|
|
|
|
|
|
Study on Information and Model of High Polarization Hyperspectral about Vegetation-Soil Mixed Pixels Based on Different Vegetation Indices |
MA Shuang, HAN Yang*, HUANG Meng-xue, WANG Ying, WU Miao-miao, JIN Lun |
School of Geographical Sciences, Northeast Normal University, Changchun 130024, China |
|
|
Abstract Hyperspectral remote sensing is increasingly used to determine the feature components of mixed pixels and their proportions. In this paper, the vegetation soil mixed pixels of different area ratio were set as the research object, and polarization means and ASD FieldSpec3 spectrometer was applied to obtain polarized reflectance spectrum curve of vegetation soil mixed pixels to calculated the proportion of 8 different vegetation index and discuss the hyperspectral polarization characteristics of vegetation soil mixed pixels under different area ratio and different polarization angle. The study found that as the increasing of the proportion of leaves, vegetation soil spectral curves increasingly appeared “5 valleys and 4 peaks”, and the positions of the peak and bottom were basically the same. The larger the angle of polarization, the greater the spectral reflectance ratio of mixed pixels was; In mixed pixels, the larger the proportion of the vegetation, the greater the influence of the polarization angle. The vegetation index and the size of vegetation in mixed pixels were in a linear relationship and the correlation coefficient between the vegetation attenuation index and the improved red edge normalized difference vegetation index was the largest, which could reach about 98%, suitable for establishing the correlation model between vegetation index and vegetation proportion of mixed pixel area.. When vegetation area changes, the sensitivity of vegetation index is better by improving the red edge ratio. In the use of spectral absorption characteristic parameters to estimate vegetation index, the two order function model of absorption valley depth and photochemical vegetation index had the strongest fitting degree with the determination coefficient R2 of 0.963 3; The two degree function model of spectral absorption index and photochemical vegetation index had the strongest fitting degree, and the coefficient of determination R2 was 0.960 5.
|
Received: 2016-03-02
Accepted: 2016-07-26
|
|
Corresponding Authors:
HAN Yang
E-mail: hany025@nenu.edu.cn
|
|
[1] Qi J,Cabot F,Moran M S,et al. Remote Sensing of Environment,1995, 54(1):71.
[2] SHEN Guang-rong, WANG Ren-chao(申广荣, 王人潮). Journal of Shanghai Jiaotong University·Agricultural Science(上海交通大学学报·农业科学版),2001, 19(4): 315.
[3] TONG Qing-xi, ZHENG Lan-fen(童庆禧, 郑兰芬). Journal of Remote Sensing(遥感学报), 1997, 1(1): 50.
[4] YANG Zhe-hai, HAN Jian-feng, GONG Da-peng, et al(杨哲海, 韩建峰, 宫大鹏, 等). Hydrographic Surveying and Charting(海洋测绘), 2003, 23(6): 55.
[5] Aoki M,Yabuki K,Totsuka T. Research Report of the National Institute of Environmental Studies of Japan,1981,66:125.
[6] Barnes J D,Balaguer L,Manrique E,et al. Environmental and Experimental Botany,1992,32(2):85.
[7] Blackburn G A. Remote Sensing of Environment,1998,66(3):273.
[8] Chappelle E W,Kim M S,McMurtrey J E III. Remote Sensing of Environment,1992,39(3):239.
[9] Datt B. International Journal of Remote Sensing,1999,20(14):2741.
[10] Filella I,Penuelas J. International Journal of Remote Sensing,1994,15(7):1459. |
[1] |
ZHAO Tai-fei1,2,WANG Chan1,LENG Yu-xin1, SONG Peng3. The Study of Polarization Characteristics of Ultraviolet Light Scattering from Chebyshev Haze Particles[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2149-2156. |
[2] |
CAO Xiao-feng, REN Hui-ru, LI Xing-zhi, YU Ke-qiang*, SU Bao-feng*. Discrimination of Winter Jujube’s Maturity Using Hyperspectral Technique Combined with Characteristic Wavelength and Spectral Indices[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2175-2182. |
[3] |
HUANG Yu-ping1,3, Renfu Lu2, QI Chao1, CHEN Kun-jie1*. Tomato Maturity Classification Based on Spatially Resolved Spectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2183-2188. |
[4] |
HUANG Di-yun, LI Jing-bin*, YOU Jia, KAN Za. The Classification of Delinted Cottonseeds Varieties by Fusing Image Information Based on Hyperspectral Image Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2227-2232. |
[5] |
XIE Ya-ping1, CHEN Feng-nong1, ZHANG Jing-cheng1, ZHOU Bin2, WANG Hai-jiang3, WU Kai-hua1*. Study on Monitoring of Common Diseases of Crops Based on Hyperspectral Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2233-2240. |
[6] |
WU Jin-hui1, 2, YU Jun-zhi1*, LIU Han-qi3, WANG Gao2, 4. Research on Polarization Spectrum Imaging System Based on Orthogonal Modulation[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2315-2319. |
[7] |
GAO Pan1, ZHANG Chu3, Lü Xin2*, ZHANG Ze2, HE Yong3*. Visual Identification of Slight-Damaged Cotton Seeds Based on Near Infrared Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1712-1718. |
[8] |
CHEN Bing1, WANG Gang4, LIU Jing-de1*, MA Zhan-hong2, WANG Jing3, LI Tian-nan1, 2. Extraction of Photosynthetic Parameters of Cotton Leaves under Disease Stress by Hyperspectral Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1834-1838. |
[9] |
TAO Chao1*, WANG Ya-jin1, ZOU Bin1, 2, TU Yu-long1, JIANG Xiao-lu1. Assessment and Analysis of Migrations of Heavy Metal Lead and Zinc in Soil with Hyperspectral Inversion Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1850-1855. |
[10] |
WANG Chao, WANG Jian-ming, FENG Mei-chen, XIAO Lu-jie, SUN Hui, XIE Yong-kai, YANG Wu-de*. Hyperspectral Estimation on Growth Status of Winter Wheat by Using the Multivariate Statistical Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(05): 1520-1525. |
[11] |
CAO Xiao-lan1, 2, CHEN Xing-ming2, ZHANG Shuai2, CUI Guo-xian1*. Ramie Variety Identification Based on the Hyperspectral Parameters and the Stepwise Discriminant Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(05): 1547-1551. |
[12] |
WANG Han1*, YANG Lei-ku1, DU Wei-bing1, LIU Pei1, SUN Xiao-bing2, 3. Inversion of Aerosol Optical Depth over Land Surface from Airborne Polarimetric Measurements[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(04): 1019-1024. |
[13] |
WANG Xiao-bin1, 2, 3, 4, 5,HUANG Wen-qian2, 3, 4, 5,WANG Qing-yan2, 3, 4, 5,LI Jiang-bo2, 3, 4, 5,WANG Chao-peng2, 3, 4, 5,ZHAO Chun-jiang1, 2, 3, 4, 5*. Application of Near Infrared Hyperspectral Imaging for Detection of Azodicarbonamidein Flour[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(03): 805-812. |
[14] |
GAO Xiao-yu1, 2, 3,LU Shan1*. The Relationship of the Leaf Surface Wettability and Degree of Reflectance Polarization[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(03): 923-928. |
[15] |
TU Yu-long, ZOU Bin*, JIANG Xiao-lu, TAO Chao, TANG Yu-qi, FENG Hui-hui. Hyperspectral Remote Sensing Based Modeling of Cu Content in Mining Soil[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(02): 575-581. |
|
|
|
|