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Radiation correction considering geometric effect of urban area and its application in temperature retrieval through remote sensing

  • role： First author第一作者
  • Affiliation：

    College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China

    Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Beijing 100048, China

  • Email：dyf_cnu@163.com
  • Introduction：狄宇飞，1994年生，男，博士研究生，研究方向为城市热环境、辐射收支的理论和应用。E-mail：dyf_cnu@163.com
DI Yufei12,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China

    Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Beijing 100048, China

  • Email：deyonghu@cnu.edu.cn
  • Introduction：胡德勇，1974年生，男，教授，研究方向为城市遥感、自然灾害监测和评估等。E-mail：deyonghu@cnu.edu.cn
HU Deyong12*,  
  • Affiliation：

    College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China

    Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Beijing 100048, China

LIU Manqing12,  
  • Affiliation：

    Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China

CHEN Shanshan3,  
  • Affiliation：

    College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China

    Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Beijing 100048, China

YU Chen12,  
  • Affiliation：

    College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China

    Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Beijing 100048, China

WANG Yichen12

resumen

The study of radiometric correction physical model for geometric effects applicable to thermal infrared remote sensing images in urban areas plays an important role in improving the inversion accuracy of underlying surface temperature. In this study, the sky view factor (V) was used to quantify the geometric feature of urban underlying surface, considering the influence mechanism of the geometric effect on radiation transfer of the urban ground target. The radiance observed by the sensor was decomposed into three parts: radiance emitted by ground target, radiance reflected by ground target and upward radiance of the atmosphere, and the radiation transfer process of each part at the “urban -atmosphere” interface was analyzed. Then we constructed the Urban Thermal Radiation Transfer (UTRT) model for the urban underlying surface, which can be applied to the geometric effect radiation correction of thermal infrared remote sensing images in urban areas. Based on Landsat 8 TIRS(Thermal Infrared Sensor) data, the UTRT model was used to inverse surface temperature in a typical urban area of ​​Beijing and compared inversion results to the model without considering geometric feature of the underlying surface. The results demonstrate that: (1)The inversion result of the UTRT model is generally lower than that without considering geometric feature of the underlying surface with the difference range 0—1.57 K and average value 0.44 K. (2) Through the analysis and comparison of different land cover types, the results show that buildings and bare land types have a more significant difference, and their spatial distribution is obviously affected by the V value. The V value is proportional to the inversion result within a specific range. (3) Sensitivity analysis of the sky view factor and ambient temperature parameters were conducted for the UTRT model. The sensitivity of the V parameter is stronger in scenes with small surface emissivity, while the sensitivity is lower due to the ambient temperature being closer to the target feature temperature. The study shows that the UTRT model can provide a solution for the radiation correction of the geometric effects of the remote sensing image in urban underlying surface. Compared with the model that does not consider the geometric feature of the underlying surface, the former can be better applied to the remote sensing inversion of urban surface temperature. This study has development potential and application prospects for advancing the quantitative remote sensing research of urban high-resolution thermal infrared.

palabra clave

thermal radiation transfer model;urban underlying surface;geometric feature;sky view factor;land surface temperature

