Downscaling Land Surface Temperature in an Urban Area: A Case Study for Hamburg, Germany

被引:132
作者
Bechtel, Benjamin [1 ]
Zaksek, Klemen [1 ,2 ]
Hoshyaripour, Gholamali [1 ]
机构
[1] Univ Hamburg, Inst Geophys, D-20146 Hamburg, Germany
[2] Ctr Excellence Space Si, SI-1000 Ljubljana, Slovenia
来源
REMOTE SENSING | 2012年 / 4卷 / 10期
关键词
land surface temperature; downscaling; urban heat island; Hamburg; HEAT-ISLAND ANALYSIS; VEGETATION INDEX; AIR-TEMPERATURE; TIME-SERIES; TM DATA; SATELLITE; CLASSIFICATION; IMPACTS; URBANIZATION; SIMULATION;
D O I
10.3390/rs4103184
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Monitoring of (surface) urban heat islands (UHI) is possible through satellite remote sensing of the land surface temperature (LST). Previous UHI studies are based on medium and high spatial resolution images, which are in the best-case scenario available about four times per day. This is not adequate for monitoring diurnal UHI development. High temporal resolution LST data (a few measurements per hour) over a whole city can be acquired by instruments onboard geostationary satellites. In northern Germany, geostationary LST data are available in pixels sized 3,300 by 6,700 m. For UHI monitoring, this resolution is too coarse, it should be comparable instead to the width of a building block: usually not more than 100 m. Thus, an LST downscaling is proposed that enhances the spatial resolution by a factor of about 2,000, which is much higher than in any previous study. The case study presented here (Hamburg, Germany) yields promising results. The latter, available every 15 min in 100 m spatial resolution, showed a high explained variance (R-2: 0.71) and a relatively low root mean square error (RMSE: 2.2 K). For lower resolutions the downscaling scheme performs even better (R-2: 0.80, RMSE: 1.8 K for 500 m; R-2: 0.82, RMSE: 1.6 K for 1,000 m).
引用
收藏
页码:3184 / 3200
页数:17
相关论文
共 57 条
[1]   A vegetation index based technique for spatial sharpening of thermal imagery [J].
Agam, Nurit ;
Kustas, William P. ;
Anderson, Martha C. ;
Li, Fuqin ;
Neale, Christopher M. U. .
REMOTE SENSING OF ENVIRONMENT, 2007, 107 (04) :545-558
[2]   Classification of Local Climate Zones Based on Multiple Earth Observation Data [J].
Bechtel, Benjamin ;
Daneke, Christian .
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2012, 5 (04) :1191-1202
[4]   Towards an urban roughness parameterisation using interferometric SAR data taking the Metropolitan Region of Hamburg as an example [J].
Bechtel, Benjamin ;
Langkamp, Thomas ;
Ament, Felix ;
Boehner, Juergen ;
Daneke, Chrstian ;
Guenzkofer, Rene ;
Leitl, Bernd ;
Ossenbruegge, Juergen ;
Ringeler, Andre .
METEOROLOGISCHE ZEITSCHRIFT, 2011, 20 (01) :29-37
[5]   Classification of hyperspectral data from urban areas based on extended morphological profiles [J].
Benediktsson, JA ;
Palmason, JA ;
Sveinsson, JR .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2005, 43 (03) :480-491
[6]  
Chen S., SPARSE MODELLING USI
[7]   Thermal remote sensing of near surface environmental variables: Application over the Oklahoma Mesonet [J].
Czajkowski, KP ;
Goward, SN ;
Stadler, SJ ;
Walz, A .
PROFESSIONAL GEOGRAPHER, 2000, 52 (02) :345-357
[8]   Downscaling of METEOSAT SEVIRI 0.6 and 0.8 μm channel radiances utilizing the high-resolution visible channel [J].
Deneke, H. M. ;
Roebeling, R. A. .
ATMOSPHERIC CHEMISTRY AND PHYSICS, 2010, 10 (20) :9761-9772
[9]  
Denis B, 2002, CLIM DYNAM, V18, P627, DOI [10.1007/s00382-001-0201-0, 10.1007/s00382-001-0210-0]
[10]   High-resolution urban thermal sharpener (HUTS) [J].
Dominguez, Anthony ;
Kleissl, Jan ;
Luvall, Jeffrey C. ;
Rickman, Douglas L. .
REMOTE SENSING OF ENVIRONMENT, 2011, 115 (07) :1772-1780