收稿：2016-01-19，
纸质出版：2017-01
移动端阅览
二向反射分布函数包含地表反射的方向性特征信息。研究二向反射分布函数BRDF(Bidirectional Reflectance Distribution Function)形状对植被结构参数的敏感性，有助于理解植被的二向性反射规律，进而反演植被参数。本文耦合双冠层反射率模型和核驱动的罗斯厚核-李氏稀疏互易核模型，利用EFAST全局敏感性分析方法，以各向异性平整指数为BRDF形状变化的衡量指标，研究了不同天空光比例(SKYL)下，各向异性平整指数AFX对植被参数敏感度的变化，以及SKYL=0.1时AFX的敏感性。结果表明：(1)在红波段，上、下层叶面积指数、上层叶绿素含量，以及上层叶倾角分布是AFX的敏感参数，在近红外波段，上、下层叶面积指数LAI是AFX的敏感参数。(2)冠层尺度上的参数敏感度总体大于叶片尺度。(3)晴天时(SKYL=0.1)，红波段主敏感度较大的参数分别是上层LAI、上层叶倾角分布和下层叶片结构参数，近红外波段主敏感度较大的参数主要是上、下层LAI。
Bidirectional Reflectance Distribution Function (BRDF) contains directional information on surface reflectance and provides important orientation information in calculating the surface time-space variable elements. The analysis of the sensitivity of BRDF shapes to vegetation structure parameters contributes to understanding the laws of vegetation bidirectional reflectance and increasing the accuracy in inversing vegetation parameters. Numerous scholars have investigated different BRDF models to determine the relationship between vegetation structure parameters and bidirectional reflectance distribution effects of the vegetation surface. Among these models
the two-layer canopy reflectance model (ACRM model) and kernel-driven model are generally accepted as physical and semiempirical models
respectively.This study explores the sensitivity of BRDF shapes to vegetation structure parameters by coupling the ACRM model and kernel-driven Ross-Thick/Li-Sparse-Reciprocal model (RTLSR model). The adopted sensitivity analysis method is the extended Fourier amplitude sensitivity test
one of the global sensitivity analysis methods. The Anisotropic Flat indeX (AFX) is employed as the metric for BRDF shapes to explore the sensitivity change in AFX to vegetation parameters under the condition of different SKY Light ratios (SKYL) and sensitivity with SKYL=0.1. From these variables
this study explores the sensitivity parameters with SKYL=0.1. Then
the relationship between the main sensitivity parameters and AFX is analyzed.Overall
AFX can indicate the change in BRDF shapes. The kernel-driven RTLSR model can fit the data produced by the ACRM model well. Results of the analysis of sensitivity indicate that the change in sky light results in different degrees of sensitivity. Given the influence of sky light
parameter sensitivity at the canopy scale is larger than that at the leaf scale. Under sunny conditions (SKYL=0.1)
the total order sensitivity index of the upper Leaf Area Index (LAI) is strongest in the red band
and lower LAI is observed in the near-infrared band. The first-order sensitivity indices of the upper LAI
upper leaf angle distribution
and lower leaf structure parameters are stronger in the red band. Meanwhile
the first-order sensitivity indices of the upper and lower LAI are stronger in the near-infrared band.AFX plays an important role in increasing inversion accuracy. The index is significant in understanding the relationship between AFX and the surface-covering physical parameters for the inversion of the parameters. This study analyzed the sensitivity of AFX to vegetation structure parameters. The results will help researchers understand the effects of changes in vegetation parameters on BRDF shapes.
Bowyer P and Danson F M. 2004. Sensitivity of spectral reflectance to variation in live fuel moisture content at leaf and canopy level. Remote Sensing of Environment, 92(3): 297–308
Bréon F M, Maignan F, Leroy M and Grant I. 2002. Analysis of hot spot directional signatures measured from space. Journal of Geophysical Research, 107(D16): AAC 1-1–AAC 1-15
Chen J M and Cihlar J. 1997. A hotspot function in a simple bidirectional reflectance model for satellite applications. Journal of Geophysical Research-Atmospheres, 102(D22): 25907–25913
Cheng Y B, Middleton E M, Zhang Q Y, Corp L A, Dandois J and Kustas W P. 2012. The photochemical reflectance index from directional cornfield reflectances: observations and simulations. Remote Sensing of Environment, 124: 444–453
Cukier R I, Fortuin C M, Shuler K E, Petschek A G and Schaibly J H. 1973. Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. I: theory. The Journal of Chemical Physics, 59(8): 3873–3878
董亚冬, 焦子锑, 张虎, 李佳悦, 焦广平, 石涵予. 2014. 改善MODIS BRDF产品热点效应的方法研究. 遥感学报, 18(4): 804–825
Dong Y D, Jiao Z T, Zhang H, Li J Y, Jiao G P and Shi H Y. 2014. Efficient algorithm for improving the hotspot effect of the operational MODIS BRDF product. Journal of Remote Sensing, 18(4): 804–825
Eriksson H M, Eklundh L, Kuusk A and Nilson T. 2006. Impact of understory vegetation on forest canopy reflectance and remotely sensed LAI estimates. Remote Sensing of Environment, 103(4): 408–418
Hirsch J E. 2005. An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences of the United States of America, 102(46): 16569–16572
