浏览全部资源
扫码关注微信
纸质出版日期： 2021-10-07 ，
扫 描 看 全 文
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.遥感学报，25（10）： 2116-2126
Hu D Y，Zhang Y N，Liu M Q，Yu C，Cao S S and Di Y F. 2021. Morphological characteristics and remote sensing application. National Remote Sensing Bulletin， 25（10）：2116-2126
准确量化地表太阳辐射量是反射率遥感反演之基础，开展城区表面辐射研究具有重要意义。本文选择天空视域因子（
V
）表征城区下垫面形态特征，并区分太阳直接辐射、天空漫辐射以及环境辐射的不同影响，构建了城区表面太阳辐射模型USSR（Urban Surface Solar Radiation）；然后，以Landsat 8可见光和近红外波段遥感数据为例，分析了USSR模型对于量化城区表面太阳辐射的应用前景。研究结论为：（1）USSR模型以天空视域因子为核心要素，清楚量化了城区表面各辐射分量，有效模拟了城区表面太阳辐射传输过程，能够较好地表达城区下垫面形态结构对入射辐射的影响；（2）将USSR模型应用于Landsat 8遥感数据可见光和近红外波段时，基于USSR模型估算得到的城区表面太阳辐射，与不考虑下垫面形态特征影响相比，前者可以较好地表达城区下垫面对入射辐射的“截留”作用；（3）USSR模型估算结果与TEB模型相比，二者具有较高的相关性，间接验证了USSR模型的可用性；（4）对USSR模型的
V
和下垫面反射率两变量进行敏感性分析，显示随着
V
值的增加，结果呈递增趋势；通常情况下的地表反射参数
P
设置值，相对于参数
V
的影响要弱，呈现不敏感性。本文提出的USSR模型可订正城区表面太阳辐射值，从而拓展城市遥感的应用深度和广度。
Accurate quantification of surface solar radiation is the basis of remote sensing inversion of reflectivity
and a research on urban surface radiation is important. The sky view factor is selected to characterize the morphological characteristics of the underlying surface of the urban area
and the Urban Surface Solar Radiation Model (USSR) is constructed. This model has distinguished the different effects of direct solar radiation
diffuse sky radiation
and environmental radiation on ground objects. The remote sensing data of Landsat 8 visible and near-infrared bands are considered the examples
and the application prospect of the USSR model for the quantification of urban surface solar radiation is analyzed. The research conclusions are as follows: (1) The USSR clearly quantifies the radiation components of urban surface based on the sky view factor (
V
)
which can effectively solve the simulation of solar radiation transfer process of urban surface and better express the influence of the morphological structure of urban underlying surface on the incident radiation. (2) When USSR is applied to the estimates of solar radiation in the visible and near-infrared bands of Landsat 8 remote sensing data
compared with the estimated values without considering the influence of the morphological characteristics of the underlying surface
the urban surface solar radiation values estimated based on the USSR model can better express the “interception” effect of urban underlying surfaces on the incident radiation. (3) Compared with the TEB model
the USSR model estimates have high correlation
which indirectly verifies the availability of the USSR model. (4) According to the sensitivity analysis of
V
and reflectivity of the underneath surface
the results show an increasing trend as the
V
value of the underlying surface increased. In general
the parameter setting value is weak and insensitive compared with the parameter
V
. The proposed USSR model can amend the estimation results of urban surface solar radiation and improve the reliability of estimation results
thereby expanding the application depth and breadth of urban remote sensing.
