Climate and Environmental Change

Leaf area index retrieval based on canopy reflectance and vegetation index in eastern China

  • 1. College of Geographical Science, Nanjing Normal University, Nanjing 210097, China;

    2. College of Humanities and Social Science, Nanjing Forestry University, Nanjing 210037, China

Received date: 2004-12-14

  Revised date: 2005-02-20

  Online published: 2005-06-25

Supported by

European Commission Project, No.ICA4-CT-2002-10004; National Natural Science Foundation of China, No.40371081; Knowledge Innovation Project of CAS, No.KZCX3-SW-146


The aim of this paper is to investigate the feasibility of using Landsat TM data to retrieve leaf area index (LAI). To get a LAI retrieval model based ground reflectance and vegetation index, detailed field data were collected in the study area of eastern China, dominated by bamboo, tea plant and greengage. Plant canopy reflectance of Landsat TM wavelength bands has been inversed using software of 6S. LAI is an important ecological parameter. In this paper, atmospheric corrected Landsat TM imagery was utilized to calculate different vegetation indices (VI), such as simple ratio vegetation index (SR), shortwave infrared modified simple ratio (MSR), and normalized difference vegetation index (NDVI). Data of 53 samples of LAI were measured by LAI-2000 (LI-COR) in the study area. LAI was modeled based on different reflectances of bands and different vegetation indices from Landsat TM and LAI samples data. There are certainly correlations between LAI and the reflectance of Tm3, TM4, TM5 and TM7. The best model through analyzing the results is LAI = 1.2097*MSR + 0.4741 using the method of regression analysis. The result shows that the correlation coefficient R2 is 0.5157, and average accuracy is 85.75%. However, whether the model of this paper is suitable for application in subtropics needs to be verified in the future.

Cite this article

JIANG Jianjun, CHEN Suozhong, CAO Shunxian, WU Hongan, ZHANG Li, ZHANG Hailong . Leaf area index retrieval based on canopy reflectance and vegetation index in eastern China[J]. Journal of Geographical Sciences, 2005 , 15(2) : 247 -254 . DOI: 10.1360/gs050213


[1] Asrar G, Fuchs M, Kanemas E T et al., 1984. Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat. Agronomic Journal, 76: 300-306.

[2] Chen J, Cihlar J, 1996. Retrieving leaf area index of boreal conifer forests using Landsat TM images. Remote Sensing of Environment, 55: 153- 162.

[3] Chen J M, Black T A, 1992. Defining leaf area index for non-flat leaves. Plant Cell Environ., 15: 421-429.

[4] Chen J M, Brown L, Cihlar J et al., 1999. Validation of Canada2wide LAI/ FPAR maps from satellite imagery. Presented at the Fourth International Airborne Remote Sensing Conference. Ottawa, Canada, 21-24 June.

[5] Chen Y H, Li X B, Shi P J, 2001. Variation in NDVI driven by climate factors across China, 1983-1992. Acta Phytoecologica Sinica, 25: 716-720. (in Chinese)

[6] Chi H K, 1995. A study on the model for estimating winter wheat yield using spectral data wheat field. Acta Phytoecologica Sinica, 19: 337-344. (in Chinese)

[7] Cohen W B, Justice C O, 1999. Validating MODIS terrestrial ecology products: linking in situ and satellite measurement. Remote Sensing of Environment, 70: 1-4.

[8] David P, Turner et al., 1999. Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites. Remote Sensing of Environment, 70: 52-68.

[9] Deering D W, 1978. Rangeland reflectance characteristics measured by aircraft and spacecraft sensors. Ph.D. dissertation, Texas A&M University, 338pp.

[10] Gao W, Lesht B M, 1997. Model inversion of satellite-measured reflectances for obtaining surface biophysical and bidirectional reflectance characteristics of grassland. Remote Sensing of Environment, 59(3): 461-471.

[11] Guo Z H, Peng S L, Wang B S et al., 1999. Estimation of radiation absorption by Guangdong vegetation using GIS and RS. Acta Ecologica Sinica, 19: 1444-1449. (in Chinese)

[12] Hall F, Shimabukuro Y, Huemmrich K, 1995. Remote sensing of forest biophysical structure using mixture decomposition and geometric reflectance models. Ecol. Appl., 5(4): 993-1013.

