 9/23
试验2 Exp.2	2005	‘武香粳14号’ WXJ 14	0, 90, 270, 420	31.5 m² (3.5 m×9 m)	7/15, 8/9, 8/16, 9/5, 9/13, 9/29, 10/10,10/23	建模 Model calibration
试验2 Exp.2	2005	27123	0, 90, 270, 420	31.5 m² (3.5 m×9 m)	7/15, 8/9, 8/16, 9/5, 9/13, 9/29, 10/10,10/23	建模 Model calibration
试验3 Exp.3	2006	‘武香粳14号’ WXJ 14	0, 90, 270, 405	25 m² (5.0 m×5.0 m)	7/28, 8/9, 8/18, 9/7, 9/17, 9/25, 10/5	建模 Model calibration
试验3 Exp.3	2006	‘27123’	0, 90, 270, 405	25 m² (5.0 m×5.0 m)	7/28, 8/9, 8/18, 9/7, 9/17, 9/25, 10/5	建模 Model calibration
试验4 Exp.4	2006	‘盐粳9967’ YJ 9967	0, 105, 210, 315, 420	8 100 m² (90 m×90 m)	7/29, 9/10	模型检验 Model validation
试验5 Exp.5	2007	‘扬辐粳8号’ YFJ 8	0, 210, 420	8 100 m² (90 m×90 m)	9/8, 10/9	模型检验 Model validation

试验和年份 Experiment & year		品种 Cultivar	施氮水平 Nitrogen rate (kg N·hm^-2)	小区面积 Plot area	采样日期 Sampling date (month / day)	数据用途 Data using
试验1 Exp.1	2004	‘武香粳9号’ WXJ 9	0, 105, 210, 315	18 m² (4 m×4.5 m)	9/2, 9/12, 9/23, 9/30,10/12	模型检验 Model validation
		‘华粳2号’ HJ 2			8/27, 9/2, 9/11, 9/23, 9/30
		‘日本晴’ NPB			8/27, 9/2, 9/11, 9/23
试验2 Exp.2	2005	‘武香粳14号’ WXJ 14	0, 90, 270, 420	31.5 m² (3.5 m×9 m)	7/15, 8/9, 8/16, 9/5, 9/13, 9/29, 10/10,10/23	建模 Model calibration
试验2 Exp.2	2005	27123	0, 90, 270, 420	31.5 m² (3.5 m×9 m)	7/15, 8/9, 8/16, 9/5, 9/13, 9/29, 10/10,10/23	建模 Model calibration
试验3 Exp.3	2006	‘武香粳14号’ WXJ 14	0, 90, 270, 405	25 m² (5.0 m×5.0 m)	7/28, 8/9, 8/18, 9/7, 9/17, 9/25, 10/5	建模 Model calibration
试验3 Exp.3	2006	‘27123’	0, 90, 270, 405	25 m² (5.0 m×5.0 m)	7/28, 8/9, 8/18, 9/7, 9/17, 9/25, 10/5	建模 Model calibration
试验4 Exp.4	2006	‘盐粳9967’ YJ 9967	0, 105, 210, 315, 420	8 100 m² (90 m×90 m)	7/29, 9/10	模型检验 Model validation
试验5 Exp.5	2007	‘扬辐粳8号’ YFJ 8	0, 210, 420	8 100 m² (90 m×90 m)	9/8, 10/9	模型检验 Model validation

红边面积参数 Red edge area index	算法 Algorithm	文献出处 Reference
625~795 nm 波段范围一阶导数光谱面积 Sum of FD between 625 and 795 nm	R₇₉₅-R₆₂₅	Elvidge & Chen, 1995
680~780 nm 波段范围一阶导数光谱面积 Sum of FD between 680 and 780 nm	R₇₈₀-R₆₈₀	Filella & Pennelas, 1994
红边与红谷面积比值 Ratio of red edge to red vale	A_{red edge}/A_{red vale}	Maccioni et al. 2001
双差指数 DD	(R_(a+Δ) -R_a)- (R_(b+Δ)-R_b)	Maire et al. 2004
归一化双峰指数 NDPS (A_a-b, A_c-d)	$\frac{\left(R_{a}-R_{b}\right)-\left(R_{c}-R_{d}\right)}{\left(R_{a}-R_{b}\right)+\left(R_{c}-R_{d}\right)}$	The present study
双峰对称度 DPS (A_a-b, A_c-d)	(R_a-R_b) / (R_c-R_d)	The present study

