基于高光谱遥感的小麦叶干重和叶面积指数监测

doi:10.3773/j.issn.1005-264x.2009.01.004

摘要/Abstract

摘要：

生物量和叶面积指数(LAI)是描述作物长势的重要参数, 叶干重和LAI的实时动态监测对小麦(Triticum aestivum)生长诊断和管理调控具有重要意义。为分析多种高光谱参数估算小麦叶干重和LAI的效果, 建立小麦叶干重和LAI的定量监测模型, 该研究连续3年采用不同小麦品种进行不同施氮水平的大田试验, 于小麦不同生育期采集田间冠层高光谱数据并测定叶片叶干重和LAI。试验结果显示, 小麦叶干重和LAI随施氮水平的提高而增加, 随生育进程呈单峰动态变化模式。小麦叶干重和LAI与光谱反射率间相关性较好的区域主要位于红光波段(590~710 nm, r<-0.60)和近红外波段(745~1 130 nm, r>0.69)。对于不同试验条件下的叶干重和LAI, 可以使用统一的光谱参数进行定量反演, 其中基于RVI (810, 560)、FD755、GM1、SARVI (MSS)和TC3等光谱参数的方程拟合效果较好。经不同年际独立试验数据的检验表明, 以参数RVI (810, 560)、GM1、SARVI (MSS)、PSSRb、(R_750-800/R_695-740) -1、VOG2和MSR705为变量建立的叶干重和LAI监测模型均给出较好的检验结果。因此, 利用关键特征光谱参数可以有效地评价小麦叶片生长状况, 尤其是光谱参数RVI (810, 560)、GM1和SARVI (MSS)可以对不同条件下小麦叶干重和LAI进行准确可靠的监测。

关键词: 小麦, 叶干重, 叶面积指数, 高光谱遥感, 监测模型

Abstract:

Aims Biomass and leaf area index (LAI) are important parameters for indicating crop growth potential and photosynthetic productivity in wheat. Non-destructive, quick assessment of leaf dry weight and LAI is necessary for growth diagnosis and cultural regulation in wheat production. The objectives of this study were to determine the relationships of leaf dry weight and LAI to ground-based canopy hyperspectral reflectance and spectral parameters and to derive regression equations for monitoring leaf dry weight and LAI in winter wheat (Triticum aestivum) with hyperspectral remote sensing.
Methods Three field experiments were conducted with different wheat varieties and nitrogen levels for three growing seasons, and time-course measurements were taken on canopy hyperspectral reflectance and leaf dry weight and LAI during the experiments. Experiment one was conducted in 2005-2006 to construct a monitoring model with four N rates of 0, 90, 180 and 270 kg·hm^-2 using cultivars ‘Ningmai9’ and ‘Yumai34’ (low and high protein types, respectively). Experiment two was undertaken in 2004-2005 to construct a monitoring model with four N rates of 0, 75, 150 and 225 kg·hm^-2 using cultivars ‘Ningmai9’, ‘Yangmai12’ and ‘Yumai34’ (low, medium, high protein types, respectively). Experiment three was conducted in 2003-2004 to test a monitoring model with four N rates of 0, 75, 150, 225 and 300 kg·hm^-2 using cultivars ‘Ningmai9’, ‘Huaimai20’ and ‘Xuzhou26’ (low, medium, high protein types, respectively).
Important findings Leaf dry weight and LAI in wheat increased with increasing nitrogen rates and with significant differences between stages of growth. The dynamics of leaf dry weight and LAI during growth exhibited single peak patterns. The sensitive spectral bands were located mostly within red light and near infrared regions, with correlation coefficients <-0.60 in 590~710 nm and >0.69 in 745~1 130 nm. The regression analyses between existing vegetation indices and leaf dry weight and LAI revealed that some key spectral parameters could accurately estimate changes in leaf dry weight and LAI across a broad range of stages of growth, nitrogen levels and growing seasons, with unified spectral parameters for each growth parameter. Among them, regression models based on RVI (810, 560), FD755, GMI, SARVI (MSS) and TC3 produced better estimation of leaf dry weight and LAI. Testing of the monitoring models with an independent dataset indicated that the spectral indices of RVI (810, 560), GMI, SARVI (MSS), PSSRb, (R_750-800/R_695-740)-1, VOG2 and mSR705 gave accurate growth estimation under the experimental conditions. Overall, leaf dry weight and LAI in wheat could be monitored by key vegetation indices, with more reliable estimation from RVI (810, 560), GMI and SARVI (MSS).