Jacquemoud S and Baret F. 1990. Prospect: a model of leaf optical properties spectra. Remote Sensing of Environment, 34(2): 75–91
Jacquemoud S and Baret F. 1993. Estimating vegetation biophysical parameters by inversion of a reflectance model on high spectral resolution data // Varlet-Grancher C, Bonhomme R and Sinoquet H, eds. Crop Structure and Light Microclimate: Characterization and Applications. Versailles (France): Editions de I’INRA: 339–350
Jacquemoud S, Verhoef W, Baret F, Bacour C, Zarco-Tejada P J, Asner G P, François C and Ustin S L. 2009. PROSPECT+SAIL models: a review of use for vegetation characterization. Remote Sensing of Environment, 113: S56–S66
Jiao Z T, Dong Y D and Li X W. 2013. An approach to improve hot spot effect for the MODIS BRDF/albedo algorithm // Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Melbourne: IEEE: 3037–3039 [DOI: 10.1109/IGARSS.2013.6723466]
Jiao Z T, Hill M J, Schaaf C B, Zhang H, Wang Z S and Li X W. 2014. An Anisotropic Flat Index (AFX) to derive BRDF archetypes from MODIS. Remote Sensing of Environment, 141: 168–187
Kuusk A. 1991. Determination of vegetation canopy parameters from optical measurements. Remote Sensing of Environment, 37(3): 207–218
Kuusk A. 2001. A two-layer canopy reflectance model. Journal of Quantitative Spectroscopy and Radiative Transfer, 71(1): 1–9
Kuusk A, Lang M and Nilson T. 2004. Simulation of the reflectance of ground vegetation in sub-boreal forests. Agricultural and Forest Meteorology, 126(1/2): 33–46
Liu S H, Liu Q, Liu Q H, Wen J G and Li X W. 2010. The angular and spectral kernel model for BRDF and albedo retrieval. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3(3): 241–256
Lucht W, Schaaf C B and Strahler A H. 2000. An algorithm for the retrieval of albedo from space using semiempirical BRDF models. IEEE Transactions on Geoscience and Remote Sensing, 38(2): 977–998
Maignan F, Bréon F M and Lacaze R. 2004. Bidirectional reflectance of Earth targets: evaluation of analytical models using a large set of spaceborne measurements with emphasis on the Hot Spot. Remote Sensing of Environment, 90(2): 210–220
McCartney H A. 1978. Spectral distribution of solar radiation. II: global and diffuse. Quarterly Journal of the Royal Meteorological Society, 104(442): 911–926
Nicodemus F E, Richmond J C, Hsia J J, Ginsberg I W and Limperis T. 1977. Geometrical Considerations and Nomenclature for Reflectance. Washington, DC: National Bureau of Standards, US Department of Commerce
Price J C. 1990. On the information content of soil reflectance spectra. Remote Sensing of Environment, 33(2): 113–121
Roujean J L, Leroy M and Deschamps P Y. 1992. A bidirectional reflectance model of the earth’s surface for the correction of remote sensing data. Journal of Geophysical Research-Atmospheres, 97(D18): 20455–20468
Saltelli A, Tarantola S and Chan K P S. 1999. A quantitative model-independent method for global sensitivity analysis of model output. Technometrics, 41(1): 39–56
Schaaf C B, Gao F, Strahler A H, Lucht W, Li X W, Tsang T, Strugnell N C, Zhang X Y, Jin Y F, Muller J P, Lewis P, Barnsley M, Hobson P, Disney M, Roberts G, Dunderdale M, Doll C, D’Entremont R P, Hu B X, Liang S L, Privette J L and Roy D. 2002. First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sensing of Environment, 83(1/2): 135–148
Schaepman-Strub G, Schaepman M E, Painter T H, Dangel S and Martonchik J V. 2006. Reflectance quantities in optical remote sensing-definitions and case studies. Remote Sensing of Environment, 103(1): 27–42
施润和, 庄大方, 牛铮, 王汶. 2005. 叶肉结构对叶片光谱及生化组分定量反演的影响. 中国科学院研究生院学报, 22(5): 589–595
Shi R H, Zhuang D F, Niu Z and Wang W. 2005. Influence of mesophyll structure on leaf spectra and biochemical inversion. Journal of the Graduate School of the Chinese Academy of Sciences, 22(5): 589–595
Sobol I M. 1993. Sensitivity analysis for nonlinear mathematical models. Mathematical Modeling and Computational Experiment, 1(4): 407–414[translation of Sobol I M. (1990). Sensitivity estimates for nonlinear mathematical models. Matematicheskoe Modelirovanie, 2(1): 112–118(in Russian)]
Wanner W, Li X and Strahler A H. 1995. On the derivation of kernels for kernel-driven models of bidirectional reflectance. Journal of Geophysical Research-Atmospheres, 100(D10): 21077–21089
肖艳芳, 周德民, 宫辉力, 赵文吉. 2015. 冠层反射光谱对植被理化参数的全局敏感性分析. 遥感学报, 19(3): 368–374
Xiao Y F, Zhou D M, Gong H L and Zhao W J. 2015. Sensitivity of canopy reflectance to biochemical and biophysical variables. Journal of Remote Sensing, 19(3): 368–374
张虎, 焦子锑, 董亚东, 李佳悦, 李小文. 2015. 利用BRDF原型和单方向反射率数据估算地表反照率. 遥感学报, 19(3): 355–367
Zhang H, Jiao Z T, Dong Y D, Li J Y and Li X W. 2015. Albedo retrieved from BRDF archetype and Surface dirface directional relectance. Journal of Remote Sensing, 19(3): 355–367
相关作者
相关机构
京公网安备11010802024621
