Modelling the diurnal variations of urban heat islands with multi-source satellite data

Zhou, Ji; Chen, Yunhao; Xu, Zaifu; Zhan, Wenfeng

doi:10.1080/01431161.2013.821576

Cited by 85 publications

(51 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the diurnal change in evapotranspiration, a diurnal temperature cycle model is helpful. This type of model can be applied to the LSTs acquired by a satellite sensor with multiple observations in the same area in a diurnal cycle, such as the thermal sensors on board the geostationary satellites [47]. However, the current diurnal cycle models have limited abilities in parameterizing the influences of varying meteorological conditions, e.g., wind and clouds.…”

Section: Discussionmentioning

confidence: 99%

Validation and Performance Evaluations of Methods for Estimating Land Surface Temperatures from ASTER Data in the Middle Reach of the Heihe River Basin, Northwest China

Zhou

Liu

et al. 2015

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Validation and performance evaluations are beneficial for developing methods that estimate the remotely sensed land surface temperature (LST). However, such evaluations for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data are rare. By selecting the middle reach of the Heihe River basin (HRB), China, as the study area, the atmospheric correction (AC), mono-window (MW), single-channel (SC), and split-window (SW) methods were evaluated based on in situ measured LSTs. Results demonstrate that the influences of surface heterogeneity on the validation are significant in the study area. For the AC, MW, and SC methods, the LSTs estimated from channel 13 are more accurate than those from channel 14 in general cases. When the in situ measured atmospheric profiles are available, the AC method has the highest accuracy, with a root-mean squared error (RMSE) of about 1.4-1.5 K at the homogenous oasis sites. In actual application without sufficient in situ measured inputs, the MW method is highly accurate; the RMSE is around 1.5-1.6 K. The SC method systematically overestimates LSTs and it is sensitive to error in the water vapor content. The two SW methods are simple to use but their performances are limited by accuracies, revealed by the simulation dataset. Therefore, when the in situ atmospheric profiles are available, the AC method is recommended to generate reliable ASTER LSTs for modeling the eco-hydrological processes in the middle reach of OPEN ACCESS Remote Sens. 2015, 7 7127 the HRB. When sufficient in situ measured inputs are not available, the MW method can be used instead.

show abstract

Section: Discussionmentioning

confidence: 99%

Validation and Performance Evaluations of Methods for Estimating Land Surface Temperatures from ASTER Data in the Middle Reach of the Heihe River Basin, Northwest China

Zhou

Liu

et al. 2015

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…These studies, however, made no effort to draw quantitative inferences between changes in land cover types and UHI differences. Moreover, although previous researchers have identified that the seasonal/diurnal variations have a significant effect in measuring UHI [30][31][32], this critical factor has been generally overlooked within existing longitudinal studies. Therefore, studies examining a quantitative causal link between land cover patterns and the UHI effect at the surface layer are relatively scarce.…”

Section: Links Between Land Cover Type and The Uhi Effectmentioning

confidence: 99%

Correlation or Causality between Land Cover Patterns and the Urban Heat Island Effect? Evidence from Brisbane, Australia

2016

View full text Add to dashboard Cite

Numerous studies have identified associations between the surface urban heat island (SUHI) effect (i.e., SUHI, hereinafter is referred to as UHI) and urban growth, particularly changes in land cover patterns. This research questions their causal links to answer a key policy question: If cities restrict urban expansion and encourage people to live within existing urban areas, will that help in controlling UHI? The question has been answered by estimating four models using data from Brisbane, Australia: Model 1-cross-sectional ordinary least square (OLS) regression-to examine the association between the UHI effect and land cover patterns in 2013; Model 2-cross-sectional geographically weighted regression (GWR)-to examine whether the outputs generated from Model 1 possess significant spatial variations; Model 3-longitudinal OLS-to examine whether changes in land cover patterns led to changes in UHI effects between 2004 and 2013; and Model 4-longitudinal GWR-to examine whether the outputs generated from Model 3 vary significantly over space. All estimations were controlled for potential confounding effects (e.g., population, employment and dwelling densities). Results from the cross-sectional OLS and GWR models were consistent with previous findings and showed that porosity is negatively associated with the UHI effect in 2013. In contrast, population density has a positive association. Results from the longitudinal OLS and GWR models confirm their causal linkages and showed that an increase in porosity level reduced the UHI effect, whereas an increase in population density increased the UHI effect. The findings suggest that even a containment of population growth within existing urban areas will lead to the UHI effect. However, this can be significantly minimized through proper land use planning, by creating a balance between urban and non-urban uses of existing urban areas.

show abstract

“…A robust and already established method is diurnal temperature cycle (DTC) modelling [30][31][32][33] which allows interpolation of missing values due to single cloudy acquisitions. For instance in [30] a Levenberg-Marquardt minimization scheme was utilized to fit a DTC model to the time series of cloud-screened brightness temperatures and describe the thermal behavior of the land surface by five determined model parameters per day, which were found to be related to relevant physical properties of the land surface, such as NDVI and thermal inertia.…”

Section: Introductionmentioning

confidence: 99%

A New Global Climatology of Annual Land Surface Temperature

Bechtel

2015

Remote Sensing

109

View full text Add to dashboard Cite

Land surface temperature (LST) is an important parameter in various fields including hydrology, climatology, and geophysics. Its derivation by thermal infrared remote sensing has long tradition but despite substantial progress there remain limited data availability and challenges like emissivity estimation, atmospheric correction, and cloud contamination. The annual temperature cycle (ATC) is a promising approach to ease some of them. The basic idea to fit a model to the ATC and derive annual cycle parameters (ACP) has been proposed before but so far not been tested on larger scale. In this study, a new global climatology of annual LST based on daily 1 km MODIS/Terra observations was processed and evaluated. The derived global parameters were robust and free of missing data due to clouds. They allow estimating LST patterns under largely cloud-free conditions at different scales for every day of year and further deliver a measure for its accuracy respectively variability. The parameters generally showed low redundancy and mostly reflected real surface conditions. Important influencing factors included climate, land cover, vegetation phenology, anthropogenic effects, and geology which enable numerous potential applications. The datasets will be available at the CliSAP Integrated Climate Data Center pending additional processing.

show abstract

Modelling the diurnal variations of urban heat islands with multi-source satellite data

Cited by 85 publications

References 44 publications

Validation and Performance Evaluations of Methods for Estimating Land Surface Temperatures from ASTER Data in the Middle Reach of the Heihe River Basin, Northwest China

Validation and Performance Evaluations of Methods for Estimating Land Surface Temperatures from ASTER Data in the Middle Reach of the Heihe River Basin, Northwest China

Correlation or Causality between Land Cover Patterns and the Urban Heat Island Effect? Evidence from Brisbane, Australia

A New Global Climatology of Annual Land Surface Temperature

Contact Info

Product

Resources

About