ZHU Xiaohua, ZHAO Yingshi, FENG Xiaoming. A Methodology for Estimating Leaf Area Index by Assimilating Remote Sensing Data into Crop Model Based on Temporal and Spatial Knowledge[J]. Chinese Geographical Science, 2013, 23(5): 550-561. doi: 10.1007/s11769-013-0621-x
Citation: ZHU Xiaohua, ZHAO Yingshi, FENG Xiaoming. A Methodology for Estimating Leaf Area Index by Assimilating Remote Sensing Data into Crop Model Based on Temporal and Spatial Knowledge[J]. Chinese Geographical Science, 2013, 23(5): 550-561. doi: 10.1007/s11769-013-0621-x

A Methodology for Estimating Leaf Area Index by Assimilating Remote Sensing Data into Crop Model Based on Temporal and Spatial Knowledge

doi: 10.1007/s11769-013-0621-x
Funds:  Foundation item: Under the auspices of Major State Basic Research Development Program of China (No. 2007CB714407), National Natural Science Foundation of China (No. 40801070), Action Plan for West Development Program of Chinese Academy of Sciences (No. KZCX2-XB2-09)
More Information
  • Corresponding author: ZHU Xiaohua,E-mail: zhuxh@aoe.ac.cn
  • Received Date: 2012-08-21
  • Rev Recd Date: 2012-12-11
  • Publish Date: 2013-09-10
  • In this paper, a methodology for Leaf Area Index (LAI) estimating was proposed by assimilating remote sensed data into crop model based on temporal and spatial knowledge. Firstly, sensitive parameters of crop model were calibrated by Shuffled Complex Evolution method developed at the University of Arizona (SCE-UA) optimization method based on phenological information, which is called temporal knowledge. The calibrated crop model will be used as the forecast operator. Then, the Taylor's mean value theorem was applied to extracting spatial information from the Moderate Resolution Imaging Spectroradiometer (MODIS) multi-scale data, which was used to calibrate the LAI inversion results by a two-layer Canopy Reflectance Model (ACRM) model. The calibrated LAI result was used as the observation operator. Finally, an Ensemble Kalman Filter (EnKF) was used to assimilate MODIS data into crop model. The results showed that the method could significantly improve the estimation accuracy of LAI and the simulated curves of LAI more conform to the crop growth situation closely comparing with MODIS LAI products. The root mean square error (RMSE) of LAI calculated by assimilation is 0.9185 which is reduced by 58.7% compared with that by simulation (0.3795), and before and after assimilation the mean error is reduced by 92.6% which is from 0.3563 to 0.0265. All these experiments indicated that the methodology proposed in this paper is reasonable and accurate for estimating crop LAI.

