Assessment of land surface temperature and urban heat island using remote sensing in the Kurdistan region, Iraq

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

The global rise of urbanization has led to the formation of surface urban heat islands and surface urban cool islands. Urban heat islands have been shown to increase thermal discomfort, which increases heat stress and heat-related diseases. In Kuwait, a hyper-arid desert climate, most of the population lives in urban and suburban areas. In this study, we characterized the spatial distribution of land surface temperatures and investigated the presence of urban heat and cool effects in Kuwait. We used historical Moderate-Resolution Imaging Spectroradiometer (MODIS) Terra satellite 8-day composite land surface temperature (LST) from 2001 to 2017. We calculated the average LSTs of the urban/suburban governorates and compared them to the average LSTs of the rural and barren lands. We repeated the analysis for daytime and nighttime LST. During the day, the temperature difference (urban/suburban minus versus governorates) was −1.1 °C (95% CI; −1.2, −1.00, p < 0.001) indicating a daytime urban cool island. At night, the temperature difference (urban/suburban versus rural governorates) became 3.6 °C (95% CI; 3.5, 3.7, p < 0.001) indicating a nighttime urban heat island. In light of rising temperatures in Kuwait, this work can inform climate change adaptation efforts in the country including urban planning policies, but also has the potential to improve temperature exposure assessment for future population health studies.

Daily air temperature is a measurement that is required by many biogeochemical models. This study compared daily maximum (T max), minimum (T min) and mean (T mean) air temperature observations collected at 678 standard meteorological stations of China in 2003 with estimates derived from daytime and night-time land surface temperature (LST) observed by the Moderate Resolution Imaging Spectroradiometer (MODIS) on board TERRA and AQUA satellites. Correlation analysis showed that the determination coefficients (R 2 > 0.81) between models using night-time LSTs and the observed air temperatures were higher than those using daytime LSTs (R 2 > 0.57), but with significant seasonal variation. Though estimates derived from coupled daytime and night-time LSTs were more accurate than using night-time or daytime LSTs alone, the available pixels were substantially reduced. Four empirical models were established for T max, T min and T mean with MODIS night-time LSTs alone, or with coupled daytime and night-time LSTs, respectively. Solar declination was incorporated into the models to simulate seasonal variation of the correlations. Model validation showed that percentage of residuals within –3°C to 3°C ranged approximately from 60.2% to 74.3%, 64.4% to 69.9% and 76.8% to 85.7% for T max, T min and T mean, respectively. It was concluded that night-time LST was the optimum predictor for estimating daily T min, T mean and even T max when considering both the performance of the models and the availability of the LST data. Moreover, there was no significant difference between LSTs of TERRA and AQUA for estimating daily air temperatures.

An urban heat island (UHI) is a significant anthropogenic modification of urban land surfaces, and its geospatial pattern can increase the intensity of the heatwave effects. The complex mechanisms and interactivity of the land surface temperature in urban areas are still being examined. The urban–rural gradient analysis serves as a unique natural opportunity to identify and mitigate ecological worsening. Using Landsat Thematic Mapper (TM), Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS), Land Surface Temperature (LST) data in 2000, 2010, and 2019, we examined the spatial difference in daytime and nighttime LST trends along the urban–rural gradient in Greater Cairo, Egypt. Google Earth Engine (GEE) and machine learning techniques were employed to conduct the spatio-temporal analysis. The analysis results revealed that impervious surfaces (ISs) increased significantly from 564.14 km2 in 2000 to 869.35 km2 in 2019 in Greater Cairo. The size, aggregation, and complexity of patches of ISs, green space (GS), and bare land (BL) showed a strong correlation with the mean LST. The average urban–rural difference in mean LST was −3.59 °C in the daytime and 2.33 °C in the nighttime. In the daytime, Greater Cairo displayed the cool island effect, but in the nighttime, it showed the urban heat island effect. We estimated that dynamic human activities based on the urban structure are causing the spatial difference in the LST distribution between the day and night. The urban–rural gradient analysis indicated that this phenomenon became stronger from 2000 to 2019. Considering the drastic changes in the spatial patterns and the density of IS, GS, and BL, urban planners are urged to take immediate steps to mitigate increasing surface UHI; otherwise, urban dwellers might suffer from the severe effects of heatwaves.

