Précédent|Prochain

Hyperspectral thermal infrared TOA image simulation and preliminary applications

  • role： First author第一作者
  • Affiliation：

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

  • Email：ouyxy@aircas.ac.cn
  • Introduction：欧阳晓莹，1983年生，女，助理研究员，研究方向为定量遥感、热红外遥感反演理论和应用。E-mail： ouyxy@aircas.ac.cn
OUYANG Xiaoying1,  
  • role： Corresponding author通信作者
  • Affiliation：

    The School of the Geo-Science & Technology, Zhengzhou University, Zhengzhou 450001, China

  • Email：zhoushugui1990@zzu.edu.cn
  • Introduction：周书贵，1990年生，男，讲师，研究方向为热红外定量遥感反演。E-mail： zhoushugui1990@zzu.edu.cn
ZHOU Shugui2*

résumé

Hyperspectral thermal infrared image simulation provides potential application in military, scientific, and commercial fields, such as target detection, atmospheric correction, Land Surface Temperature (LST) and emissivity (LSE) separation and validation, and future satellite sensor bandwidth/resolution setup and optimization. Therefore, thermal infrared image simulation is an effective means for quantitative remote sensing research. In this paper, we simulated the Top of Atmosphere (TOA) hyperspectral thermal infrared radiation data with a spectral resolution of 0.25 cm-1, ranging from 8—14 μm (714—1250 cm-1), using ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) land surface temperature (AST08), land surface emissivity (AST05) product and the Seebor V5.0 atmospheric profiles. The surface hyperspectral emissivity is obtained from ASTER multi-spectral emissivity data using the principal component regression, while the various atmospheric parameters are obtained using the hyperspectral atmospheric radiative transfer model 4A/OP (operational release for automatized atmospheric absorption atlas) with the Seebor V5.0 atmospheric profile. At-nadir observations are considered in the simulations in this study. Finally, based on the above-mentioned simulation method of thermal infrared hyperspectral data, and further considering the influence of instrument observation error and other factors, two atmospheric correction algorithms based on image information, namely AAC algorithm (Autonomous Atmospheric Compensation) and combined ISAC (In Scene Atmospheric Correction) and AAC algorithm, and Iterative Spectrally Smooth Temperature Emissivity Separation (ISSTES) algorithm were evaluated respectively. And we also analyze the sensitivity of these methods to different noise levels and other error sources. The results show that the ISSTES temperature and emissivity separation algorithm is reliable and feasible when the accuracy of the atmospheric profile is relatively high, but when instrument noises and errors in the atmospheric profile are considered, the accuracy of ISSTES algorithm decreases. In the absence of instrument noise, the accuracy of AAC and ISAC-AAC atmospheric correction methods is close and can obtain high-precision atmospheric parameters of atmospheric transmittance, atmospheric downward radiation and atmospheric upwelling radiation, which are required in the atmospheric radiative transfer equation. When 1 K instrument noise is added, the accuracy of ISAC-AAC is significantly higher than that of AAC. And under the same instrument noise, with the increase of water vapor content, the accuracy of AAC algorithm decreases rapidly, while the accuracy of ISAC-AAC does not decrease significantly. The above evaluation results are consistent with the previous evaluation results based on the measured data. In general, the thermal infrared image simulation method proposed in this paper is feasible and can provide effective data for evaluating different atmospheric correction algorithms and temperature and emissivity separation algorithms.

mots-clés

remote sensing;hyperspectral thermal infrared;image simulation;land surface temperature;land surface emissivity;atmospheric correction

