Modeling Directional Thermal Radiance Anisotropy for Urban Canopy
one of the significant factors for improving the
accuracy of Land Surface Temperature (LST) retrieval is the correct
understanding of the directional anisotropy for thermal radiance. In
this paper, the multiple scattering effect between heterogeneous
non-isothermal surfaces is described rigorously according to the
concept of configuration factor, based on which a directional thermal
radiance model is built, and the directional radiant character for urban
canopy is analyzed. The model is applied to a simple urban canopy
with row structure to simulate the change of Directional Brightness
Temperature (DBT). The results show that the DBT is aggrandized
because of the multiple scattering effects, whereas the change range of
DBT is smoothed. The temperature difference, spatial distribution,
emissivity of the components can all lead to the change of DBT. The
“hot spot" phenomenon occurs when the proportion of high
temperature component in the vision field came to a head. On the other
hand, the “cool spot" phenomena occur when low temperature
proportion came to the head. The “spot" effect disappears only when
the proportion of every component keeps invariability. The model
built in this paper can be used for the study of directional effect on
emissivity, the LST retrieval over urban areas and the adjacency effect
of thermal remote sensing pixels.
[1] F. Becker and Z. Li, "Surface temperature and emissivity at various
scales: Definition, measurement and related problems," Remote Sensing
Reviews, vol. 12, pp. 225-253, 1995.
[2] Z. Qin and A. Karnieli, "Progress in the remote sensing of land surface
temperature and ground emissivity using NOAA-AVHRR data,"
International Journal of Remote Sensing, vol. 20, pp. 2367-2393, 1999.
[3] P. Dash, F. Göttsche, F. Olesen and F. Fischer, "Land surface temperature
and emissivity estimation from passive sensor data: theory and
practice-current trends," International Journal of Remote Sensing, vol.
23, pp. 2563-2594, 2002.
[4] J. P. Lagouarde and M. Irvine, "Directional anisotropy in thermal infrared
measurements over Toulouse city centre during the CAPITOUL
measurement campaigns: First results," Meteoro. Atmos. Phys., vol. 102,
pp. 173-185, 2008.
[5] J. A. Voogt and T. R. Oke, "Effects of urban surface geometry on
remotely-sensed surface temperature," International Journal of Remote
Sensing, vol. 19, pp. 895-890, 1998.
[6] X. Li and A. H. Strahler, "Modeling the gap probability of a
discontinuous vegetation canopy," IEEE Transactions on Geoscience and
Remote Sensing, vol. 26, pp. 161-170, 1988.
[7] G. Yan, L. Jiang, J. Wang, L. Chen and X. Li, "Thermal bidirectional gap
probability model for row crop canopies and validation," Science in
China Series D: Earth Sciences, vol. 46, pp. 1241-1249, 2003.
[8] L. Chen, Z. Li, Q. Liu and S. Chen, "Definition of component effective
emissivity for heterogeneous and non-isothermal surface and its
approximate caculation," International Journal of Remote Sensing, vol.
25, pp. 231-244, 2004.
[9] T. YU, X. GU, G. Tian, M. Legrand, F. Baret, J. Hanocq, et al.,"Modeling
directional brightness temperature over a maize canopy in row structure,"
IEEE transactions on geoscience and remote sensing, vol. 42, pp.
2290-2304, 2004.
[10] C. Fran├ºois, C. Ottlé and L. Prévot, "Analytical parameterization of
canopy directional emissivity and directional radiance in the thermal
infrared. Application on the retrieval of soil and foliage temperatures
using two directional measurements," International Journal of Remote
Sensing, vol. 18, pp. 2587-2621, 1997.
[11] Q. Liu, H. Huang and W. Qin, "An extended 3-D radiosity-graphics
combined model for studying thermal-emission directionality of crop
canopy," IEEE transactions on geoscience and remote sensing, vol. 45,
pp. 2900-2918, 2007.
[12] A. Soux, J. A. Voogt, and T. R. Oke, "A model to calculate what a remote
sensor ÔÇÿsees-of an urban surface," Boundary-Layer Meteorology, vol.
111, pp. 109-132, 2004.
[13] J. P. Gastellu-Etchegorry, E. Martin and F. Gascon , "DART: A 3-D
model for simulating satellite images and surface radiation budget,"
International Journal of Remote Sensing, vol. 25, pp. 75-96, 2004.
[14] E. S. Krayenhoff and J. A. Voogt, "A microscale three-dimensional urban
energy balance model for studying surface temperatures,"
Boundary-Layer Meteorology, vol. 123, pp. 433-461, 2007.
