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Field observations of background thermal radiation directionality in natural forests

  • role： First author第一作者
  • Affiliation：

    Key Laboratory for Silviculture and Conservation of the Ministry of Education, Beijing Forestry University, Beijing 100083, China

  • Email：785010487@qq.com
  • Introduction：连君(1992—　)，女，硕士研究生，主要研究方向为林业定量遥感。E-mail：785010487@qq.com
LIAN Jun,  
  • Affiliation：

    Key Laboratory for Silviculture and Conservation of the Ministry of Education, Beijing Forestry University, Beijing 100083, China

SUN Hao,  
  • Affiliation：

    Key Laboratory for Silviculture and Conservation of the Ministry of Education, Beijing Forestry University, Beijing 100083, China

LIN Qinan,  
  • Affiliation：

    Key Laboratory for Silviculture and Conservation of the Ministry of Education, Beijing Forestry University, Beijing 100083, China

CAO Xuejia,  
  • Affiliation：

    Key Laboratory for Silviculture and Conservation of the Ministry of Education, Beijing Forestry University, Beijing 100083, China

LI Chong,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory for Silviculture and Conservation of the Ministry of Education, Beijing Forestry University, Beijing 100083, China

  • Email：huaguo_huang@bjfu.edu.cn
  • Introduction：黄华国(1978—　)，男，教授，主要研究方向为林业定量遥感。E-mail：huaguo_huang@bjfu.edu.cn
HUANG Huaguo*

résumé

Thermal radiation directionality is a key factor that affects the accurate retrieval of land surface temperature from remote sensing images. To understand the characteristics and law of thermal radiation directionality of a natural forest background, multi angular observations of directional brightness temperatures (DBT) were conducted in both summer and autumn on herbaceous vegetation and partially bare soil in a natural forest in northeast China. A FLIR T420 thermal imager was used to capture DBTon the east–west and north–south planes at 10:30 AM and 14:30 PM of each day. The View Zenith Angle (VZA) changed from –50° to 50°. Then, time effects on DBT were removed with a linear correction method during observations using continuous thermal images that were taken by FLIR i7 in the nadir direction. (1) The brightness temperature of then atural forest background had a visible directional affect, which is related to the ground vegetation structure and cannot be omitted. (2) In the summer, the forest background was mostly covered by herbaceous vegetation and shaded by dense tree crowns, which account for the small variations in small DBT (1—1.5 ℃). The highest DBT was observed at VZA of 40° in the east for 10:30 AM (solar zenith angle (SZA), 30.5±0.5°;solar azimuthal angle (SAA), 149±1°) and in the west for 14:30 PM (SZA, 43.5±1.5°; SAA, 247.5±1.5°). (3) In autumn, the forest background was covered by a 10-cm thick litter layer. DBT is 1 ℃ higher than those in the summer. The maximum DBT appeared at VZA of 50° in the east for 10:30 AM (SZA, 57.5±0.5°; SAA, 169±2°) and in the west for 14:30 PM (SZA, 73±1°; SAA, 231±1°). (4) Although forest age influenced DBT, the seasonal effects were more significant. The directional effects of the brightness temperature of the forest background may contribute to pixel-scale DBT, which should not be neglected in future studies.

