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

Peng Fu, Yanhua Xie, Qihao Weng, Soe Myint, Katherine Meacham-Hensold, Carl Bernacchi

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Article number111191
JournalRemote Sensing of Environment
Volume230
DOIs
StatePublished - Sep 1 2019

Fingerprint

physical models
surface temperature
land surface
Mean square error
Temperature
moderate resolution imaging spectroradiometer
contaminated land
canopy
methodology
Interfacial energy
clear sky
surface energy
MODIS
weather
Pixels
pixel
vector-borne diseases
method
Energy balance
heat island

Keywords

  • Cloud contamination
  • Land surface temperature
  • MODIS
  • Thermal infrared
  • Urban canopy model
  • WRF

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences

Cite this

A physical model-based method for retrieving urban land surface temperatures under cloudy conditions. / Fu, Peng; Xie, Yanhua; Weng, Qihao; Myint, Soe; Meacham-Hensold, Katherine; Bernacchi, Carl.

In: Remote Sensing of Environment, Vol. 230, 111191, 01.09.2019.

Research output: Contribution to journalArticle

Fu, Peng ; Xie, Yanhua ; Weng, Qihao ; Myint, Soe ; Meacham-Hensold, Katherine ; Bernacchi, Carl. / A physical model-based method for retrieving urban land surface temperatures under cloudy conditions. In: Remote Sensing of Environment. 2019 ; Vol. 230.
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