References

  1. 1.
    Bian Z, Cao B, Hua L, Du Y, Xiao Q and Liu Q. 2018. The Effect of Trunks on Directional Brightness Temperatures of a Leafless Forest Using a Geometrical Optical Model. IGARSS 2018—2018 IEEE International Geoscience and Remote Sensing Symposium
  2. 2.
    Bian Z, Roujean J L, Cao B, Du Y, Li H, Gamte P, Fang J, Xiao Q and Liu Q. 2021. Modeling the directional anisotropy of fine-scale TIR emissions over tree and crop canopies based on UAV measurements. Remote Sensing of Environment, 252
  3. 3.
    Bian Z J, Qi J B, Wu S B, Wang Y S, Liu S Y, Xu B D, Du Y M, Cao B, Li H, Huang H G, Xiao Q and Liu Q H. 2021. A review on the development and application of three dimensional computer simulation mode of optical remote sensing. National Remote Sensing Bulletin, 25(2): 559-576
  4. 4.
    Bonczak B and Kontokosta C E. 2019. Large-scale parameterization of 3D building morphology in complex urban landscapes using aerial LiDAR and city administrative data. Computers, Environment and Urban Systems, 73:126-142
  5. 5.
    Brown M J, Grimmond S and Ratti C. 2001. Comparison of methodologies for computing sky view factor in urban environments. Los Alamos National Lab., NM (US).
  6. 6.
    Cai H and Xu X . 2017. Impacts of Built-Up Area Expansion in 2D and 3D on Regional Surface Temperature. Sustainability, 9(10):1862.
  7. 7.
    Cai Z and Han G. 2018. Assessing Land Surface Temperature in the Mountain City with Different Urban Spatial Form Based on Local Climate Zone Scheme. Mountain Research. 4:617-627
  8. 8.
    Duan X, Hu D, Cao S, Yu C and Zhang Y. 2019. A study of the parametric method of sky view factor on complex underlying surface in urban area: A case study of national sport stadium area in Beijing. REMOTE SENSING FOR LAND & RESOURCES, 31(3): 29-35
  9. 9.
    Gál T and Unger J. 2014. A new software tool for SVF calculations using building and tree-crown databases. Urban Climate, 10: 594-606.
  10. 10.
    González-Aparicio I, Hidalgo J, Baklanov A, Korsholm U, Nuterman R, Mahura A and Santa-Coloma O. 2012. Urban boundary layer analysis in the complex coastal terrain of Bilbao using Enviro-HIRLAM. Theoretical and Applied Climatology, 113(3-4), 511-527
  11. 11.
    Grimmond C S B , Blackett M, Best M J, Baik J J, Belcher S E, Beringer J and Zhang N. 2010. Initial results from Phase 2 of the international urban energy balance model comparison. International Journal of Climatology, 31(2), 244-272
  12. 12.
    Guo G, Zhou X, Wu Z, Xiao R and Chen Y. 2016. Characterizing the impact of urban morphology heterogeneity on land surface temperature in Guangzhou, China. Environmental Modelling & Software, 84, 427-439
  13. 13.
    Guo M, Cao B , Fan W ,Ren H, Cui Y, Du Y and Liu Q.2019. Scattering Effect Contributions to the Directional Canopy Emissivity and Brightness Temperature Based on CE-P and CBT-P Models. IEEE Geoscience and Remote Sensing Letters, 1-5
  14. 14.
    Hu D Y, Zhang Y N, Liu M Q, Yu C, Cao S S and Di Y F. 2021. Solar radiation transfer model considering urban morphological characteristics and its remote sensing application. Journal of Remote Sensing
  15. 15.
    Hu T, Du Y, Cao B, Hua L, Bian Z, Sun D and Liu Q.2016 Estimation of Upward Longwave Radiation From Vegetated Surfaces Considering Thermal Directionality. IEEE Transactions on Geoscience & Remote Sensing, 54(11):6644-6658
  16. 16.
    Lan X, Zhao E, Li Z, Labed J and Nerry F. 2020. Deep Mixture Model-Based Land Surface Temperature Retrieval for Hyperspectral Thermal IASI Sensor. IEEE Access, 8, 218122-218130
  17. 17.
    Lian J, Sun H, Lin Q N, Cao X J, Li C and Huang H G. 2017. Field observations of background thermal radiation directionality in natural forests. Journal of Remote Sensing, 21(3): 365-374
  18. 18.
    Liu Q, Cao B, Bian Z, Du Y and Li H. 2019. Progresses on Thermal Radiation Directionality Modeling for Vegetation Canopy. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
  19. 19.
    Liu Q, Cao B, Zeng Y, Li J, Du Y, Wen J, Fan W, Zhao J and Yang L. 2016. Recent progresses on the remote sensing radiative transfer modeling over heterogeneous vegetation canopy. Journal of Remote Sensing, 20(5): 933-945
  20. 20.
    Li Z, Duan S, Tang B, Wu H, Ren H, Yan G, Tang R and Leng P. 2016. Review of methods for land surface temperature derived from thermal infrared remotely sensed data. Journal of Remote Sensing, 20(5): 899–920
  21. 21.
    Machete R, Falcão A P, Gomes M G and Rodrigues A M. 2018. The use of 3D GIS to analyse the influence of urban context on buildings’ solar energy potential. Energy and Buildings,177,290-302
  22. 22.
    Mousivand A, Verhoef W, Menenti M and Gorte B. 2015. Modeling Top of Atmosphere Radiance over Heterogeneous Non-Lambertian Rugged Terrain. Remote Sensing, 7(6), 8019-8044
  23. 23.
