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| Research on Spectrum Variance of Vegetation Leaves and Estimation Model for Leaf Chlorophyll Content Based on the Spectral Index |
| LI Zhe1, 2, ZHANG Fei1, 2, 3*, CHEN Li-hua4, ZHANG Hai-wei1, 2 |
1. College of Resources & Environmental Science, Xinjiang University, Urumqi 830046, China
2. Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China
3. General Institutes of Higher Learning Key Laboratory of Smart City and Environmental Modeling, Xinjiang University, Urumqi 830046, China
4. Area Management Bureau of Ebinur Lake Wetland Natural Reserve, Bole 833400, China |
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Abstract The chlorophyll can effectively monitoring vegetation growth status, currently the hyperspectral vegetation index (VI) is one of the common methods that have been widely applied to estimate the leaf chlorophyll content (LCC) inversion. Non-destructive rapid estimation of chlorophyll using hyperspectral remote sensing technology is a prerequisite to dynamically monitor chlorophyll content, and it is an important research issue of vegetation remote sensing. The author measured the leaf spectral reflectance and chlorophyll relative content of desert plants, analyzed the spectral curves of different desert plants under the same chlorophyll content, then transformed the SPAD value, compared Pearson and VIP methods to study the correlation between chlorophyll content and vegetation index of desert vegetation. Finally, the author selected the best fitting model from accuracy test. The results showed that: Based on the comparative analysis between Pearson and VIP, established the chlorophyll content estimation model by VIP method, selected 7 vegetation indices, which was NDVI705，ARVI，CIred edge，PRI，VARI，PSRI and NPCI respectively, the value of VIP all greater than 0.8, thus these 7 vegetation indices were the optimal vegetation indices. The prediction results indicated that the correlation of all models was more than 0.7, the best correlation between the predicted value and the measured value was the SPAD value of the reciprocal transformation, R=0.816, RMSE=0.007. The inversion model based on VIP method can estimate the chlorophyll content of vegetation in the study area, it provides an important theoretical basis and technical support for the practical application in the diagnosis of plant chlorophyll content.
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Received: 2017-05-02
Accepted: 2017-11-18
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Corresponding Authors:
ZHANG Fei
E-mail: zhangfei3s@163.com
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[1] Yang X G, Yu Y, Fan W Y. Environmental Monitoring Assessment, 2015, 187(7): 1.
[2] FENG Wei, ZHU Yan, TIAN Yong-chao, et al(冯 伟，朱 艳，田永超，等). Acta Ecologica Sinica(生态学报)，2008，28(10)： 4902.
[3] Le Maire G, Francois C, Dufrene E. Remote Sensing of Environment, 2004, 89(1): 1.
[4] ZHANG Si-nan, WANG Quan, JIN Jia, et al(张思楠, 王 权, 靳 佳, 等). Arid Zone Research(干旱区研究), 2016, 33(5): 1088.
[5] Dash J, Curran P J. IEEE International Symposium on Geoscience and Remote Sensing(IGARSS), 2004, 39(1): 254.
[6] Daughtry C, Walthall C L, Kim M S, et al. Remote Sensing of Environment, 2000，74(2): 229.
[7] LIAO Qin-hong, ZHANG Dong-yan, WANG Ji-hua, et al(廖钦洪, 张东彦, 王纪华, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2014, 34(6): 1599.
[8] YANG Ai-xia, DING Jian-li, LI Yan-hong, et al(杨爱霞, 丁建丽, 李艳红, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2016, 36(3): 691.
[9] Uddling J, Gelang-Alfredsson J, Piikki K, et al. Photosynthesis Research, 2007, 91(1): 37.
[10] Maccioni A, Agati G, Mazzinghi P. Journal of Photochemistry and Photobiology, 2001, 61(1-2): 52.
[11] Rouse Jr J W, Haas R H, Schell J A, et al. NASA Special Publication, 1974, 351: 309.
[12] Dian Y Y, Le Y, Fang S H, et al. J. Indian Society of Remote Sensing, 2016, 44(4): 583.
[13] TIAN Jing-guo, WANG Shu-dong, ZHANG Li-fu, et al(田静国, 王树东, 张立福, 等). Science Technology and Engineering(科学技术与工程), 2016, 16(15): 1671.
[14] GUO Ni(郭 铌). Arid Meteorology(干旱气象), 2003, 21(4): 71.
[15] Francisco F S, John M K, Francisco F V. Wetlands Ecol. Manage, 2013, 21(3): 193.
[16] JIA Kun, YAO Yun-jun, WEI Xiang-qin, et al(贾 坤, 姚云军, 魏香琴, 等). Advances in Earth Science(地球科学进展), 2013, 28(7): 774.
[17] ZHANG Jian-yong, GAO Ran, HU Jun, et al(张建勇, 高 冉, 胡 骏, 等). Journal of Chifeng University·Natural Science Edition(赤峰学院学报·自然科学版), 2014, 30(11): 1.
[18] Svante W, Michael S, Lennart E. Chemometrics and Intelligent Laboratory Systems, 2001, 58(2): 109.
[19] WANG Li-wen, WEI Ya-xing(王莉雯, 卫亚星). Scientia Geographica Sinica(地理科学), 2016, 36(1): 135. |
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