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Advances in Earth Science  2009, Vol. 24 Issue (7): 756-768    DOI: 10.11867/j.issn.1001-8166.2009.07.0756
Articles     
Estimating Time Series Leaf Area Index Based on Recurrent Neural Networks
Chai Linna1,2,Qu Yonghua1,2,Zhang Lixin1,2,Liang Shunlin3,Wang Jindi1,2
1. State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing Applications of Chinese Academy of Sciences, Beijing  100875, China;
2. School of Geography and Remote Sensing Science, Beijing Normal University, Beijing  100875, China;
3. Department of Geography, University of Maryland, Maryland MD20742, USA
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Abstract  

Leaf Area Index is a key parameter of many meteorological, environmental and agricultural models. At present, global LAI products of several sensors have been released. However, due to the limitations of the retrieval methods and the qualities of the reflectance products, the released LAI products, generally retrieved from a single sensor data, have been found maybe not continuous in time series and can not characterize the natural growing vegetation well. Many research results in different domain have found that the neural network has nearly perfect performance in recognizing patterns in complex, nonlinear data, e.g., a three-layered neural network could be used to approximate any continuous function if nonlinear increasing function and linear function are used respectively in its hidden layers and output layer. As to a span of the LAI values of an individual vegetation type in a same area, if there were no natural disasters or human destructions, they should present as a nonlinear continuous time series. In this study, by fusing the MODIS and VEGETATION products, time series LAI were used to construct recurrent neural networks, namely the NARX neural network, for six typical vegetation types. Meanwhile, time series reflectances in red, near infrared and shortwave infrared bands and the corresponding sun-viewing angles were introduced into the NARX neural networks as the exogenous inputs to estimate time series LAI. The validation results show that the NARX neural network is competent to estimate time series LAI and the predicted LAI of it is more continuous and smoother than that of the original MODIS LAI in time series. Thus the proposed method may be helpful to improve the quality of LAI products of the typical vegetation types.

Key words:  Time series      Leaf area index      Data fusion      NARX neural network      MODIS      VEGETATION     
Received:  08 January 2009      Published:  10 July 2009
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JUE Yong-Hua
ZHANG Li-Xin
LIANG Shun-Lin
WANG Jin-De
CHAI Lin-Na

Cite this article: 

Chai Linna,Qu Yonghua,Zhang Lixin,Liang Shunlin,Wang Jindi. Estimating Time Series Leaf Area Index Based on Recurrent Neural Networks. Advances in Earth Science, 2009, 24(7): 756-768.

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http://www.adearth.ac.cn/EN/10.11867/j.issn.1001-8166.2009.07.0756     OR     http://www.adearth.ac.cn/EN/Y2009/V24/I7/756