Key words: wheat (Triticum aestivum), leaf dry weight, leaf area index (LAI), hyperspectral remote sensing, monitoring model

冯伟, 朱艳, 姚霞, 田永超, 曹卫星. 基于高光谱遥感的小麦叶干重和叶面积指数监测. 植物生态学报, 2009, 33(1): 34-44. DOI: 10.3773/j.issn.1005-264x.2009.01.004

FENG Wei, ZHU Yan, YAO Xia, TIAN Yong-Chao, CAO Wei-Xing. MONITORING LEAF DRY WEIGHT AND LEAF AREA INDEX IN WHEAT WITH HYPERSPECTRAL REMOTE SENSING. Chinese Journal of Plant Ecology, 2009, 33(1): 34-44. DOI: 10.3773/j.issn.1005-264x.2009.01.004

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https://www.plant-ecology.com/CN/Y2009/V33/I1/34

图/表 6

参考文献 28

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光谱参数 Spectral parameter	定义与算法 Definition and algorithm	参考文献 Reference
RVI	两波段反射率比值 Ratio of reflectance at λ1 and λ2 Rλ1/Rλ2	Rouse et al. (1974)
SARVI (MSS)	基于MSS波段的土壤调整比值植被指数 Soil adjusted ratio vegetation indices on data from MSS MSS7/(MSS5+b/a)	Lyon et al. (1998)
TC3	穗帽变换第三分量 The third variable extracted using Tasseled Cap transformation -0.829MSS4+0.522MSS5-0.039MSS6+0.194MSS7	Kauth & Thomas (1976)
GM1	750 nm与550 nm处反射率比值 Ratio of reflectance at 750 nm to reflectivity at 550 nm R750/R550	Gitelson & Merzlyak (1994)
VOG2	红边光谱比值植被指数 Red edge reflectance-ratio indices (R734-R747)/(R715+R726)	Zarco-Tejada et al. (2001)
MSR705	修正SR705植被指数 Simple ratio indices modified on SR705 R₇₅₀-R₄₄₅/R₇₀₅-R₄₄₅	Sims & Gamon (2002)
PSSRb	Ratio of reflectance at 800 and 635 nm R800/R635	Blackburn (1998)
(R_750-800/R_695-740)-1	宽波段695~740 nm反射率倒数与750~800 nm反射率组合指数 Broad spectral bands indice with reciprocal reflectance from 695 to 740 nm and reflectance from 750 to 800 nm (R_750-800/R_695-740)-1	Gitelson et al. (2003)
Dr/Db	红边内一阶微分最大值与蓝边内一阶微分最大值的比值 Radio of the red edge amplitude and the blue edge amplitude Max (dR(λi)/dλi)／max (dR(λj)/dλj), i=680-760 nm, j=490-530 nm	Wang XZ (王秀珍) et al. (2003)
SDr/SDb	红边内一阶微分总和与蓝边内一阶微分总和的比值 Radio of the red-edge integral areas and the blue-edge integral areas $\sum\limits_{j=680}^{755}(R_{i+1}-R_{i-1})/2 / \sum\limits_{i=490}^{530}(R_{i+1}-R_{i-1})/2$	Wang XZ (王秀珍) et al. (2003)
MSR	修正型简单比值指数 Modified simple ratio (Rλ1/Rλ2-1)/[(Rλ1/Rλ2)^0.5-1]	Chen (1996)

光谱参数 Spectral parameter	定义与算法 Definition and algorithm	参考文献 Reference
RVI	两波段反射率比值 Ratio of reflectance at λ1 and λ2 Rλ1/Rλ2	Rouse et al. (1974)
SARVI (MSS)	基于MSS波段的土壤调整比值植被指数 Soil adjusted ratio vegetation indices on data from MSS MSS7/(MSS5+b/a)	Lyon et al. (1998)
TC3	穗帽变换第三分量 The third variable extracted using Tasseled Cap transformation -0.829MSS4+0.522MSS5-0.039MSS6+0.194MSS7	Kauth & Thomas (1976)
GM1	750 nm与550 nm处反射率比值 Ratio of reflectance at 750 nm to reflectivity at 550 nm R750/R550	Gitelson & Merzlyak (1994)
VOG2	红边光谱比值植被指数 Red edge reflectance-ratio indices (R734-R747)/(R715+R726)	Zarco-Tejada et al. (2001)
MSR705	修正SR705植被指数 Simple ratio indices modified on SR705 R₇₅₀-R₄₄₅/R₇₀₅-R₄₄₅	Sims & Gamon (2002)
PSSRb	Ratio of reflectance at 800 and 635 nm R800/R635	Blackburn (1998)
(R_750-800/R_695-740)-1	宽波段695~740 nm反射率倒数与750~800 nm反射率组合指数 Broad spectral bands indice with reciprocal reflectance from 695 to 740 nm and reflectance from 750 to 800 nm (R_750-800/R_695-740)-1	Gitelson et al. (2003)
Dr/Db	红边内一阶微分最大值与蓝边内一阶微分最大值的比值 Radio of the red edge amplitude and the blue edge amplitude Max (dR(λi)/dλi)／max (dR(λj)/dλj), i=680-760 nm, j=490-530 nm	Wang XZ (王秀珍) et al. (2003)
SDr/SDb	红边内一阶微分总和与蓝边内一阶微分总和的比值 Radio of the red-edge integral areas and the blue-edge integral areas $\sum\limits_{j=680}^{755}(R_{i+1}-R_{i-1})/2 / \sum\limits_{i=490}^{530}(R_{i+1}-R_{i-1})/2$	Wang XZ (王秀珍) et al. (2003)
MSR	修正型简单比值指数 Modified simple ratio (Rλ1/Rλ2-1)/[(Rλ1/Rλ2)^0.5-1]	Chen (1996)

