A physical model-based method for retrieving urban land surface temperatures under cloudy conditions

REMOTE SENSING OF ENVIRONMENT

卷:230

Article

Fu, Peng¹  Xie, Yanhua²  Weng, Qihao³  Myint, Soe⁴  Meacham-Hensold, Katherine¹  Bernacchi, Carl^1,5 

[1] Univ Illinois, Carl R Woese Inst Genom Biol, Dept Plant Biol, Urbana, IL 61820 USA

[2] Univ Wisconsin, Nelson Inst Ctr Sustainabil & Global Environm SAG, Madison, WI 53726 USA

[3] Indiana State Univ, Dept Earth & Environm Syst, Ctr Urban & Environm Change, Terre Haute, IN 47809 USA

[4] Arizona State Univ, Sch Geog Sci & Urban Planning, Tempe, AZ 85287 USA

[5] ARS, USDA, Global Change & Photosynthesis Res Unit, Urbana, IL 61801 USA

关键词: Land surface temperature;    MODIS;    Cloud contamination;    Urban canopy model;    WRF;    Thermal infrared;   

DOI  :  10.1016/j.rse.2019.05.010

来源: Elsevier

【 摘 要 】

Satellite-derived land surface temperature (LST), due to its synoptic coverage, has been widely used for understanding surface energy and carbon fluxes at local, regional, and global scales. Despite great achievements in developing practical algorithms to estimate LSTs from thermal infrared (TIR) data, the retrieval of LSTs for overcast conditions has received much less attention. Existing techniques, such as passive microwave (PMW) approaches, surface energy balance (SEB) models, and reconstruction algorithms relying on spatial/temporal information, for estimating LSTs under cloudy skies cannot fulfill the need for a spatially and temporally consistent LST dataset. Inspired by recent advancements in physically-based urban canopy models (UCMs), this study synergistically used the coupled Weather Research and Forecasting Model (WRF)/UCM system and the random forest (RF) regression technique (hereafter named as WRFF) to effectively estimate LSTs under cloudy conditions. Taking the Baltimore-Washington metropolitan region as a test site, the WRF/UCM simulations (LSTs) were performed from April 28 to May 20, 2011. The MODIS LST images of the same period were used to evaluate the effectiveness of the method introduced in this study. LSTs under cloudy conditions for a partially cloudy image were retrieved using the RF model calibrated by clear-sky pixels from the same image. For a fully cloud-contaminated image, clear-sky pixels from its temporally adjacent images were used to calibrate the RF model to estimate LSTs under cloudy conditions. Results showed that the modeling system could well capture diurnal air temperature variations but tended to underestimate temperature values. The correlation coefficient between MODIS LSTs and simulated LSTs exhibited a wide range from 0.5 to 0.9 with the RMSE (root mean square error) value varying from 1.0 to 9.0 K across different land covers. The utilization of the RF regression technique for estimating LSTs under cloudy conditions from a partially cloud-contaminated LST image greatly reduced the RMSE to similar to 1.8 K with an improved correlation coefficient. For fully cloud-contaminated LST images, LSTs were estimated with the correlation coefficient and RMSE values of similar to 0.75 and similar to 2.0 K, respectively. Overall, the WRFF method has potential to generate a consistent and reliable LST dataset for various applications such as quantification of urban heat island intensity and vulnerability analysis of humans to vector-borne diseases. Further research should be made to examine the impact of the temporal distance between the target image and its temporally adjacent images on the performance of the proposed method.

【 授权许可】

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