TY - JOUR
T1 - A physical model-based method for retrieving urban land surface temperatures under cloudy conditions
AU - Fu, Peng
AU - Xie, Yanhua
AU - Weng, Qihao
AU - Myint, Soe
AU - Meacham-Hensold, Katherine
AU - Bernacchi, Carl
N1 - Publisher Copyright:
© 2019 Elsevier Inc.
PY - 2019/9/1
Y1 - 2019/9/1
N2 - 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 ~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 ~0.75 and ~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.
AB - 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 ~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 ~0.75 and ~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.
KW - Cloud contamination
KW - Land surface temperature
KW - MODIS
KW - Thermal infrared
KW - Urban canopy model
KW - WRF
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U2 - 10.1016/j.rse.2019.05.010
DO - 10.1016/j.rse.2019.05.010
M3 - Article
AN - SCOPUS:85065906777
SN - 0034-4257
VL - 230
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
M1 - 111191
ER -