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Journal of Zhejiang University SCIENCE A 2007 Vol.8 No.6 P.883-895

http://doi.org/10.1631/jzus.2007.A0883


Estimation of vegetation biophysical parameters by remote sensing using radial basis function neural network


Author(s):  YANG Xiao-hua, HUANG Jing-feng, WANG Jian-wen, WANG Xiu-zhen, LIU Zhan-yu

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

Corresponding email(s):   yxhua1@tom.com, hjf@zju.edu.cn

Key Words:  Artificial neural network (ANN), Radial basis function (RBF), Remote sensing, Rice, Vegetation index (VI)


YANG Xiao-hua, HUANG Jing-feng, WANG Jian-wen, WANG Xiu-zhen, LIU Zhan-yu. Estimation of vegetation biophysical parameters by remote sensing using radial basis function neural network[J]. Journal of Zhejiang University Science A, 2007, 8(6): 883-895.

@article{title="Estimation of vegetation biophysical parameters by remote sensing using radial basis function neural network",
author="YANG Xiao-hua, HUANG Jing-feng, WANG Jian-wen, WANG Xiu-zhen, LIU Zhan-yu",
journal="Journal of Zhejiang University Science A",
volume="8",
number="6",
pages="883-895",
year="2007",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2007.A0883"
}

%0 Journal Article
%T Estimation of vegetation biophysical parameters by remote sensing using radial basis function neural network
%A YANG Xiao-hua
%A HUANG Jing-feng
%A WANG Jian-wen
%A WANG Xiu-zhen
%A LIU Zhan-yu
%J Journal of Zhejiang University SCIENCE A
%V 8
%N 6
%P 883-895
%@ 1673-565X
%D 2007
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2007.A0883

TY - JOUR
T1 - Estimation of vegetation biophysical parameters by remote sensing using radial basis function neural network
A1 - YANG Xiao-hua
A1 - HUANG Jing-feng
A1 - WANG Jian-wen
A1 - WANG Xiu-zhen
A1 - LIU Zhan-yu
J0 - Journal of Zhejiang University Science A
VL - 8
IS - 6
SP - 883
EP - 895
%@ 1673-565X
Y1 - 2007
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2007.A0883


Abstract: 
Hyperspectral reflectance (350~2500 nm) data were recorded at two different sites of rice in two experiment fields including two cultivars, and three levels of nitrogen (N) application. Twenty-five Vegetation Indices (VIs) were used to predict the rice agronomic parameters including Leaf Area Index (LAI, m2 green leaf/m2 soil) and Green Leaf Chlorophyll Density (GLCD, mg chlorophyll/m2 soil) by the traditional regression models and Radial Basis Function Neural Network (RBF). RBF emerged as a variant of Artificial Neural Networks (ANNs) in the late 1980’s. A large variety of training algorithms has been tested for training RBF networks. In this study, Original RBF (ORBF), Gradient Descent RBF (GDRBF), and Generalized Regression Neural Network (GRNN) were employed. Results showed that green waveband Normalized Difference Vegetation Index (NDVIgreen) and TCARI/OSAVI have the best prediction power for LAI by exponent model and ORBF respectively, and that TCARI/OSAVI has the best prediction power for GLCD by exponent model and GDRBF. The best performances of RBF are compared with the traditional models, showing that the relationship between VIs and agronomic variables are further improved when RBF is used. Compared with the best traditional models, ORBF using TCARI/OSAVI improves the prediction power for LAI by lowering the Root Mean Square Error (RMSE) for 0.1119, and GDRBF using TCARI/OSAVI improves the prediction power for GLCD by lowering the RMSE for 26.7853. It is concluded that RBF provides a useful exploratory and predictive tool when applied to the sensitive VIs.

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

Reference

[1] Ahlrichs, J.S., Bauer, M.E., 1983. Relation of agronomic and multispectral reflectance characteristics of spring wheat canopies. Agronomy Journal, 75:987-993.

[2] Aparicio, N., Villegas, D., Casadesus, J., Araus, J.L., Royo, C., 2000. Spectral vegetation indices as non-destructive tools for determining durum wheat yield. Agronomy Journal, 92:83-91.

[3] Asseng, S., Keulen, H., Stol, W., 2000. Performance and application of the APSIMN wheat model in the Netherlands. European Journal of Agronomy, 12:37-54.

[4] Best, R.G., Harlan, J.C., 1985. Spectral estimation of green leaf area index of oats. Remote Sensing of Environment, 17:27-36.