太阳辐射城区表面形态特征天空视域因子下垫面反射率
solar radiationurban surfacemorphological characteristicssky view factorunderlying surface reflectance
Achour-Younsi S and Kharrat F. 2016. Outdoor thermal comfort: impact of the geometry of an urban street canyon in a Mediterranean subtropical climate–case study Tunis, Tunisia. Procedia-Social and Behavioral Sciences, 216: 689-700 [DOI: 10.1016/j.sbspro.2015.12.062http://dx.doi.org/10.1016/j.sbspro.2015.12.062]
Barlow J F. 2014. Progress in observing and modelling the urban boundary layer. Urban Climate, 10: 216-240 [DOI: 10.1016/j.uclim.2014.03.011http://dx.doi.org/10.1016/j.uclim.2014.03.011]
Cantelli A, Monti P and Leuzzi G. 2015. Numerical study of the urban geometrical representation impact in a surface energy budget model. Environmental Fluid Mechanics, 15(2): 251-273 [DOI: 10.1007/s10652-013-9309-0http://dx.doi.org/10.1007/s10652-013-9309-0]
Chen L, Ng E, An X P, Ren C, Lee M, Wang U and He Z J. 2012. Sky view factor analysis of street canyons and its implications for daytime intra-urban air temperature differentials in high-rise, high-density urban areas of Hong Kong: a GIS-based simulation approach. International Journal of Climatology, 32(1): 121-136 [DOI: 10.1002/joc.2243http://dx.doi.org/10.1002/joc.2243]
Chen L, Yu B L, Yang F and Mayer H. 2016. Intra-urban differences of mean radiant temperature in different urban settings in Shanghai and implications for heat stress under heat waves: a GIS-based approach. Energy and Buildings, 130: 829-842 [DOI: 10.1016/j.enbuild.2016.09.014http://dx.doi.org/10.1016/j.enbuild.2016.09.014]
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]
Groleau D and Mestayer P G. 2013. Urban morphology influence on urban albedo: a revisit with the SOLENE model. Boundary-Layer Meteorology, 147(2): 301-327 [DOI: 10.1007/s10546-012-9786-6http://dx.doi.org/10.1007/s10546-012-9786-6]
Guo G H, Zhou X Q, Wu Z F, Xiao R B and Chen Y B. 2016. Characterizing the impact of urban morphology heterogeneity on land surface temperature in Guangzhou, China. Environmental Modelling and Software, 84: 427-439. [DOI: 10.1016/j.envsoft.2016.06.021http://dx.doi.org/10.1016/j.envsoft.2016.06.021]
Kondo A, Ueno M, Kaga A and Yamaguchi K. 2001. The influence of urban canopy configuration on urban albedo. Boundary-Layer Meteorology, 100(2): 225-242 [DOI: 10.1023/A:1019243326464http://dx.doi.org/10.1023/A:1019243326464]
Liu Q H, Cao B, Zeng Y L, Li J, Du Y M, Wen J G, Fan W L, 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]
Loridan T and Grimmond C S B. 2012. Multi‐site evaluation of an urban land‐surface model: intra‐urban heterogeneity, seasonality and parameter complexity requirements. Quarterly Journal of the Royal Meteorological Society, 138(665): 1094-1113. [DOI: 10.1002/qj.963http://dx.doi.org/10.1002/qj.963]
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]
Martinelli L and Matzarakis A. 2017. Influence of height/width proportions on the thermal comfort of courtyard typology for Italian climate zones. Sustainable Cities and Society, 29: 97-106 [DOI: 10.1016/j.scs.2016.12.004http://dx.doi.org/10.1016/j.scs.2016.12.004]
Masson V. 2000. A physically-based scheme for the urban energy budget in atmospheric models. Boundary-Layer Meteorology, 94(3): 357-397 [DOI: 10.1023/A:1002463829265http://dx.doi.org/10.1023/A:1002463829265]
Masson V. 2006. Urban surface modeling and the meso-scale impact of cities. Theoretical and Applied Climatology, 84(1/3): 35-45 [DOI: 10.1007/s00704-005-0142-3http://dx.doi.org/10.1007/s00704-005-0142-3]
Meier F, Scherer D and Richters J. 2010. Determination of persistence effects in spatio-temporal patterns of upward long-wave radiation flux density from an urban courtyard by means of Time-Sequential Thermography. Remote Sensing of Environment, 114(1): 21-34 [DOI: 10.1016/j.rse.2009.08.002http://dx.doi.org/10.1016/j.rse.2009.08.002]
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]
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]
