两种用于作物冠层叶绿素含量提取的改进光谱指数

doi:10.11867/j.issn.1001-8166.2009.05.0548

地球科学进展 ›› 2009, Vol. 24 ›› Issue (5): 548 -554. doi: 10.11867/j.issn.1001-8166.2009.05.0548

关丽 ¹,刘湘南 ²

1.北京大学遥感与GIS研究所，北京 100871; 2.中国地质大学（北京）信息工程学院，北京 100083

收稿日期:2008-11-21 修回日期:2009-04-08 出版日期:2009-05-10
通讯作者: 关丽 E-mail:binger02600@163.com
基金资助:
国家自然科学基金项目“基于ICA和模糊推理技术构建东北典型区黑土污染高光谱反演模型”（编号：40771155）;中国高技术研究发展计划项目“复杂隐蔽性农田面源污染特征遥感识别与反演技术”（编号：2007AA12Z174）资助.

Two Kinds of Modified Spectral Indices for Retrieval of Crop Canopy Chlorophyll Content

Guan Li ¹,Liu Xiangnan ²

1.Institute of Remote Sensing and GIS, Peking University, Beijing 100871, China;
2.School of Information Engineering, China University of Geosciences, Beijing 100083, China

Received:2008-11-21 Revised:2009-04-08 Online:2009-05-10 Published:2009-05-10

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在深入探讨目前广泛使用的提取叶绿素含量的植被指数的光谱响应机制基础上，利用PROSPECT+SAIL模型模拟的作物冠层反射率样本数据对比分析了这些植被指数对叶绿素含量变化的敏感性差异，包括PSSRa、PSSRb、PSNDa、PSNDb、NPCI、PRI、MCARI和TVI等。结果表明，上述植被指数或对土壤背景变化敏感，或受高值LAI影响趋于饱和，对作物叶绿素含量反演效果均不理想。提出了4种基于TVI和MCARI的改进植被指数MTVI1、MTVI2、MCARI1和MCARI2，揭示了它们对土壤背景和LAI不敏感，对叶绿素含量变化更为敏感的光谱机制，并根据实验数据对其进行验证。实验表明，改进的植被指数MTVI2和MCARI2是作物冠层叶绿素含量较好的预测器，可据此建立作物冠层叶绿素含量反演模型。

关键词: 叶绿素含量, 改进光谱指数, MTVI2, MCARI2

Based on the thorough analysis of the spectral response mechanism of vegetation indices, which are used widely to retrieve chlorophyll content at present, taking advantage of sample reflectance data of crop canopy, simulated by models PROSPECT+SAIL, the sensitivity differences of these vegetation indices for chlorophyll content are compared and discussed, including Pigment-Specific Simple Ratio a (PSSRa), Pigment-Specific Simple Ratio b (PSSRb), Pigment-Specific Normalized Difference a (PSNDa), Pigment-Specific Normalized Difference b (PSNDb), Normalized Pigments Chlorophyll ratio Index (NPCI), Pigment Ratio Index (PRI), Modified Chlorophyll Absorption Ratio Index (MCARI) and Triangular Vegetation Index (TVI). The analysis results indicated that the above vegetation indices were either sensitive to changes of soil background or affected by saturation at high LAI levels. Therefore, it is not an ideal way to retrieve chlorophyll content by these vegetation indices. Then, four new kinds of modified spectral indices based on TVI and MCARI are proposed, including Modified Triangular Vegetation Index 1(MTVI1), Modified Triangular Vegetation Index 2(MTVI2), Modified Chlorophyll Absorption Ratio Index 1(MCARI1) and Modified Chlorophyll Absorption Ratio Index 2(MCARI2). The spectral response mechanism of four modified spectral indices was disclosed that they are both less sensitive to soil background and green LAI and more sensitive to the variation of chlorophyll content, and their performances of chlorophyll content retrieval were validated with the experimental data. The experiment demonstrated that two modified spectral indices, MTVI2 and MCARI2, are proved to be the better predictors for chlorophyll content, and the retrieval model of chlorophyll content of crop canopy can be established based on them.

