Analysing the day/night seasonal and annual changes and trends in land surface temperature and surface urban heat island intensity (SUHII) for Indian cities

Siddiqui, Asfa; Kushwaha, Gautami; Nikam, Bhaskar R.; Srivastav, S. K.; Shelar, Ankita; Kumar, Pramod

doi:10.1016/j.scs.2021.103374

Cited by 83 publications

(27 citation statements)

References 122 publications

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“…Under arid conditions, LST was mainly positively correlated with air temperature. However, LST was negatively correlated with air temperature in some cities with less urban–rural vegetation differences and low vegetation cover [ 70 ]. Under the conditions of global climate change, extreme heat waves and droughts with little rain will be more frequent, vegetation plays a neutralizing and regulating role in this link, and increasing urban greening construction can, to a certain extent, reduce the adverse effects brought by global climate change.…”

Section: Discussionmentioning

confidence: 99%

Spatial and Temporal Variation of Land Surface Temperature and Its Spatially Heterogeneous Response in the Urban Agglomeration on the Northern Slopes of the Tianshan Mountains, Northwest China

Zhang

Kasimu

Liang

et al. 2022

IJERPH

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An in-depth study of the influence mechanism of oasis land surface temperature (LST) in arid regions is essential to promote the stable development of the ecological environment in dry areas. Based on MODIS, MYD11A2 long time series data from 2003 to 2020, the Mann–Kendall nonparametric test, the Sen slope, combined with the Hurst index, were used to analyze and predict the trend of LST changes in the urban agglomeration on the northern slopes of the Tianshan Mountains. This paper selected nine influencing factors of the slope, aspect, air temperature, normalized vegetation index (NDVI), precipitation (P), nighttime light index (NTL), patch density (PD), mean patch area (AREA_MN), and aggregation index (AI) to analyze the spatial heterogeneity of LST from global and local perspectives using the geodetector (GD) model and multi-scale geo-weighted regression (MGWR) model. The results showed that the average LSTs of the urban agglomeration on the northern slopes of the Tianshan Mountains in spring, summer, autumn, and winter were 31.53 °C, 47.29 °C, 22.38 °C, and −5.20 °C in the four seasons from 2003 to 2020, respectively. Except for autumn, the LST of all seasons showed an increasing trend, bare soil and grass land had a warming effect, and agricultural land had a cooling effect. The results of factor detection showed that air temperature, P, and NDVI were the dominant factors affecting the spatial variation of LST. The interaction detection results showed that the interaction between air temperature and NDVI was the most significant, and the two-factor interaction was more effective than the single-factor effect on LST. The MGWR model results showed that the effects of PD on LST were positively correlated, and the impact of AREA_MN and AI on LST were negatively correlated, indicating that the dense landscape of patches has a cooling effect on LST. Overall, this study provides information for managers to carry out more targeted ecological stability regulations in arid zone oases and facilitates the development of regulatory measures to maintain the cold island effect and improve the environment.

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Section: Discussionmentioning

confidence: 99%

Spatial and Temporal Variation of Land Surface Temperature and Its Spatially Heterogeneous Response in the Urban Agglomeration on the Northern Slopes of the Tianshan Mountains, Northwest China

Zhang

Kasimu

Liang

et al. 2022

IJERPH

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“…Taking the Fifth Ring Road in Beijing as an example, we show the unique advantages of ECOSTRESS LST data in analyzing the urban thermal environment, and provide a new perspective for the study of urban thermal environment. When there are data from multiple time periods from the same area, we can study the diurnal variation of surface temperature in urban areas more accurately; such as the diurnal variation in surface temperature and surface UHII in three cities in India was analyzed by Siddiqui et al ( 69 ) using MODIS LST data. Lu et al ( 70 ) combined MODIS and Landsat images to analyze diurnal differences and seasonal characteristics of surface UHII variability in Hefei, China.…”

Section: Discussionmentioning

confidence: 99%

Exploring seasonal diurnal surface temperature variation in cities based on ECOSTRESS data: A local climate zone perspective

