Please wait a minute...
img

官方微信

遥感技术与应用  2006, Vol. 21 Issue (4): 271-276    DOI: 10.11873/j.issn.1004-0323.2006.4.271
研究与应用     
基于模拟退火算法的植被参数遥感反演
黄春林, 李 新, 卢 玲
( 中国科学院寒区旱区环境与工程研究所, 甘肃兰州 730000)
A Simulated Annealing Algorithm for Retrieval of Vegetation Parameter from Optical Remote Sensing Data
HUANG Chun-lin, LI Xin, LU Ling
 全文: PDF 
摘要:

提出了基于模拟退火( SA, Simulated Annealing ) 算法的植被参数( 叶面积指数和叶绿素含量) 反演方案。该方案以冠层反射率模型( SAIL, Scat tering by Arbit rarily Inclined Leav es) 作为正向模型, 分别以Bo ltzman 模拟退火( BSA , Bolt zman Simulated Annealing) 、快速模拟退火( FSA,Fast Simulated Annealing ) 、极快速模拟再退火( VFSA, Very Fast Simulated Anneal ing ) 算法为优化方法, 并采用模型输出的光谱反射率和观测的光谱反射率的残差平方和作为目标函数。模拟反演结果表明: ①模拟退火算法能够跳出局部最优, 得到全局最优解; ②极快速模拟再退火算法在时间效率和反演精度上都优于Bo ltzman 模拟退火和快速模拟退火;③ 在给定的光谱数据没有误差的情况下, 利用模拟退火算法反演SAIL 模型, 能够得到高精度的叶面积指数和叶绿素含量。

关键词: 植被参数 反演 SAIL 模型模拟退火 光学遥感    
Abstract:

 The optimization approach is one of the most promising methods for retrieval of vegetation parameter from canopy reflectance model based on optical remote sensing data. In this study , a canopy reflectance model ( SAIL, Scattering by Arbitrarily Inclined Leaves) is adopted as forward model and three different simulated annealing algorithms( Boltzman simulated annealing, fast simulated annealing and very fast simulated re-annealing ) are developed as global optimization scheme to simultaneously retrieve leaf area index and content of chlorophy ll, respectively . The Sum of Squared Residuals (SSR) between spectral reflectance by SAIL model and by observation is selected as cost function. The performance of these algorithms is demonstrated with simulated data sets. We can draw following conclusions: ① this algorithm is able to escape local energy minima and can converge to a global energy minimum; ② the very fast simulated re-annealing algorithm priorto Boltzman simulated annealing and fast simulated annealing ;  ③under no noise conditions, we can obtain the estimation of leaf area index and chlorophyll content accurately .

Key words: Vegetation parameter    Inversion    SAIL    Simulated annealing algorithm    Optical remote sensing
收稿日期: 2005-12-14 出版日期: 2011-09-27
:  TP 79  
基金资助:

 国家重点基础发展项目( 2001CB309404) 、国家自然科学基金( 90202014) 和中国科学院寒区旱区环境与工程研究所创新课题( CACX2003102) 资助。

作者简介: 黄春林( 1979- ) , 男, 博士研究生, 主要从事陆面数据同化、定量遥感的研究。
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  

引用本文:

黄春林, 李 新, 卢 玲. 基于模拟退火算法的植被参数遥感反演[J]. 遥感技术与应用, 2006, 21(4): 271-276.

HUANG Chun-lin, LI Xin, LU Ling. A Simulated Annealing Algorithm for Retrieval of Vegetation Parameter from Optical Remote Sensing Data. Remote Sensing Technology and Application, 2006, 21(4): 271-276.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2006.4.271        http://www.rsta.ac.cn/CN/Y2006/V21/I4/271


