Full Text:   <3794>

CLC number: S127

On-line Access: 

Received: 2008-07-06

Revision Accepted: 2008-11-03

Crosschecked: 2008-11-12

Cited: 8

Clicked: 6233

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
Open peer comments

Journal of Zhejiang University SCIENCE B 2008 Vol.9 No.12 P.953-963


Optimal waveband identification for estimation of leaf area index of paddy rice

Author(s):  Fu-min WANG, Jing-feng HUANG, Qi-fa ZHOU, Xiu-zhen WANG

Affiliation(s):  Institute of Agricultural Remote Sensing and Information Application, Zhejiang University, Hangzhou 310029, China; more

Corresponding email(s):   hjf@zju.edu.cn

Key Words:  Rice, Hyperspectral reflectance, Leaf area index (LAI), Wavebands identification

Fu-min WANG, Jing-feng HUANG, Qi-fa ZHOU, Xiu-zhen WANG. Optimal waveband identification for estimation of leaf area index of paddy rice[J]. Journal of Zhejiang University Science B, 2008, 9(12): 953-963.

@article{title="Optimal waveband identification for estimation of leaf area index of paddy rice",
author="Fu-min WANG, Jing-feng HUANG, Qi-fa ZHOU, Xiu-zhen WANG",
journal="Journal of Zhejiang University Science B",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Optimal waveband identification for estimation of leaf area index of paddy rice
%A Fu-min WANG
%A Jing-feng HUANG
%A Qi-fa ZHOU
%A Xiu-zhen WANG
%J Journal of Zhejiang University SCIENCE B
%V 9
%N 12
%P 953-963
%@ 1673-1581
%D 2008
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B0820211

T1 - Optimal waveband identification for estimation of leaf area index of paddy rice
A1 - Fu-min WANG
A1 - Jing-feng HUANG
A1 - Qi-fa ZHOU
A1 - Xiu-zhen WANG
J0 - Journal of Zhejiang University Science B
VL - 9
IS - 12
SP - 953
EP - 963
%@ 1673-1581
Y1 - 2008
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.B0820211

The objectives of the study were to select suitable wavebands for rice leaf area index (LAI) estimation using the data acquired over a whole growing season, and to test the efficiency of the selected wavebands by comparing them with feature positions of rice canopy spectra. In this study, the field experiment in 2002 growing season was conducted at the experimental farm of Zhejiang University, Hangzhou, China. Measurements of hyperspectral reflectance (350~2500 nm) and corresponding LAI were made for a paddy rice canopy throughout the growing season. And three methods were employed to identify the optimal wavebands for paddy rice LAI estimation: correlation coefficient-based method, vegetation index-based method, and stepwise regression method. This research selected 15 wavebands in the region of 350~2 500 nm, which appeared to be the optimal wavebands for the paddy rice LAI estimation. Of the selected wavebands, the most frequently occurring wavebands were centered around 554, 675, 723, and 1 633 nm. They were followed by 444, 524, 576, 594, 804, 849, 974, 1 074, 1 219, 1 510, and 2 194 nm. Most of them made physical sense and had their counterparts in spectral known feature positions, which indicates the promising potential of the 15 selected wavebands for the retrieval of paddy rice LAI.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1] Becker, B.L., Lusch, D.P., Qi, J., 2005. Identifying optimal spectral bands from in situ measurements of Great Lakes coastal wetlands using second-derivative analysis. Remote Sens. Environ., 97(2):238-248.

[2] Bouman, B.A.M., 1995. Crop modeling and remote sensing for yield prediction. Neth. J. Agric. Sci., 43:143-161.

[3] Brown, L., Chen, J.M., Leblanc, S.G., Cihlar, J., 2000. A shortwave infrared modification to the simple ratio for LAI retrieval in boreal forests: an image and model analysis. Remote Sens. Environ., 71(1):16-25.

[4] Casanova, D., Epema, G.F., Goudriaan, J., 1998. Monitoring rice reflectance at field level for estimating biomass and LAI. Field Crop. Res., 55(1-2):83-92.

[5] Chappelle, E.W., Kim, M.S., Mcmurtrey, J.E., 1993. Ratio analysis of reflectance spectra (RARS): an algorithm for the remote estimation of concentration of chlorophyll a, chlorophyll b, and carotenoids in soybean leaves. Remote Sens. Environ., 39(3):239-247.

[6] Confalonieri, R., Bocchi, S., 2005. Evaluation of CropSyst for simulating the yield of flooded rice in northern Italy. Eur. J. Agron., 23(4):315-332.

[7] Curran, P.J., 1989. Remote sensing of foliar chemistry. Remote Sens. Environ., 30(3):271-278.

[8] Curran, P.J., Dungan, J.L., Peterson, D.L., 2001. Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: testing the Kokaly and Clark methodologies. Remote Sens. Environ., 76(3):349-359.