mots-clés

thermal radiation directionality;natural forest;under storey;time effect

References

  1. 1.
    Chen L F, Zhuang J L, Xu X R, Liu Q H and Xiang Y Q. 2000a. The simulation of thermal radiant directionality of continuous vegetation using Monte Carlo method. Journal of Remote Sensing, 4(4): 261–265
  2. 2.
    Chen L F, Zhuang J L, Liu Q H, Xu X R and Tian G L. 2000b. Study on the law of radiant directionality of row crops. Science in ChinaSeries E: Technological Sciences, 43(S1): 70–82
  3. 3.
    Chen L F and Liu Q H. 2001. The thermal radiant directionality of continuous vegetation.Journal of Remote Sensing, 5(6): 407–415
  4. 4.
    Chen L F, Fan W J and Liu Q H. 2001. The study on thermal infrared radiant directionality of non isothermal land surface. Progress in Geography, 20(3): 262–267
  5. 5.
    Chen L F, Liu Q H, Fan W J,Li X W, Xiao Q, Yan G J and Tian G L. 2002. The porosity model of the thermal radiant directionality of row cops. Science in China(Series D), 32(4): 290–298
  6. 6.
    Chopping M J, Su L H, Rango A and Maxwell C. 2004. Modelling the reflectance anisotropy of Chihuahuan desert grass–shrub transition canopy–soil complexes. International Journal of Remote Sensing, 25(14): 2725–2745
  7. 7.
    Fan W J and Xu X R. 2003. The affecting factors of thermal radiant directionality of the row winter wheat. Geography and Geo-Information Science, 19(4): 84–88
  8. 8.
    Huang H G, Liu Q H, Liu Q, Xiao Q and Chen L F.2007a. Using multidirectional thermal images to obtain canopy component temperature and directional brightness temperature.Journal of Beijing Normal University(Natural Science), 43(3): 292–297
  9. 9.
    Huang H G, Liu Q H, Liu Q, Du Y M and Li X W. 2007b. Simulation of time effect on thermal emission directionality measurement. Journal of System Simulation, 19(15): 3586–3590
  10. 10.
    Lagouarde J P, Kerr Y H and Brunet Y. 1995. An experimental study of angular effects on surface temperature for various plant canopies and bare soils. Agricultural and Forest Meteorology, 77(3/4): 167–190
  11. 11.
    Lagouarde J P, Ballans H, Moreau P, Guyon D and Coraboeuf D. 2000. Experimental study of brightness surface temperature angular variations of maritime pine (Pinuspinaster) stands. Remote Sensing of Environment, 72(1): 17–34
  12. 12.
    Liu Q, Chen L F, Liu Q H and Xiao Q. 2003. A radiation transfer model to predict canopy radiation in thermal infrared band. Journal of Remote Sensing, 7(3): 161–167
  13. 13.
    Masuda K, Takashima T and Takay ama Y. 1988. Emissivity of pure and sea waters for the model sea surface in the infrared window regions. Remote Sensing of Environment, 24(2): 313–329
  14. 14.
    Paw U K T, Ustin S L and Zhang C A. 1989. Anisotropy of thermal infrared exitance in sunflower canopies.Agricultural and Forest Meteorology, 48(1/2): 45–58
  15. 15.
    Rasmussen M O, Pinheiro A C, Proud S R and Sandholt I. 2010. Modeling angular dependences in land surface temperatures from the SEVIRI instrument onboard the geostationary meteosat second generation satellites. IEEE Transactions on Geoscience and Remote Sensing, 48(8): 3123–3133
  16. 16.
    Rautiainen M and Stenberg P. 2005. Application of photon recollision probability in coniferous canopy reflectance simulations. Remote Sensing of Environment, 96(1): 98–107
  17. 17.
    Verbrugghe M and Cierniewski J. 1998.Influence and modelling of view angles and microrelief on surface temperature measurements of bare agricultural soils. ISPRS Journal of Photogr ammetry and Remote Sensing, 53(3): 166–173
  18. 18.
    Voogt J A and Oke T R. 2003.Thermal remote sensing of urban climates.Remote Sensing of Environment, 86(3): 370–384
  19. 19.
    Yang G J, Liu Q H, Liu Q, Xiao Q and Huang W J.2010. Directional simulation of thermal infrared radiation and 3D radiative transfer model of canopy. Journal of Infrared and Millimeter Waves, 29(1): 38–44
  20. 20.
    Zhao L M, Gu X F, Yu T, Wan W, Zhang L and Xie Y H. 2012. A directional thermal radiance model for multiple scattering over surfaces. Journal of Infrared and Millimeter Waves, 31(6): 528–535

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