光谱参数 Spectral parameter	叶干重 Leaf dry weight			叶面积指数 LAI
光谱参数 Spectral parameter	回归方程 Equation	R²	SE	回归方程 Equation	R²	SE
RVI (810, 560)	y = 0.024 9x + 0.007 7	0.819	0.033	y = 0.561 3x + 0.183 3	0.767	0.846
FD755	y = 0.609 2x + 0.054 6	0.817	0.033	y = 13.957x + 1.212	0.788	0.820
GM1	y = 0.031 4x - 0.006 4	0.817	0.033	y = 0.703 2x - 0.104 4	0.751	0.872
SARVI (MSS)	y = 0.012 6x + 0.040 6	0.814	0.033	y = 0.278 7x + 0.967 2	0.736	0.896
TC3	y = 0.047 3x - 0.027	0.806	0.034	y = 1.065 5x - 0.590 2	0.751	0.877
RVI (900, 680)	y = 0.008 7x + 0.050 5	0.807	0.034	y = 0.192 5x + 1.200 4	0.719	0.923
MSR (800, 670)	y = 0.054 2x + 0.033 4	0.814	0.034	y = 1.192 1x + 0.826 5	0.724	0.923
SDr/SDb	y = 0.011 6x + 0.019 5	0.805	0.034	y = 0.259 7x + 0.464 7	0.746	0.874
PSSRb	y = 0.011 7x + 0.045 8	0.806	0.034	y = 0.259 3x + 1.089	0.724	0.913
(R_750-800/R_695-740)-1	y = 0.107 2x + 0.030 6	0.793	0.035	y = 2.422 2x + 0.698 1	0.744	0.884
VOG2	y = -0.540 6x + 0.049 8	0.788	0.035	y = -12.231x + 1.127 9	0.741	0.885
MSR705	y = 0.056 8x + 0.014 2	0.795	0.035	y = 1.265 6x + 0.364 9	0.727	0.910
Dr/Db	y = 0.023 6x + 0.012	0.774	0.036	y = 0.530 7x + 0.296 6	0.717	0.913
R870	y = 0.006 7x - 0.082	0.714	0.041	y = 0.145 8x - 1.689 3	0.629	1.022

光谱参数 Spectral parameter	叶干重 Leaf dry weight			叶面积指数 LAI
光谱参数 Spectral parameter	回归方程 Equation	R²	SE	回归方程 Equation	R²	SE
RVI (810, 560)	y = 0.024 9x + 0.007 7	0.819	0.033	y = 0.561 3x + 0.183 3	0.767	0.846
FD755	y = 0.609 2x + 0.054 6	0.817	0.033	y = 13.957x + 1.212	0.788	0.820
GM1	y = 0.031 4x - 0.006 4	0.817	0.033	y = 0.703 2x - 0.104 4	0.751	0.872
SARVI (MSS)	y = 0.012 6x + 0.040 6	0.814	0.033	y = 0.278 7x + 0.967 2	0.736	0.896
TC3	y = 0.047 3x - 0.027	0.806	0.034	y = 1.065 5x - 0.590 2	0.751	0.877
RVI (900, 680)	y = 0.008 7x + 0.050 5	0.807	0.034	y = 0.192 5x + 1.200 4	0.719	0.923
MSR (800, 670)	y = 0.054 2x + 0.033 4	0.814	0.034	y = 1.192 1x + 0.826 5	0.724	0.923
SDr/SDb	y = 0.011 6x + 0.019 5	0.805	0.034	y = 0.259 7x + 0.464 7	0.746	0.874
PSSRb	y = 0.011 7x + 0.045 8	0.806	0.034	y = 0.259 3x + 1.089	0.724	0.913
(R_750-800/R_695-740)-1	y = 0.107 2x + 0.030 6	0.793	0.035	y = 2.422 2x + 0.698 1	0.744	0.884
VOG2	y = -0.540 6x + 0.049 8	0.788	0.035	y = -12.231x + 1.127 9	0.741	0.885
MSR705	y = 0.056 8x + 0.014 2	0.795	0.035	y = 1.265 6x + 0.364 9	0.727	0.910
Dr/Db	y = 0.023 6x + 0.012	0.774	0.036	y = 0.530 7x + 0.296 6	0.717	0.913
R870	y = 0.006 7x - 0.082	0.714	0.041	y = 0.145 8x - 1.689 3	0.629	1.022