利用高光谱数据快速估算高羊茅牧草光合色素的研究

草业学报 ›› 2009, Vol. 18 ›› Issue (4): 94-102.

利用高光谱数据快速估算高羊茅牧草光合色素的研究

钱育蓉^1,2,杨峰¹,李建龙^1*,干晓宇¹,杨齐¹,王卫源³

1.南京大学生命科学学院,江苏南京 210093;
2.新疆大学软件学院,新疆乌鲁木齐 830046;
3.新疆军区司令部机要处,新疆乌鲁木齐 830042

收稿日期:2008-10-28 出版日期:2009-08-20 发布日期:2009-08-20
作者简介:钱育蓉(1980-),女,满族,山东武城人,讲师,博士。E-mail: QYR@xju.edu.cn
基金资助:
国家高技术(863计划)专题项目(2007AA10Z231)资助。

Estimation of photosynthetic pigment of Festuca arundinacea using hyper-spectral data

QIAN Yu-rong^1,2, YANG Feng¹, LI Jian-long¹, GAN Xiao-yu¹, YANG Qi¹, WANG Wei-yuan³

1.School of Life Science, Nanjing University, Nanjing 210093, China; 2.Software College, Xinjiang
University, Urumqi 830046, China; 3.Confidential Department, Xinjiang
Military Area Command, Urumqi 830042, China

Received:2008-10-28 Online:2009-08-20 Published:2009-08-20

摘要/Abstract

摘要： 光合色素是植物体进行初级生产的重要物质, 能够间接反映植被的健康状况与光合能力,同时,高光谱遥感为快速、大面积监测植被的色素变化提供了可能。本研究以高羊茅牧草为材料,实测了高羊茅冠层的高光谱反射率与光合色素含量数据, 对二者进行了相关分析,从5大类12个高光谱特征变量{R_g、R₇₁₀、Dλ_r、Dλ₇₀₀、D[log(1/λ_i)]、S_g、RVI_a、RVI_b、PSSR、PSND、R_ch、CARI}中挑选了光合色素敏感参数,建立了植被指数光合色素估算模型。结果表明,光合色素叶绿素a、叶绿素b、总叶绿素和类胡萝卜素与原始光谱进行相关性比较分析时,叶绿素b效果最好;原始光谱、光谱一阶导和倒数对数一阶导3种光谱形式与光合色素进行比较分析时,光谱一阶导在700 nm附近与光合色素相关性最好,相关系数为-0.897;通过12个高光谱特征变量与光合色素相关性比较,选择达到极显著水平的6个变量:R_g、Dλ₇₀₀、D[log(1/R₇₃₀)]、RVI_b、R_ch、CARI进行光合色素含量的回归模型建立,这为利用物理方法快速、无损伤探测高羊茅牧草的营养状况及牧草质量提供了理论支持。

Abstract: Photosynthetic pigments of vegetation are a primary product and important materials that can indirectly reflect health status and photosynthetic capacity of vegetation. Hyper-spectral remote sensing provides the possibility for rapid, large-scale monitoring of vegetation chlorophyll change. In this study, canopy reflectance spectra and chlorophyll content of cool-season Festuca arundinacea were measured and their relationship analyzed. The top-five chlorophyll sensitive parameters were selected from 12 hyperspectral characteristic variables, and then used to establish a vegetation index model to estimate photosynthetic pigments. 1) The relationship between Chlb and the original spectrum was the best among the photosynthetic pigments: Chla, Chlb, Chls, and Cars. 2) The first derivative spectra gave a better relationship (correlation coefficient: -0.897) with vegetation photosynthetic pigments near 700 nm wavelength than the other two hyperspectrum forms {R and D[log(1/R)]}. Finally, after comparing the correlation between photosynthetic pigment concentrations and hyperspectral data, the most significant variables {R_g, Dλ₇₀₀, D[log(1/R₇₃₀)], RVI_b, R_ch, CARI} were selected from twelve variables to establish the regression model of photosynthetic pigments. All of these provide a theoretical basis for rapid, non-invasive detection of nutritional status and meadow quality of F. arundinacea.

