[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. |