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遥感技术与应用  2019, Vol. 34 Issue (3): 476-487    DOI: 10.11873/j.issn.1004-0323.2019.3.0476
荧光遥感专栏     
陆地生态系统碳监测卫星远红波段叶绿素荧光反演算法设计
王思恒1,2,黄长平1,张立福1,高显连3,付安民3
(1.中国科学院遥感与数字地球研究所,北京100101;
2.中国科学院大学,北京100049;
3.国家林业和草原局调查规划设计院,北京100013)
Designment and Assessment of Far-Red Solar-Induced Chlorophyll Fluorescence Retrieval Method for the Terrestrial Ecosystem Carbon Inventory Satellite
Wang Siheng1,2,Huang Changping1,Zhang Lifu1,Gao Xianlian3,Fu Anmin3
 (1.Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing 100101,China;
2.University of Chinese Academy of Sciences,Beijing 100049,China;3.Academy of Forestry Investigation and Planning,National Forestry and Grassland Administration,Beijing 100013,China)
 全文: PDF(11440 KB)  
摘要:  太阳诱导叶绿素荧光(Solar-Induced Chlorophyll Fluorescence,SIF)是陆表植被在自然光照条件下进行光合作用时释放的一种光信号。SIF携带有重要的光合作用信息,通过卫星遥感的方法探测区域—全球SIF信号为大尺度植被光合作用监测打开了新世界的大门。我国预计于2020年前后发射的陆地生态系统碳卫星(陆碳卫星)将搭载超光谱分辨率载荷,有望成为全球范围内第一个针对陆表植被SIF进行专门观测的卫星传感器。数据驱动算法是陆碳卫星远红波段SIF反演的主算法,作为一种半经验算法,需结合传感器指标进行参数优化。依据陆碳卫星超光谱载荷的设计指标,基于仿真数据进行了端对端反演模拟,讨论了不同潜在反演窗口下SIF反演的精度(精密度与准确度),确定了不同反演窗口所需的主成分个数(nv),分析了不同反演窗口内反演精度对荧光波形函数(hF)的敏感性。结果表明:随着反演窗口的拓宽,SIF反演的精密度(鲁棒性)提升,但准确度下降,且对nv及hF的敏感性增强。综合考虑各因素,确定了735~758 nm作为陆碳卫星远红波段SIF反演的主窗口,同时nv取6,hF设置为单峰高斯函(μ=740 nm,σ=30 nm)。基于近地表和航空遥感数据的SIF反演结果验证了所设计算法的可行性和合理性。研究结果将为陆碳卫星升空后SIF的反演及产品发布提供重要参考。
 
 
关键词: 太阳诱导叶绿素荧光(SIF);陆地生态系统碳监测卫星;数据驱动算法;高光谱遥感
 
    
Abstract: Solar-Induced Chlorophyll Fluorescence (SIF),which is emitted by photosystem during photosynthesis under natural illumination,carries important information of actual photosynthesis of plants.Spaceborne remote sensing of SIF provides an unprecedented opportunity for monitoring global photosynthesis at regional to global scales.Up to date,in-orbit operational spaceborne sensors that are available for SIF retrieval are originally designed for atmosphere monitoring.The hyperspectral sensor onboard Chinese Terrestrial Ecosystem Carbon Inventory Satellite (CTECS) is expected to be the first operational spaceborne sensor that is specifically designed for sensing SIF from space (scheduled to be launched around 2020,2 years before the Fluorescence Explorer (FLEX) Mission).Data-driven approach has been selected as the main algorithm for far-red SIF retrieval for CTECS,but is to be refined and assessed according to sensor specifications (e.g.spectral resolution and signal-to-noise ratio).In this context,this study aims to improve the designment of far-red SIF retrieval method for CTECS.based on end-to-end simulation,we evaluate the precision and accuracy of SIF retrieval in several potential windows.We then analyze the sensitivity of SIF retrieval to number of features (nv) and fluorescence spectral shape function (hF) in the forward model in different windows.Results show that a broader fitting window increases retrieval precision,but is accompanied with lower accuracy and stronger sensitivity to nv and  hF.Considering both retrieval precision and accuracy,the window of 735~758 nm with nv set to 6 and hFset as single peak Gaussian function (μ=740 nm and σ=30 nm) is selected as optimal fitting window for CTECS.SIF retrieval results based on proximal and airborne remote sensing data demonstrate the feasibility and reasonability of the designed method.Our results provide an important reference for far-red SIF retrieval for CTECS.
Key words: Solar-Induced Chlorophyll Fluorescence(SIF)    Chinese Terrestrial Ecosystem Carbon Inventory Satellite    Data-driven approach    Hyperspectral remote sensing
收稿日期: 2019-02-25 出版日期: 2019-07-01
ZTFLH:  TP75  
基金资助:  国家重点研发计划项目“星机地协同的大地震灾后灾情快速调查关键技术研究”(2017YFC1500900),空基科研星工程先期攻关项目“陆地生态系统碳监测卫星林业产品地面数据处理及反演技术研究”(2016K-10),中国科学院青年创新促进会项目(2017086)。
作者简介: 王思恒(1992-),男,河北石家庄人,博士研究生,主要从事高光谱植被遥感研究。E-mail:wangsh@radi.ac.cn。
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引用本文:

王思恒, 黄长平, 张立福, 高显连, 付安民. 陆地生态系统碳监测卫星远红波段叶绿素荧光反演算法设计[J]. 遥感技术与应用, 2019, 34(3): 476-487.

Wang Siheng, Huang Changping, Zhang Lifu, Gao Xianlian, Fu Anmin. Designment and Assessment of Far-Red Solar-Induced Chlorophyll Fluorescence Retrieval Method for the Terrestrial Ecosystem Carbon Inventory Satellite. Remote Sensing Technology and Application, 2019, 34(3): 476-487.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2019.3.0476        http://www.rsta.ac.cn/CN/Y2019/V34/I3/476

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