光谱学与光谱分析 |
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Remote Sensing of Chlorophyll Fluorescence at Airborne Level Based on Unmanned Airship Platform and Hyperspectral Sensor |
YANG Pei-qi1, 2, 3, LIU Zhi-gang1, 2, 3*,NI Zhuo-ya1, 2, 3, WANG Ran1, 2, 3,WANG Qing-shan1, 2, 3 |
1. State Key Laboratory of Remote Sensing Science,Beijing Normal University, Beijing 100875, China 2. School of Geography, Beijing Normal University, Beijing 100875, China 3. Beijing Key Laboratory of Environmental Remote Sensing and Digital City, Beijing Normal University, Beijing 100875, China |
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Abstract The solar-induced chlorophyll fluorescence (ChlF) has a close relationship with photosynthetic and is considered as a probe of plant photosynthetic activity. In this study, an airborne fluorescence detecting system was constructed by using a hyperspectral imager on board an unmanned airship. Both Fraunhofer Line Discriminator (FLD) and 3FLD used to extract ChlF require the incident solar irradiance, which is always difficult to receive at airborne level. Alternative FLD (aFLD) can overcome the problem by selecting non-fluorescent emitter in the image. However, aFLD is based on the assumption that reflectance is identical around the Fraunhofer line, which is not realistic. A new method, a3FLD, is proposed, which assumes that reflectance varies linearly with the wavelength around Fraunhofer line. The result of simulated data shows that ChlF retrieval error of a3FLD is significantly lower than that of aFLD when vegetation reflectance varies near the Fraunhofer line. The results of hyperspectral remote sensing data with the airborne fluorescence detecting system show that the relative values of retrieved ChlF of 5 kinds of plants extracted by both aFLD and a3FLD are consistent with vegetation growth stage and the ground-level ChlF. The ChlF values of aFLD are about 15% greater than a3FLD. In addition, using aFLD, some non-fluorescent objects have considerable ChlF value, while a3FLD can effectively overcome the problem.
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Received: 2013-01-31
Accepted: 2013-04-18
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Corresponding Authors:
LIU Zhi-gang
E-mail: zhigangliu@bnu.edu.cn
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[1] Meroni M, Rossini M, Guanter L, et al. Remote Sensing of Environment, 2009,113(10): 2037. [2] Baker N R. Annu. Rev. Plant Biol., 2008,59: 89. [3] Damm A, Elber J, Erler A, et al. Global Change Biology, 2010, 16(1): 171. [4] WANG Ran,LIU Zhi-gang, YANG Pei-qi(王 冉, 刘志刚, 杨沛琦). Advances in Earth Science(地球科学进展),2012, 27(11): 1221. [5] Maier S W. United States Patent,6329660. 2001. [6] LIU Liang-yun, ZHANG Yong-jiang, WANG Ji-hua, et al(刘良云, 张永江, 王纪华,等). Journal of Remote Sensing(遥感学报),2006,10(1): 130. [7] Entcheva Campbell P K, Middleton E M, Corp L A, et al. Science of the Total Environment, 2008, 404: 433. [8] Moya M, Colombo R. Remote Sensing of Environment, 2006,103(4): 438. [9] Plascyk J A, Gabriel F C. IEEE Transactions on Instrumentation and Measurement, 1975, 24(2): 306. [10] Maier S W, Günther K P, Stellmes M. The Vniversity of Michigan Press, 2003. 202. |
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