Précédent|Prochain

Design and realization of ground object background spectral library for surveying and mapping

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, the Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：zhongsy@radi.ac.cn
  • Introduction：钟守熠，1995年生, 男, 硕士研究生, 研究方向为定量遥感产品真实性检验。E-mail: zhongsy@radi.ac.cn
ZHONG Shouyi16,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, the Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：xiaoqing@radi.an.cn
  • Introduction：肖青，1971年生, 男, 研究员, 研究方向为定量遥感反演机理和模型。E-mail: xiaoqing@radi.an.cn
XIAO Qing16*,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, the Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

WEN Jianguang16,  
  • Affiliation：

    Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

ZHENG Xingming2,  
  • Affiliation：

    Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China

    School of Geography Sciences, Southwest University, Chongqing 400715, China

MA Mingguo35,  
  • Affiliation：

    School of Geography, Beijing Normal University, Beijing 100875, China

QU Yonghua4,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, the Chinese Academy of Sciences, Beijing 100101, China

ZHENG Ke1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, the Chinese Academy of Sciences, Beijing 100101, China

CHI Tianhe1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, the Chinese Academy of Sciences, Beijing 100101, China

TANG Yong1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, the Chinese Academy of Sciences, Beijing 100101, China

YOU Dongqin1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, the Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

HAO Dalei16,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, the Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

CHENG Juan16,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, the Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

HE Min16,  
  • Affiliation：

    Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

JIANG Tao2,  
  • Affiliation：

    Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China

JIN Rui3,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, the Chinese Academy of Sciences, Beijing 100101, China

YAO Xiaojing1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, the Chinese Academy of Sciences, Beijing 100101, China

ZHAO Lijun1

résumé

The spectral characteristics of ground objects are the theoretical basis for target detection and identification using remote sensing. The spectral library plays an important role in improving the level of remote sensing applications. In this study, we established the “Ground Object Background Spectral Library for Surveying and Mapping (GOSPEL)” under the National Science and Technology Foundation Project to standardize and enrich China’s spectral database and promote its application in the fields of surveying and mapping. Notable progress was observed in the construction of the ground feature spectrum library, warehousing, and preliminary exploration of application demonstrations. Moreover, the Ground Spectral Spectrum and Supporting Non-spectral Data Collection and Summary Standards, Spectral and Supporting Non-spectral Parameter Test Technical Specifications, and Feature Classification Code Standards were established on the basis of the existing spectrum acquisition and processing specifications. Since the implementation of the project, more than 14,000 existing spectral data were compiled and stored in accordance with the data norms and standards formulated by the project. In addition, nationwide spectrum collection experiments were organized and implemented involving the major regions in China, which acquired more than 17,000 ground feature spectra. Full-band experiments (visible–near infrared, infrared, and microwave bands) were performed in north and northeast China to obtain full-band data of snow, soil, vegetation canopy, and artificial target. To facilitate the application and sharing of spectral data, a ground feature spectral database (i.e., GOSPEL) and a data sharing platform containing more than 30,000 data were constructed. Twenty-five datasets were formed according to the type of ground feature spectral data and application requirements, including full-band typical feature spectrum dataset, multi-angle spectral reflectance dataset, multiscale typical feature reflectance dataset, and long-term series spectral dataset. We also performed application demonstrations on ground feature classification, remote sensing mechanism simulation, remote sensing quantitative inversion, and remote sensing product validation based on the current ground feature spectrum from GOSPEL.

mots-clés

spectral library;object spectrum;surveying and mapping;remote sensing;system;data sharing

