سابِق|تال

Asia-Oceania GEOSS: A GEO initiative to implement GEOSS for sustainable development in Asia-Oceania

  • role： First author第一作者
  • Affiliation：

    吉林大学 地球探测科学与技术学院，长春 130026

  • Email：1215433225@qq.com
  • Introduction：宋江涛，1993年生，女，硕士研究生，研究方向为遥感图像处理。E-mail：1215433225@qq.com
宋江涛,  
  • role： Corresponding author通信作者
  • Affiliation：

    吉林大学 地球探测科学与技术学院，长春 130026

  • Email：panj@jlu.edu.cn
  • Introduction：潘军，1971年生，男，副教授，研究方向为遥感与地理信息系统。E-mail：panj@jlu.edu.cn
潘军*,  
  • Affiliation：

    吉林大学 地球探测科学与技术学院，长春 130026

  • No information about the author is available
邢立新,  
  • Affiliation：

    吉林大学 地球探测科学与技术学院，长春 130026

  • No information about the author is available
蒋立军,  
  • Affiliation：

    吉林大学 地球探测科学与技术学院，长春 130026

  • No information about the author is available
孙也涵,  
  • Affiliation：

    吉林大学 地球探测科学与技术学院，长春 130026

  • No information about the author is available
张雪峰,  
  • Affiliation：

    吉林大学 地球探测科学与技术学院，长春 130026

  • No information about the author is available
仲伟敬,  
  • Affiliation：

    吉林大学 地球探测科学与技术学院，长春 130026

  • No information about the author is available
范博文

ملخص

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。

مفهوم

线性光谱混合模型;微面元;积分线性光谱;观测尺度;数值模拟;瞬时视场角

References

  1. 1.
    Chen J, Ma L, Chen X H and Rao Y H. 2016. Research progress of spectral mixture analysis. Journal of Remote Sensing, 20(5): 1102–1109 (
  2. 2.
    Chen J M. 1999. Spatial scaling of a remotely sensed surface parameter by contexture. Remote Sensing of Environment, 69(1): 30–42
  3. 3.
    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 (
  4. 4.
    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 (
  5. 5.
    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
  6. 6.
    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
  7. 7.
    Li J. 2008. The study on comparing non-linear unmixing model and linear unmixing model of mixed pixels. Harbin: Northeast Forestry University, 24–26
  8. 8.
    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
  9. 9.
    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 (
  10. 10.
    Li X W and Wang Y T. 2013. Prospects on future developments of quantitative remote sensing. ACTA GEOGRAPHICA SINICA, 68(9): 1163–1169 (
  11. 11.
    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 (
  12. 12.
    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 (
  13. 13.
    Lv C C, Wang Z W and Qian S M. 2003. A review of pixel unmixing models. Remote Sensing Information(3): 55–58, 60 (
  14. 14.
    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 (
  15. 15.
    Woodcock C E and Strahler A H. 1987. The factor of scale in remote sensing. Remote Sensing of Environment, 21(3): 311–332
  16. 16.
    Zhao Y S. 2013. Theory and Method of Remote Sensing Application Analysis. Second Edition. Beijing: Science Press, 13-85
  17. 17.
    Zhou Z F. 2001. Analysis of standard deviation and standard deviation coefficient. Shanghai Statistics, 8: 26-27

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