[9] de Jong, S.M., Pebesma, E.J., Lacaze, B., 2003. Above-ground biomass assessment of Mediterranean forests using airborne imaging spectrometry: the DAIS Peyne experiment. Int. J. Remote Sens., 24(7):1505-1520.

[10] Dobermann, A., Pampolino, M.F., 1995. Indirect leaf area index measurement as a tool for characterizing rice growth at the field scale. Commun. Soil Sci. Plant Anal., 26(9):1507-1523.

[11] Eklundh, L., Harrie, L., Kuusk, A., 2001. Investigating relationships between Landsat ETM+ sensor data and leaf area index in a boreal conifer forest. Remote Sens. Environ., 78(3):239-251.

[12] Gitelson, A.A., 2004. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. J. Plant Physiol., 161(2):165-173.

[13] Gitelson, A.A., Kaufman, Y.J., Merzlyak, M.N., 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ., 58(3):289-298.

[14] Grossman, Y.L, Ustin, S.L., Jacquemoud, J., 1996. Critique of stepwise multiple linear regression for the extraction of leaf biochemistry information from leaf reflectance data. Remote Sens. Environ., 56(3):182-193.

[15] Haboudane, D., Mille, J.R., Pattey, E., 2004. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sens. Environ., 90(3):337-352.

[16] Huang, Z., Turner, B.J., Dury, S.J., Wallis, I.R., Foley, W.J., 2004. Estimating foliage nitrogen concentration from HYMAP data using continuum removal analysis. Remote Sens. Environ., 93(1-2):18-29.

[17] Jacquemoud, S., Verdebout, J., Schmuck, G., Andreoli, G., Hosgood, B., 1995. Investigation of leaf biochemistry by statistics. Remote Sens. Environ., 54(3):180-188.

[18] Jiminez, L., Landgrebe, D., 1999. Hyperspectral data analysis and supervised feature reduction via project pursuit. IEEE Trans. Geosci. Remote Sensing, 37(6):2653-2667.

[19] Koger, C.H., Bruce, L.M., Shaw, D.R., Reddy, K.N., 2003. Wavelet analysis of hyperspectral reflectance data for detecting pitted morningglory (Ipomoea lacunosa) in soybean (Glycine max). Remote Sens. Environ., 86(1):108-119.

[20] Lee, K., Warren, B.C., Robert, E.K., 2004. Hyperspectral versus multispectral data for estimating leaf area index in four different biomes. Remote Sens. Environ., 91(3-4):508-520.

[21] Mutanga, O., Skidmore, A.K., 2004. Narrow band vegetation indices overcome the saturation problem in biomass estimation. Int. J. Remote Sens., 25(19):3999-4014.

[22] Shibayama, M., Akiyama, T., 1989. Seasonal visible, near-infrared and mid-infrared spectra of rice canopies in relation to LAI and above-ground dry phytomass. Remote Sens. Environ., 27(2):119-127.

[23] Sims, D.A., Gamon, J.A., 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens. Environ., 81(2-3):337-354.

[24] Strachan, I.B., Pattey, E., Boisvert, J.B., 2002. Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance. Remote Sens. Environ., 80(2):213-224.

[25] Thenkabail, P.S., Smith, R.B., Pauw, E.D., 2000. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sens. Environ., 71(2):158-182.

[26] Thenkabail, P.S., Enclona, E.A., Ashton, M.S., Legg, C., Dieu, M.J.D., 2004a. Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests. Remote Sens. Environ., 90(1):23-43.

[27] Thenkabail, P.S., Enclona, E.A., Ashton, M.S., Meer, B.V.D., 2004b. Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote Sens. Environ., 91(3-4):354-376.

[28] Tsai, F., Philpot, W., 1998. Derivative analysis of hyperspectral data. Remote Sens. Environ., 66(1):41-51.

[29] Vaesen, K., Gilliams, S., Nackaerts, K., Coppin, P., 2001. Ground-measured spectral signatures as indicators of ground cover and leaf area index: the case of paddy rice. Field Crop. Res., 69(1):13-25.

[30] Wang, F.M., Huang, J.F., Wang, X.Z., 2008. Identification of optimal hyperspectral bands for estimation of rice biophysical parameters. J. Integr. Plant Biol., 50(3):291-299.

[31] Xiao, X., He, L., Salas, W., Li, C., Moore, B., Zhao, R., Frolking, S., Boles, S., 2002. Quantitative relationships between field-measured leaf area index and vegetation index derived from VEGETATION images for paddy rice fields. Int. J. Remote Sens., 23(18):3595-3604.

[32] Yang, X., Huang, J., Wang, J., Wang, X., Liu, Z., 2007. Estimation of vegetation biophysical parameters by remote sensing using radial basis function neural network. J. Zhejiang Univ. Sci. A, 8(6):883-895.

[33] Zhao, D., Huang, L., Li, J., Qi, J., 2007. A comparative analysis of broadband and narrowband derived vegetation indices in predicting LAI and CCD of a cotton canopy. ISPRS-J. Photogramm. Remote Sens., 62(1):25-33.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE