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Review on spectral libraries: Progress and application

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：zhangyt02@radi.ac.cn
  • Introduction：张莹彤(1992—    )，女，硕士研究生，研究方向为BRDF模型及反照率反演研究。E-mail：zhangyt02@radi.ac.cn
ZHANG Yingtong12,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

  • Email：xiaoqing@radi.ac.cn
  • Introduction：肖青(1971—    )，男，研究员，研究方向为定量遥感反演机理和模型研究。E-mail：xiaoqing@radi.ac.cn
XIAO Qing1*,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

WEN Jianguang1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

YOU Dongqin1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

    State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

DOU Baocheng13,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

TANG Yong1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

PENG Shi12

resumen

A spectrum is the distribution of electromagnetic radiation that has been reflected, emitted, or transmitted from ground objects because of their inherent physical characteristics. Remote sensing measurements provide a solid theoretical basis for applications such as target detection and identification fields considering that different materials possess distinct abilities for reflection, emission, and scattering mechanisms over different wavelength ranges. To support such studies, a spectral library is necessary for quantitative remote sensing modeling, land surface parameter inversion, and ecological environment monitoring. For nearly half a century, domestic and international research institutes have designed various spectral libraries by assembling spectral data of typical materials. These spectral libraries include a long-term, reliable, accumulative observation for a certain period and indispensable standard ancillary data. Such spectral data are essential resources for ground cover classification and target identification.This study reviews the history of the development of spectral libraries by analyzing and comparing each spectral library with those of different wavelength ranges, types, ancillary data, and resources. The applications of two kinds of spectral libraries for different disciplines have been summarized. In this study, the general spectral library refers to a database with more comprehensive and abundant ground objects, including the United States Geological Survey Spectral Library, Advanced Spaceborne Thermal Emission and Reflection Radiometer Spectral Library, and Spectral Database System of Typical Objects in China. Meanwhile, the professional spectral libraries that were established focus mainly on the specific disciplines.Comparison results reveal that spectrum data are relatively insufficient in the microwave band because previous research focused more on visible and near-infrared bands during the last century, especially on geological and ecological applications. This observation can also account for the over whelming quantity of minerals, rocks, and crops spectral data in existing data bases. However, most of the available spectra data that exist still exclude canopy or urban grassland spectra, although Nonetheless, vegetation spectra data have been considerable. Based on the research demand investigation for spectral library, the study should mainly focus on data management, quality control standard in measurements, and data utilization efficiency.The establishment and full sharing of a comprehensive spectral library are crucial steps for domestic research. The waveband and the spatial and temporal gradients of the ground objects present the issues for accurate classification and identification applications. Thus, the full-wave band, spatial scales, or phenological information are significant in the completion of future spectral libraries.

