High Land Surface Temperature Research Articles

Analysis of Land Use Land Cover and Land Surface Temperature in Karst Area: A Case Study Wonogiri Regency

<p class="Abstract">Urban development and global climate change drive increased Land Surface Temperature (LST). Wonogiri Regency is an area that is within the development range of Surakarta City. Analysis of land surface temperature has become Important in Wonogiri Regency as a mitigation measure for urban heat islands. This study aims to measure Land Surface Temperature (LST) in Wonogiri Regency and determine the controlling factor in the form of Land Use Land Cover (LULC), Normalized Difference Vegetation Index (NDVI), seasons, and zones by landform. Using the Google Earth Engine platform, LST calculations, LULC classifications, and NDVI calculations can compute imagery. The dataset used is USGS Landsat 8. The analytical technique used in this study is comparative descriptive, which compares other controlling factors. The analysis results show the vital role of LULC, NDVI, seasonality, and landform on LST. LULC types of built-up land, low NDVI values, dry season, and southern zone (karst) tend to have higher LST than other factors.</p>

GIS-BASED REGRESSION ANALYSIS OF AIR QUALITY AND LAND SURFACE TEMPERATURE IN METRO MANILA AIRSHEDS

Abstract. This study examines the spatial relationship of land surface temperature (LST) derived from MODIS, elevation, and air quality parameters (CO, NO2, and SO2) derived from Sentinel 5P in the Metro Manila airsheds from January 2019 to March 2023. Using Ordinary Least Squares (OLS) and Generalized Linear Regression (GLR), mean LST and heat index from the Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA) ground stations exhibit a strong positive correlation, allowing the use of LST for further analysis. Across different combinations between LST, elevation, and air quality parameters, a weak to low negative correlation was seen between DEM to LST, CO, and NO2. In addition, weak to low positive correlation was seen between LST to CO and NO2. Almost no correlation was found between DEM and SO2, and LST and SO2. These results may be unreliable due to overfitting, non-stationarity, bias, or misspecification as implied by their statistical parameters. To enhance the reliability, it is recommended to investigate additional air quality parameters such as Normalized Difference Built-Up Index (NDBI) as high LST, CO, and NO2 have shown clustering in urban areas of Metro Manila. Moreover, it suggests exploring other regression modeling and methodologies, such as training and test sets, to identify the best-fit model. In conclusion, this study provides an exploratory foundation for future research and comparative assessment on using different methods for modeling these variables. This comprehensive approach enhances understanding of the complex interplay between temperature, elevation, and air quality, aiding the development of informed urban climate adaptation strategies.

Long‒term trends in surface thermal environment and its potential drivers along the urban development gradients in rapidly urbanizing regions of China

Urban areas are experiencing extensive expansion and intensive warming, accompanied by the deterioration of environmental quality and increased health risks. However, the complex effect of global climate change and human activity on the thermal environment evolution under different stages of urbanization remains unclear. In this study, land surface temperature (LST) and a newly-proposed surface urban heat island intensity (SUHII) were used to reveal the thermal environment and its evolution during 2002‒2020 in the Yangtze River Delta (YRD), a rapidly urbanizing region in eastern China. The potential drivers of LST evolution were detected with random forest algorithm. We found that the thermal environment in the YRD had undergone deterioration trends, with the fastest warming trend observed in urban new towns (2.30 °C/decade). Although the urban cores had the highest LST, 15.91 % of urban cores exhibited a cooling trend, especially in cities with higher urbanization levels, which was caused by restored vegetation through urban renewal. Population density had the highest positive contributions in urban cores (21.08 %), while anthropogenic heat emissions accounted 21.87 % of the warming trend in urban new towns. Appropriate urban renewal and green infrastructure construction are effective strategies to alleviate the thermal environmental risks in highly urbanized areas.

Soil moisture mapping for drought monitoring in urban areas

Soil moisture is an important indicator for drought monitoring. Mapping of soil moisture in this study uses remote sensing, namely Landsat 9 OLI imagery, because it can be relied upon as a cheap source of information, and its good temporal resolution or revisit period. Two parameters indirectly related to soil moisture, namely vegetation were analyzed using the Normalized Difference Vegetation Index (NDVI) approach, and land surface temperature (LST). Drought analysis was verified using the Normalized Difference Drought Index (NDDI). The remote sensing imagery used in this study is Landsat 9 OLI imagery by selecting images with 30% cloud cover from 1 January 2022 to 31 December 2022 with the support of the cloud-based Google Earth Engine computing platform. The results of the analysis indicate high LST values in the southern part of the study area whose dominant land use is built-up areas, namely in the sub-districts of Depok, Gamping, Ngaglik, and Mlati. The effect of vegetation on soil moisture is indicated by the NDVI value, which has a relatively strong positive correlation with SMI (R= 0.46). The SMI value is in contrast to LST, where the spatial distribution of high SMI is spread in the northern part, namely Pakem, Cangkringan, and Turi districts. On the other hand, a low SMI is spread across the central and southern parts of the study area, which have a high drought index (extreme moderate). Overall, it is concluded that the SMI has the potential to map drought and is a reliable index for initial analysis of drought risk management.