[15] T. Poglio, S. Mathieu-Marni, T. Ranchin, E. Savaria and L. Wald,
"OSIrIS: a physically based simulation tool to improve training in thermal
infrared remote sensing over urban areas at high spatial resolution,"
Remote Sensing of Environment, vol. 104, pp. 238-246, 2006.
[16] J. A. Voogt, "Assessment of an Urban Sensor View Model for thermal
anisotropy," Remote Sensing of Environment, vol. 112, pp. 482-495,
2008.
[17] J. P. Lagouarde, A. Hénon, B. Kurz, P. Moreau, M. Irvine, J. Voogt, et
al., "Modelling daytime thermal infrared directional anisotropy over
Toulouse city centre," Remote Sensing of Environment, vol. 114, pp.
87-105, 2010.
[18] J. Sheng, J. P. Wilson and S. Lee, "Comparison of land surface
temperature (LST) modeled with a spatially-distributed solar radiation
model (SRAD) and remote sensing data," Environmental Modelling &
Software, vol. 24, pp. 436-443, 2009.
[19] J. A. Sobrino, J. C. Jiménez-Mu├▒oz and W. Verhoef, "Canopy directional
emissivity: Comparison between models," Remote Sensing of
Environment, vol. 99, pp. 304-314, 2005.
[20] M. Menenti, L. Jia and Z. Li, "Multi-angular thermal infrared
observations of terrestrial vegetation," in Advances in Land Remote
Sensing: System, Modeling, Inversion and Application, S. Liang, Ed., ed
Berlin: Springer, 2008, pp. 51-93.
[21] A. J. Prata, "A new long-wave formula for estimating downward
clear-sky radiation at the surface," Quarterly Journal of the Royal
Meteorological Society, vol. 122, pp. 1127-1151, 1996.
[22] S. Niemelä, P. Räisänen and H. Savijärvi, "Comparison of surface
radiative flux parameterizations: Part I: Longwave radiation,"
Atmospheric Research, vol. 58, pp. 1-18, 2001.
[23] M. G. Iziomon, H. Mayer and A. Matzarakis, "Downward atmospheric
longwave irradiance under clear and cloudy skies: Measurement and
parameterization," Journal of Atmospheric and Solar-Terrestrial Physics,
vol. 65, pp. 1107-1116, 2003.
[1] F. Becker and Z. Li, "Surface temperature and emissivity at various
scales: Definition, measurement and related problems," Remote Sensing
Reviews, vol. 12, pp. 225-253, 1995.
[2] Z. Qin and A. Karnieli, "Progress in the remote sensing of land surface
temperature and ground emissivity using NOAA-AVHRR data,"
International Journal of Remote Sensing, vol. 20, pp. 2367-2393, 1999.
[3] P. Dash, F. Göttsche, F. Olesen and F. Fischer, "Land surface temperature
and emissivity estimation from passive sensor data: theory and
practice-current trends," International Journal of Remote Sensing, vol.
23, pp. 2563-2594, 2002.
[4] J. P. Lagouarde and M. Irvine, "Directional anisotropy in thermal infrared
measurements over Toulouse city centre during the CAPITOUL
measurement campaigns: First results," Meteoro. Atmos. Phys., vol. 102,
pp. 173-185, 2008.
[5] J. A. Voogt and T. R. Oke, "Effects of urban surface geometry on
remotely-sensed surface temperature," International Journal of Remote
Sensing, vol. 19, pp. 895-890, 1998.
[6] X. Li and A. H. Strahler, "Modeling the gap probability of a
discontinuous vegetation canopy," IEEE Transactions on Geoscience and
Remote Sensing, vol. 26, pp. 161-170, 1988.
[7] G. Yan, L. Jiang, J. Wang, L. Chen and X. Li, "Thermal bidirectional gap
probability model for row crop canopies and validation," Science in
China Series D: Earth Sciences, vol. 46, pp. 1241-1249, 2003.
[8] L. Chen, Z. Li, Q. Liu and S. Chen, "Definition of component effective
emissivity for heterogeneous and non-isothermal surface and its
approximate caculation," International Journal of Remote Sensing, vol.
25, pp. 231-244, 2004.
[9] T. YU, X. GU, G. Tian, M. Legrand, F. Baret, J. Hanocq, et al.,"Modeling
directional brightness temperature over a maize canopy in row structure,"
IEEE transactions on geoscience and remote sensing, vol. 42, pp.
2290-2304, 2004.
[10] C. Fran├ºois, C. Ottlé and L. Prévot, "Analytical parameterization of
canopy directional emissivity and directional radiance in the thermal
infrared. Application on the retrieval of soil and foliage temperatures
using two directional measurements," International Journal of Remote
Sensing, vol. 18, pp. 2587-2621, 1997.