palabra clave

spectra;database;visible-NIR;thermal infrared;microwave

References

  1. 1.
    Baldridge A M, Hook S J, Grove C I and Rivera G. 2009. The ASTER spectral library version 2.0. Remote Sensing of Environment, 113(4): 711–715
  2. 2.
    Bian X L, Shao Y, Zhang F L and Fu X Y. Construction of the library of targets microwave properties. 2015. Remote Sensing for Land and Resources, 27(4): 189–194
  3. 3.
    Cao R Y, Chen Y H and Huang W J. 2008. Design and development of hyperspectrallibrary for recognizing disease of crops. Journal of Natural Disasters, 17(6): 73–76
  4. 4.
    Charette M P, McCord T B, Pieters C and Adams J B. 1974. Application of remote spectral reflectance measurements to lunar geology classification and determination of titanium content of lunar soils. Journal of Geophysical Research, 79(11): 1605–1613
  5. 5.
    Chen S P and Zhou S G. 1986.Review of airborne remote sensing experiment in Tengchong. Remote Sensing Information, (2): 11–12
  6. 6.
    陈水森. 2005. 基于波谱库的作物纯像元识别与种植面积遥感估算. 北京: 中国科学院研究生院Chen S S. 2005.Remote Sensing Method of Pure Crop Pixel Identification andPlanting Area Estimation Based on Spectral Library. Beijing: Graduate University of Chinese Academy of Science
  7. 7.
    Chen S S, Fang L G, Liu Q H, Chen L F and Liu Q. 2005. The frame design and application demonstration of the standardized spectral library for featured crops in Guangdong province. Remote Sensing for Land and Resources, 17(3): 74–78
  8. 8.
    Christensen P R, Bandfield J L, Hamilton V E, Howard D A, Lane M D, Piatek J L, Ruff S W and Stefanov W L. 2000. A thermal emission spectral library of rock-forming minerals. Journal of Geophysical Research, 105(E4): 9735–9739
  9. 9.
    Clark R N, Swayze G A, Gallagher A J, King T V V and Calvin W M. 1993. The U.S. Geological Survey Digital Spectral Library: Version 1 (0.2 to 3.0 μm). ’Open File Report 93-592
  10. 10.
    Clark R N, Swayze G A, Livo K E, Kokaly R F, Sutley S J, Dalton J B, McDougal R R and Gent C A. 2003. Imaging spectroscopy: earth and planetary remote sensing with the USGS Tetracorder and expert systems. Journal of Geophysical Research: Planets (1991-2012), 108(E12)
  11. 11.
    Clark R N, Swayze G A, Wise R, Livo K E, Heofen T M, Kokaly R F and Sutley S J. 2004. USGS digital spectral library (splib05a). Spectroscopy Lab, US Geological Surey
  12. 12.
    Clark R N, Swayze G A, Wise R A, Livo K E, Hoefen T M, Kokaly R F and Sutley S J. 2007. USGS digital spectral library splib06a[EB/OL]. Reston, VA:US Geological Survey.
  13. 13.
    Clark R N, Swayze G A, Leifer I, Livo K E, Kokaly R, Hoefen T, Lundeen S, Eastwood M, Green R O, Pearson N, Sarture C, McCubbin I, Roberts D, Bradley E, SteeleD, Ryan T and Dominguez R. 2010. A method for quantitative mapping of thick oil spills using imaging spectroscopy. Open-File Report 2010-1167. US Geological Survey
  14. 14.
    Creese R G, Glasby T M, West G and Gallen C. 2009. Mapping the Habitats of NSW Estuaries. Nelson Bay, NSW: Australian Government
  15. 15.
    Drake N A, Mackin S and Settle J J. 1999. Mapping vegetation, soils, and geology in semiarid shrublands using spectral matching and mixture modeling of SWIR AVIRIS imagery. Remote Sensing of Environment, 68(1): 12–25
  16. 16.
    Dudley K L, Dennison P E, Roth K L, Roberts D A and Coates A R. 2015. A multi-temporal spectral library approach for mapping vegetation species across spatial and temporal phenological gradients. Remote Sensing of Environment, 167: 121–134
  17. 17.
    范俊甫. 2011. 兖州矿区典型地物波谱数据库建设与应用研究. 青岛: 山东科技大学Fan J F. 2011. Study on Construction and Implementation of Typical Ground Object Spectrum Database in YanzhouMining Area. Qingdao: Shandong University of Science and Technology
  18. 18.
    Garrity D, Bindraban P. 2003. A Globally Distributed Soil Spectral Library Visible Near Infrared Diffuse Reflectance Spectra [EB/OL]. [2015-10-24].
  19. 19.
    Gong P. 2009. Some essential questions in remote sensing science and technology.Journal of Remote Sensing, 13(1): 1–23
  20. 20.
    Goudge T A, Mustard J F, Head J W, Salvatore M R and Wiseman S M. 2015. Integrating CRISM and TES hyperspectral data to characterize a halloysite-bearing deposit in Kashira crater, Mars.Icarus, 250: 165–187
  21. 21.
    Green R O, Eastwood M L, Sarture C M, Chrien T G, Aronsson M, Chippendale B J, Faust J A, Pavri B E, Chovit C J, Solis M, Olah M R and Williams O. 1998. Imaging spectroscopy and the airborne visible/infrared imaging spectrometer (AVIRIS). Remote Sensing of Environment, 65(3): 227–248
  22. 22.
    Green R O. 2012. HyspIRI ecosystem spectral library[EB/OL]. [2015-03-06]
  23. 23.
    Hapke B. 1981. Bidirectional reflectance spectroscopy: 1. Theory. Journal of Geophysical Research: Solid Earth, 86(B4): 3039–3054
  24. 24.
    Herold M, Gardner M E and Roberts D A. 2003. Spectral resolution requirements for mapping urban areas. IEEE Transactions on Geoscience and Remote Sensing, 41(9): 1907–1919
  25. 25.
    Herold M, Roberts D A, Smadi O and Noronha V. 2004a. Road condition mapping with hyperspectral remote sensing//Proceedings of the 2004 AVIRIS workshop.Pasadena, CA: AVIRIS
  26. 26.