[5] Boegh, E., Soegaard, H., Broge, N., Hasager, C.B., Jensen, N.O., Schelde, K., Thomsen, A., 2002. Airborne multispectral data for quantifying leaf area index, nitrogen concentration, and photosynthetic efficiency in agriculture. Remote Sensing of Environment, 81:179-193.

[6] Bors, A.G., Gabbouj, G., 1994. Minimal topology for a radial basis function neural network for pattern classification. Digital Signal Processing, 4(3):173-188.

[7] Bors, A.G., Pitas, I., 1996. Median radial basis functions neural network. IEEE Trans. on Neural Networks, 7(6):1351-1364.

[8] Bors, A.G., Pitas, I., 1998. Optical flow estimation and moving object segmentation based on median radial basis function network. IEEE Trans. on Image Processing, 7(5):693-702.

[9] Bors, A.G., Pitas, I., 1999. Object classification in 3-D images using alpha-trimmed mean radial basis function network. IEEE Trans. on Image Processing, 8(12):1744-1756.

[10] Broge, N.H., Leblanc, E., 2001. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment, 76:156-172.

[11] Broge, N.H., Mortensen, J.V., 2002. Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data. Remote Sensing of Environment, 81:45-57.

[12] Broomhead, D.S., Lowe, D., 1988. Multivariable functional interpolation and adaptive networks. Complex Systems, 2:321-355.

[13] Casdagli, M., 1989. Nonlinear prediction of chaotic time series. Phys. D, 35:335-356.

[14] Cha, I., Kassam, S.A., 1996. RBFN restoration of nonlinearly degraded images. IEEE Trans. on Image Processing, 5(6):964-975.

[15] Chatzis, V., Bors, A.G., Pitas, I., 1999. Multimodal decision-level fusion for person authentification. IEEE Trans. on Systems, Man, and Cybernetics, Part A: Systems and Humans, 29(6):674-680.

[16] Chen, S., Cowan, C.F.N., Grant, P.M., 1991. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans. on Neural Networks, 2(2):302-309.

[17] Cheng, Q., Huang, J., Wang, X., Wang, R., 2003. In situ hyperspectral data analysis for pigment content estimation of rice leaves. J. Zhejiang Univ. Sci., 4(6):727-733.

[18] Cheng, Q., 2006. Multisensor comparisons for validation of MODIS vegetation indices. Pedosphere, 16(3):362-370.

[19] Christensen, S., Goudriaan, J., 1993. Deriving light interception and biomass from spectral reflectance ratio. Remote Sensing of Environment, 43:87-95.

[20] Curran, P.J., Dungan, J.L., Peterson, D.L., 2001. Estimating the foliar biochemical concentration of leaves with reflectance spectrometry-testing the Kolaly and Clark methodologies. Remote Sensing of Environment, 76:349-359.

[21] Daughtry, C.S.T., Walthall, C.L., Kim, M.S., Brown de Colstoun, E., McMurtrey III, J.E., 2000. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74:229-239.

[22] 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 Sensing of Environment, 58:289-298.

[23] Gitelson, A.A., Kaufman, Y.J., Stark, R., Rundquist, D., 2002. Novel algorithm for remote estimation of vegetation fraction. Remote Sensing of Environment, 80:76-87.

[24] Goel, N.S., Qi, W., 1994. Influences of canopy architecture on relationships between various vegetation indices and LAI and FPAR: a computer simulastion. Remote Sensing of Environment, 10:309-347.

[25] Gong, P., Pu, R., Binging, G.S., Larrieu, M.R., 2003. Estimation of forest leaf area index using vegetation indices derived from hyperion hyperspectral data. IEEE Trans. on Geoscience and Remote Sensing, 41(6):1355-1362.

[26] Haboudane, D., Miller, J.R., Tremblay, N., Zarco-Tejada, P.J., Dextraze, L., 2002. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81:416-426.

[27] Haykin, S., 1994. Neural Networks: A Comprehensive Foundation. Upper Saddle River, Prentice Hall, NJ.

[28] Huete, A.R., 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25:295-309.

[29] Jamieson, P.D., Porter, J.R., Goudrian, J., Ritchie, J.T., Keulen, H., Stol, W., 1998. A comparison of the models AFRCWHEAT2, CERES-wheat, Sirius, SUCROS2 and SWHEAT with measurements from wheat grown under drought. Field Crops Research, 55:23-44.