〔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〕 Sellers P J, Mintz Y. A Simple Biosphere Model ( SiB) for Use Within General Circulation Models 〔J〕.Journal of the
Atmosphere Sciences, 1986, 43: 505~531.
〔3〕 Sellers P J , Randall D A, Collatz G J, et al . A Revised Land Surface Parametrization ( SIB2 ) for Atmospheric GCMs :
Part I. Model for mulation〔J〕.Journ al of Climate, 1996, 9:676~705.
〔4〕 Sellers P J , Tuck er C J, Collatz G J, et al . A Global 1 by 1 NDVI Data Set for Climate Studies : Part 2. The Generation of Global Fields of Terrestrial Biophysical Parameters from the NDVI〔J〕. International Journal of Remote Sensing ,1994, 15: 3519~3545.
〔5〕 Chen J M , Cihlar J . Retrieving Leaf Area Index of Boreal Conifer Forests Using Landsat TM Images〔J〕.Remote Sensing of Environment , 1996, 55: 153~162.
〔6〕 Chen J M , Rich P M, Gower S T, et al. Leaf Area Index of Boreal Forests : Theory , Techniques and Measurements〔J〕.
Journal of Geophysical Research, 1997, 102: 29429~29443.
〔7〕 Turner D P, Cohen W B, Kennedy R E , et al. Relationship Between Leaf Area Index and Landsat TM Spectral Vegetation
Indices Across Three Temperate Zone Sites〔J〕.Remote Sensing of Environment , 1999, 70: 52~68.
〔8〕 Fassnacht K S , Gower S T , MacKenzie M D, et al. Estimating the Leaf Area Index of North Central Wiscons in Forests Using the Landsat Thematic Mapper〔J〕.Remote Sensing of Environment, 1997, 61: 229~245.
〔9〕 Knyazikhin Y, Martonchik 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〕.J Geophys Res , 1998, 103:32239~32256.
〔10〕 Goel N S, Thompson R L. Inversion of Vegetation Canopy Reflectance Models for Estimating Agronomic Variables . V.Estimation of Leaf Area Index and Average Leaf Inclination Angle Using Measured Canopy Reflectance 〔J 〕. Remote Sensing Environ, 1984, 15: 69~85.
〔11〕 Geol N S, Deering D. Evaluati on of A Canopy Reflectance Model for LAI Estimation Through Its Inversion〔J〕.IEEE Trans Geosci And Remote Sensing, 1989, 23: 674~684.
〔12〕 Liang S, Strahler A H. An Analytic BRDF Model of Canopy Radiative Transfer and Its Inversion〔J〕.IEEE Trans Geosci And Remote Sensing, 1993, 31: 1081~1092.
〔13〕 Gong P, Wang S X, Liang S. Inverting A Canopy Reflectance Model Using A Neural Network〔J〕.Int J Remote Sensing, 1999, 20: 111~122.
〔14〕 Fang H, Liang S. Retrieve LAI from Landsat 7 ETM+ Data with A Neural Network Method: Simulation and Validation Study〔J〕. IEEE Transactions on Geosciences and Remote Sensing, 2003, 41: 2052~2062.
〔15〕 Fang H, Liang S. Retrieving LAI Using A Genetic Algorithm with a Canopy Radiative Transfer Model〔J〕.Remote Sensing of Environment , 2003, 85: 257~270.
〔16〕 Weiss M, Baret F, Leroy M , et al . Validation of Neural Net Techniques to Estimate Canopy Biophysical Variables from
Remote Sensing Data〔J〕.Agronomie, 2002, 22: 547~553.
〔17〕 Kirkpatrick S, Gelatt Jr C D, Vecchi M P. Optimization by Simulated Annealing〔J〕.Science, 1983, 220: 671~680.
〔18〕 Li Xin, Koike T , Pathmathevan M. A Very Fast Simulated Re-annealing ( VFSA) Approach for Land Data Assimilation〔J〕.
Computer & Geosciences, 2004, 30: 239~248.
〔19〕 Verhoef W. Light Scattering by Leaf Layers with Application to Canopy Reflectance Modeling: The SAIL Model〔J〕.Remot e Sensing Environ , 1984, 16: 125~141.
〔20〕 Jacquemoud S , Bacour C, Polve H, et al . Comparison of Four Radiative Transfer Models to Simulate Plan t Canopies Reflectance: Direct and Inverse Model〔J〕.Remote Sensing Environ, 2000, 74: 471~481.
〔21〕 Andrieu B, Bar et F, Jacquemoud S, et al . Evaluation of An Improved Version of SAIL Model to Simulate bi-directional Reflectance of Sugar Beet Canopies〔J〕.Remote Sensing Environ,1997, 60: 247~257.
〔22〕 Badwahr G, Bunnik N, Verhoef W. Comparative Study of Suits and SAIL Canopy Reflectance Models 〔J〕. Remote Sensing Environ, 1985, 17: 179~195.
〔23〕 Major D, Schaalje G, Wiegand C, et al . Accuracy and Sensitivity Analysis of SAIL Model-predicted Reflectance of Maize
〔J〕.Remote Sensing Environ, 1992, 41: 61~70.
〔24〕 Jacquemoud S. Inversion of the PROSPECT + SAIL Canopy Reflectance Model from AVIRIS Equivalent Spectra: Theoretical Study〔J〕. Remote Sensing Environ, 1993, 44: 281~292.
〔25〕 Jacquemoud S , Baret F. PROSPECT : A Model of Leaf Optical Properties Spectra〔J〕.Remote Sensing Environ, 1990,34: 75~91.
〔26〕 Ingber L. Very Fast Simulated Re-ann ealing〔J〕.Mathematical Computer Modelling, 1989, 12: 967~973.
〔27〕 Ingber L. Simulated Annealing: Practice Versus Theory〔J〕.Mathematical Computer Modeling, 1993, 18: 29~57.

