Spatiotemporal trends of land surface temperature and surface urban heat island intensification across India.

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

Urban heat islands (UHIs) aggravate urban heat stress and, therefore, exacerbate heat-related morbidity and mortality as global warming continues. Numerous studies used surface urban heat island intensity (SUHII) to quantify the change in the UHI effect and its drivers for heat mitigation. However, whether the variations in SUHII among cities can demonstrate the physical difference and fluctuation of the urban thermal environment is poorly understood. Here, we present a comparison study on the temporal trends of SUHII and LST in urban and nonurban areas in 13 cities of the Beijing–Tianjin–Hebei (BTH) megaregion in China and further identify different types of changes in SUHII based on the temporal trends of land surface temperature (LST) in urban and nonurban areas from 2000 to 2020. We also measured the effect of the changes in four socioecological factors (i.e., population density, vegetation greenness (EVI), GDP, and built-up area) on the trends of SUHII to understand the dynamic interaction between the UHI effect and socioecological development. We found the following. (1) Nine out of thirteen cities showed a significant increasing trend in SUHII, indicating that the SUHI effects have been intensified in most of the cities in the BTH megaregion. (2) The spatial pattern of summer mean SUHII and LST in urban areas varied greatly. Among the 13 cities, Beijing had the highest mean SUHII, but Handan had the highest urban temperature, which suggests that a city with stronger SUHII does not necessarily have a higher urban temperature or hazardous urban thermal environment. (3) Four types of changes in SUHII were identified in the 13 cities, which resulted from different temporal trends of LST in urban areas and nonurban areas. In particular, one type of increasing trend of SUHII in seven cities resulted from a greater warming trend (increasing LST) in urban than nonurban areas (SUHII↑1), and another type of increasing trend of SUHII in Beijing and Chengde was attributed to the warming trends (increasing LST) in urban areas and the cooling trends (decreasing LST) in nonurban areas (SUHII↑2). Meanwhile, the third type of increasing trend of SUHII in Zhangjiakou was due to a greater cooling (decreasing LST) trend in nonurban areas than in urban areas (SUHII↑3). In contrast, three cities with a decreasing trend of SUHII were caused by the increase in LST in urban and nonurban areas, but the warming trend in nonurban areas was greater than in urban areas (SUHII↓1). (4) Among the relationship between the trend of SUHII (TrendSUHII) and the changes in socioecological factors (Trendpopulation density, TrendGDP per captica, TrendEVI, and Trendbuild-up area), a significantly positive correlation between TrendSUHII and TrendEVI indicated that the change in SUHII was significantly related to an increased rate of EVI. This is mainly because increased vegetation in nonurban areas would result in lower temperatures in nonurban areas.

This study assessed the relationship between land surface temperature (LST) and vegetation using MODIS NDVI and LST timeseries data in Kaduna Metropolis. MOD13Q1 and MOD11A2 datasets were accessed using Google Earth Engine. Mann-Kendall trend test was used to analyse the trends in LST and NDVI. Pearson Moment Correlation Coefficient and Linear Regression were used to examine the relationship between LST and NDVI. Mann-Kendall trend test revealed monotonic downward trend in NDVI with a Z-statistics of -1.2758, but upward trend in daytime and nighttime LST, with a Z-statistics of 0.567 and 2.107 respectively. For the relationship, vegetation showed strong negative relationship with daytime LST with -0.704. Vegetation also showed weak positive relationship with nighttime LST. The linear regression analysis revealed that vegetation was able to predict 49.5% of LST in Kaduna Metropolis, with R2 value of 0.495 and a standard error of estimate is 2.459. The study concluded that loss of vegetation is responsible for the increase in land surface temperature. The study therefore recommended regulatory agencies should ensure that trees are planted whenever they are removed due to infrastructural development in order to prevent UHI phenomenon and planting of trees should be encouraged in order to regulate the urban climate.

Urban air temperature studies usually focus on the urban canopy heat island phenomenon, whereby the city center experiences higher near surface air temperatures compared to its surrounding non-urban areas. The Land Surface Temperature (LST) is used instead of urban air temperature to identify the Surface Urban Heat Island (SUHI). In this study, the nighttime LST and SUHI characteristics and trends in the seventeen largest Mediterranean cities were investigated, by analyzing satellite observations for the period 2001–2012. SUHI averages and trends were based on an innovative approach of comparing urban pixels to randomly selected non-urban pixels, which carries the potential to better standardize satellite-derived SUHI estimations. A positive trend for both LST and SUHI for the majority of the examined cities was documented. Furthermore, a 0.1 °C decade−1 increase in urban LST corresponded to an increase in SUHI by about 0.04 °C decade−1. A longitudinal differentiation was found in the urban LST trends, with higher positive values appearing in the eastern Mediterranean. Examination of urban infrastructure and development factors during the same period revealed correlations with SUHI trends, which can be used to explain differences among cities. However, the majority of the cities examined show considerably increased trends in terms of the enhancement of SUHI. These findings are considered important so as to promote sustainable urbanization, as well as to support the development of heat island adaptation and mitigation plans in the Mediterranean.