&amp;lt;p&amp;gt;The increasing accessibility to high resolution land surface temperature (LST) data unbalances recently the investigation of the urban heat island (UHI) towards approaches based on these remote sensing tools. However, for a holistic assessment of UHI, a need of comparison of the resulted surface urban heat island (SUHI) with the air urban heat island(AUHI) remains of great interest. In our study we respond to this demand by taking to account all the MODIS LST images and their corresponding synchronous air temperature observations from 9 in-situ monitoring points evenly distributed over the city of Ia&amp;amp;#537;i for 2013-2020. This way, using a total of 2901 satellite images, the main diurnal and seasonal characteristics of clear-sky SUHI have been outlined for Ia&amp;amp;#537;i city.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The results obtained describe accurately the intensity of the SUHI, but also its relation with the urban land use categories. During summer season in daytime the spatial extent of SUHI reaches its maximum, SUHI being bounded by the 35&amp;amp;#176;C isotherm in direct relation with the highest imperviousness ratio. In the winter season instead, SUHI is almost absent during the day especially due to the high frequency of temperature inversions in this area. Also, the geometry of SUHI tends to be compact and regular during the nighttime and more irregular during the daytime, as a result of the higher and more complex energy input.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The comparison with the in-situ observations indicates that the differences between SUHI and AUHI are highest during the daytime in spring and summer, when LST is 5 to 7&amp;amp;#176;C higher than the air temperature in classical sheltered conditions, while during winter no major difference can be observed. For the nighttime the LST is 1 to 3&amp;amp;#176;C lower than air conditions regardles of the seasons. The analysis is detailed with the influence of land use categories and imperviousness ratio on SUHI, but also on the difference between SUHI and AUHI. As well, using a k-means atmospheric circulation classification we identified the weather patterns that are capable to increase both the SUHI intensity, and the difference between SUHI and AUHI.&amp;lt;/p&amp;gt;

The process of the surface urban heat island (SUHI) varies with latitude, climate, topography and meteorological conditions. This study investigated the seasonal variability of SUHI in the Tehran metropolitan area, Iran, with respect to selected surface biophysical variables. Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) was retrieved as nighttime LST data, while daytime LST was retrieved from Landsat 8 Thermal Infrared Sensor (TIRS) using the split-window algorithm. Both data covered the time period from September 2013 to September 2015. To assess SUHI intensity, we employed three SUHI indicators, i.e., the LST difference of urban-rural, that of urban-agriculture and that of urban-water. Physical and biophysical surface variables, including land use and land cover (LULC), elevation, impervious surface (IS), fractional vegetation cover (FVC) and albedo, were selected to estimate the relationship between LST seasonal variability and the surface properties. Results show that an inversion of the SUHI phenomenon (i.e., surface urban cool island) existed at daytime with the maximal value of urban-rural LST difference of −4 K in March; whereas the maximal value of SUHI at nighttime yielded 3.9 K in May. When using the indicators of urban-agriculture and urban-water LST differences, the maximal value of SUHI was found to be 8.2 K and 15.5 K, respectively. Both results were observed at daytime, suggesting the role of bare soils in the inversion of the SUHI phenomenon with the urban-rural indicator. Maximal correlation was observed in the relationship between night LST and elevation in spring (coefficient: −0.76), night LST and IS in spring (0.60), night LST and albedo in winter (−0.53) and day LST with fractional vegetation cover in summer (−0.41). The relationship between all surface properties with LST possessed large seasonal variations, and thus, using these relationships for SUHI modeling may not be effective. The only exception existed in the correlation between elevation and IS, which may be useful to simulate the SUHI at night. This study suggests that in semi-arid cities, such as Tehran, with the urban-rural indicator, a surface urban cool island may be observed in daytime while SUHI at nighttime; with other indicators, SUHI can be observed in both day and night. Thus, SUHI studies require the acquisition of remote sensing image data at both daytime and nighttime and careful selection of SUHI indicators.

Understanding the spatial and temporal variations of land surface temperature (LST) and its influencing factors is crucial for improving the urban thermal environment and promoting sustainable urban development. However, few studies have explored the impact of urban morphology on daytime and nighttime LST. Therefore, this study utilizes SDGSAT-1 data to investigate the influence of multi-dimensional urban morphology on summer daytime and nighttime LST. The results indicate that higher LST during daytime is primarily concentrated in low-rise, high-density urban blocks, whereas higher LST during nighttime is more prominent in high-rise, high-density blocks. Correlation analysis and the importance evaluation of the random forest (RF) regression model reveal that 2D spatial morphology exhibits a stronger correlation with daytime LST (|p| > 0.45), contributing over 50%. In contrast, 3D building morphology shows a higher correlation with nighttime LST (|p| > 0.2), contributing more than 60%. Further analysis using the multi-scale geographic weighting regression (MGWR) model highlights significant spatial heterogeneity in the impact of urban morphology on summer daytime LST, demonstrating that MGWR outperforms global models in capturing spatial variations of LST. These findings provide a scientific foundation for guiding future urban development, particularly in formulating differentiated planning strategies to mitigate urban heat stress.

The urban heat island (UHI) effect and its associated extreme weather events have adverse impacts on human environment-coupled systems. However, the spatiotemporal variations in the UHI effect, as well as potential influencing factors, across climate zones remain poorly understood. This study explored how climate zones influenced the spatiotemporal variation in, trends in, and drivers of summer daytime surface UHI intensity (SUHII) in 220 Chinese cities located in five climate zones from 2000 to 2020. SUHII was quantified using MODIS land surface temperature (LST) data and remote sensing-derived urban built-up area masks were used to quantify SUHII. The Mann–Kendall test was applied to detect long-term SUHII trends, while Pearson correlation and stepwise multiple regression analyses were performed to identify key climatic and geographic drivers across different climate zones. The results indicated summer daytime SUHII values of 1.75 °C ± 1.19 °C, 1.74 °C ± 0.81 °C, 2.37 °C ± 0.75 °C, 2.14 °C ± 1.00 °C, and 2.36 °C ± 0.91 °C for the middle temperate zone (MTZ), south temperate zone (STZ), north subtropical zone (NSZ), middle subtropical zone (MSZ), and south subtropical zone (SSZ), respectively. In most cities, the SUHII increased significantly over time (p &lt; 0.05). Pearson’s correlation analysis indicated that the enhanced vegetation index (EVI) and net radiation (NR) were moderately correlated with the SUHII in the MTZ, with correlation coefficients (r) of 0.465 and 0.42 (p &lt; 0.05). Using a multivariate stepwise regression model, the relative contributions of various influencing factors to the UHI effect were quantified, explaining 27.1% to 57.2% of the variation across different climate zones. In particular, the economic vulnerability index and population density were the main factors affecting the SUHII in the MTZ and SSZ. Our findings support the development of policies aimed at mitigating the UHI effect by addressing the specific requirements of different climate zones to reduce.

Abstract. Moderate Resolution Imaging Spectroradiometer (MODIS) daytime and nighttime land surface temperature (LST) data are often used as proxies for estimating daily maximum (Tmax) and minimum (Tmin) air temperatures, especially for remote mountainous areas due to the sparseness of ground measurements. However, the Tibetan Plateau (TP) has a high daily cloud cover fraction (&gt; 45 %), which may affect the air temperature (Tair) estimation accuracy. This study comprehensively analyzes the effects of clouds on Tair estimation based on MODIS LST using detailed half-hourly ground measurements and daily meteorological station observations collected from the TP. It is shown that erroneous rates of MODIS nighttime cloud detection are obviously higher than those achieved in daytime. Large errors in MODIS nighttime LST data were found to be introduced by undetected clouds and thus reduce the Tmin estimation accuracy. However, for Tmax estimation, clouds are mainly found to reduce the estimation accuracy by affecting the essential relationship between Tmax and daytime LST. The errors of Tmax estimation are obviously larger than those of Tmin and could be attributed to larger MODIS daytime LST errors that result from higher degrees of LST heterogeneity within MODIS pixel compared to those of nighttime LST. Constraining MODIS observations to non-cloudy observations can efficiently screen data samples for accurate Tmin estimation using MODIS nighttime LST. As a result, the present study reveals the effects of clouds on Tmax and Tmin estimation through MODIS daytime and nighttime LST, respectively, so as to help improve the Tair estimation accuracy and alleviate the severe air temperature data sparseness issues over the TP.