  • [1] Bateer Bake, 2005. Studies On Modeling Crop Production in Fen River Irrigation District and Water Balance Model of the Watershed. Beijing: China Agricultural University. (in Chinese)
    [2] Baret F, Guyot G, 1991. Potentials and limits of vegetation indices for LAI and FAPAR assessment. Remote Sensing of Environment, 35(2-3): 161-173. doi: 10.1016/0034-4257(91) 90009-U
    [3] Boogaard H L, van Diepen C A, Rotter R P et al., 1998. User's Guide for the WOFOST 7. 1 Crop Growth Simulation Model and WOFOST Control Center. DLO Wageningen: Winand Staring Centre, 1-40.
    [4] Burgers R, Concha F, 1998. Mathematical model and numerical simulation of the setting of flocculated suspensions. International Journal of Multiphase Flow, 24(6): 1005-1023. doi:  10.1016/S0301-9322(98)00026-3
    [5] Doraiswamy P C, Moulin S, Cook P W et al., 2003. Crop yield assessment from remote sensing. Photogrammetric Engineering and Remote Sensing, 69(6): 665-674. doi: 10.1080/ 01431160410001698870
    [6] Dorigo W A, Zurita-Milla R, de Wit A J W et al., 2007. A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. International Journal of Applied Earth Observation and Geoinformation, 9(2): 165-193. doi:  10.1016/j.jag.2006.05.003
    [7] Duan Qingyun, Gupta V K, Sorooshian S, 1993. A shuffled complex evolution approach for effective and efficient global minimization. Journal of Optimization Theory and Applications, 76(3): 501-521. doi:  10.1007/bf00939380
    [8] Duan Qingyun, Sorooshian S, Gupta V K, 1994. Optimal use of the SCE-UA global optimization method for calibrating watershed models. Journal of Hydrology, 158(3-4): 265-284. doi:  10.1016/0022-1694(94)90057-4
    [9] Evensen G, 1994. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte-Carlo methods to forecast error statistics. Journal of Geophysical Research, 99(5): 10143-10162. doi:  10.1029/94jc00572
    [10] Fang Hongliang, Liang Shunlin, Gerrit H, 2010. Integration of MODIS LAI and vegetation index products with the CSM-CERES-Maize model for corn yield estimation. International Journal of Remote Sensing, 32(4): 1039-1065. doi: 10.1080/ 01431160903505310
    [11] Garrigues S, Allard B D, Baret F, 2006. Influence of landscape spatial heterogeneity on the non-linear estimation of leaf area index from moderate spatial resolution remote sensing date. Remote Sensing of Environment, 105(4): 286-298. doi:  10.1016/j.rse.2006.07.013
    [12] Hazarika M K, Yasuoka Y, Ito A et al., 2005. Estimation of net primary productivity by integrating remote sensing data with an ecosystem model. Remote Sensing of Environment, 94(3): 298-310. doi:  10.1016/j.rse.2004.10.004
    [13] Jacquemoud S, Baret F, 1990. PROSPECT: A model of leaf optical properties spectra. Remote Sensing of Environment, 34(2): 75-91. doi:  10.1016/0034-4257(90)90100-Z
    [14] Kuusk A, 1985. The hot spot effect of a uniform vegetative cover. Soviet Journal of Remote Sensing, 3(4): 645-658.
    [15] Kuusk A, 2001. A two-layer canopy reflectance model. Journal of Quantitative Spectroscopy & Radiative Transfer, 71(1): 1-9. doi:  10.1016/S0022-4073(01)00007-3
    [16] Li Xiaowen, Gao Feng, Wang Jingdi et al., 1997. Uncertainty and sensitivity matrix of parameters in inversion of physical BRDF model. Journal of Remote Sensing, 1(1): 5-14. (in Chinese)
    [17] Per J, Eklundhc L, 2004. TIMESAT—A program for analyzing time-series of satellite sensor data. Computers and Geosciences, 30(8): 833-845. doi:  10.1016/j.cageo.2004.05.006
    [18] Price J C, 1990. On the information content of soil reflectance spectra. Remote Sensing of Environment, 33(2): 113-121. doi:  10.1016/0034-4257(90)90037-M
    [19] Song Xingyuan, Shu Quanying, Wang Haibo et al., 2009. Comparison and application of SCE-UA, genetic algorithm and simplex method. Engineering Journal of Wuhan University, 4(10): 6-15. (in Chinese)
    [20] Sun Huasheng, Huang Jingfeng, Peng Dialing, 2009. Detecting major growth stages of paddy rice using MODIS data. Journal of Remote Sensing, 13(6): 1130-1137. (in Chinese)
    [21] Tian Y H, Woodcock C E, Wang Y J, 2002. Multi-scale analysis and validation of the MODIS LAI product, I. Uncertainty assessment. Remote Sensing of Environment, 83(3): 414-430. doi:  10.1016/S0034-4257(02)00047-0
    [22] Verhoef W, 1984. Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model. Remote Sensing of Environment, 16(2): 125-141. doi:  10.1016/0034-4257(84)90057-9
    [23] Wang Dongwei, Wang Jindi, Liang Shunlin, 2010. Retrieving crop leaf area index by assimilation of MODIS data into crop growth model. Science China Earth Science, 53(5): 721-730. (in Chinese)
    [24] Wang Tao, Lu Change, Yu Bohua, 2010. Assessing the potential productivity of winter wheat using WOFOST in the Beijing-Tianjin-Hebei Region. Journal of Natural Resources, 25(3): 475-487. (in Chinese)
    [25] Wu Dingrong, Ou Yangzhu, Zhao Xiaomin et al., 2003. The applicability research of WOFOST model in North China Plain. Acta Phytoecologica Sinica, 27(5): 594-602. (in Chinese)
    [26] Xiao Zhiqiang, Liang Shunlin, Wang Jindi et al., 2011. Real-time retrieval of Leaf Area Index from MODIS time series data. Remote Sensing of Environment, 115(1): 97-106. doi: 10.1016/ j.rse.2010.08.009
    [27] Xie Wenxia, Wang Guanghuo, Zhang Qichun, 2006. Development of WOFOST (World Food Studies) and its Application. Chinese Journal of Soil Science, 37(1): 154-158. (in Chinese)
    [28] Yannick C, Allard J.W, Duveiller G et al., 2011. Potential performances of remotely sensed LAI assimilation in WOFOST model based on an OSS Expeniment. Agricultural and Forest Meteorology, 151(12): 1843-1855. doi: 10.1016/j.agrformet. 2011.08.002
    [29] Zhang Renhua, Sun Xiaomin, Su Hongbo et al., 1999. Remote sensing and scale transferring of levity parameters on earth surface. Remote Sensing for Land & Resources, 3(2): 51-59. (in Chinese)
    [30] Zhang X Y, Friedl M A, Schaaf C B et al., 2003. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment, 84: 471-475. doi:  10.1016/S0034-4257(02)00135-9
    [31] Zhang Xiuying, Jiang Hong, Han Ying, 2010. Lamd data assimilation system and its application in global change research. Remote Sensing Information, (4): 135-143. (in Chinese)
    [32] Zhu Xiaohua, Feng Xiaoming, Zhao Yingshi et al., 2010. Scale effect and error analysis of crop LAI inversion. Journal of Remote Sensing, 14(3): 586-599. (in Chinese)
    [33] Zhu Xiaohua, Feng Xiaoming, Zhao Yingshi, 2012. Multi-scale MSDT inversion based on LAI spatial knowledge. Science China Earth Science, 55(8): 1297-1305.
  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Article Metrics