Urban expansion has been changing the urban thermal environment. Understanding the spatial distribution and temporal trends in the urban thermal environment is important in guiding sustainable urbanization. In this study, we focused on the land use/land cover (LULC) changes and urban expansion in Nanchang city, Jiangxi province, China. The four elements in the remote sensing-based ecological index (RSEI) are heat, greenness, dryness, and wetness, which correspond to the land surface temperature (LST), NDVI, NDBSI, and WET, respectively. According to the synthetic images of the average indices, we conducted temporal trend analysis together with statistical significance test for these images. We conducted partial correlation analyses between LST and NDVI, NDVSI, as well as WET. In addition, we used the LULC maps to analyze the multi-year trends in urban expansion. Then, we superimposed the trends in daytime and nighttime LST in summer on urban expansion area to extract the LST trends at sample locations. The results showed that LULC in Nanchang has substantially changed during the study period. The areas with statistically significant trends in LST coincided with the urban expansion areas. Land cover change was the main reason for LST change in Nanchang. In particular, artificial surfaces showed the greatest increase in LST; for per 100 km2 expansion in artificial surfaces, the daytime and nighttime LST increased by 0.8 °C and 0.7 °C, respectively. Among all the study land cover types, water bodies showed the greatest differences in LST change between the daytime and nighttime. There were statistically significant correlations between increases in LST and increases in NDBSI as well as decreases in NDVI and WET. In view of the considerable impact of urban expansion on the urban thermal environment, we urge local authorities to emphasize on urban greening when carrying out urban planning and construction.

Recent trends of land surface temperature in relation to the influencing factors using Google Earth Engine platform and time series products in megacities of India

Land cover change effects on land surface temperature trends in an African urbanizing dryland region

This study quantifies the thermal environmental impact of Pokhara International Airport in Nepal by analyzing land surface temperature (LST) trends before and after its construction. Using the Google Earth Engine (GEE) platform, TerraClimate (1987-2025) and MODIS datasets are processed to perform a spatiotemporal analysis across two distinct periods from pre-construction (i.e., 1987-2017) and post-construction (i.e., 2018-2025) of Pokhara International Airport. The derived LST trends is validated through zonal analysis of urban heat island (UHI) intensity using urban (500 m, 1 km) and rural (2-4 km) buffers and correlated with a time-series analysis of the Normalized Difference Vegetation Index (NDVI). The results indicate a pronounced acceleration in warming following the airport’s construction, with the LST trend rising from 0.44◦C/decade in the pre-construction period to 2.53◦C/decade afterwards. Statistical validation via the Mann–Kendall test and Sen’s slope estimator confirmed a significant increasing trend (p < 0.001). This warming is coupled with a strengthening of the UHI effect, where the mean urban–rural LST difference increased from 0.5◦C to 1.34◦C, and a sustained decline in NDVI, signifying vegetation loss. The findings demonstrate that major infrastructure projects can rapidly alter local microclimates, exacerbating surface warming and UHI effects.

This study examined the impact of sea surface temperature (SST) on urban temperature across four cities located in three different countries (United States of America, Japan, and Morocco), all at nearly the same latitude, focusing on the summer season over the period from 2003 to 2020, because previously no one attempted to analyze the impact of SST on land surface temperature (LST). Data were acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) for LST and SST to evaluate the correlation between urban temperature and SST, the trends over time, and the relationship between urban areas and LST. The novelty of this study lies in its being the first to investigate the impact of SST on urban temperature based on a city’s proximity to warm and cold ocean currents. The findings revealed a positive correlation between LST and SST across all cities analyzed in this study (San Francisco, Tangier, Tokyo, and Atlantic City), and in some instances a significant positive relationship was observed at a 95% confidence level, but still the significance is in the range of weak to moderate. Specifically, the study found that during both daytime and nighttime, Tangier exhibited a decreasing trend in LST (99% confidence level) and SST. On the contrary, San Francisco displayed an increasing trend in both LST and SST during the daytime, but at nighttime, while SST continued to rise, LST showed a decreasing trend. Further analysis differentiated cities influenced by warm ocean currents (Tokyo and Atlantic City) from those affected by cold currents (San Francisco and Tangier). In Tokyo, influenced by a warm ocean current, there was a decreasing trend in LST despite increased SST. Conversely, Atlantic City, also influenced by warm ocean currents, showed an increasing trend in both LST and SST during the daytime. At nighttime, both Tokyo and Atlantic City exhibited increasing trends in LST and SST. Additionally, this study explored the correlation between urban areas and LST, finding that cities influenced by warm ocean currents (Tokyo and Atlantic City) showed a positive correlation between urban areas and LST. In contrast, cities influenced by cold ocean currents (San Francisco and Tangier) displayed a negative correlation between urban areas and LST. Overall, this research highlights the complex interplay between SST and urban temperatures, demonstrating how ocean currents and urbanization can influence temperature trends differently in cities at similar latitudes.

