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植物光学模型估算叶片类胡萝卜素含量的一种双归一化差值-比值植被指数 被引量:6

Dual NDVI Ratio Vegetation Index:A Kind of Vegetation Index Assessing Leaf Carotenoid Content Based on Leaf Optical Properties Model
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摘要 运用高光谱技术进行植物叶片探测具有快速、无损、高精度等特点,在叶片色素等生化成分含量估算方面应用前景广阔。类胡萝卜素作为叶片中重要光合色素之一,因其在可见光区域与叶绿素的光谱吸收特征存在重叠,且其含量远低于叶绿素,导致利用光谱信息估算叶片类胡萝卜素含量存在困难,国内外少有针对类胡萝卜素含量的植被指数。利用高光谱数据光谱信息丰富的特点,提出一种以波段组合遍历与相关分析为基础,通过多指数协同来构建组合式的植被光谱指数的新方法。在PROSPECT叶片辐射传输模型模拟出大量具有不同生化和生物物理特征的叶片光谱的基础上,成功构建了一种在叶片水平下具有良好稳定性的类胡萝卜素含量估算新指数RVI_(DNDVI)。结果表明,该方法构建的叶片类胡萝卜素光谱指数由两部分组成:由532和405 nm构建的窄波段NDVI(与类胡萝卜素、叶绿素均强相关)和由548和498 nm构建的窄波段NDVI(仅与叶绿素强相关)进行比值组合,能较好消除叶绿素含量对指数的干扰;通过减去对叶片结构高敏感的916 nm处反射率,能消除叶肉结构参数的影响,进一步提高指数的抗干扰能力。该研究得到的指数RVI_(DNDVI)仅对叶片类胡萝卜素具有高敏感性,相关系数达到—0.94,对其进行指数拟合的R^2达到0.834 4。经与模拟数据和实测数据的验证,该指数有较好的估算效果。 With characteristics of rapidness ,non-destructiveness and high precision in detecting plant leaves ,hyperspectral tech-nology is promising in assessing the contents of leaf pigments and other biochemical components .Because the spectral absorption features of carotenoid and chlorophyll are overlapped in visible light region and that foliar carotenoid content is far lower than chlorophyll content ,studies about constructing vegetation indices (VIs) for carotenoid is rare at home and abroad though carote-noid is one of the most important photosynthetic pigments .Hyperspectral data has abundant spectral information ,so this paper proposed a multiple spectral indices collaborative algorithm to construct VIs on the basis of band-combination traversal and corre-lation analysis .Through a large number of simulated leaf reflectance spectra under different biochemical components contents run on PROSPECT model ,a radiative transfer model ,we successfully constructed a new kind of stable vegetation index (VI) for as-sessing carotenoid content at leaf level :RVIDNDVI .Our results indicate that RVIDNDVI is composed of two parts :(1)Narrow band NDVI constructed with 532 and 405 nm is high correlated with both carotenoid content and chlorophyll content while narrow band NDVI constructed with 548 and 498 nm is highly correlated with carotenoid content .The influence of chlorophyll content on RVIDNDVI can be eliminated with the ratio combination of these two indices .(2) The influence of mesophyll structure parame-ter can be weakened by subtracting the reflectance at 916 nm ,which has strong correlation with mesophyll structure parameter . RVIDNDVI only has high sensitivity to carotenoid content (the correlation coefficient is -0.94) at leaf level and R2 of its exponen-tial fit is 0.834 4 .The estimation of RVIDNDVI to carotenoid content can be verified with the validations of both simulated data and measured data .
出处 《光谱学与光谱分析》 SCIE EI CAS CSCD 北大核心 2016年第7期2189-2194,共6页 Spectroscopy and Spectral Analysis
基金 国家自然科学基金项目(41201358) 上海市科委重点项目(15dz1207805 13231203804) 上海市卫计委重点学科建设项目(15GWZK0201)资助
关键词 类胡萝卜素含量 多指数协同法 植被指数 PROSPECT模型 RVIDNDVI Carotenoid content Multiple spectral indices collaborative algorithm Vegetation index PROSPECT model RVIDNDVI
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参考文献15

  • 1Biswal113. Journal of Photochemistry and Photobiology, 1995, 30(1): 3.
  • 2Grossman Y L, Ustin S L, Jacquemoud S, et al. Remote Sensing of Environment, 1996, 56(3) : 182.
  • 3Jacquemoud S, Baret F. Remote Sensing of Environment, 1990, 34(2) : 75.
  • 4Fret J B, Frangois C, Asner G P, et al. Remote Sensing of Environment, 2008, 112(6): 3030.
  • 5Main R, Cho M A, Mathieu R, et al. ISPRS Journal of Photogrammetry and Remote Sensing, 2011, 66(6).. 751.
  • 6Chappelle E W, Kim M S, McMurtrey J E. Remote Sensing of Environment, 1992, 39(3).- 239.
  • 7Blackburn G A. Remote Sensing of Environment, 1998, 66(3) : 273.
  • 8Datt B. Remote Sensing of Environment, 1998, 66(2): 111.
  • 9Gitelson A A, Zur Y, Chivkunova O B, et al. Photochemistry and Photobiology, 2002, 75(3) .. 272.
  • 10Pefiuelas J, Baret F, Filella I. Photosynthetica, 1995, 31.. 221.

二级参考文献21

  • 1Yoder B J, Pettigrew-Crosby R E. Predicting Nitrogen and Chlorophyll Content and Concentrations from Reflectance Spectra (400-2500nm) at Leaf and Canopy Scales [J]. Remote Sensing of Environment, 1995, 53:199-211.
  • 2Kokaly R F, Clark R N. Spectroscopic Determination of Leaf Biochemistry Using Band-depth Analysis of Absorption Features and Stepwise Multiple Linear Regression [J]. Remote Sensing of Environment, 1999, 67:267-287.
  • 3Serrano L, Penuelas J, Ustin S L. Remote Sensing of Nitrogen and Lignin in Mediterranean Vegetation from AVIRIS Data: Decomposing Biochemical from Structural Signals [J]. Remote Sensing of Environment, 2002, 81:355-364.
  • 4Kokaly R F. Investigating a Physical Basis for Spectroscopic Estimates of Leaf nitrogen Concentration [J]. Remote Sensing of Environment, 2001, 75:153-161.
  • 5Martin M E, Aber J D. High Spectral Resolution Remote Sensing of Forest Canopy Lignin, Nitrogen and Ecosystem Processes [J]. Ecological Applications, 1997, 7:431-443.
  • 6Elvidge C D. Visible and Near Infrared Reflectance Characteristics of Dry Plant Materials [J]. Int. J. Remote Sensing, 1990, 11:1775-1795.
  • 7Ingle J D. Spectrochemical Analysis [M]. New Jersey: Prentice-Hall, Inc., 1988.
  • 8Clark R N, Roush T L. Reflectance Spectroscopy: Quantitative Analysis Techniques for Remote Sensing Applications [J]. Journal of Geophysical Research, 1984, 89:6329-6340.
  • 9Clark R N. Spectroscopy of Rocks and Minerals and Principles of Spectroscopy [A]. Remote Sensing for the Earth Sciences: Manual of Remote Sensing 3rd Edition [C], New York: John Wiley and Sons, Inc., 1999.
  • 10http://www-eosdis.ornl.gov/daacpages/accp.html

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