[11] Q. Liu, H. Huang and W. Qin, "An extended 3-D radiosity-graphics
combined model for studying thermal-emission directionality of crop
canopy," IEEE transactions on geoscience and remote sensing, vol. 45,
pp. 2900-2918, 2007.
[12] A. Soux, J. A. Voogt, and T. R. Oke, "A model to calculate what a remote
sensor ÔÇÿsees-of an urban surface," Boundary-Layer Meteorology, vol.
111, pp. 109-132, 2004.
[13] J. P. Gastellu-Etchegorry, E. Martin and F. Gascon , "DART: A 3-D
model for simulating satellite images and surface radiation budget,"
International Journal of Remote Sensing, vol. 25, pp. 75-96, 2004.
[14] E. S. Krayenhoff and J. A. Voogt, "A microscale three-dimensional urban
energy balance model for studying surface temperatures,"
Boundary-Layer Meteorology, vol. 123, pp. 433-461, 2007.
[15] T. Poglio, S. Mathieu-Marni, T. Ranchin, E. Savaria and L. Wald,
"OSIrIS: a physically based simulation tool to improve training in thermal
infrared remote sensing over urban areas at high spatial resolution,"
Remote Sensing of Environment, vol. 104, pp. 238-246, 2006.
[16] J. A. Voogt, "Assessment of an Urban Sensor View Model for thermal
anisotropy," Remote Sensing of Environment, vol. 112, pp. 482-495,
2008.
[17] J. P. Lagouarde, A. Hénon, B. Kurz, P. Moreau, M. Irvine, J. Voogt, et
al., "Modelling daytime thermal infrared directional anisotropy over
Toulouse city centre," Remote Sensing of Environment, vol. 114, pp.
87-105, 2010.
[18] J. Sheng, J. P. Wilson and S. Lee, "Comparison of land surface
temperature (LST) modeled with a spatially-distributed solar radiation
model (SRAD) and remote sensing data," Environmental Modelling &
Software, vol. 24, pp. 436-443, 2009.
[19] J. A. Sobrino, J. C. Jiménez-Mu├▒oz and W. Verhoef, "Canopy directional
emissivity: Comparison between models," Remote Sensing of
Environment, vol. 99, pp. 304-314, 2005.
[20] M. Menenti, L. Jia and Z. Li, "Multi-angular thermal infrared
observations of terrestrial vegetation," in Advances in Land Remote
Sensing: System, Modeling, Inversion and Application, S. Liang, Ed., ed
Berlin: Springer, 2008, pp. 51-93.
[21] A. J. Prata, "A new long-wave formula for estimating downward
clear-sky radiation at the surface," Quarterly Journal of the Royal
Meteorological Society, vol. 122, pp. 1127-1151, 1996.
[22] S. Niemelä, P. Räisänen and H. Savijärvi, "Comparison of surface
radiative flux parameterizations: Part I: Longwave radiation,"
Atmospheric Research, vol. 58, pp. 1-18, 2001.
[23] M. G. Iziomon, H. Mayer and A. Matzarakis, "Downward atmospheric
longwave irradiance under clear and cloudy skies: Measurement and
parameterization," Journal of Atmospheric and Solar-Terrestrial Physics,
vol. 65, pp. 1107-1116, 2003.
@article{"International Journal of Engineering, Mathematical and Physical Sciences:64366", author = "Limin Zhao and Xingfa Gu and C. Tao Yu", title = "Modeling Directional Thermal Radiance Anisotropy for Urban Canopy", abstract = "one of the significant factors for improving the
accuracy of Land Surface Temperature (LST) retrieval is the correct
understanding of the directional anisotropy for thermal radiance. In
this paper, the multiple scattering effect between heterogeneous
non-isothermal surfaces is described rigorously according to the
concept of configuration factor, based on which a directional thermal
radiance model is built, and the directional radiant character for urban
canopy is analyzed. The model is applied to a simple urban canopy
with row structure to simulate the change of Directional Brightness
Temperature (DBT). The results show that the DBT is aggrandized
because of the multiple scattering effects, whereas the change range of
DBT is smoothed. The temperature difference, spatial distribution,
emissivity of the components can all lead to the change of DBT. The
“hot spot" phenomenon occurs when the proportion of high
temperature component in the vision field came to a head. On the other
hand, the “cool spot" phenomena occur when low temperature
proportion came to the head. The “spot" effect disappears only when
the proportion of every component keeps invariability. The model
built in this paper can be used for the study of directional effect on
emissivity, the LST retrieval over urban areas and the adjacency effect
of thermal remote sensing pixels.", keywords = "Directional thermal radiance, multiple scattering,configuration factor, urban canopy, hot spot effect", volume = "5", number = "11", pages = "1779-4", }