<p>The present work deals with the time series analysis of remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST). While many works have been published concerning the trends of nighttime and daytime LST at the regional or local scale, little attention has been paid to structural changes observed within the LST time series in various sub-periods. This could be of much interest not only for climate studies but also for unveiling the possible relation between natural disasters such as wildfires and global changes. In this work we tested the hypothesis of a constant trend in LST time series from 2000 to 2019 and highlighted the existence of periods with changing trends. The methodology was applied in an area of approximately 17.000 km<sup>2</sup> located in NE Greece and South Bulgaria. The nighttime and daytime LST time series data were initially subjected to a gap filling algorithm to account for missing values and were then aggregated at the catchment level. Furthermore, LST time series were analyzed using the Breaks For Additive Season and Trend (BFAST) method. Results indicated that an abrupt change in both nighttime and daytime LST trends was observed in all examined time series, indicating a transition from a decreasing LST regime from 2002 to 2006 to an abrupt increasing thereafter until today. An initial comparison with the existing inventory of wildfires in the area for the last 20 years indicated an increase of wildfire events which coincides with the LST breakpoint, indicating thus possible connections between rising LST and wildfire events.</p>

Trends in land surface temperature and its drivers over the High Mountain Asia

Urban heat island (UHI) attenuation is an essential aspect for maintaining environmental sustainability at a local, regional, and global scale. Although impervious surfaces (IS) and green spaces have been confirmed to have a dominant effect on the spatial differentiation of the urban land surface temperature (LST), comprehensive temporal and quantitative analysis of their combined effects on LST and surface urban heat island intensity (SUHII) changes is still partly lacking. This study took the plain area of Beijing, China as an example. Here, rapid urbanization and a large-scale afforestation project have caused distinct IS and vegetation cover changes within a small range of years. Based on 8 scenes of Landsat 5 TM/7ETM/8OLI images (30 m × 30 m spatial resolution), 920 scenes of EOS-Aqua-MODIS LST images (1 km × 1 km spatial resolution), and other data/information collected by different approaches, this study characterized the interrelationship of the impervious surface area (ISA) dynamic, forest cover increase, and LST and SUHII changes in Beijing’s plain area during 2009–2018. An innovative controlled regression analysis and scenario prediction method was used to identify the contribution of ISA change and afforestation to SUHII changes. The results showed that percent ISA and forest cover increased by 6.6 and 10.0, respectively, during 2009–2018. SUHIIs had significant rising tendencies during the decade, according to the time division of warm season days (summer days included) and cold season nights (winter nights included). LST changes during warm season days responded positively to a regionalized ISA increase and negatively to a regionalized forest cover increase. However, during cold season nights, LST changes responded negatively to a slight regionalized ISA increase, but positively to an extensive regionalized ISA increase, and LST variations responded negatively to a regionalized forest cover increase. The effect of vegetation cooling was weaker than ISA warming on warm season days, but the effect of vegetation cooling was similar to that of ISA during cold season nights. When it was assumed that LST variations were only caused by the combined effects of ISA changes and the planting project, it was found that 82.9% of the SUHII rise on warm season days (and 73.6% on summer days) was induced by the planting project, while 80.6% of the SUHII increase during cold season nights (and 78.9% during winter nights) was caused by ISA change. The study presents novel insights on UHI alleviation concerning IS and green space planning, e.g., the importance of the joint planning of IS and green spaces, season-oriented UHI mitigation, and considering the thresholds of regional IS expansion in relation to LST changes.