[1] 王恺宁,王修信,黄凤荣,罗涟玲. 喀斯特城市地表温度遥感反演算法比较[J]. 遥感技术与应用, 2018, 33(5): 803-810.
[2] 金点点,宫兆宁. 基于Landsat 系列数据地表温度反演算法对比分析—以齐齐哈尔市辖区为例[J]. 遥感技术与应用, 2018, 33(5): 830-841.
[3] 廖凯涛,齐述华,王成,王点. 结合GLAS和TM卫星数据的江西省森林高度和生物量制图[J]. 遥感技术与应用, 2018, 33(4): 713-720.
[4] 李珊珊,蒋耿明. 基于通用分裂窗算法和Landsat-8数据的地表温度反演研究[J]. 遥感技术与应用, 2018, 33(2): 284-295.
[5] 汤玉明,邓孺孺,刘永明,熊龙海. 大气气溶胶遥感反演研究综述[J]. 遥感技术与应用, 2018, 33(1): 25-34.
[6] 王纪坤,陈正华,余克服,黄荣永,王英辉. 珊瑚礁区多光谱遥感水深反演研究[J]. 遥感技术与应用, 2018, 33(1): 61-67.
[7] 张王菲,陈尔学,李增元,赵磊,姬永杰. 干涉、极化干涉SAR技术森林高度估测算法研究进展[J]. 遥感技术与应用, 2017, 32(6): 983-997.
[8] 张雅,尹小君,王伟强. 基于Landsat 8 OLI遥感影像的天山北坡草地地上生物量估算[J]. 遥感技术与应用, 2017, 32(6): 1012-1021.
[9] 吴仪,邓孺孺,秦雁,梁业恒,熊龙海. 新丰江水库叶绿素浓度时空分布特征的遥感反演研究[J]. 遥感技术与应用, 2017, 32(5): 825-834.
[10] 谢亚楠,周明亮,刘志坤. 合成孔径雷达反演降雨量算法的研究进展[J]. 遥感技术与应用, 2017, 32(4): 624-633.
[11] 周爱明,鲍艳松,魏鸣,陆其峰. FY-3近红外与热红外资料大气柱水汽总量反演对比[J]. 遥感技术与应用, 2017, 32(4): 651-659.
[12] 陈坤堂,董晓龙,徐星欧,郎姝燕. 微波散射计反演海面风场的神经网络方法研究[J]. 遥感技术与应用, 2017, 32(4): 683-690.
[13] 韩冰,许遐祯,刘焕彬,张康宇,郭乔影,黄敬峰,王秀珍. 同极化Radarsat-2数据的海面风速反演研究[J]. 遥感技术与应用, 2017, 32(3): 419-426.
[14] 于惠,吴玉锋,金毅,张峰. 基于MODIS SWIR数据的干旱区草地地上生物量反演及时空变化研究[J]. 遥感技术与应用, 2017, 32(3): 524-530.
[15] 葛美香,赵军,仲波,杨爱霞. FY-3/VIRR及MERSI与EOS/MODIS植被指数比较与差异原因分析[J]. 遥感技术与应用, 2017, 32(2): 262-273.