红边面积参数 Red edge area index	算法 Algorithm	文献出处 Reference
625~795 nm 波段范围一阶导数光谱面积 Sum of FD between 625 and 795 nm	R₇₉₅-R₆₂₅	Elvidge & Chen, 1995
680~780 nm 波段范围一阶导数光谱面积 Sum of FD between 680 and 780 nm	R₇₈₀-R₆₈₀	Filella & Pennelas, 1994
红边与红谷面积比值 Ratio of red edge to red vale	A_{red edge}/A_{red vale}	Maccioni et al. 2001
双差指数 DD	(R_(a+Δ) -R_a)- (R_(b+Δ)-R_b)	Maire et al. 2004
归一化双峰指数 NDPS (A_a-b, A_c-d)	$\frac{\left(R_{a}-R_{b}\right)-\left(R_{c}-R_{d}\right)}{\left(R_{a}-R_{b}\right)+\left(R_{c}-R_{d}\right)}$	The present study
双峰对称度 DPS (A_a-b, A_c-d)	(R_a-R_b) / (R_c-R_d)	The present study

参数 Parameter	模型形式 Model type	R²	根均方差 RMSE (%)	平均相对误差 RE
DPS (A_730-755, A_675-700)	直线 Linear	0.76	14.00	0.11
DPS (A_730-755, A_675-700)	曲线 Curve	0.81	10.11	0.09
DD (A_730-755, A_675-700)	直线 Linear	0.72	19.82	0.15
NDPS (A_730-755, A_675-700)	直线 Linear	0.80	10.00	0.09
DPS (A_675-700, A_700-755)	直线 Linear	0.78	12.10	0.11
DPS (A_675-700, A_700-755)	曲线 Curve	0.82	10.26	0.09
DD (A_675-700, A_700-755)	直线 Linear	0.58	23.63	0.19
NDPS (A_675-700, A_700-755)	直线 Linear	0.80	10.51	0.09
NDPS (A_675-700, A_700-755)	曲线 Curve	0.81	9.95	0.09
DPS (A_675-730, A_730-755)	曲线 Curve	0.77	10.40	0.09
DD (A_675-730, A_730-755)	直线 Linear	0.59	22.08	0.16
NDPS (A_675-730, A_730-755)	直线 Linear	0.77	11.13	0.10
DPS (A_675-720, A_720-755)	曲线 Curve	0.79	10.78	0.14
DD (A_675-720, A_720-755)	直线 Linear	0.74	18.10	0.09
NDPS (A_675-720, A_720-755)	直线 Linear	0.79	10.23	0.09
DPS (A_720-730, A_700-720)	直线 Linear	0.77	11.24	0.10
DPS1 (A_700-720, A_700-730)	直线 Linear	0.77	10.60	0.09
DD (A_720-730, A_700-720)	直线 Linear	0.49	25.22	0.18
NDPS (A_720-730, A_700-720)	直线 Linear	0.77	10.60	0.09
DPS (A_675-720, A_675-755)	直线 Linear	0.79	11.19	0.10
DPS (A_675-730, A_675-755)	直线 Linear	0.77	10.51	0.09
DPS (A_675-700, A_675-755)	直线 Linear	0.80	10.51	0.09
DPS (A_675-700, A_675-755)	曲线 Curve	0.81	9.95	0.09
A_{red edge}/A_{red vale}	曲线 Curve	0.54	13.54	0.12
R_795-R₆₂₅	直线 Linear	0.39	23.59	0.19
R_780-R₆₈₀	直线 Linear	0.41	23.94	0.20