[1] Bonan G B. Importance of leaf area index and forest type when estimating photosynthesis in boreal forests[J].Remote Sensing of Environment,1993,43:303-314.
[2] Bonan G B. The land surface climatology of the NCAR land surface model coupled to the NCAR community climate model[J].Journal of Climate,1998, 11: 1 307-1 326.
[3] Bonan G B, Oleson K W, Vertenstein M, et al.The land surface climatology of the community land model coupled to the NCAR community climate model[J].Journal of Climate, 2002, 15:3 123-3 149.
[4] Fang H, Liang S, Kuusk A. Retrieving leaf area index using a genetic algorithm with a canopy radiative transfer model[J].Remote Sensing of Environment,2003, 85: 257-270.
[5] Sellers P, Schimel D. Remote sensing of the land biosphere and biogeochemistry in the EOS era: Science priorities, methods and implementation-EOS land biosphere and biogeochemical cycles panels[J].Global and Planetary Change,1993, 7: 279-297.
[6] Kergoat L. A model for hydrological equilibrium of leaf area index on a global scale[J].Journal of Hydrology, 1998,(212/213): 268-286.
[7] Sakamoto T, Yokozawa M, Toritani H, et al. A crop phenology detection method using time-series MODIS data[J].Remote Sensing of Environment,2005, 96: 366-374.
[8] Liu R, Chen J M, Liu J, et al. Application of a new leaf area index algorithm to China′s landmass using MODIS data for carbon cycle research[J].Remote Sensing of Environment, 2007, 85: 649-658.
[9] Verger A, Baret F, Weiss M. Performances of neural networks for deriving LAI estimates from existing CYCLOPES and MODIS products[J].Remote Sensing of Environment,2008, 112: 2 798-2 803.
[10] Liang S. Quantitative Remote Sensing of Land Surface[M]. New York: John Wiley and Sons, Inc, 2004.
[11] Walthall C, Dulaney W, Anderson M, et al. A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+ imagery[J].Remote Sensing of Environment, 2004, 92: 465-474.
[12] Qu Y, Wang J, Wan H,et al. A bayesian network algorithm for retrieving the characterization of land surface vegetation[J].Remote Sensing of Environment, 2008, 112: 613-622.
[13] Walthall C L. A study of reflectance anisotropy and canopy structure using a simple empirical model[J]. Remote Sensing of Environment,1997, 61: 118-128.
[14] Brown L, Chen J M, Leblanc S G, et al. A shortwave infrared modification to the simple ratio for LAI retrieval in boreal forests an image and model analysis[J].Remote Sensing of Environment,2000, 71: 16-25.
[15] Jacquemoud S, Baret F. A model of leaf optical properties spectra[J].Remote Sensing of Environment,1990, 34: 75-91.
[16] Myneni R B, Nemani R R, Running S W. Estimation of global leaf area index and absorbed par using radiative transfer models[J].IEEE Transactions on Geoscience and Remote Sensing,1997, 35: 1 380-1 393.
[17] Dawson T P, Curran P J, Plummer S E. LIBERTY-modeling the effects of leaf biochemical concentration on reflectance spectra[J].Remote Sensing of Environment,1998, 65: 50-60.
[18] Kuusk A. A two-layer canopy reflectance model[J].Journal of Quantitative Spectroscopy & Radiative Transfer, 2001, 71: 1-9.
[19] Verstraete M M, Pinty B, Myneini R B. Potential and limitations of information extraction on the terrestrial biosphere from satellite remote sensing[J].Remote Sensing of Environment, 1996, 58: 201-214.
[20] Noble P A, Tribou E H. Neuroet: An easy-to-use artificial neural network for ecological and biological modeling[J].Ecological Modelling,2007, 203: 87-98.
[21] Fang H, Liang S. Retrieving leaf area index with a neural network method: Simulation and validation[J].IEEE Transactions on Geoscience and Remote Sensing,2003, 41: 2 052-2 062.
[22] Gong P, Wang S, Liang S. Inverting a canopy reflectance model using a neural network[J].International Journal of Remote Sensing, 1999, 20: 111-122.
[23] Trombetti M, Riano D, Rubio M A, et al. Multi-temporal vegetation canopy water content retrieval and interpretation using artificial neural networks for the continental USA[J].Remote Sensing of Environment,2008, 112: 203-215.
[24] Funahashi K.On the approximate realization of continuous mappings by neural networks[J].Neural Networks, 1989,2:183-192.
[25] Cotter N E, Guillerm T J. The DMAC and a theorem of Kolmogorov[J].Neural Networks,1992, 5: 221-228.
[26] Kürková V. Kolmogorov′s theorem and multilayer neural networks[J].Neural Networks,1992, 5: 501-506.
[27] Katsuura H, Sprecher D A. Computational aspects of Kolmogorov′s superposition theorem[J].Neural Networks,1994, 7: 455-461.
[28] Zhang G P.A neural network ensemble method with jittered training data for time series forecasting[J]. Information Sciences, 2007, 177: 5 329-5 346.
[29] Assaad M, Bon  R, Cardot H. A new boosting algorithm for improved time-series forecasting with recurrent neural networks[J].Information Fusion,2008, 9: 41-55.
[30] Yu L,Wang S, Lai K K. A neural-network-based nonlinear metamodeling approach to financial time series forecasting[J].Applied Soft Computing, 2007, 9(2): 563-574.
[31] Hussain A J, Knowles A, Lisboa P J G, et al. Financial time series prediction using polynomial pipelined neural networks[J].Expert Systems with Applications, 2008, 35: 1 186-1 199.
[32] Zhang G P, Qi M. Neural network forecasting for seasonal and trend time series[J].European Journal of Operational Research, 2005, 160: 501-514.
[33] Chen Y, Zhang W, Yong B. Retrieving leaf area index using a neural network based on classification knowledge[J].Acta Ecologica Sinica, 2007, 27: 2 785-2 793.
[34] Sun W, Liang S, Xu G, et al. Mapping plant functional types from MODIS data using multisource evidential reasoning[J].Remote Sensing of Environment, 2008, 112: 1 010-1 024.
[35] Pisek J, Chen J M. Comparison and validation of MODIS and VEGETATION global LAI products over four BigFoot sites in north America[J].Remote Sensing of Environment,2007, 109: 81-94.
[36] Weiss M, Baret F, Garrigues S, et al. LAI and fAPAR CYCLOPES global products derived from VEGETATION. Part 2: Validation and comparison with MODIS collection 4 products[J].Remote Sensing of Environment,2007, 110: 317-331.
[37] Knyazikhin Y, Martonchin J V, Diner D J, et al. Estimation of vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from atmosphere-corrected MISR data[J].Journal of Geophysical Research, 1998, 103: 32 239-32 256.
[38] Baret F, Hagolle O, Geiger B, et al. LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION. Part 1: Principles of the algorithm[J].Remote Sensing of Environment, 2007, 110: 275-286.
[39] Demuth H, Beal M, Hagan M. User′s Guide: Neural network toolbox TM 6[Z]. Natick: The Mathworks, MA, 2008.
[40] Wang Q, Tenhunen J, Dinh N Q, et al. Evaluation of seasonal variation of MODIS derived leaf area index at two European deciduous broadleaf forest sites[J].Remote Sensing of Environment,2005, 96: 475-484.

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