Pigeon G, Zibouche K, Bueno B, Le Bras J and Masson V. 2014. Improving the capabilities of the Town Energy Balance model with up-to-date building energy simulation algorithms: an application to a set of representative buildings in Paris. Energy and Buildings, 76: 1-14 [DOI: 10.1016/j.enbuild.2013.10.038http://dx.doi.org/10.1016/j.enbuild.2013.10.038]
Richter R and Schläpfer D. 2002. Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: atmospheric/topographic correction. International Journal of Remote Sensing, 23(13): 2631-2649 [DOI: 10.1080/01431160110115834http://dx.doi.org/10.1080/01431160110115834]
Sugawara H and Takamura T. 2006. Longwave radiation flux from an urban canopy: evaluation via measurements of directional radiometric temperature. Remote Sensing of Environment, 104(2): 226-237 [DOI: 10.1016/j.rse.2006.01.024http://dx.doi.org/10.1016/j.rse.2006.01.024]
Wang J D, Zhang L X, Liu Q H, Zhang B and Yin Q. 2009. Knowledge Base of Typical Ground Objects Spectrum in China. Beijing: Science Press
王锦地, 张立新, 柳钦火, 张兵, 尹球. 2009. 中国典型地物波谱知识库. 北京: 科学出版社
Wang K C and Dickinson R E. 2013. Contribution of solar radiation to decadal temperature variability over land. Proceedings of the National Academy of Sciences of the United States of America, 110(37): 14877-14882 [DOI: 10.1073/pnas.1311433110http://dx.doi.org/10.1073/pnas.1311433110]
Wang K C and Liang S L. 2009. Estimation of daytime net radiation from shortwave radiation measurements and meteorological observations. Journal of Applied Meteorology and Climatology, 48(3): 634-643 [DOI: 10.1175/2008JAMC1959.1http://dx.doi.org/10.1175/2008JAMC1959.1]
Wang Q and Li P H. 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 P 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]
Wen J G, Liu Q H, Xiao Q, Liu Q and Li X W. 2008. Modeling the land surface reflectance for optical remote sensing data in rugged terrain. Science in China Series D: Earth Sciences, 51(8): 1169-1178
闻建光, 柳钦火, 肖青, 刘强, 李小文. 2008. 复杂山区光学遥感反射率计算模型. 中国科学 D辑: 地球科学, 38(11): 1419-1427 [DOI: 10.1007/s11430-008-0085-5http://dx.doi.org/10.1007/s11430-008-0085-5]
Wen J G, Liu Q, Tang Y, Dou B C, You D Q, Xiao Q, Liu Q H and Li X W. 2015. Modeling land surface reflectance coupled BRDF for HJ-1/CCD data of rugged terrain in Heihe river basin, China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(4): 1506-1518 [DOI: 10.1109/JSTARS.2015.2416254http://dx.doi.org/10.1109/JSTARS.2015.2416254]
Wen J G, Liu Q, Xiao Q, Liu Q H, You D Q, Hao D L, Wu S B and Lin X W. 2018. Characterizing land surface anisotropic reflectance over rugged terrain: a review of concepts and recent developments. Remote Sensing, 10(3): 370 [DOI: 10.3390/rs10030370http://dx.doi.org/10.3390/rs10030370]
Wu S B, Wen J G, Gastellu-Etchegorry J, Liu Q H, You D Q, Xiao Q, Hao D L, Lin X W and Yin T G. 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]
Yang F, Qian F and Lau S S Y. 2013. Urban form and density as indicators for summertime outdoor ventilation potential: a case study on high-rise housing in Shanghai. Building and Environment, 70: 122-137 [DOI: 10.1016/j.buildenv.2013.08.019http://dx.doi.org/10.1016/j.buildenv.2013.08.019]
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]
Zeng L Y, Lu J, Li W Y and Li Y C. 2018. A fast approach for large-scale sky view factor estimation using street view images. Building and Environment, 135: 74-84 [DOI: 10.1016/j.buildenv.2018.03.009http://dx.doi.org/10.1016/j.buildenv.2018.03.009]
Zhang H L, Zhu S Y, Gao Y and Zhang G X. 2016. The relationship between urban spatial morphology parameters and urban heat island intensity under fine weather condition. Journal of Applied Meteorological Science, 27(2): 249-256
张海龙, 祝善友, 高玚, 张桂欣. 2016. 城市空间形态学参数与晴好天气下热岛强度关系. 应用气象学报, 27(2): 249-256 [DOI: 10.11898/1001-7313.20160213http://dx.doi.org/10.11898/1001-7313.20160213]
Zhang X T, Liang S L, Wild M and Jiang B. 2015. Analysis of surface incident shortwave radiation from four satellite products. Remote Sensing of Environment, 165: 186-202 [DOI: 10.1016/j.rse.2015.05.015http://dx.doi.org/10.1016/j.rse.2015.05.015]
Zhu S Y, Guan H D, 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]
相关文章
相关作者
相关机构
京公网安备11010802024621