References

  1. 1.
    Abrams M. 2000. The advanced spaceborne thermal emission and reflection radiometer (ASTER): data products for the high spatial resolution imager on NASA's Terra platform. International Journal of Remote Sensing, 21(5): 847-859
  2. 2.
    Borbas E, Suzanne S S, Huang H L, Li J and Menzel W P. 2005. Global profile training database for satellite regression retrievals with estimates of skin temperature and emissivity//Proceedings of the Fourteenth International ATOVS Study Conference. Beijing: CIMSS: 763-770
  3. 3.
    Borel C C. 1998. Surface emissivity and temperature retrieval for a hyperspectral sensor//IGARSS'98.
  4. 4.
    Borel C C. 2008. Error analysis for a temperature and emissivity retrieval algorithm for hyperspectral imaging data. International Journal of Remote Sensing, 29(17/18): 5029-5045
  5. 5.
    Cheng G D and Li X. 2019. Heihe River Basin Model Integration. Beijing: Science Press
  6. 6.
    Duan S B, Ru C, Li Z L, Wang M M, Xu H Q, Li H, Wu P H, Zhan W F, Zhou J, Zhao W, Ren H Z, Wu H,Tang B H, Zhang X, Shang Guo F and Qin Z H. 2021. Reviews of methods for land surface temperature retrieval from Landsat thermal infrared data. National Remote Sensing Bulletin, 25(8): 1591-1617
  7. 7.
    Gillespie A R, Abbott E A, Gilson L, Hulley G, Jiménez-Muñoz J C and Sobrino J A. 2011. Residual errors in ASTER temperature and emissivity standard products AST08 and AST05. Remote Sensing of Environment, 115(12): 3681-3694
  8. 8.
    Gillespie A R, Matsunaga T, Rokugawa S and Hook S J. 1996. Temperature and emissivity separation from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images//Proceedings of SPIE 2817, Infrared Spaceborne Remote Sensing IV. Denver: SPIE: 82-94
  9. 9.
    Gu D, Gillespie A R, Kahle A B and Palluconi F D. 2000. Autonomous atmospheric compensation (AAC) of high resolution hyperspectral thermal infrared remote-sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 38(6): 2557-2570
  10. 10.
    Li X, Li X W, Li Z Y, Wang J, Ma M G, Liu Q, Xiao Q, Hu Z Y, Che T, Wang J M, Liu Q H, Chen E X, Yan G J, Liu S M, Wang W Z, Zhang L X, Wang J D, Niu Z, Jin R, Ran Y H and Wang L X. 2012. Progresses on the Watershed Allied Telemetry Experimental Research (WATER). Remote Sensing Technology and Application, 27(5): 637-649
  11. 11.
    Li Z L, Tang B H, Wu H, Ren H Z, Yan G J, Wan Z M, Trigo I F and Sobrino J A. 2013. Satellite-derived land surface temperature: current status and perspectives. Remote Sensing of Environment, 131: 14-37
  12. 12.
    Liu Y B, Hiyama T and Yamaguchi Y. 2006. Scaling of land surface temperature using satellite data: a case examination on ASTER and MODIS products over a heterogeneous terrain area. Remote Sensing of Environment, 105(2): 115-128
  13. 13.
    Manolakis D, Pieper M, Truslow E, Lockwood R, Weisner A, Jacobson J and Cooley T. 2019. Longwave infrared hyperspectral imaging: principles, progress, and challenges. IEEE Geoscience and Remote Sensing Magazine, 7(2): 72-100
  14. 14.
    Ouyang X Y, Kang G T, Zeng F N, Qi E Y and Li Z L. 2013. Preliminary applications of a land surface temperature retrieval method to IASI and AIRS data. International Journal of Remote Sensing, 34(9/10): 3128-3139
  15. 15.
    Ouyang X Y, Wang N, Wu H and Li Z L. 2010. Errors analysis on temperature and emissivity determination from hyperspectral thermal infrared data. Optics Express, 18(2): 544-550
  16. 16.
    Sabol D E, Gillespie A R, Abbott E and Yamada G. 2009. Field validation of the ASTER temperature–emissivity separation algorithm. Remote Sensing of Environment, 113(11): 2328-2344
  17. 17.
    Verhoef W and Bach H. 2012. Simulation of Sentinel-3 images by four-stream surface–atmosphere radiative transfer modeling in the optical and thermal domains. Remote Sensing of Environment, 120: 197-207
  18. 18.
    Wang K C and Liang S L. 2009. Evaluation of ASTER and MODIS land surface temperature and emissivity products using long-term surface longwave radiation observations at SURFRAD sites. Remote Sensing of Environment, 113(7): 1556-1565
  19. 19.
    Wu H, Li X J, Li Z L, Duan S B and Qian Y G. 2021. Hyperspectral thermal infrared remote sensing:current status and perspectives. National Remote Sensing Bulletin, 25(8): 1567-1590
  20. 20.
    Yang Y K, Li H, Sun L, Du Y M, Cao B, Liu Q H and Zhu J S. 2019. Land surface temperature and emissivity separation from GF-5 visual and infrared multispectral imager data. Journal of Remote Sensing, 23(6): 1132-1146
  21. 21.
    Yin M, Tian S F and Li S J. 2016. Atmospheric compensation based on combined autonomous atmospheric compensation algorithms. Journal of Remote Sensing, 20(3): 450-458
  22. 22.
    Young S J, Johnson B R and Hackwell J A. 2002. An in-scene method for atmospheric compensation of thermal hyperspectral data. Journal of Geophysical Research: Atmospheres, 107(D24): ACH-1-ACH 14-20
  23. 23.
    Zhang R H. 1999. Some thinking on quantiative thermal infrared remote sensing. Remote Sensing for Land and Resources, (1): 1-6
  24. 24.
    Zhou X M, Wang N and Wu H. 2012. Comparison of two methods for atmospheric correction of hyper-spectral thermal infrared data. Journal of Remote Sensing, 16(4): 796-808
  25. 25.
    Zhu X L, Duan S B, Li Z L, Zhao W, Wu H, Leng P, Gao M F and Zhou X M. 2020. Retrieval of land surface temperature with topographic effect correction from landsat 8 thermal infrared data in mountainous areas. IEEE Transactions on Geoscience and Remote Sensing, 1-14

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