中图分类号:

钱育蓉,杨峰,李建龙,干晓宇,杨齐,王卫源. 利用高光谱数据快速估算高羊茅牧草光合色素的研究[J]. 草业学报, 2009, 18(4): 94-102.

QIAN Yu-rong, YANG Feng, LI Jian-long, GAN Xiao-yu, YANG Qi, WANG Wei-yuan. Estimation of photosynthetic pigment of Festuca arundinacea using hyper-spectral data[J]. Acta Prataculturae Sinica, 2009, 18(4): 94-102.

参考文献

[1] 王小利,刘正书,牟琼,等. 高羊茅遗传多样性RAPD分析[J]. 草业学报,2007,16(4):82-86.
[2] 杨志民,陈煜,韩烈保.不同光照强度对高羊茅形态和生理指标的影响[J].草业学报,2007,16(6):23-29.
[3] Filella D, Perruelas J. The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status[J]. International Journal of Remote Sensing, 1994, 15(7): 1459-1470.
[4] Madeira A C, Mendonca A, Ferreira M E, et al. Relationship between spectroradiometric and chlorophyll measurements in green Beans[J]. Communication in Soil Science and Plant Analysis, 2000, 31(5-6): 631-643.
[5] 吴长山, 童庆禧, 郑兰芬,等. 水稻、玉米的光谱数据与叶绿素的相关分析[J]. 应用基础与工程科学学报, 2000, 8(1): 31-37.
[6] 唐延林, 黄敬峰, 王秀珍,等.玉米叶片高光谱特征及与叶绿素、类胡萝卜素相关性的研究[J].玉米科学, 2008, 16(2):71-76.
[7] Blackburn G A. Quantifying chlorophylls and caroteniods at leaf and canopy scales: An evaluation of some hyperspectral approaches[J]. Remote Sensing of Environment, 1998, 66:273-285.
[8] 程乾, 黄敬峰, 王人潮,等. MODIS植被指数与水稻叶面积指数及叶片叶绿素含量相关性研究[J]. 应用生态学报, 2004, 15(8):1363-1367.
[9] 赵祥,刘素红,王培,等.基于高光谱数据的小麦叶绿素含量反演[J].地理与地理信息科学, 2004, 20(3): 36-39.
[10] 高俊风. 植物生理学实验指导[M]. 北京: 高等教育出版社, 2006. 101-103.
[11] Gupta R K, Vijayan D, Prasad T S. New hyperspectral vegetation characterization parameters[J]. Advances in Space Research, 2001, 28(1):201-206.
[12] Borge N H, Lebianc E. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy cholorophyll density[J]. Remote Sensing of Environment, 2000, 76:156-172.
[13] 宋开山, 张柏, 王宗明,等.小波分析在大豆叶绿素含量高光谱反演中的应用[J]. 中国农学通报, 2008, 22(9):101-108.
[14] 王秀珍, 王人潮, 黄敬峰.微分光谱遥感及其在水稻农学参数测定上的应用研究[J]. 农业工程学报, 2002, 18(1): 9-13.
[15] 王秀珍,王人潮,李云梅,等.不同氮素营养水平的水稻冠层光谱红边参数及其应用研究[J]. 浙江大学学报(农业与生命科学版),2001, 27(3): 301-306.
[16] 浦瑞良,宫鹏. 森林生物化学与CASI 高光谱分辨率遥感数据的相关分析[J]. 遥感学报, 1997, 1(2): 115-123.
[17] 吴长山, 项月琴, 郑兰芬, 等. 利用高光谱数据对作物群体叶绿素密度估算的研究[J].遥感学报, 2000, 4(3): 228-232.
[18] 张金恒, 王珂, 王人潮. 高光谱评价植被叶绿素含量的研究进展[J]. 上海交通大学学报(农业科学版), 2003, 21(1): 74-80.