Shi

Yang

Wang

et al. 2022

Front. Public Health

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High urban temperatures affect city livability and may be harmful for inhabitants. Analyzing spatial and temporal differences in surface temperature and the thermal impact of urban morphological heterogeneity can promote strategies to improve the insulation of the urban thermal environment. Therefore, we analyzed the diurnal variation of land surface temperature (LST) and seasonal differences in the Fifth Ring Road area of Beijing from the perspective of the Local Climate Zone (LCZ) using latest ECOSTRESS data. We used ECOSTRESS LST data with a resolution of 70 m to accurately interpret the effects of urban morphology on the local climate. The study area was dominated by the LCZ9 type (sparse low-rise buildings) and natural LCZ types, such as LCZA/B (woodland), LCZD (grassland), and LCZG (water body), mainly including park landscapes. There were significant differences in LST observed in different seasons as well as day and night. During daytime, LST was ranked as follows: summer > spring > autumn > winter. During night-time, it was ranked as follows: summer > autumn > spring > winter. All data indicated that the highest and lowest LST was observed in summer and winter, respectively. LST was consistent with LCZ in terms of spatial distribution. Overall, the LST of each LCZ during daytime was higher than that of night-time during different seasons (except winter), and the average LST of each LCZ during the diurnal period in summer was higher than that of other seasons. The LST of each LCZ during daytime in winter was lower than that of the corresponding night-time, which indicates that it is colder in the daytime during winter. The results presented herein can facilitate improved analysis of spatial and temporal differences in surface temperature in urban areas, leading to the development of strategies aimed at improving livability and public health in cities.

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“…Land surface temperature (LST) is an important physical quantity that can be used for studying the interaction between the Earth"s surface and the atmosphere [1]- [3]. Until now, LST has been applied widely in research on regional drought monitoring [4], [5], land surface evapotranspiration estimation [6], [7] and the heat island effect [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Filling Then Spatio-Temporal Fusion for All-Sky MODIS Land Surface Temperature Generation

Tang

Wang

Atkinson

2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The thermal infrared (TIR) band of the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra/Aqua satellite can provide daily, 1 km land surface temperature (LST) observations. However, due to the influence of cloud contamination, spatial gaps are common in the LST product, restricting its application greatly at the regional scale. In this paper, to deal with the challenge of large gaps (especially complete data loss) in MODIS LST for local monitoring, a filling then spatio-temporal fusion (FSTF) method is proposed, which utilizes another type of product with all-sky coverage, but coarser spatial resolution (i.e., the 7 km China Land Data Assimilation System (CLDAS) LST product). Due to the great temporal heterogeneity of LST, temporally closer auxiliary MODIS LST images are considered to be preferable choices for spatio-temporal fusion of CLDAS and MODIS LST time-series. However, such data are always abandoned inappropriately in conventional spatio-temporal fusion if they contain gaps. Accordingly, pre-gap filling is performed in FSTF to make fuller use of the valid information in temporally close MODIS LST images with small gaps. Through evaluation in both the spatial and temporal domains for three regions in China, FSTF was found to be more accurate in reconstructing MODIS LST images than the original spatio-temporal fusion methods. FSTF, thus, has great potential for updating the current MODIS LST product at the global scale.  Index Terms-Land surface temperature (LST), MODIS, spatio-temporal fusion, gap filling.

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Analysing the day/night seasonal and annual changes and trends in land surface temperature and surface urban heat island intensity (SUHII) for Indian cities

Cited by 83 publications

References 122 publications

Spatial and Temporal Variation of Land Surface Temperature and Its Spatially Heterogeneous Response in the Urban Agglomeration on the Northern Slopes of the Tianshan Mountains, Northwest China

Spatial and Temporal Variation of Land Surface Temperature and Its Spatially Heterogeneous Response in the Urban Agglomeration on the Northern Slopes of the Tianshan Mountains, Northwest China

Exploring seasonal diurnal surface temperature variation in cities based on ECOSTRESS data: A local climate zone perspective

Filling Then Spatio-Temporal Fusion for All-Sky MODIS Land Surface Temperature Generation

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