光谱学与光谱分析 |
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Vegetation Water Content Retrieval and Application of Drought Monitoring Using Multi-Spectral Remote Sensing |
WANG Li-tao1,WANG Shi-xin1,ZHOU Yi1,LIU Wen-liang1,2,WANG Fu-tao1,2 |
1. The State Key Laboratory of Remote Sensing Science,Institute of Remote Sensing Application, Chinese Academy of Sciences, Beijing 100101, China 2. Graduate University of Chinese Academy of Sciences, Beijing 100039, China |
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Abstract The vegetation is one of main drying carriers. The change of Vegetation Water Content (VWC) reflects the spatial-temporal distribution of drought situation and the degree of drought. In the present paper, a method of retrieving the VWC based on remote sensing data is introduced and analyzed, including the monitoring theory, vegetation water content indicator and retrieving model. The application was carried out in the region of Southwest China in the spring, 2010. The VWC data was calculated from MODIS data and spatially-temporally analyzed. Combined with the meteorological data from weather stations, the relationship between the EWT and weather data shows that precipitation has impact on the change in vegetation moisture to a certain extent. However, there is a process of delay during the course of vegetation absorbing water. So precipitation has a delaying impact on VWC. Based on the above analysis, the probability of drought monitoring and evaluation based on multi-spectral VWC data was discussed. Through temporal synthesis and combined with auxiliary data (i.e. historical data), it will help overcome the limitation of data itself and enhance the application of drought monitoring and evaluation based on the multi-spectral remote sensing.
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Received: 2010-12-14
Accepted: 2011-03-07
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Corresponding Authors:
WANG Li-tao
E-mail: wanglt@irsa.ac.cn
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[1] Tugrul Yilmaz M, Raymond Hunt E Jr, Jackson Thomas J. Remote Sensing of Environment, 2008, 112(5): 2514. [2] Sepulcre-Cantó G, Zarco-Tejada P J, Jiménez-Muoz J C, et al. Agricultural and Forest Meteorology, 2006, 136(1-2): 31. [3] Hornbuckle Brian K, England Anthony W, Anderson Martha C, et al. Agricultural and Forest Meteorology, 2006, 138(1-4): 180. [4] Ceccato P, Gobron N, Flasse S, et al. Remote Sensing of Environment, 2002, 82: 198. [5] Gao B C. Remote Sensing of Environment, 1996, 58(3): 257. [6] Zhang R H, Su H B, Li Z L, et al. Science in China (Series D), 2001, 44(2): 112. [7] Zhang R H, Sun X M, Li Z L, et al. Science in China (Series D), 2003, 46(4): 344. [8] LI Zhen, GUO Hua-dong, SHI Jian-cheng(李 震,郭华东,施建成). Journal of Remote Sensing(遥感学报), 2002, 6(6): 481. [9] Sandholt I, Rasmussen K,Andersen J. Remote Sensing of Environment, 2002, 79: 213. [10] Jackson R D,Idso S B,Reginato R J,et al. Water Resource Research, 1981, 17(4): 1133. [11] ZHANG Chang-chun, WANG Xiao-yan, SHAO Jing-li(张长春,王晓燕,邵景力). Resources Science(资源科学), 2005, 27(1): 86. [12] Goward S N, Xue Y K,Czajkowski K P. Remote Sensing of Environment, 2002, 79:225. [13] Sandholt I, Rasmussen K,Andersen J. Remote Sensing Environment, 2002, 79: 213. [14] ZHAO Ying-shi, et al(赵英时, 等). Remote Sensing Application Principles and Methods(遥感应用分析原理与方法). Beijing: Science Press(北京:科学出版社), 2003. 366. [15] Hunt E R Jr, Rock B N. Remote Sensing of Environment, 1989, 30: 43. [16] Ceccato P, Gobron N, Flasse S, et al. Remote Sensing of Environment, 2002, 82: 188. [17] Palacios-Orueta A, Khanna S, Litago J, et al. Proceedings of the First International Conference of Remote Sensing and Geoinformation Processing. Trier, Germany, 2006. 207.
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