    Herold M, Roberts D A, Gardner M E and Dennison P E. 2004b. Spectrometry for urban area remote sensing-Development and analysis of a spectral library from 350 to 2400 nm. Remote Sensing of Environment, 91(3/4): 304–319
  27. 27.
    Korb A R, Dybwad P, Wadsworth W and Salisbury J W. 1996. Portable Fourier transform infrared spectroradiometer for field measurements of radiance and emissivity. Applied Optics, 35(10): 1679–1692
  28. 28.
    李少鹏. 2013. 新疆典型荒漠植物光谱数据库系统设计与实现. 乌鲁木齐: 新疆农业大学Li S P. 2013. The Design and Development of Xinjiang Typical Desert Plants Spectral Database System.Urumqi: Xinjiang Agricultural University
  29. 29.
    Li X W, Zhao H R, Zhang H and Wang J D. 2002. Globalchange study and quantitative remote sensing for land surface parameters. 2002. Earth Science Frontiers, 9(2): 365–370
  30. 30.
    Li X W. 2006. Remote sensing science and quantitative remote sensing. Science Focus,2006, 1(5): 45
  31. 31.
    Peng W S and Liu G K. 1982. Infrared Spectra of Minerals. Beijing: Science Press
  32. 32.
    Qiu H L, Wu H Q, Fan Y M and Zhong J P. 2003. The hyperspectral database of salinized soilsand salinized vegetations in Xinjiang.Journal of Xinjiang Agricultural University, 26(2): 6–8
  33. 33.
    Qu Y H, Liu S H, Wang J D, Li X W and Su L H. 2004. Study and design on spectral database system oftypical objects in China(SpeLib). Remote Sensing Information, (2): 5–8.
  34. 34.
    Salisbury J W, Wald A and D’Aria D M. 1994. Thermal-infrared remote sensing and Kirchhoff’s law: 1. Laboratory measurements. Journal of Geophysical Research: Solid Earth (1978-2012), 99(B6): 11897–11911
  35. 35.
    Shepherd K D, Palm C A, Gachengo C N and Vanlauwe B. 2003. Rapid characterization of organic resource quality for soil and livestock management in tropical agroecosystems using near-infrared spectroscopy. Agronomy Journal, 95(5): 1314–1322
  36. 36.
    Shi J, Liu Q H, Wen J G, Tang Y, Dou B C, Wang J D and Zhang L X. 2011. Design and realization of the service platform for typical ground objects spectrum data in China based on E-government. Remote Sensing Technology and Application,26(4): 520–526
  37. 37.
    Su L H, Li X W, Liang S L and Wang J D. 2002. Data frame and spectral simulation for remote sensing spectral data base. Geo-information Science, 4(4): 7–15
  38. 38.
    Swayze G A, Smith K S, Clark R N, Sutley S J, Pearson R M, Vance J S, Hageman P L, Briggs P H, Meier A L, Singleton M J and Roth S. 2000. Using imaging spectroscopy to map acidic mine waste. Environmental Science and Technology, 34(1): 47–54
  39. 39.
    Swayze G A, Higgins C T, Clinkenbeard J P, Kokaly R F, Clark R N, Meeker G P and Sutley S J. 2004. Preliminary Report on Using Imaging Spectroscopy to Map Ultramafic Rocks, Serpentinites, and Tremolite-actinolite-bearing Rocks in California. Open-File Report 2004-1304. US Geological Survey
  40. 40.
    Tang J W. 1996. Analytical and processing system on ocean spectral data. Ocean Technology, 15(4): 10–17
  41. 41.
    Tian Q J and Gong P. 2002. Current status and trend of object spectral libraries. Remote Sensing Information, (3): 2–6, 46
  42. 42.
    Tong Q X, Zhang B and Zheng L F. 2006. Hyperspectral Remote Sensing: Principle, Technique and Application. Beijing: Higher Education Press
  43. 43.
    Vahtmäe E and Kutser T. 2013. Classifying the baltic sea shallow water habitats using image-based and spectral library methods. Remote Sensing, 5(5): 2451–2474
  44. 44.
    Vane G and Goetz A F H. 1993. Terrestrial imaging spectrometry: current status, future trends. Remote Sensing of Environment, 44(2/3): 117–126
  45. 45.
    Vincent R K. 1976. Data gaps in the NASA Earth resources spectral information system[EB/OL]. [2015-11-20]
  46. 46.
    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
  47. 47.
    Wang J D, Zhang G, Xiao Y T and Qu Y H. 2007.The prior knowledge of the crop growth parameters constructed from the spectrum database of land surface objects. Journal of Beijing Normal University (Natural Science), 43(3): 284–291
  48. 48.
    Wang J D, Zhang L X, Liu Q H, Zhang B and Yin Q. 2009. Spectral Library of Typical Objects in China.Beijing: Science Press
  49. 49.
    Wang P J, Liu S H, Zhu Q J, Qu Y H and Guo X Y. 2004. The design of the application demonstration system based on the standard spectral knowledge based on the typical surface features in China. Computer Engineering and Applications, 40(13): 212–214
  50. 50.
    Wen J G, Liu Q, Liu Q H, Xiao Q and Li X W. 2015. Remote Sensing Modeling for Land Bidirectional Reflectance Characteristics and the Retrieval of Albedo.Beijing: Science Press
  51. 51.
    Yao Y J, Chen L F, Liu Q H, Liu Q, Du Y M and Li X W. 2006. Inversion and validation of Leaf Area Index based onthe spectralandknowledge database usingMODIS data. Journal of Remote Sensing, 10(6): 869–878
  52. 52.
    Zhang Y, Bo L Q and Lu X C. 2002.Design and development of object spectrumdatabase in Jingyuetan of Changchun. Remote Sensing Information, (2): 25–27
  53. 53.
    Zhou X H and Zhou D W. 2009.Review of digital ground object spectral library. Spectroscopy and Spectral Analysis, 29(6): 1616–1622

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