|
|
|
|
|
|
Inversion of Vegetation Leaf Water Content Based on Spectral Index |
ZHANG Hai-wei1, 2, ZHANG Fei1, 2, 3*, ZHANG Xian-long 1, 2, LI Zhe1, 2, Abduwasit Ghulam1, 4, SONG Jia1, 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. Center for Sustainability, Saint Louis University, St. Louis, MO 63108, USA |
|
|
Abstract Monitoring the water status of vegetation by spectral technique is one of the important means to understand the physiological status and growth trend of vegetation. In this study, the Ebinur Lake Wetland Nature Reserve is chosen as the target area. By using cluster analysis, variable importance projection (VIP) and sensitivity analysis method, the vegetation water content was classified, estimated and validated. The Results showed that in the clustering analysis method based on Euclidean distance of the vegetation moisture content is divided into three grades with higher water content, medium water content and low water content, whose ranges are around 70.76%~80.69%, 53.27%~70.76% and 31%~53.27%, respectively. From 1 350 to 2 500 nm wavelength range, the spectral reflectance of water content is the lowest ,however there is no law from 380 to 1 350 nm wavelength range. By using VIP method, all vegetation water index VIP value of more than 0.8, indicated that vegetation water index estimation ability of water content of vegetation leaves is strong and the difference is not obvious. The MSI, or GVMI and vegetation water content cubic equation fitting is the best, the fitting coefficients of R2 were 0.657 5 and 0.674 2 respectively. The RWC in the range of 30%~45%, the MSI value of the NE index is the lowest. In the range of 45%~90%, the GVMI value of the NE index is the lowest. About 70% of NE value NDWI1240 index has undulation, it shows that the NDWI1240 index of the vegetation water content is at about 70% and the prediction ability is poor. Through the error analysis, the error of GVMI exponent inversion is the smallest, different vegetation indices have obvious difference in vegetation estimation results with different water contents. Therefore, it is necessary to estimate vegetation water content. In summary, using hyper spectral remote sensing technology to monitor vegetation growth and drought environment in Ebinur Lake Reserve Area is feasible.The results provide a theoretical basis for the large area inversion of satellite borne hyper spectral sensors for vegetation water content.
|
Received: 2017-04-22
Accepted: 2017-10-25
|
|
Corresponding Authors:
ZHANG Fei
E-mail: zhangfei3s@163.com
|
|
[1] El B S, Caliot E, Bens M, et al. International Journal of Remote Sensing, 2002, 23(11): 2145.
[2] ZHAO Yun-sheng, DU Jia, SONG Kai-shan, et al(赵云升, 杜 嘉, 宋开山, 等). Scientia Geographica Sinica(地理科学), 2006, 26(1): 70.
[3] Thomas J R, Namken L N, Oerther G F, et al. Agronomy Journal, 1971, 63(6): 845.
[4] Curran P J. Remote Sensing of Environment, 1990, 30(3): 271.
[5] Carter G A. American Journal of Botany, 1991, 78(7): 916.
[6] DENG Bing, YANG Wu-nian, MU Nan, et al(邓 兵, 杨武年, 慕 楠, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2016, 36(8): 2546.
[7] 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, 29(5): 1403.
[8] YANG Ai-xia, DING Jian-li(杨爱霞, 丁建丽) . Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 21(18): 162.
[9] MA Hui-ying, YANG Xiao-dong, Lü Guang-hui, et al(马辉英, 杨晓东, 吕光辉, 等). Acta Ecologica Sinica(生态学报), 2017, 37(3): 1.
[10] Feng Wei, Zhang Haiyan, Zhang Yuanshuai, et al. Field Crops Research, 2016, 198: 238.
[11] CHEN Xiao-ping, WANG Shu-dong, ZHANG Li-fu, et al(陈小平, 王树东, 张立福, 等). Remote Sensing Information(遥感信息), 2016, 31(1): 48.
[12] Erjr H, Rock B N. Remote Sensing of Environment, 1989, 30(1): 43.
[13] Penuelas J, Filella I, Serrano L, et al. International Journal of Remote Sensing, 1996, 17(2): 373.
[14] Zarco P J. Remote Sensing of Environment, 2003, 85(1): 109.
[15] Wu C, Niu Z, Tang Q, et al. Journal of Plant Research, 2009, 122(3): 317.
[16] Gao B C. Proc Spie, 1996, 58(3): 257.
[17] Chen D, Huang J, Jackson T J. Remote Sensing of Environment, 2005, 98(2-3): 225.
[18] Wang L, Qu J J, Hao X, et al. International Journal of Remote Sensing, 2008, 29(24): 7065.
[19] Ceccato P, Gobron N, Flasse S, et al. Remote Sensing of Environment, 2002, 82(2-3): 188.
[20] Feng W, Zhang H Y, Zhang Y S, et al. Field Crops Research, 2016, 198: 238.
