| 光谱学与光谱分析 |
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| Estimating Leaf Area Index by Fusing MODIS and MISR Data |
| WAN Hua-wei1,2, WANG Jin-di1*, LIANG Shun-lin3, QIN Jun4 |
1. School of Geography and Remote Sensing Science, Beijing Normal University, State Key Laboratory of Remote Sensing Science, Beijing 100875, China
2. Environment Satellite Center, Ministry of Environmental Protection of the People’s Republic of China, Beijing 100029, China
3. Department of Geography, University of Maryland, College Park, MD 20742, USA
4. Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100085, China |
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Abstract Moderate-resolution imaging spectrometer (MODIS) and multi-angle imaging spectroradiometer (MISR) are two important sensors on TERRA satellite. The authors can have more spectral and multi-angular observations on the land surface objects by combining these two datasets. In the present paper, both MODIS and MISR observations were combined to estimate leaf area index (LAI) of land surface. The adjoining model and trust-region optimal algorithm were introduced into the framework of physical model inversion to speed up the running of the model inversion algorithm. And the algorithm allows the prior knowledge on the retrieved parameters to be input into the inversion procedure. The uncertainty and sensitivity matrix (USM) based analysis is helpful for selecting the observed data subset with more information and less noise to retrieve LAI. The measured LAI in situ and estimated LAI from ETM data were scaling-up to MODIS/MISR LAI product scale, and were taken as the ground truth to evaluate the new approach. The result suggests that combining two sensors datasets can improve the accuracy of LAI estimation, and the developed inversion method in this paper can be applied to the large area remote sensed image data effectively.
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Received: 2008-11-06
Accepted: 2009-02-08
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Corresponding Authors:
WANG Jin-di
E-mail: wangjd@bnu.edu.cn
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[1] Myneni R B, Hoffman S, Knyazikhin Y, et al. Remote Sens. Environ., 2002, 83: 214.
[2] Knyazikhin Y, Martonchik J V, Diner D J, et al. J. Geophy. Res., 1998, 103: 32; 239.
[3] Liang S, Fang H, Townshend J, et al. Data Assimilation Method for Improving Estimation of Land Surface Variables from Satellite Observations. In: The 9th International Symposium on Physical Measurements and Signatures in Remote Sensing (ISPMSRS), Beijing, 2005.
[4] Morisette J T, Baret F, Privette J L, et al. IEEE Trans. Geosci. Remote. Sens., 2006, 44(7): 1804.
[5] Fang H, Liang S, Kuusk A. Remote Sens. Environ., 2003, 85: 257.
[6] Fang H, Liang S. IEEE Trans. Geosci. Remote Sens., 2003, 41(9): 2052.
[7] Myneni R B, Nemani R R, Running S W. IEEE Trans. Geosci. Remote. Sens., 1997, 35(6): 13803.
[8] Knyazikhin Y, Martonchik J V, Myneni R B, et al. J. Geophy. Res., 1998, 103(D24): 32; 257.
[9] Fang H, Liang S. Remote Sens. Environ., 2005, 94: 405.
[10] Kuusk A. Journal of Quantitative Spectroscopy and Radiative Transfer，2001, 71(1): 1.
[11] Qin J, Liang S, Li X, et al. IEEE Trans. Geosci. Remote Sens., 2008, 46(7): 2028.
[12] Powell M J D. A Fast Algorithm for Nonlinearly Constrained Optimization Calculations. Numerical Analysis, Proceedings, Biennial Conference, Dundee, 1977.
[13] Fletcher R. Practical Methods of Optimization. Chichester: John Wiley & Sons, 1981.
[14] Zhou J L, Tits A L, Lawrence C T. User’s Guide for FFSQP Version 3. 7: A FORTRAN Code for Solving Constrained Nonlinear (Minimax) Optimization Problems, Generating Iterates Satisfying All Inequality and Linear Constraints. 1997.
[15] QI Xue-feng, FAN Ding(戚学峰, 樊 丁). Computer Simulation(计算机仿真), 2004, 21(5): 53.
[16] LI Xiao-wen, GAO Feng, WANG Jin-di, et al(李小文, 高 峰, 王锦地，等). Journal of Remote Sensing (遥感学报), 1997, 1(1): 5.
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