[13] House P, Cosgrove B, 2002. LDAS--Land Data Assimilation System' web site: ( published by NASA, USA accessed April 20.

[14] Hui Fengming, Tian Qingjiu, Jin Zhenyu et al., 2003. Research and quantitative analysis of the correlation between vegetation index and leaf area index. Remote Sensing Information, (2): 10-13. (in Chinese)

[15] Jiang D, Wang N B, Yang X H et al., 2002. Principles of the interaction between NDVI profile and the growing situation of crops. Acta Ecologica Sinica, 22: 247-252. (in Chinese)

[16] Jordan C F, 1969. Derivation of leaf area index from quality of light on the forest floor. Ecology, 50: 663-666.

[17] Kuusk A, 1995. A Markov chain model of canopy reflectance. Agricultural and Forest Meteorology, 76: 221-236.

[18] Leonard Brown, Chen J M et al., 1999. A shortwave infrared modification to the simple ration for LAI retrieval in boreal forests: an image and model analysis. Remote Sensing of Environment, 71: 16-25.

[19] Li B G, Tao S, 2000. Correlation between AVHRR/NDVI and climate factors. Acta Ecologica Sinica, 20: 898-902. (in Chinese)

[20] Li X, Strahler A H, Woodcock C E, 1995. A hybrid geometric optical-radiative transfer approach for modeling albedo and directional reflectance of discontinuous canopies. IEEE Trans. Geosci. Remote Sens., 33: 466-480.

[21] Li X B, Shi P J, 1999. Research on regulation of NDVI change of Chinese primary vegetation types based on NOAA/AVHRR Data. Acta Botanica Sinica, 41: 314-324. (in Chinese)

[22] Peddle D R, Hall F G, Ledrew E F, 1999. Spectral mixture analysis and geometrical-optical reflectance modeling of boreal forest biophysical structure. Remote Sensing of Environment, 67: 288-297.

[23] Privette J L, Myneni R B, Knyazikhin Y et al., 2002. Early spatial and temporal validation of MODIS LAI product in the Southern Africa Kalahari. Remote Sensing of Environment, 83: 232-243.

[24] Rosema A W, Verhoef W, Noorbergen H et al., 1992. A new forest light interaction model in support of forest monitoring. Remote Sensing of Environment, 42: 23-41.

[25] Sun R, Liu C M, Zhu Q J, 2001. Relationship between the fractional vegetation and cover change and rainfall in the Yellow River basin. Acta Geographica Sinica, 56: 667-672. (in Chinese)

[26] Sun R, Zhu Q J, 2000. Distribution and seasonal change of net primary productivity in China from April, 1992 to March, 1993. Acta Geographica Sinica, 55(1): 36-45. (in Chinese)

[27] United Nations FAO, Online distribution: http://www.fao.Org/gtos/tems/variables, Accessed February, 2002.

[28] Vermote E, Tanré D et al. Second Simulation of the Satellite Signal in the Solar Spectrum (6S), Formerly affiliated to Laboratoire d'Optique Atmosphérique, 218pp.

[29] White J D, Running S W, Nemani R et al., 1997. Measurement and remote sensing of LAI in rocky mountain montane ecosystems. Canadian Journal of Forest Research, 27: 1714-1727.

[30] Zhang J, Ge J P, Guo Q X, 2001. The relation between the change of NDVI of the main vegetation types and the climatic factors in the northeast of China. Acta Ecologica Sinica, 21: 522-527. (in Chinese)

[31] Zhang Xiaoyang, Li Jinfeng, 1995. The derivation of a reflectance model for the estimation of leaf area index using perpendicular vegetation index. Remote Sensing Technology and Application, 10: 13-18. (in Chinese)

[32] Zhao M S, Fu C B, Yan X D et al., 2001. Study on the relationship between different ecosystems and climate in China using NOAA/AVHRR data. Acta Geographica Sinica, 56(3): 287-296. (in Chinese)

[33] Zheng Y R, Zhou G S, 2000. A forest vegetation NPP model based on NDVI. Acta Phytoecologica Sinica, 24: 9-12. (in Chinese)