浏览全部资源
扫码关注微信
纸质出版日期： 2021-08-07 ，
扫 描 看 全 文
狄宇飞，胡德勇，刘曼晴，陈姗姗，于琛，王一辰.2021.城市区域几何效应的辐射校正及其温度遥感反演.遥感学报，25（8）： 1821-1835
Di Y F，Hu D Y，Liu M Q，Chen S S，Yu C and Wang Y C. 2021. Radiation correction considering geometric effect of urban area and its application in temperature retrieval through remote sensing. National Remote Sensing Bulletin， 25（8）：1821-1835
开展适用于城市地区热红外遥感图像几何效应的辐射校正物理模型研究，对提升城区下垫面温度反演精度具有重要作用。本文利用天空视域系数SVF（Sky View Factor，
V
）量化城区下垫面几何特征，并考虑几何特征对地物目标辐射传输的影响机理。将遥感传感器入瞳处的辐射亮度分解为地物目标发射辐射、地物目标反射辐射以及大气上行路径辐射3部分，并分析了各部分辐射在“城区—大气”界面的辐射传输过程，构建适用于城市下垫面的热辐射传输UTRT（Urban Thermal Radiation Transfer）模型，可应用于城区热红外遥感图像的几何效应辐射校正。基于Landsat 8 TIRS（Thermal Infrared Sensor）数据，将UTRT模型应用于北京市典型城区地表温度的反演，并对UTRT模型和不考虑下垫面几何特征反演结果进行对比分析，结果显示：（1）研究区内UTRT模型反演结果数值上整体低于不考虑下垫面几何特征的反演结果，差值范围为0—1.57 K，均值为0.44 K；（2）通过对不同地表覆被类型的分析对比，模型结果差值较大的区域为建筑和裸地，其空间分布明显受
V
值的影响，在一定范围内，
V
值越大反演结果数值越低；（3）对UTRT模型天空视域系数和环境温度两个参数进行敏感性分析，在地表发射率较小的场景中
V
参数敏感性较强，由于环境温度与目标地物温度较为接近，其参数的敏感性较低。结论为：UTRT模型可为城市下垫面遥感图像几何效应的辐射校正提供解决方案，对比不考虑下垫面几何特征的模型，前者可以更好地应用于城区地表温度遥感反演。该研究对推进城市高分辨率热红外定量遥感具有发展潜力和应用前景。
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.
热辐射传输模型城区下垫面几何特征天空视域系数地表温度
thermal radiation transfer modelurban underlying surfacegeometric featuresky view factorland surface temperature
Bian Z, Cao B, Hua L, Du Y, Xiao Q andLiu 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 [DOI: 10.1109/IGARSS.2018.8517583http://dx.doi.org/10.1109/IGARSS.2018.8517583]
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 [DOI:10.1016/j.rse.2020.112150http://dx.doi.org/10.1016/j.rse.2020.112150]
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
卞尊健, 漆建波, 吴胜标, 王雨生, 刘守阳, 徐保东, 杜永明, 曹彪, 历华, 黄华国, 肖青, 柳钦火. 2021. 光学遥感三维计算机模拟模型的研究进展与应用. 遥感学报, 25(2): 559-576 [DOI: 10.11834/jrs.20219274http://dx.doi.org/10.11834/jrs.20219274]
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 [DOI: 10.1016/j.compenvurbsys.2018.09.004http://dx.doi.org/10.1016/j.compenvurbsys.2018.09.004]
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).
Cai H and Xu X . 2017. Impacts of Built-Up Area Expansion in 2D and 3D on Regional Surface Temperature. Sustainability, 9(10):1862. [DOI: 10.3390/su9101862http://dx.doi.org/10.3390/su9101862]
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
蔡智, 韩贵锋. 2018.山地城市空间形态的地表热环境效应——基于LCZ的视角.山地学报, 4:617-627 [DOI: 10.16089/j.cnki.1008-2786.000358http://dx.doi.org/10.16089/j.cnki.1008-2786.000358]
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
段欣, 胡德勇, 曹诗颂, 于琛, 张亚妮. 2019.城区复杂下垫面天空视域因子参数化方法——以北京鸟巢周边地区为例. 国土资源遥感, 31(3): 29-35 [DOI:10.6046/gtzyyg.2019.03.04http://dx.doi.org/10.6046/gtzyyg.2019.03.04]
Gál T and Unger J. 2014. A new software tool for SVF calculations using building and tree-crown databases. Urban Climate, 10: 594-606.[DOI: 10.1016/j.uclim.2014.05.004http://dx.doi.org/10.1016/j.uclim.2014.05.004]
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 [DOI:10.1007/s00704-012-0808-6http://dx.doi.org/10.1007/s00704-012-0808-6]
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 [DOI:10.1002/joc.2227http://dx.doi.org/10.1002/joc.2227]
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 [DOI:10.1016/j.envsoft.2016.06.021http://dx.doi.org/10.1016/j.envsoft.2016.06.021]
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 [DOI:10.1109/LGRS.2018.2886606http://dx.doi.org/10.1109/LGRS.2018.2886606]
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
胡德勇, 张亚妮, 刘曼晴, 于琛, 曹诗颂, 狄宇飞. 2021. 顾及城区形态特征的太阳辐射传输模型及其遥感应用初探. 遥感学报) [DOI: 10.11834/jrs.20209199http://dx.doi.org/10.11834/jrs.20209199]
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 [DOI: 10.1109/TGRS.2016.2587695http://dx.doi.org/10.1109/TGRS.2016.2587695]
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 [DOI: 10.1109/ACCESS.2020.3040780http://dx.doi.org/10.1109/ACCESS.2020.3040780]
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
连君, 孙浩, 林起楠, 曹雪佳, 李翀, 黄华国. 2017. 天然林林下地表热辐射方向性观测. 遥感学报, 21(3): 365-374 [DOI: 10.11834/jrs.20176097http://dx.doi.org/10.11834/jrs.20176097]
