The Research of Vegetation Water Content Based on Spectrum Analysis and Angle Slope Index
DENG Bing1, YANG Wu-nian1*, MU Nan2, ZHANG Chao1
1. Key Laboratory of Geoscience Spatial Information Technology, Ministry of Land and Resources, Chengdu University of Technology, Chengdu 610059, China 2. Sichuan Institute of Land Planning and Survey, Chengdu 610045, China
Abstract：Vegetation water content is an important indicator of vegetal state, and a vital parameter of studying agriculture, ecological and hydrological. The diagnosis of vegetation water content has great significance for forest fire forecast and natural vegetation drought condition monitoring. The correlation analysis of the vegetation spectral reflectance and vegetation water content shows that the relativity between the spectral reflectance of different wavelengths and the vegetation water content varies considerably. The spectral reflectance of red band of visible light (620～700 nm) and the near-infrared band(800～1 350, 1 600～1 950, 2 200～2 400 nm) had a higher correlation with the vegetation water content. The slope angle indexes were used as parameters for estimating the vegetation water content based on analyzing the relation between the slope angle indexes and vegetation water content. An evaluation model of vegetation water content was set up by utilizing statistical linear regression model method. The band of 660, 850, 1 630, 2 200 nm were selected as RED, NIR, SWIR1 and SWIR2 band value of the slope angle index based on the analysis of the correlation between spectral reflectance and vegetation water content. A large amount of vegetation spectral information and vegetation water content were collected in the study area(the upstream of Minjiang River), and the linear regression model of the slope angle index (SANI, SASI, ANIR) and vegetation water content (FMC) was build. The linear regression model of ANIR and FMC has the highest of linear fitting and the linearity is up to 0.791. The near infrared angle index(ANIR)was improved on the basis of the analysis the linear regression results of angle slope vegetation index and water content. Near infrared angle normalized index (NANI) and near infrared angle slope index (NASI) were defined, and the linear regression model was established. Compared with the slope angle index (SANI, SASI, ANIR) which were proposed by Palacios-Orueta, NANI had more advantages in the vegetation water content inversion in the study area. The determination coefficient (R2) of the inversion model increased from 0.791 to 0.853, and root-mean-square error (RMSE) reduced from 0.047 to 0.039. Angle slope index had higher linear fitting and estimation accuracy by improving the angle of slope index. NANI and FMC linear regression model was established to estimate the vegetation water content in the study area. In this paper, the main innovation point is that the slope angle index NANI and NASI has been proposed on the basis of predecessors’ research results, and the improved angle slope index has higher linear fitting and estimation accuracy compared with SANI, SASI, ANIR.
Key words：Spectrum analysis;Angle slope index;Vegetation water content;The upstream of Minjiang River
邓 兵1，杨武年1*，慕 楠2，张 超1 . 基于光谱分析与角度斜率指数的植被含水量研究 [J]. 光谱学与光谱分析, 2016, 36(08): 2546-2552.
DENG Bing1, YANG Wu-nian1*, MU Nan2, ZHANG Chao1 . The Research of Vegetation Water Content Based on Spectrum Analysis and Angle Slope Index . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(08): 2546-2552.
[1] Ceccato P, Gobron N, Flasse S, et al. Remote Sens. Environ., 2002, 82: 188. [2] Zarco-Tejada P J, Rueda C A, Ustin S L. Remote Sens. Environ., 2003, 85: 109. [3] Kramer P J. Water Relations of Plants: Academic Press, 1983. 489. [4] Carter G A. American Journal of Botany, 1991，(7): 916. [5] Williams C W R A. Journal of the Optical Society of America, 1971，(10): 1316. [6] LI Yu-xia, YANG Wu-nian, TONG Ling, et al(李玉霞，杨武年，童 玲, 等). Acta Optica Sinica (光学学报), 2009，(5): 1403. [7] ZHANG Jia-hua, XU Yun, YAO Feng-mei, et al(张佳华，许 云，姚凤梅, 等). Scientia Sinica Technologica (中国科学: 技术科学), 2010，(10): 1121. [8] Grace J. Journal of Ecology, 1982，(2): 696. [9] Garratt C J T A. Applied Optics, 1977，(3): 635. [10] Bc G, Afh G. Remote Sensing of Environment, 1995, (3): 155. [11] PAN Pei-fen, YANG Wu-nian, JIAN Ji, et al(潘佩芬，杨武年，简 季, 等). Remote Sensing Information(遥感信息), 2013，(3): 69. [12] PAN Pei-fen, YANG Wu-nian, DAI Xiao-ai(潘佩芬，杨武年，戴晓爱). Remote Sensing for Land & Resources(国土资源遥感), 2013，(3): 38. [13] Tucker C J. Remote Sensing of Environment, 1980，(1): 23. [14] Khanna S, Palacios-Orueta A, Whiting M L, et al. Remote Sensing of Environment, 2007, 109(2): 154. [15] Palacios-Orueta A, Susan U. Remote Sensing of Environment, 1996, 57(2): 108. [16] YU Jun-ming, LAN Chao-zhen, ZHOU Yi, et al(于君明，蓝朝桢，周 艺, 等). Geomatics and Information Science of Wuhan University(武汉大学学报·信息科学版), 2009，(2): 210. [17] ZHANG Wen-hui, LU Tao, ZHOU Jian-yun, et al(张文辉，卢 涛，周建云, 等). Acta Botanica Boreali-Occidentalia Sinica(西北植物学报), 2003，(6): 888. [18] WU Hai-long，YU Xin-xiao，ZHANG Zhen-ming, et al(吴海龙，余新晓，张振明, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2014，34(6): 1615.