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

[31] Kim, M.S., Daughtry, C.S.T., Chappelle, E.W., McMurtrey III, J.E., Walthall, C.L., 1994. The Use of High Spectral Resolution Bands for Estimating Absorbed Photosynthetically Active Radiation (Apar). Proc. 6th Symposium on Physical Measurements and Signatures in Remote Sensing. Val D’Isere, France, p.299-306.

[32] Kohonen, T.K., 1989. Self-organization and Associative Memory. Springer-Verlag, Berlin.

[33] Lukina, E.V., Stone, M.L., Raun, W.R., 1999. Estimating vegetation coverage in wheat using digital images. J. Plant Nutr., 22:341-350.

[34] Matej, S., Lewitt, R.M., 1996. Practical considerations for 3-D image reconstruction using spherically symmetric volume elements. IEEE Trans. on Medical Imaging, 15(1):68-78.

[35] Moody, J., 1989. Fast learning in networks of locally-tuned processing units. Neural Computation, 1:281-294.

[36] Musavi, M.T., Ahmed, W., Chan, K.H., Faris, K.B., Hummels, D.M., 1992. On the training of radial basis function classifiers. Neural Networks, 5:595-603.

[37] Mutanga, O., Skidmore, A.K., 2004. Integrating imaging spectroscopy and neural networks to map grass quality in the Kruger National Park, South Africa. Remote Sensing of Environment, 90:104-115.

[38] Niranjan, M., Fallside, F., 1990. Neural networks and radial basis functions in classifying static speech patterns. Computer Speech and Language, 4:275-289.

[39] O'Neal, M.R., Engel, B.A., Ess, D.R., Frankenberger, J.R., 2002. Neural network prediction of maize yield using alternative data coding algorithms. Biosystems Engineering, 83(1):31-45.

[40] Park, J., Sandberg, J.W., 1991. Universal approximation using radial basis functions network. Neural Computation, 3:246-257.

[41] Pearson, R.L., Miller, L.D., 1972. Remote Mapping of Standing Crop Biomass for Estimation of the Productivity of the Short-grass Prairie, Pawnee National Grasslands, Colorado. Proc. 8th International Symposium on Remote Sensing of Environment, p.1357-1381.

[42] Peňuelas, J., Baret, F., Filella, I., 1995. Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica, 31(2):221-230.

[43] Poggio, T., Girosi, F., 1990. Networks for approximation and learning. Proc. IEEE, 78(9):1481-1497.

[44] Qi, J., Chehbouni, A., Huete, A.R., Kerr, Y.H., Sorooshian, S., 1994. A modified soil adjusted vegetation index. Remote Sensing of Environment, 48:119-126.

[45] Rondeaux, G., Steven, M., Baret, F., 1996. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55:95-107.

[46] Roujean, J.L., Breon, F.M., 1995. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment, 51(3):375-384.

[47] Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., Harlan, J.C., 1974. Monitoring the Vernal Advancements and Retrogradation of Natural Vegetation. NASA/GSFC, Final Report. Greenbelt, MD, USA, p.1-137.

[48] Sanner, R.M., Slotine, J.E., 1992. Gaussian networks for direct adaptive control. IEEE Trans. on Neural Networks, 3(6):837-863.

[49] Serrano, L., Filella, I., Peňuelas, J., 2000. Remote sensing of biomass and yield of winter wheat under different nitrogen supplies. Crop Sci., 40:723-731.

[50] 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 Sensing of Environment, 81(2-3):337-354.

[51] Tang, Y., Wang, R., Huang, J., 2004. Relations between red edge characteristics and agronomic parameters of crop. Pedosphere, 4(4):467-474.

[52] Thenkabail, P.S., Smith, R.B., de Pauw, E., 2000. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment, 71:158-182.

[53] Tou, J.T., Gonzalez, R.C., 1974. Pattern Recognition. Reading, Addison-Wesley, MA.

[54] Wei, G.Q., Hirzinger, G., 1997. Parametric shape-from-shading by radial basis functions. IEEE Trans. on Patt. Anal. & Machine Intell., 19(4):353-365.

[55] Yang, X., Huang, J., Wang, F., Wang, X., Yi, Q., Wang, Y., 2006. A modified chlorophyll absorption continuum index for chlorophyll estimation. J. Zhejiang Univ. Sci. A, 7(12):2002-2006.

[56] Zhang, J., Wang, K., Bailey, J.S., Wang, R., 2006. Predicting nitrogen status of rice using multispectral data at canopy scale. Pedosphere, 16(1):108-117.

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