Comparison of land surface and air temperatures for quantifying summer and winter urban heat island in a snow climate city

Yang, Chaobin; Yan, Fengqin; Zhang, Shuwen

doi:10.1016/j.jenvman.2020.110563

Cited by 70 publications

(77 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Yang et al found that the surface UHI intensity was considerably stronger than that of air temperature in summer (the surface UHI was 6.83 • C and air temperature UHI was 0.27 • C). Meanwhile, this research also considers that the relationships between land-surface temperature and air temperature were all statistically significant [38].…”

Section: Air Temperature Urban Heat Islandmentioning

confidence: 99%

“…The air temperature UHI is typically characterized by air temperature, while the surface temperature UHI is reflected by surface temperature [37]. The combination of air temperature and land-surface temperature would add value in exploring UHI effects [38]. Yang et al found that the surface UHI intensity was considerably stronger than that of air temperature in summer (the surface UHI was 6.83 • C and air temperature UHI was 0.27 • C).…”

Section: Air Temperature Urban Heat Islandmentioning

confidence: 99%

See 1 more Smart Citation

Transformation of Urban Surfaces and Heat Islands in Nanjing during 1984–2018

Zhang

Zhu

et al. 2020

Sustainability

View full text Add to dashboard Cite

One of the many consequences of urbanization is the expansion of cities into rural areas, which leads to the transformation of lands from natural surfaces to developed surfaces. It is widely considered an established fact that urbanization generally increases the heat island effect. The objective of this study is to understand the pattern of urban surface transformation in the city of Nanjing since 1980 and to find, if any, the correlation between such transformation and the urban heat island effect. The supervised classification technique was used to analyze the remote sensing data obtained from Landsat to identify the different kinds of underlying surfaces. Land surface temperatures were calculated using a subset of Landsat data. The correlation between the transformation of underlying surfaces and the heat island effect was established through analytical and statistical approaches. The results clearly show that the proportion of developed surfaces has been steadily rising in Nanjing in the past 30 years and that the urban heat island effect is positively correlated with the expansion of hard pavement and the deterioration of green surfaces and water bodies considering the general trend.

show abstract

Section: Air Temperature Urban Heat Islandmentioning

confidence: 99%

Section: Air Temperature Urban Heat Islandmentioning

confidence: 99%

Transformation of Urban Surfaces and Heat Islands in Nanjing during 1984–2018

Zhang

Zhu

et al. 2020

Sustainability

View full text Add to dashboard Cite

show abstract

“…El análisis térmico requiere de estimaciones precisas de la emisividad de las coberturas de la superficie terrestre. Uno de los métodos habitualmente empleados (Jiménez-Muñoz et al, 2014;Li y Meng, 2018;Sobrino et al, 2008;Yang et al, 2020;Yu et al, 2014) es mediante el índice de vegetación normalizado (Sobrino y Raissouni, 2000) en función de su correlación con las bandas visibles y del infrarrojo cercano. Los autores Becker y Li, (1995) establecen que las distribuciones de TST y emisividad dentro del píxel son medibles desde el espacio y por tanto, este método se recomienda para la determinación de la TST.…”

Section: Emisividadunclassified

Determinación de la temperatura de la superficie terrestre mediante imágenes Landsat 8: Estudio comparativo de algoritmos sobre la ciudad de Granada

García

2021

Rev. Teledetec.

View full text Add to dashboard Cite

<p>The use of satellite images has become, in recent decades, one of the most common ways to determine the Land Surface Temperature (LST). One of them is through the use of Landsat 8 images that requires the use of single-channel (MC) and two-channel (BC) algorithms. In this study, the LST of a medium-sized city, Granada (Spain) has been determined over a year by using five Landsat 8 algorithms that are subsequently compared with ambient temperatures. Few studies compare the data source with the seasonal variations of the same metropolis, which together with its geographical location, high pollution and the significant thermal variations it experiences make it a suitable place for the development of this research. As a result of the statistical analysis process, the regression coefficients R<sup>2</sup>, mean square error (RMSE), mean error bias (MBE) and standard deviation (SD) were obtained. The average results obtained reveal that the LST derived from the BC algorithms (1.0 °C) are the closest to the ambient temperatures in contrast to the MC (-5.6 °C), although important variations have been verified between the different zones of the city according to its coverage and seasonal periods. Therefore, it is concluded that the BC algorithms are the most suitable for recovering the LST of the city under study.</p>

show abstract

“…The different data may cause different results. Another question is how to define urban and rural areas [15]. Different definitions may also generate different results.…”

Section: Monthly Variation Of Suhi Intensitymentioning

confidence: 99%

“…Nevertheless, the limited stations fail to provide detailed spatial information regarding CUHI. Previous studies have shown that the relationships between LST and air temperature are statistically significant, and UHI, quantified using retrieved LST from thermal remote sensing, has been defined as the surface UHI (SUHI) [15][16][17]. LST is one of the primary indicators for examining SUHI because it is related to surface radiation and energy exchange, and helps us to explicitly reveal sufficient spatial patterns of the urban thermal environment, despite being subject to the time of satellite overpass [18,19].…”

Section: Introductionmentioning

confidence: 99%

Investigating Seasonal Effects of Dominant Driving Factors on Urban Land Surface Temperature in a Snow-Climate City in China

et al. 2020

Self Cite

View full text Add to dashboard Cite

Land surface temperature (LST) is a crucial parameter in surface urban heat island (SUHI) studies. A better understanding of the driving mechanisms, influencing variations in LST dynamics, is required for the sustainable development of a city. This study used Changchun, a city in northeast China, as an example, to investigate the seasonal effects of different dominant driving factors on the spatial patterns of LST. Twelve Landsat 8 images were used to retrieve monthly LST, to characterize the urban thermal environment, and spectral mixture analysis was employed to estimate the effect of the driving factors, and correlation and linear regression analyses were used to explore their relationships. Results indicate that, (1) the spatial pattern of LST has dramatic monthly and seasonal changes. August has the highest mean LST of 38.11 °C, whereas December has the lowest (−19.12 °C). The ranking of SUHI intensity is as follows: summer (4.89 °C) > winter with snow cover (1.94 °C) > spring (1.16 °C) > autumn (0.89 °C) > winter without snow cover (−1.24 °C). (2) The effects of driving factors also have seasonal variations. The proportion of impervious surface area (ISA) in summer (49.01%) is slightly lower than those in spring (56.64%) and autumn (50.85%). Almost half of the area is covered with snow (43.48%) in winter. (3) The dominant factors are quite different for different seasons. LST possesses a positive relationship with ISA for all seasons and has the highest Pearson coefficient for summer (r = 0.89). For winter, the effect of vegetation on LST is not obvious, and snow becomes the dominant driving factor. Despite its small area proportion, water has the strongest cooling effect from spring to autumn, and has a warming effect in winter. (4) Human activities, such as agricultural burning, harvest, and different choices of crop species, could also affect the spatial patterns of LST.

show abstract

Comparison of land surface and air temperatures for quantifying summer and winter urban heat island in a snow climate city

Cited by 70 publications

References 66 publications

Transformation of Urban Surfaces and Heat Islands in Nanjing during 1984–2018

Transformation of Urban Surfaces and Heat Islands in Nanjing during 1984–2018

Determinación de la temperatura de la superficie terrestre mediante imágenes Landsat 8: Estudio comparativo de algoritmos sobre la ciudad de Granada

Investigating Seasonal Effects of Dominant Driving Factors on Urban Land Surface Temperature in a Snow-Climate City in China

Contact Info

Product

Resources

About