线性光谱混合模型的适用观测尺度分析
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摘要
线性光谱混合模型是目前应用最广泛的光谱混合模型,但由于遥感观测多分辨率的特点,模型的适用性会受到尺度效应的影响。为探索该模型在不同观测尺度下的适用程度,本文从地物辐射原理出发,通过理论推导微面元辐射通量表达式,得出地物辐射通量除了与端元反射率和面积比有关外,也与天顶角存在显著的非线性关系。因此,在线性光谱混合模型和微面元辐射通量的基础上,推导了更具普适性的积分线性光谱混合模型的表达式,再采用数值模拟的方法,计算了两模型的相对差值Δρ,结果表明Δρ的大小仅与探测单元的半瞬时视场角β有关,并通过实测光谱实验对上述推论进行了验证。研究表明,当β<13°时,Δρ较小,线性光谱混合模型是积分线性光谱混合模型的一种近似表达形式,β完全可作为确定线性光谱混合模型适用观测尺度的关键依据,并且该模型的适用程度随β的增大而降低。
The Linear Spectral Mixture Model is the most widely used spectral mixing model. Due to the multi-resolution characteristics of remote sensing observations, the applicability of the model will be affected by the scale effect. In order to explore the applicability of the model in different observational scales, this paper starts from the radiation principle of ground objects and derives the radiative flux expression of the ground surface micro-surface features theoretically. The results show that the radiative flux is not only related to the endmember reflectance and the area ratio, there is also a significant non-linear relationship with the zenith angle. Based on the Linear Spectral Mixture Model and the micro-facet radiant flux, the expression of the Integral Linear Spectral Mixture Model is deduced. Then through numerical simulation, the relative difference(Δρ) of the two models is calculated. The analysis results show that Δρ is only related to the semi-instantaneous field of view(β) of the detection unit. The above inferences were verified by actual spectroscopy experiments. The results indicate that when β<13°, Δρ is smaller. The Linear Spectral Mixture Model is an approximate representation of the Integral Linear Spectral Mixture Model. Besides, β can be used as the key basis for determining the applicable scale of the Linear Spectral Mixture Model, and the applicability of the model decreases with the increase of β.
The Linear Spectral Mixture Model is the most widely used spectral mixing model. Due to the multi-resolution characteristics of remote sensing observations, the applicability of the model will be affected by the scale effect. In order to explore the applicability of the model in different observational scales, this paper starts from the radiation principle of ground objects and derives the radiative flux expression of the ground surface micro-surface features theoretically. The results show that the radiative flux is not only related to the endmember reflectance and the area ratio, there is also a significant non-linear relationship with the zenith angle. Based on the Linear Spectral Mixture Model and the micro-facet radiant flux, the expression of the Integral Linear Spectral Mixture Model is deduced. Then through numerical simulation, the relative difference(Δρ) of the two models is calculated. The analysis results show that Δρ is only related to the semi-instantaneous field of view(β) of the detection unit. The above inferences were verified by actual spectroscopy experiments. The results indicate that when β<13°, Δρ is smaller. The Linear Spectral Mixture Model is an approximate representation of the Integral Linear Spectral Mixture Model. Besides, β can be used as the key basis for determining the applicable scale of the Linear Spectral Mixture Model, and the applicability of the model decreases with the increase of β.
引文
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Li X W,Wang J D and Strahler.2000.Scale effects and scaling-up by geometric-optical model.Science in China Series E:Technological Sciences,43(S1):17-22[DOI:10.1007/BF02916574]
Li X W,Zhao H R,Zhang H and Wang J D.2002.Global change study and quantitative remote sensing for land surface parameters.Earth Science Frontiers,9(2):365-370(李小文,赵红蕊,张颢,王锦地.2002.全球变化与地表参数的定量遥感.地学前缘,9(2):365-370)[DOI:10.3321/j.issn:1005-2321.2002.02.015]
Li X W and Wang Y T.2013.Prospects on future developments of quantitative remote sensing.ACTA GEOGRAPHICA SINICA,68(9):1163-1169(李小文,王祎婷.2013.定量遥感尺度效应刍议.地理学报,68(9):1163-1169)[DOI:10.11821/dlxb201309001]
Liu Y and Li Y.2014.Observations of spectral data and characteristics analysis of snow-bare soil mixed pixel generated by micro-simulation.Spectroscopy and Spectral Analysis,34(7):1903-1908(刘艳,李杨.2014.雪-土混合像元微观/宏观尺度光谱混合机制差异分析.光谱学与光谱分析,34(7):1903-1908)[DOI:10.3964/j.issn.1000-0593(2014)07-1903-06]