Key words: Chlorophyll content, Modified spectral indices, MTVI2, MCARI2

中图分类号:

S127

[1] Grossman Y L, Ustin S L, Jacquemoud S, et al. Critique of stepwise multiple linear regression for the extraction of leaf biochemistry information from leaf reflectance data[J].Remote Sensing of Enviornment, 1996, 56: 182-193.
[2] Wu Changshan, Xiang Yueqin, Zheng Lanfen, et al. Estimating chlorophyll density of crop canopies by using hyperspectral data[J].Journal of Remote Sensing,2000,4(3):228-232.[吴长山,项月琴,郑兰芬,等.利用高光谱数据对作物群体叶绿素密度估算的研究[J]. 遥感学报, 2000,4(3):228-232.]
[3] Liu Weidong, Xiang Yueqing, Zheng Lanfen,et al. Relationships between rice LAI, CH.D and hyperspectral data[J].Journal of Remote Sensing,2000,4(4):279-283.[刘伟东,项月琴,郑兰芬,等.高光谱数据与水稻叶面积指数及叶绿素密度的相关分析[J]. 遥感学报, 2000,4(4):279-283.]
[4] Jacquemoud S, Ustin S L, Verdebout J, et al. Estimating leaf biochemistry using the prospect leaf optical properties model[J].Remote Sensing of Enviornment, 1996, 56(3):194-202.
[5] Dawson T P, Curran P R J, Ranson K J. The potential for understanding the biochemical signal in the spectra of forest canopies using a coupled leaf and canopy model[C]//Guyot G, Phulpin T, eds.Physical Measurements and Signatures in Remote Sensing, 1997: 463-470.
[6] Zhao Dehua,Li Jianlong, Song Zijian. Hyperspectral remote sensing for estimating biochemical variables of canopy[J].Advances in Earth Science, 2003,18(1):94-99.[赵德华,李建龙,宋子键.高光谱技术提取植被生化参数机理与方法研究进展[J].地球科学进展, 2003, 18(1):94-99.]
[7] Chen J,Pavlic G, Brown L,et al. Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements[J].Remote Sensing of Enviornment, 2002,55:153-162.
[8] Gitelson A, Rundquist D, Derry D,et al. Using remote sensing to quantify vegetation fraction in corn canopies[C]//Third Conference on Geospatial Information in Agriculture and Forestry, Denver, Colorado, 2001:3-7.
[9] Yan Chunyan,Niu Zheng,Wang Jihua,et al.The assessment of spectral indices applied in chlorophyll content retrieval and a modified crop canopy chlorophyll content retrieval model[J].Journal of Remote Sensing,2005,9(6):742-749.[颜春燕,牛铮,王纪华,等.光谱指数用于叶绿素含量提取的评价及一种改进的农作物冠层叶绿素含量提取模型[J].遥感学报,2005, 9(6):742-749.]
[10] Daughtry C S T, Walthall C L, KimM S,et al. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance[J].Remote Sensng of Enviornment, 2000,74:229-239.
[11] Haboudane D, Miller J R, TremblayN, et al. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture[J].Remote Sensng of Enviornment,2002,81:416-426.
[12] Blackburn G A.Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves[J].International Journal of Remote Sensing, 1998,19:657-675.
[13] Kim M S, Daughtry C S T, Chappelle E W, et al. The use of high spectral resolution bands for estimating absorbed photosynthetically active radiation[C]//Proceedings of the 6th Symposium on Physical Measurements and Signatures in Remote Sensing, Val D′Isere, France,1994.
[14] Broge N H, Leblanc E. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density[J].Remote Sensing of Enviornment,2000,76:156-172.
[15] Huete A R. A soil vegetation adjusted index (SAVI)[J].Remote Sensing of Enviornment, 1988,25:295-309.
[16] Qi J, Chehbouni A, Huete A R,et al. A modified soil vegetation adjusted index[J].Remote Sensing of Enviornment, 1994,48:119-126.
[17] Datt B, McVicar T R, van Niel T G, et al. Preprocessing EO-1 hyperion hyperspectral data to support the application of agricultural indexes[J].IEEE Transactions on Geoscience and Remote Sensing, 2003,41:1 246-1 259.
[18] Govaerts Y M, Verstraete M M, Pinty B,et al. Designing optimal spectral indices: A feasibility and proof of concept study[J].International Journal of Remote Sensing, 1999,20:1 853-1 873.
[19] Jacquemoud S. Inversion of the PROSPECT+SAIL canopy reflectance model from AVIRIS equivalent spectra: Theoretical Study[J].Remote Sensing of Environment, 1993,44:281-292.
[20] Jacquemoud S, Baret F, Andrieu B, et al. Extraction of vegetation biophysical parameters by inversion of the PROSPECT+SAIL models on sugar beet canopy reflectance data. Application to TM and AVIRIS Sensors[J]. Remote Sensing of Environment,1995, 52: 163-172.
[21] Haboudane D, Miller J R, Pattey E, et al. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture[J].Remote Sensing of Environment, 2004, 90: 337-352.

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