Article views(273) PDF downloads(1684) Cited by()

Proportional views
Related

A Methodology for Estimating Leaf Area Index by Assimilating Remote Sensing Data into Crop Model Based on Temporal and Spatial Knowledge

doi: 10.1007/s11769-013-0621-x
Funds:  Foundation item: Under the auspices of Major State Basic Research Development Program of China (No. 2007CB714407), National Natural Science Foundation of China (No. 40801070), Action Plan for West Development Program of Chinese Academy of Sciences (No. KZCX2-XB2-09)
    Corresponding author: ZHU Xiaohua,E-mail: zhuxh@aoe.ac.cn

Abstract: 

In this paper, a methodology for Leaf Area Index (LAI) estimating was proposed by assimilating remote sensed data into crop model based on temporal and spatial knowledge. Firstly, sensitive parameters of crop model were calibrated by Shuffled Complex Evolution method developed at the University of Arizona (SCE-UA) optimization method based on phenological information, which is called temporal knowledge. The calibrated crop model will be used as the forecast operator. Then, the Taylor's mean value theorem was applied to extracting spatial information from the Moderate Resolution Imaging Spectroradiometer (MODIS) multi-scale data, which was used to calibrate the LAI inversion results by a two-layer Canopy Reflectance Model (ACRM) model. The calibrated LAI result was used as the observation operator. Finally, an Ensemble Kalman Filter (EnKF) was used to assimilate MODIS data into crop model. The results showed that the method could significantly improve the estimation accuracy of LAI and the simulated curves of LAI more conform to the crop growth situation closely comparing with MODIS LAI products. The root mean square error (RMSE) of LAI calculated by assimilation is 0.9185 which is reduced by 58.7% compared with that by simulation (0.3795), and before and after assimilation the mean error is reduced by 92.6% which is from 0.3563 to 0.0265. All these experiments indicated that the methodology proposed in this paper is reasonable and accurate for estimating crop LAI.

ZHU Xiaohua, ZHAO Yingshi, FENG Xiaoming. A Methodology for Estimating Leaf Area Index by Assimilating Remote Sensing Data into Crop Model Based on Temporal and Spatial Knowledge[J]. Chinese Geographical Science, 2013, 23(5): 550-561. doi: 10.1007/s11769-013-0621-x
Citation: ZHU Xiaohua, ZHAO Yingshi, FENG Xiaoming. A Methodology for Estimating Leaf Area Index by Assimilating Remote Sensing Data into Crop Model Based on Temporal and Spatial Knowledge[J]. Chinese Geographical Science, 2013, 23(5): 550-561. doi: 10.1007/s11769-013-0621-x
Reference (33)

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return