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Lv C C,Wang Z W and Qian S M.2003.A review of pixel unmixing models.Remote Sensing Information(3):55-58,60(吕长春,王忠武,钱少猛.2003.混合像元分解模型综述.遥感信息(3):55-58,60)[DOI:10.3969/j.issn.1000-3177.2003.03.016]
Wan H W,Wang J D,Qu Y H,Jiao Z T and Zhang H.2008.Preliminary research on scale effect and scaling-up of the vegetation spectrum.Journal of Remote Sensing,12(4):538-545(万华伟,王锦地,屈永华,焦子锑,张灏.2008.植被波谱空间尺度效应及尺度转换方法初步研究.遥感学报,12(4):538-545)[DOI:10.3321/j.issn:1007-4619.2008.04.002]
Woodcock C E and Strahler A H.1987.The factor of scale in remote sensing.Remote Sensing of Environment,21(3):311-332[DOI:10.1016/0034-4257(87)90015-0]
Zhao Y S.2013.Theory and Method of Remote Sensing Application Analysis.Second Edition.Beijing:Science Press,13-85(赵英时.2013.遥感应用分析原理与方法.第二版.北京:科学出版社,2013:13-85)
Zhou Z F.2001.Analysis of standard deviation and standard deviation coefficient.Shanghai Statistics,8:26-27(周冶芳.2001.浅析标准差和标准差系数.上海统计,8:26-27)
Chen J M.1999.Spatial scaling of a remotely sensed surface parameter by contexture.Remote Sensing of Environment,69(1):30-42[DOI:10.1016/S0034-4257(99)00006-1]
Huang Y,Tian Q J,Geng J,Wang L and Luan H J.2016.Review of spectral and spatial scale effects of remotely sensed biophysical and biochemical vegetation parameters.Acta Ecologica Sinica,36(3):883-891(黄彦,田庆久,耿君,王磊,栾海军.2016.遥感反演植被理化参数的光谱和空间尺度效应.生态学报,36(3):883-891)[DOI:10.5846/stxb201405150996]
Hui W W,Yi D P,Liao C X and Qu L.2007.The study of decomposing mix element.Forestry Science and Technology Information,39(1):2-3(惠巍巍,衣德萍,廖彩霞,曲林.2007.混合像元分解研究综述.林业科技情报,39(1):2-3)[DOI:10.3969/j.issn.1009-3303.2007.01.002]
Ichoku C,Karnieli A.1996.A review of mixture modeling techniques for sub-pixcel land cover estimation.Remote Sensing Reviews,13(3/4):161-186[DOI:10.1080/02757259609532303]
Li J.2004.Wavelet-based feature extraction for improved endmember abundance estimation in linear unmixing of hyperspectral signals.IEEE Transactions on Geoscience and Remote Sensing,42(3):644-649[DOI:10.1109/TGRS.2003.822750]
Li J.2008.The study on comparing non-linear unmixing model and linear unmixing model of mixed pixels.Harbin:Northeast Forestry University,24-26(李君.2008.线性与非线性混合像元分解模型的比较研究.哈尔滨:东北林业大学,24-26)
Li X W,Wang J D and Strahler.2000.Scale effects and scaling-up by geometric-optical model.Science in China Series E:Technological Sciences,43(S1):17-22[DOI:10.1007/BF02916574]
Li X W,Zhao H R,Zhang H and Wang J D.2002.Global change study and quantitative remote sensing for land surface parameters.Earth Science Frontiers,9(2):365-370(李小文,赵红蕊,张颢,王锦地.2002.全球变化与地表参数的定量遥感.地学前缘,9(2):365-370)[DOI:10.3321/j.issn:1005-2321.2002.02.015]
Li X W and Wang Y T.2013.Prospects on future developments of quantitative remote sensing.ACTA GEOGRAPHICA SINICA,68(9):1163-1169(李小文,王祎婷.2013.定量遥感尺度效应刍议.地理学报,68(9):1163-1169)[DOI:10.11821/dlxb201309001]
Liu Y and Li Y.2014.Observations of spectral data and characteristics analysis of snow-bare soil mixed pixel generated by micro-simulation.Spectroscopy and Spectral Analysis,34(7):1903-1908(刘艳,李杨.2014.雪-土混合像元微观/宏观尺度光谱混合机制差异分析.光谱学与光谱分析,34(7):1903-1908)[DOI:10.3964/j.issn.1000-0593(2014)07-1903-06]
Luan H J,Tian Q J,Yu T,Hu X L,Huang Y,Liu L,Du L T and Wei X.2013.Review of up-scaling of quantitative remote sensing.Advances in Earth Science,28(6):657-664(栾海军,田庆久,余涛,胡新礼,黄彦,刘李,杜灵通,魏曦.2013.定量遥感升尺度转换研究综述.地球科学进展,28(6):657-664)
Lv C C,Wang Z W and Qian S M.2003.A review of pixel unmixing models.Remote Sensing Information(3):55-58,60(吕长春,王忠武,钱少猛.2003.混合像元分解模型综述.遥感信息(3):55-58,60)[DOI:10.3969/j.issn.1000-3177.2003.03.016]
Wan H W,Wang J D,Qu Y H,Jiao Z T and Zhang H.2008.Preliminary research on scale effect and scaling-up of the vegetation spectrum.Journal of Remote Sensing,12(4):538-545(万华伟,王锦地,屈永华,焦子锑,张灏.2008.植被波谱空间尺度效应及尺度转换方法初步研究.遥感学报,12(4):538-545)[DOI:10.3321/j.issn:1007-4619.2008.04.002]
Woodcock C E and Strahler A H.1987.The factor of scale in remote sensing.Remote Sensing of Environment,21(3):311-332[DOI:10.1016/0034-4257(87)90015-0]
Zhao Y S.2013.Theory and Method of Remote Sensing Application Analysis.Second Edition.Beijing:Science Press,13-85(赵英时.2013.遥感应用分析原理与方法.第二版.北京:科学出版社,2013:13-85)
Zhou Z F.2001.Analysis of standard deviation and standard deviation coefficient.Shanghai Statistics,8:26-27(周冶芳.2001.浅析标准差和标准差系数.上海统计,8:26-27)