Modeling the effective emissivity of the urban canopy using sky view factor

Abstract Surface emissivity is a critical parameter for studying city-, meso-, and micro-scale climate and energy balance. The emissivity of complex surfaces e.g. a forest or an urban canopy is an effective surface property since it depends on both surface materials and geometry. This study presents a novel methodology for estimating effective emissivity using sky view factor retrieved from airborne Lidar data, building GIS data, and land use and land cover classification data. First, a high correlation between the effective emissivity retrieved from ASTER TIR bands 10–14 and the sky view factor was observed (r2 = 0.93, 0.99, 0.99, 0.97, 0.97). When the sky view factor decreases, the effective emissivity tends to increase, which is mainly due to multiple scattering (cavity effect), thus increases the effective emissivity. A simplified model which assumes that reflection and scattering only occurs within a single pixel was developed. Results showed that the correlations between the modeled and the spectral (band) emissivity retrieved from the ASTER multispectral TIR data (five spectral bands) are high (r2 = 0.93, 0.99, 0.98, 0.93, 0.97), and with low RMSE (0.019, 0.016, 0.012, 0.003 and 0.004 from band 10–14 respectively). The emissivity derived from this simplified model, however, tends to be overestimated in band 10–12. Thus, a refined urban emissivity model based on sky view factor (UEM-SVF) which considers the scattering and reflection from adjacent pixels was developed in this study. Results show a good agreement with ASTER spectral (band) emissivity: r2 = 0.90, 0.98, 0.96, 0.94 and 0.96, and very low RMSE (0.006, 0.003, 0.004, 0.002 and 0.004). This study illustrates that the UEM-SVF can be useful for estimation of land surface emissivity of complex surfaces, and can further be used for accurate land surface temperature retrieval.

[1]  S. Hook,et al.  The ASTER spectral library version 2.0 , 2009 .

[2]  Katsuhito Yamaguchi,et al.  The Influence Of Urban Canopy Configuration On Urban Albedo , 2001 .

[3]  Li Dong,et al.  Assessing the effects of landscape design parameters on intra-urban air temperature variability: The case of Beijing, China , 2014 .

[4]  José A. Sobrino,et al.  Land surface temperature retrieval from LANDSAT TM 5 , 2004 .

[5]  C. Ren,et al.  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 , 2012 .

[6]  José A. Sobrino,et al.  Land surface emissivity retrieval from airborne sensor over urban areas , 2012 .

[7]  José A. Sobrino,et al.  Error sources on the land surface temperature retrieved from thermal infrared single channel remote sensing data , 2006 .

[8]  Shuichi Rokugawa,et al.  A temperature and emissivity separation algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images , 1998, IEEE Trans. Geosci. Remote. Sens..

[9]  Alan R. Gillespie,et al.  Performance of a thermal-infrared radiosity and heat-diffusion model for estimating sub-pixel radiant temperatures over the course of a day , 2012 .

[10]  S. Grimmond Urbanization and global environmental change: local effects of urban warming , 2007 .

[11]  Exams Tuk PHOTOGRAMMETRY & REMOTE SENSING , 2016 .

[12]  Aya Hagishima,et al.  A Simple Energy Balance Model for Regular Building Arrays , 2005 .

[13]  Antonio J. Plaza,et al.  Land Surface Emissivity Retrieval From Different VNIR and TIR Sensors , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Changshan Wu,et al.  Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery , 2004 .

[15]  Martin J. Wooster,et al.  Derivation of an urban materials spectral library through emittance and reflectance spectroscopy , 2014 .

[16]  Klemen Zaksek,et al.  Sky-View Factor as a Relief Visualization Technique , 2011, Remote. Sens..

[17]  H. Baltes I On the Validity of Kirchhoff'S Law of Heat Radiation for a Body in a Nonequilibrium Environment* , 1976 .

[18]  José A. Sobrino,et al.  Satellite-derived land surface temperature: Current status and perspectives , 2013 .

[19]  Juan C. Jiménez-Muñoz,et al.  Emissivity mapping over urban areas using a classification-based approach: Application to the Dual-use European Security IR Experiment (DESIREX) , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[20]  C. Ratti,et al.  COMPARISON OF METHODOLOGIES FOR COMPUTING SKY VIEW FACTOR IN URBAN ENVIRONMENTS , 2001 .

[21]  J. Nichol An Emissivity Modulation Method for Spatial Enhancement of Thermal Satellite Images in Urban Heat Island Analysis , 2009 .

[22]  Marie K. Svensson,et al.  Sky view factor analysis – implications for urban air temperature differences , 2004 .

[23]  B. Holmer,et al.  Cooling rates, sky view factors and the development of intra‐urban air temperature differences , 2007 .

[24]  Timothy R. Oke,et al.  Parameterization of Net All-Wave Radiation for Urban Areas , 2003 .

[25]  Wout Verhoef,et al.  A practical algorithm to infer soil and foliage component temperatures from bi-angular ATSR-2 data , 2003 .

[26]  Michael Rast,et al.  Estimation of soil and vegetation temperatures with multiangular thermal infrared observations: IMGRASS, HEIFE, and SGP 1997 experiments , 2001 .

[27]  Fran Li,et al.  Surface temperature and emissivity at various scales: Definition, measurement and related problems , 1995 .

[28]  Manabu Kanda,et al.  A Simple Theoretical Radiation Scheme for Regular Building Arrays , 2005 .

[29]  Pablo J. Zarco-Tejada,et al.  Land surface temperature derived from airborne hyperspectral scanner thermal infrared data , 2006 .

[30]  B. Dousseta,et al.  Satellite multi-sensor data analysis of urban surface temperatures and landcover , 2003 .