Space-based data have provided important advances in understanding climate systems and processes in arid and semi-arid regions, which are hot-spot regions in terms of climate change and variability. This study assessed the performance of land surface temperatures (LSTs), retrieved from the Moderate-Resolution Imaging Spectroradiometer (MODIS) Aqua platform, over Egypt. Eight-day composites of daytime and nighttime LST data were aggregated and validated against near-surface seasonal and annual observational maximum and minimum air temperatures using data from 34 meteorological stations spanning the period from July 2002 to June 2015. A variety of accuracy metrics were employed to evaluate the performance of LST, including the bias, normalized root-mean-square error (nRMSE), Yule–Kendall (YK) skewness measure, and Spearman’s rho coefficient. The ability of LST to reproduce the seasonal cycle, anomalies, temporal variability, and the distribution of warm and cold tails of observational temperatures was also evaluated. Overall, the results indicate better performance of the nighttime LSTs compared to the daytime LSTs. Specifically, while nighttime LST tended to underestimate the minimum air temperature during winter, spring, and autumn on the order of −1.3, −1.2, and −1.4 °C, respectively, daytime LST markedly overestimated the maximum air temperature in all seasons, with values mostly above 5 °C. Importantly, the results indicate that the performance of LST over Egypt varies considerably as a function of season, lithology, and land use. LST performs better during transitional seasons (i.e., spring and autumn) compared to solstices (i.e., winter and summer). The varying interactions and feedbacks between the land surface and the atmosphere, especially the differences between sensible and latent heat fluxes, contribute largely to these seasonal variations. Spatially, LST performs better in areas with sandstone formations and quaternary sediments and, conversely, shows lower accuracy in regions with limestone, igneous, and metamorphic rocks. This behavior can be expected in hybrid arid and semi-arid regions like Egypt, where bare rocks contribute to the majority of the Egyptian territory, with a lack of vegetation cover. The low surface albedo of igneous and limestone rocks may explain the remarkable overestimation of daytime temperature in these regions, compared to the bright formations of higher surface albedo (i.e., sandy deserts and quaternary rocks). Overall, recalling the limited coverage of meteorological stations in Egypt, this study demonstrates that LST obtained from the MODIS product can be trustworthily employed as a surrogate for or a supplementary source to near-surface measurements, particularly for minimum air temperature. On the other hand, some bias correction techniques should be applied to daytime LSTs. In general, the fine space-based climatic information provided by MODIS LST can be used for a detailed spatial assessment of climate variability in Egypt, with important applications in several disciplines such as water resource management, hydrological modeling, agricultural management and planning, urban climate, biodiversity, and energy consumption, amongst others. Also, this study can contribute to a better understanding of the applications of remote sensing technology in assessing climatic feedbacks and interactions in arid and semi-arid regions, opening new avenues for developing innovative algorithms and applications specifically addressing issues related to these regions.

Roles of horizontal and vertical tree canopy structure in mitigating daytime and nighttime urban heat island effects

This study assesses the spatial and temporal characteristics of nighttime surface urban heat island (SUHI) effects over Greater Cairo: the largest metropolitan area in Africa. This study employed nighttime land surface temperature (LST) data at 1 km resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua sensor for the period 2003–2019. We presented a new spatial anomaly algorithm, which allowed to define SUHI using the most anomalous hotspot and cold spot of LST for each time step over Greater Cairo between 2003 and 2019. Results demonstrate that although there is a significant increase in the spatial extent of SUHI over the past two decades, a significant decrease in the mean and maximum intensities of SUHI was noted. Moreover, we examined the dependency between SUHI characteristics and related factors that influence energy and heat fluxes between atmosphere and land in urban environments (e.g., surface albedo, vegetation cover, climate variability, and land cover/use changes). Results demonstrate that the decrease in the intensity of SUHI was mainly guided by a stronger warming in daytime and nighttime LST in the neighborhood of urban localities. This warming was accompanied by a decrease in surface albedo and diurnal temperature range (DTR) over these areas. Results of this study can provide guidance to local urban planners and decision-makers to adopt more effective mitigation strategies to diminish the negative impacts of urban warming on natural and human environments.

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.

&amp;lt;p&amp;gt;The urban heat island (UHI) effect is primarily related to the atmosphere, but may also refer to the surfaces. The atmospheric UHI (AUHI), determined using air temperature (Tair), and the surface UHI (SUHI), assessed using land surface temperature (LST), are distinguished. There is undoubtedly a relationship between SUHI and AUHI due to the modulation of Tair by LST. On hot days in the summer months, the SUHI/AUHI effect may increase the heat load, which is dangerous to the health and comfort of people staying in the city. Detailed characteristics of the spatial distribution of Tair and LST in urban areas are required to identify the parts of the city with the highest heat load. Spatially continuous Tair data, enabling better characterizing AUHI, can be obtained by modelling. Satellite thermal data (LST) can be used as input to the Tair spatial distribution model. Satellite data with 1 km spatial resolution, due to availability several times a day, are most useful in characterizing SUHI diurnal variability and the relationship of LST with Tair. The detailed knowledge of LST and Tair correlation should be helpful in the development of the Tair estimation algorithm based on the LST values. Better recognition of the relationship between LST and Tair, and thus improving the quality of modelling the spatial distribution of Tair in urban area, can possibly be achieved through downscaling of LST data to higher spatial resolution. In the study the method of LST downscaling from 1 km to 100 m was developed, using LST derived from AVHRR, Landsat, ASTER and ECOSTRESS data. The LST-Tair correlation in the diurnal course was examined and the influence of LST downscaling on the correlation was assessed. A Tair regression model was developed based on LST, depending on local climate zone (LCZ). LST and Tair maps for Krak&amp;amp;#243;w and its vicinities were prepared, and on the basis of them the intensities of AUHI and SUHI in the multi-year period (2010-2019) in the summer months (June, July, August) were determined, separately for day and night.&amp;lt;/p&amp;gt;

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.