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 [DOI: 10.1109/IGARSS.2019.8897963http://dx.doi.org/10.1109/IGARSS.2019.8897963]
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
柳钦火, 曹彪, 曾也鲁, 李静, 杜永明, 闻建光, 范渭亮, 赵静, 杨乐. 2016. 植被遥感辐射传输建模中的异质性研究进展.遥感学报, 20(5): 933-945 [DOI:10.11834/jrs.20166280http://dx.doi.org/10.11834/jrs.20166280]
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
李召良, 段四波, 唐伯惠, 吴骅, 任华忠, 阎广建, 唐荣林, 冷佩. 2016. 热红外地表温度遥感反演方法研究进展. 遥感学报, 20(5): 899-92 [DOI:10.11834/jrs.20166192http://dx.doi.org/10.11834/jrs.20166192]
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 [DOI:10.1016/j.enbuild.2018.07.064http://dx.doi.org/10.1016/j.enbuild.2018.07.064]
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 [DOI:10.3390/rs70608019http://dx.doi.org/10.3390/rs70608019]
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 [DOI: 10.1016/j.rse.2019.111524http://dx.doi.org/10.1016/j.rse.2019.111524]
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 [DOI:10.1016/j.uclim.2015.11.004http://dx.doi.org/10.1016/j.uclim.2015.11.004]
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 [DOI:10.1016/j.atmosenv.2015.11.038http://dx.doi.org/10.1016/j.atmosenv.2015.11.038]
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
潘玥, 廖明伟, 廖明,韩飞,陈竹安.2017. 鄱阳湖核心流域地区城市地表形态的异质性对地表温度的影响研究. 生态环境学报, 26(008):1358-1367 [DOI: 10.16258/j.cnki.1674-5906.2017.08.011http://dx.doi.org/10.16258/j.cnki.1674-5906.2017.08.011]
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
彭硕，唐伯惠，李召良,吴骅,唐荣林.2016.热红外地表方向性辐射温度与半球辐射温度关系研究[J].地球信息科学学报 ,18(1):106-116 [DOI:10.3724/SP.J.1047.2016.00106http://dx.doi.org/10.3724/SP.J.1047.2016.00106]
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,
suppl (覃志豪,李文娟,徐斌,张万昌. 2004. 利用Landsat TM6反演地表温度所需地表辐射率参数的估计方法, 海洋科学进展, 22, 增刊)
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 [DOI:10.1016/j.buildenv.2014.03.014http://dx.doi.org/10.1016/j.buildenv.2014.03.014]
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 [DOI:10.1073/pnas.1311433110http://dx.doi.org/10.1073/pnas.1311433110]
Wang Q and Li P. 2013. Canopy vertical heterogeneity plays a critical role in reflectance simulation. Agricultural and Forest Meteorology, 169, 111-121 [DOI:10.1016/j.agrformet.2012.10.004http://dx.doi.org/10.1016/j.agrformet.2012.10.004]
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 [DOI:10.1089/ees.2013.0430http://dx.doi.org/10.1089/ees.2013.0430]
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 [DOI:10.1016/j.rse.2019.01.005http://dx.doi.org/10.1016/j.rse.2019.01.005]
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 [DOI:10.1016/j.rse.2015.10.026http://dx.doi.org/10.1016/j.rse.2015.10.026]
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 [DOI:10.1016/j.isprsjprs.2015.04.006http://dx.doi.org/10.1016/j.isprsjprs.2015.04.006]
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 [DOI:10.1016/j.isprsjprs.2015.09.001http://dx.doi.org/10.1016/j.isprsjprs.2015.09.001]
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 [DOI:10.1016/j.isprsjprs.2016.09.007http://dx.doi.org/10.1016/j.isprsjprs.2016.09.007]
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 [DOI:10.1016/j.rse.2019.111540http://dx.doi.org/10.1016/j.rse.2019.111540]
Zakšek K, Oštir K and Kokalj Ž. 2011. Sky-view factor as a relief visualization technique. Remote sensing, 3(2): 398-415 [DOI: 10.3390/rs3020398http://dx.doi.org/10.3390/rs3020398]
Zhang H L. 2015. Study of urban thermal environment based on remote sensing and urban spatial morphology. Nanjing University of Information Science & Technology
张海龙.2015.基于遥感和城市空间形态学方法的城市热环境研究.南京信息工程大学
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).
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.
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 [DOI:10.1080/01431161.2011.617396http://dx.doi.org/10.1080/01431161.2011.617396]
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[DOI:10.1002/joc.3660http://dx.doi.org/10.1002/joc.3660]
相关作者
相关机构
京公网安备11010802024621