    Morrison W, Yin T, Lauret N, Guilleux J and Kotthaus S, Gastellu-Etchegorry J, Norford L and Grimmond S. 2020. Atmospheric and emissivity corrections for ground-based thermography using 3D radiative transfer modelling. Remote Sensing of Environment, 237:111524
  24. 24.
    Overby M, Willemsen P, Bailey B N, Halverson S and Pardyjak E R. 2016. A rapid and scalable radiation transfer model for complex urban domains. Urban Climate, 15, 25-44
  25. 25.
    Park C, Schade G W, Werner N D, Sailor D J and Kim C H.2016. Comparative estimates of anthropogenic heat emission in relation to surface energy balance of a subtropical urban neighborhood. Atmospheric Environment, 126: 182-191
  26. 26.
    Pan Y, Liao M, Liao M, Han F and Chen Z. 2017. Study on the impacts of urban morphology heterogeneity on land temperature in the core area of Poyang Lake. Ecology and Environmental Sciences,26(8):1358-1367
  27. 27.
    Peng S, Tang B H, Li Z L, Wu H and Tang R L. 2016. Study of the relationship between thermal infrared directional and hemispherical radiative temperatures. Journal of Geo-information Science, 18(1):106-116
  28. 28.
    Qin Z, Li W, Xu B and Zhang W. 2004. Estimation method of land surface emissivity for retrieving land surface temperature from landsat TM6 data. Advances in marine science. 22,
  29. 29.
    Taleghani M, Kleerekoper L, Tenpierik M and van den Dobbelsteen A. 2015. Outdoor thermal comfort within five different urban forms in the Netherlands. Building and Environment, 83, 65-78
  30. 30.
    Wang K and Dickinson R E. 2013. Contribution of solar radiation to decadal temperature variability over land. Proceedings of the National Academy of Sciences, 110(37), 14877-14882
  31. 31.
    Wang Q and Li P. 2013. Canopy vertical heterogeneity plays a critical role in reflectance simulation. Agricultural and Forest Meteorology, 169, 111-121
  32. 32.
    Wang Y and Akbari H. 2014. Effect of Sky View Factor on Outdoor Temperature and Comfort in Montreal. Environmental Engineering Science, 31(6), 272-287
  33. 33.
    Wu, S, Wen, J, Gastellu-Etchegorry, J P, Liu Q, You D, Xiao Q, Hao D L, Lin X and Yin T. 2019. The definition of remotely sensed reflectance quantities suitable for rugged terrain. Remote Sensing of Environment, 225, 403-415
  34. 34.
    Yan G, Wang T, Jiao Z, Mu X, Zhao J and Chen L. 2016. Topographic radiation modeling and spatial scaling of clear-sky land surface longwave radiation over rugged terrain. Remote Sensing of Environment, 172, 15-27
  35. 35.
    Yang J, Wong M S, Menenti M and Nichol J. 2015. Modeling the effective emissivity of the urban canopy using sky view factor. ISPRS Journal of Photogrammetry and Remote Sensing, 105, 211-219
  36. 36.
    Yang J, Wong M S, Menenti M and Nichol J. 2015. Study of the geometry effect on land surface temperature retrieval in urban environment. ISPRS Journal of Photogrammetry and Remote Sensing, 109, 77-87
  37. 37.
    Yang J, Wong M S, Menenti M, Nichol J, Voogt J, Krayenhoff E S and Chan P W. 2016. Development of an improved urban emissivity model based on sky view factor for retrieving effective emissivity and surface temperature over urban areas. ISPRS Journal of Photogrammetry and Remote Sensing, 122, 30-40
  38. 38.
    Yang J, Wong M S, Ho H C, Krayenhoff E S, Chan P W, Abbas S and Menenti M. 2020. A semi-empirical method for estimating complete surface temperature from radiometric surface temperature, a study in Hong Kong city. Remote Sensing of Environment, 237, 111540
  39. 39.
    Zakšek K, Oštir K and Kokalj Ž. 2011. Sky-view factor as a relief visualization technique. Remote sensing, 3(2): 398-415
  40. 40.
    Zhang H L. 2015. Study of urban thermal environment based on remote sensing and urban spatial morphology. Nanjing University of Information Science & Technology
  41. 41.
    Zheng X , Li Z, Chen K, Zhang X and Shang G.2019. Assessment of the Urban Three-dimensional Structural Influence on the Satellite Thermal Infrared Measurement. PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring).
  42. 42.
    Zheng Y, Ren H, Sui J, Dong J and Qin Q. 2018. Urban Thermal Radiation Simulation Using High Resolution Digital Surface Models and Multispectral Images. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE.
  43. 43.
    Zhou J, Li J, Zhang L, Hu D and Zhan W. 2012. Intercomparison of methods for estimating land surface temperature from a Landsat-5 TM image in an arid region with low water vapour in the atmosphere. International Journal of Remote Sensing, 33(8), 2582-2602
  44. 44.
    Zhu S, Guan H, Bennett J, Clay R, Ewenz C, Benger S, Maghrabi A and Millington A C. 2013. Influence of sky temperature distribution on sky view factor and its applications in urban heat island. International Journal of Climatology, 33(7), 1837-1843

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