[21] Viña A, Gitelson A A, Nguy-Robertson A L, et al. Remote Sensing of Environment, 2011, 115(12): 3468.
[22] Wold S, Sjöström M, Eriksson L. Chemometrics and Intelligent Laboratory Systems, 2001, 58(2): 109. |
[1] |
GUO Zhou-qian1, 2, LÜ Shu-qiang1, 2, HOU Miao-le1, 2*, SUN Yu-tong1, 2, LI Shu-yang1, 2, CUI Wen-yi1. Inversion of Salt Content in Simulated Mural Based on Hyperspectral
Mural Salt Index[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3272-3279. |
[2] |
ZHU Yu-chen1, 2, WANG Yan-cang3, 4, 5, LI Xiao-fang6, LIU Xing-yu3, GU Xiao-he4*, ZHAO Qi-chao3, 4, 5. Study on Quantitative Inversion of Leaf Water Content of Winter Wheat Based on Discrete Wavelet Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2902-2909. |
[3] |
GAO Yu1, SUN Xue-jian1*, LI Guang-hua2, ZHANG Li-fu1, QU Liang2, ZHANG Dong-hui1, CHANG Jing-jing2, DAI Xiao-ai3. Study on the Derivation of Paper Viscosity Spectral Index Based on Spectral Information Expansion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2960-2966. |
[4] |
SONG Cheng-yang1, GENG Hong-wei1, FEI Shuai-peng2, LI Lei2, GAN Tian2, ZENG Chao-wu3, XIAO Yong-gui2*, TAO Zhi-qiang2*. Study on Yield Estimation of Wheat Varieties Based on Multi-Source Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2210-2219. |
[5] |
CAO Yue1, BAO Ni-sha1, 2*, ZHOU Bin3, GU Xiao-wei1, 2, LIU Shan-jun1, YU Mo-li1. Research on Remote Sensing Inversion Method of Surface Moisture Content of Iron Tailings Based on Measured Spectra and Domestic Gaofen-5 Hyperspectral High-Resolution Satellites[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1225-1233. |
[6] |
HU Yi-bin1, BAO Ni-sha1, 2*, LIU Shan-jun1, 2, MAO Ya-chun1, 2, SONG Liang3. Research on Hyperspectral Features and Recognition Methods of Typical Camouflage Materials[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 297-302. |
[7] |
WANG Dong-sheng1, WANG Hai-long1, 2, ZHANG Fang1, 3*, HAN Lin-fang1, 3, LI Yun1. Near-Infrared Spectral Characteristics of Sandstone and Inversion of Water Content[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3368-3372. |
[8] |
WANG Jin-jie1, 2, 3, 4, 5, DING Jian-li1, 4, 5*, GE Xiang-yu1, 4, 5, ZHANG Zhe1, 4, 5, HAN Li-jing1, 4, 5. Application of Fractional Order Differential Technology in the Estimation of Soil Moisture Content Using UAV-Based Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3559-3567. |
[9] |
LI De-hui1, WU Tai-xia1*, WANG Shu-dong2*, LI Zhe-hua1, TIAN Yi-wei1, FEI Xiao-long1, LIU Yang1, LEI Yong3, LI Guang-hua3. Hyperspectral Indices for Identification of Red Pigments Used in Cultural Relic[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1588-1594. |
[10] |
FU Yan-hua1, LIU Jing2*, MAO Ya-chun2, CAO Wang2, HUANG Jia-qi2, ZHAO Zhan-guo3. Experimental Study on Quantitative Inversion Model of Heavy Metals in Soda Saline-Alkali Soil Based on RBF Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1595-1600. |
[11] |
YU Yue, YU Hai-ye, LI Xiao-kai, WANG Hong-jian, LIU Shuang, ZHANG Lei, SUI Yuan-yuan*. Hyperspectral Inversion Model for SPAD of Rice Leaves Based on Optimized Spectral Index[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1092-1097. |
[12] |
LI Xue-ying1, 2, LI Zong-min3*, CHEN Guang-yuan4, QIU Hui-min2, HOU Guang-li2, FAN Ping-ping2*. Prediction of Tidal Flat Sediment Moisture Content Based on Wavelet Transform[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1156-1161. |
[13] |
DUAN Wei-na1, 2, JING Xia1*, LIU Liang-yun2, ZHANG Teng1, ZHANG Li-hua3. Monitoring of Wheat Stripe Rust Based on Integration of SIF and Reflectance Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 859-865. |
[14] |
TANG Yu-zhe, HONG Mei, HAO Jia-yong, WANG Xu, ZHANG He-jing, ZHANG Wei-jian, LI Fei*. Estimation of Chlorophyll Content in Maize Leaves Based on Optimized Area Spectral Index[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 924-932. |
[15] |
LI Peng-cheng1, 2, LIU Han1, 2, ZHAO Long-lian1, 2, LI Jun-hui1, 2*. Key Parameters for Maize Leaf Moisture Measurement Using NIR Camera With Filters Based on Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3184-3188. |
|
|
|
|