Land Surface Temperature (LST) is a key parameter for the estimation of urban fluxes as well as for the assessment of the presence and strength of the surface urban heat island (SUHI). In an urban environment, LST depends on the way the city has been planned and developed over time. To this end, the estimation of LST needs adequate spatial and temporal data at the urban scale, especially with respect to land cover/land use. The present study is divided in two parts: at first, satellite data from MODIS-Terra 8-day product (MOD11A2) were used for the analysis of an eighteen-year time series (2001–2017) of the LST spatial and temporal distribution in five major cities of the Mediterranean during the summer months. LST trends were retrieved and assessed for their statistical significance. Secondly, LST values and trends for each city were examined in relation to land cover characteristics and patterns in order to define the contribution of urban development and planning on LST; this information is important for the drafting of smart urbanization policies and measures. Results revealed (a) positive LST trends in the urban areas especially during nighttime ranging from +0.412 °K in Marseille to +0.923 °K in Cairo and (b) the SUHI has intensified during the last eighteen years especially during daytime in European Mediterranean cities, such as Rome (+0.332 °K) and Barcelona (+0.307 °K).

In our current global warming climate, the growth of record-breaking heat waves (HWs) is expected to increase in its frequency and intensity. Consequently, the considerably growing and agglomerated world’s urban population becomes more exposed to serious heat-related health risks. In this context, the study of Surface Urban Heat Island (SUHI) intensity during HWs is of substantial importance due to the potential vulnerability urbanized areas might have to HWs in comparison to their surrounding rural areas. This article discusses Land Surface Temperatures (LST) reached during the extreme HW over Western North America during the boreal summer of 2021 using Thermal InfraRed (TIR) imagery acquired from TIR Sensor (TIRS) (30 m spatial resolution) onboard Landsat-8 platform and Moderate Resolution Imaging Spectroradiometer (MODIS) (1 km spatial resolution) onboard Terra/Aqua platforms. We provide an early assessment of maximum LSTs reached over the affected areas, as well as impacts in terms of SUHI over the main cities and towns. MODIS series of LST from 2000 to 2021 over urbanized areas presented the highest recorded LST values in late June 2021, with maximum values around 50 °C for some cities. High spatial resolution LSTs (Landsat-8) were used to map SUHI intensity as well as to assess the impact of SUHI on thermal comfort conditions at intraurban space by means of a thermal environmental quality indicator, the Urban Field Thermal Variance Index (UFTVI). The same high resolution LSTs were used to verify the existence of clusters and employ a Local Indicator of Spatial Association (LISA) to quantify its degree of strength. We identified the spatial distribution of heat patterns within the intraurban space as well as described its behavior across the thermal landscape by fitting a polynomial regression model. We also qualitatively analyze the relationship between both UFTVI and LST clusters with different land cover types. Findings indicate that average daytime SUHI intensity for the studied cities was typically within 1 to 5 °C, with some exceptional values surpassing 7 °C and 9 °C. During night, the SUHI intensity was reduced to variations within 1–3 °C, with a maximum value of +4 °C. The extreme LSTs recorded indicate no significant influence of HW on SUHI intensity. SUHI intensity maps of the intraurban space evidence hotspots of much higher values located at densely built-up areas, while urban green spaces and dense vegetation show lower values. In the same manner, UTFVI has shown “no” SUHI for densely vegetated regions, water bodies, and low-dense built-up areas with intertwined dense vegetation, while the “strongest” SUHI was observed for non-vegetated dense built-up areas with low albedo material such as concrete and pavement. LST was evidenced as a good marker for assessing the influence of HWs on SUHI and recognizing potential thermal environmental consequences of SUHI intensity. This finding highlights that remote-sensing based LST is particularly suitable as an indicator in the analysis of SUHI intensity patterns during HWs at different spatial resolutions. LST used as an indicator for analyzing and detecting extreme temperature events and its consequences seems to be a promising means for rapid and accurate monitoring and mapping.

Earth's surface temperature continues to rise steadily over time. A Long-term assessment of Earth's Land Surface Temperature (LST) trend and changes in land use/land cover (LULC) can help understand its cause and effect on the environment. This study aims to use LST trend and LULC change information to analyze its impact on the state of Meghalaya. As an indicator, the trend in LST is used to examine the effects of natural and human-induced activities, supported by the decadal land use change assessment. The study utilizes a Mann-Kendall test to identify the trend in LST from the last 22 years (2000-2021). Similarly, a machine learning technique (Random Forest) is used to analyze the dynamics of decadal LULC change. Inter class differences in conjunction with LST trends are then cross-validated to draw any conclusion. The test suggests an increasing trend in LST for the Meghalayan region. Corresponding analysis of LULC change suggests that the conversion of vegetation land to buildup areas rose from 28% in 2000-2010 to 45% in 2010-2021. Similarly, 20% of forest land was converted to buildup areas in the first half, with a slight decrease to 14% in the second half. Furthermore, the importance of temperature, rainfall, and DEM in modelling LST variations was also outlined as a part of the parameter importance analysis performed in this study. By utilizing these insights and methodology, effective measures can be developed to counter the harmful effects of deforestation and urbanization, thus preserving the ecological balance of our ecosystems for future generations.