Spatial-temporal change of land surface temperature across 285 cities in China: An urban-rural contrast perspective

Peng, Jian; Ma, Junjun; Liu, Qianyuan; Liu, Yanxu; Hu, Yi’na; Li, Yingru; Yue, Yuemin

doi:10.1016/j.scitotenv.2018.04.105

Cited by 196 publications

(123 citation statements)

References 54 publications

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“…Second, cities with significant increasing trends of daytime SUHII in winter were generally located near the equatorial region (Figure 1i), which can be explained by the different vegetation species between this region (mainly covered by evergreen forest) and others (mainly covered by deciduous forest, seasonal crop, and grass; Yao et al, 2017). This is similar to Chakraborty and Lee (2019) and may be attributed to the soil moisture effect (Peng et al, 2018;Yao et al, 2017;Zhou et al, 2014). Thus, the interannual variations in evergreen forest rather than other vegetation types will drive the SUHII in winter.…”

Section: Temporal Trends Of Suhiisupporting

confidence: 76%

“…Thus, the interannual variations in evergreen forest rather than other vegetation types will drive the SUHII in winter. Drier soil (in relatively arid regions) transformed into built-up areas with urbanization may result in higher slopes of nighttime SUHII than wetter soil (in relatively humid regions; Peng et al, 2018;Yao et al, 2017;Zhou et al, 2014). Third, it seems that nighttime SUHII increased in most cities in relatively arid regions (Western United States and Northern China), while it was not true for cities in relatively humid regions (Eastern United States, Western Europe, and Southern China).…”

Section: Temporal Trends Of Suhiimentioning

confidence: 96%

“…Pixels classified as urban areas (digital number = 13) and water bodies (digital number = 17) in land cover maps (raster files) were converted into polygon (vector files) using the Environment for Visualizing Images 5.1 software (Menashe & Friedl, 2018). Using urban areas in 2016 can reveal overall trends of EVI and SUHII and their relationships, including the information from old (without urbanization) and new urban areas (with urbanization) (Peng et al, 2018;Yao, Wang, Huang, Zhang, et al, 2018). The urban areas in 2016 can include old and new urban areas due to urbanization.…”

Section: Extracting Urban and Rural Areasmentioning

confidence: 99%

“…The urban areas in 2016 can include old and new urban areas due to urbanization. Using urban areas in 2016 can reveal overall trends of EVI and SUHII and their relationships, including the information from old (without urbanization) and new urban areas (with urbanization) (Peng et al, 2018;Yao, Wang, Huang, Zhang, et al, 2018). Cities with urban area size larger than 200 km 2 were analyzed; thus, a total of 397 cities were investigated (Table S1).…”

Section: Extracting Urban and Rural Areasmentioning

confidence: 99%

“…Relatively arid regions generally have higher nighttime SUHII than relatively humid regions, since in relatively arid region the soil is dry and the rural LST decreases rapidly at night. Drier soil (in relatively arid regions) transformed into built-up areas with urbanization may result in higher slopes of nighttime SUHII than wetter soil (in relatively humid regions; Peng et al, 2018;Yao et al, 2017;Zhou et al, 2014).…”

Section: 1029/2018gl081816mentioning

confidence: 99%

See 4 more Smart Citations

Greening in Rural Areas Increases the Surface Urban Heat Island Intensity

Yao

Wang

Huang

et al. 2019

Geophysical Research Letters

184

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In this study, Moderate Resolution Imaging Spectroradiometer land cover, land surface temperature (LST), and enhanced vegetation index (EVI) data were used to investigate the trends of surface urban heat island intensity (SUHII, urban LST minus rural LST) and their relations with vegetation in 397 global big cities during 2001–2017. Major findings include the following: (1) Annual daytime and nighttime SUHII increased significantly (p < 0.05, Mann‐Kendall trend test) in 42.1% and 30.5% cities, respectively. (2) The daytime SUHII in the growing season was significantly and positively correlated with rural EVI in 58.9% cities. This is because high rural EVI can increase the EVI difference between urban and rural areas. (3) Rural greening contributed 22.5% of the increased daytime SUHII in the growing season at the global scale. This study highlights that the effect of greening in rural areas was a significant and widespread driver for the increased daytime SUHII.

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Section: Temporal Trends Of Suhiisupporting

confidence: 76%

Section: Temporal Trends Of Suhiimentioning

confidence: 96%

Section: Extracting Urban and Rural Areasmentioning

confidence: 99%

Section: Extracting Urban and Rural Areasmentioning

confidence: 99%

Section: 1029/2018gl081816mentioning

confidence: 99%

See 3 more Smart Citations

Greening in Rural Areas Increases the Surface Urban Heat Island Intensity

Yao

Wang

Huang

et al. 2019

Geophysical Research Letters

184

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Long memory conditional random fields on regular lattices

et al. 2023

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This paper draws its motivation from applications in geophysics, agricultural, and environmental sciences where empirical evidence of slow decay of correlations have been found for data observed on a regular lattice. Spatial ARFIMA models represent a widely used class of spatial models for analyzing such data.Here, we consider their generalization to conditional autoregressive fractional integrated moving average (CARFIMA) models, a larger class of long memory models which allows a wider range of correlation behavior. For this class we provide detailed descriptions of important representative models, make the necessary comparison with some other existing models, and discuss some important inferential and computational issues on estimation, simulation and long memory process approximation. Results from model fit comparison and predictive performance of CARFIMA models are also discussed through a statistical analysis of satellite land surface temperature data.

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Changes in temperature extremes and their relationship with ENSO in Malaysia from 1985 to 2018

Tan

Juneng

Tangang

et al. 2020

Intl Journal of Climatology

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Understanding the changes in temperature extremes is important for managing and coping with the risks associated with regional climate change. However, the climatological characteristics of temperature extremes and their variabilities is still not adequately studies in Malaysia. This study evaluates the spatial and temporal variations of temperature extremes over Malaysia for the period 1985-2018. This study includes four phases: (a) collection, quality control and homogeneity analysis of temperature data; (b) general (TXmean, TNmean, TMmean and DTR), warm (TXx, TNx, TX90p and TN90p) and cool (TXn, TNn, TX10p and TN10p) temperature extreme indices calculations; (c) trend analysis of temperature extremes using the Mann-Kendall and Sens's slope test; and (d) analyses of correlations between temperature extremes and El Niño-Southern Oscillation (ENSO). The results indicate a warming of surface temperature across Malaysia, particularly in Peninsular Malaysia. In general, TXmean, TNmean and TMmean increased significantly at 5% significance level by 0.

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Spatial-temporal change of land surface temperature across 285 cities in China: An urban-rural contrast perspective

Cited by 196 publications

References 54 publications

Greening in Rural Areas Increases the Surface Urban Heat Island Intensity

Greening in Rural Areas Increases the Surface Urban Heat Island Intensity

Long memory conditional random fields on regular lattices

Changes in temperature extremes and their relationship with ENSO in Malaysia from 1985 to 2018

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