References

  1. 1.
    Acito N and Diani M. 2018. Atmospheric column water vapor retrieval from hyperspectral VNIR data based on low-rank subspace projection. IEEE Transactions on Geoscience and Remote Sensing, 56(7): 3924-3940
  2. 2.
    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
  3. 3.
    Cheriyadat A M. 2014. Unsupervised feature learning for aerial scene classification. IEEE Transactions on Geoscience and Remote Sensing, 52(1): 439-451
  4. 4.
    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: Planets, 105(E4): 9735-9739
  5. 5.
    Garrity D and Bindraban P. 2004. A Globally Distributed Soil Spectral Library Visible near Infrared Diffuse Reflectance Spectra. Soil-Plant Spectral Diagnostics Laboratory
  6. 6.
    Geng L Y, Ma M G, Yu W P, Wang X F and Jia S Z. 2014. Validation of the MODIS NDVI products in different land-use types using in situ measurements in the Heihe River Basin. IEEE Geoscience and Remote Sensing Letters, 11(9): 1649-1653
  7. 7.
    Gomis-Cebolla J, Jimenez J C and Sobrino J A. 2018. LST retrieval algorithm adapted to the Amazon evergreen forests using MODIS data. Remote Sensing of Environment, 204: 401-411
  8. 8.
    Hao D L, Xiao Q, Wen J G, You D Q, Wu X D, Lin X W and Wu S B. 2018. Advances in upscaling methods of quantitative remote sensing. Journal of Remote Sensing, 22(3): 408-423
  9. 9.
    Jin M J, Zheng X M, Jiang T, Li X F, Li X J and Zhao K. 2017. Evaluation and improvement of SMOS and SMAP soil moisture products for soils with high organic matter over a forested area in northeast China. Remote Sensing, 9(4): 387
  10. 10.
    Kokaly R F, Clark R N, Swayze G A, Livo K E, Hoefen T M, Pearson N C, Wise R A, Benzel W M, Lowers H A, Driscoll R L and Klein A J. 2017. USGS Spectral Library Version 7, U.S. Geological Survey Data Series 1035. U.S. Department of the Interior, U.S. Geological Survey
  11. 11.
    Li X, Liu S M, Xiao Q, Ma M G, Jin R, Che T, Wang W Z, Hu X L, Xu Z W, Wen J G and Wang L X. 2017. A multiscale dataset for understanding complex eco-hydrological processes in a heterogeneous oasis system. Scientific Data, 4: 170083
  12. 12.
    Li X W. 2006. Remote sensing science and quantitative remote sensing. Science Focus, 1(5): 45
  13. 13.
    Peng S, Wen J G, Xiao Q, You D Q, Dou B C, Liu Q and Tang Y. 2017. Multi-staged NDVI dependent snow-free land-surface shortwave albedo narrowband-to-broadband (NTB) coefficients and their sensitivity analysis. Remote Sensing, 9(1): 93
  14. 14.
    Puri S, Stephen H and Ahmad S. 2011. Relating TRMM precipitation radar land surface backscatter response to soil moisture in the Southern United States. Journal of Hydrology, 402(1/2): 115-125
  15. 15.
    Qu Y H, Liu S H, Wang J D, Li X W and Su L H. 2004. Study and design on spectral database system of typical objects in China(SpeLib). Remote Sensing Information, (2): 5-8
  16. 16.
    Román M O, Gatebe C K, Schaaf C B, Poudyal R, Wang Z S and King M D. 2011. Variability in surface BRDF at different spatial scales (30 m–500 m) over a mixed agricultural landscape as retrieved from airborne and satellite spectral measurements. Remote Sensing of Environment, 115(9): 2184-2203
  17. 17.
    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
  18. 18.
    Tian Q J and Gong P. 2002. Current status and trend of object spectral libraries. Remote Sensing Information, (3): 2-6, 46
  19. 19.
    Vincent R K. 1971. Data gaps in the NASA Earth resources spectral information system[EB/OL]. [2020-01-23].
  20. 20.
    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
  21. 21.
    Wei L. 2018. Experimental Study on Dielectric and Microwave Radiation Characteristics of Saline Soil. Lanzhou:Northwest Institute of Eco-Environmental Resources, Chinese Academy of Sciences
  22. 22.
    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
  23. 23.
    Wen J G, Liu Q, Tang Y, Dou B C, You D Q, Xiao Q, Liu Q H and Li X W. 2015. Modeling land surface reflectance coupled BRDF for HJ-1/CCD data of rugged terrain in Heihe River Basin, China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(4): 1506-1518
  24. 24.
    Wu S B, Wen J G, Xiao Q, Liu Q H, Hao D L, Lin X W and You D Q. 2019. Derivation of kernel functions for kernel-driven reflectance model over sloping terrain. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(2): 396-409
  25. 25.
    Yang B X, Du Y M, Xiao Q, Li H, Cao B and Liu Q H. 2017. Study of effecting factors on emissivity measurement by integrating sphere with Monte Carlo simulation. Remote Sensing Technology and Application, 32(1): 149-157
  26. 26.
    You D Q, Wen J G, Liu Q, Liu Q H and Tang Y. 2014. The angular and spectral kernel-driven model: assessment and application. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(4): 1331-1345
  27. 27.
    Yu W P, Ma M G, Li Z L, Tan J L and Wu A A. 2017. New scheme for validating remote-sensing land surface temperature products with station observations. Remote Sensing, 9(12): 1210
  28. 28.
    Zhang Y, Bo L Q and Lu X C. 2002. Design and development of object spectrum database in Jingyuetan of Changchun. Remote Sensing Information, (2): 25-27
  29. 29.
    Zhang Y T, Xiao Q, Wen J G, You D Q, Dou B C, Tang Y and Peng S. 2017. Review on spectral libraries: progress and application. Journal of Remote Sensing, 21(1): 12-26
  30. 30.
    Zhao L J, Zheng K, Shi L L, Bai Y, Tang J W, Zhang W, Rao M B, Zou S and Li Y Y. 2019. Remote sensing image sample dataset of land cover types in China. China Scientific Data, 2019, 42
  31. 31.
    Zheng X M, Li X F, Jin M J, Jiang T and Zhao K. 2017. Characteristics of L-band transmissivity and effective scattering albedo of boreal forests: a case study in northeast China. Remote Sensing Letters, 8(12): 1200-1209

Lire l'article complet

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website