Deep Interpolation of Remote Sensing Land Surface Temperature Data with Partial Convolutions.

Land Surface Temperature (LST) is an important resource for a variety of tasks. The data are mostly free of charge and combine high spatial and temporal resolution with reliable data collection over a historical timeframe. When remote sensing is used to provide LST data, such as the MODA11 product using information from the MODIS sensors attached to NASA satellites, data acquisition can be hindered by clouds or cloud shadows, occluding the sensors' view on different areas of the world. This makes it difficult to take full advantage of the high resolution of the data. A common solution to interpolating LST data is statistical interpolation methods, such as fitting polynomials or thin plate spine interpolation. These methods have difficulties in incorporating additional knowledge about the research area and learning local dependencies that can help with the interpolation process. We propose a novel approach to interpolating remote sensing LST data in a fixed research area considering local ground-site air temperature measurements. The two-step approach consists of learning the LST from air temperature measurements, where the ground-site weather stations are located, and interpolating the remaining missing values with partial convolutions within a U-Net deep learning architecture. Our approach improves the interpolation of LST for our research area by 44% in terms of RMSE, when compared to state-of-the-art statistical methods. Due to the use of air temperature, we can provide coverage of 100%, even when no valid LST measurements were available. The resulting gapless coverage of high resolution LST data will help unlock the full potential of remote sensing LST data.

Identification of geothermal potential zone associated with land surface temperature derived from Landsat 8 data using split-window algorithm

This study was conducted in the geothermal potential area of Mount Seulawah, Aceh province. Study was conducted using remote sensing methods in order to estimate and map the land surface temperature (LST). Data used in this study is Landsat 8 OLI and TIRS. Methods used in this study were split-window algorithm (SWA) to obtain the LST through NDVI analysis and landcover classification, and image enhancement to obtain the lineament density through visual interpretation referring to the results of band ratio and filter techniques. The results of the NDVI analysis show that the study area has a value in the range of -0.57 – 0.97. The results of the LST analysis show that the surface temperature in the area has a value in the range of 21.32 - 32.88 oC. Areas that have high LST values can be estimated as areas that have anomalies. Based on the results of the lineament density analysis, the higher density value in an area can indicate the better permeability where geothermal manifestations come out. The direction of the lineaments that control the study area is dominated by the northwest-southeast in accordance with the direction of the main stress, namely the Seulimum fault and the Aceh fault which also has a northwest-southeast direction. The overall results show that the Seulawah geothermal area is quite potential to be developed for the sustainable natural resource.

Demographic disparity in diurnal surface urban Heat Island exposure across local climate zones: A case study of Chongqing, China

Surface urban heat island (SUHI) exposure significantly harms human health during rapid urbanization. Identifying the areas and demographic groups under high SUHI exposure is critical for mitigating heat-related hazards. However, despite broad concern in US-European countries, rare studies discuss the diurnal SUHI exposure of demographic subgroups across Local Climate Zones (LCZs) in Chinese cities. Therefore, taking Chongqing as the case study, we measured the diurnal SUHI exposure of demographic subgroups (e.g., gender, age, and income) across different LCZs (compact, open, and sparsely-built zones) by coupling the ECOSTRESS data and mobile phone signaling data. The results indicated that Chongqing's compact high/middle-rise zones suffered a higher SUHI exposure due to high land surface temperature (LST) and a larger size of population than open zones. Despite a relatively low population density, extremely high LST in compact low-rise zones (e.g., industrial parks) contributes to considerable accumulated SUHI exposure. The SUHI exposure risk exhibited the differences between daytime and nighttime, resulting from SUHI variation and population flow. The demographic analysis showed that Chongqing's demographic subgroups are exposed disproportionately to SUHI. Elderly groups suffered relatively high exposure in compact high-rise zones. Low-incomers witnessed a high exposure in open zones. These findings call for alleviating SUHI exposure risk by targeting vulnerable groups and high-intensity exposure areas.

Comparing Characteristics of the Urban Thermal Environment Based on the Local Climate Zone in Three Chinese Metropolises

Urban landscape has important effects on urban climate, and the local climate zone (LCZ) framework has been widely applied in related studies. However, few studies have compared the relative contributions of LCZ on the urban thermal environment across different cities. Therefore, Beijing, Shanghai, and Shenzhen in China were selected to conduct a comparative study to explore the relationship between LCZ and land surface temperature (LST). The results showed that (1) both the composition and spatial configuration of LCZ had obvious differences among the three cities. Beijing had a higher area proportion of compact mid-rise and low-rise LCZ types. The spatial pattern of LCZ in Shenzhen was especially quite different from those of Beijing and Shanghai. (2) Shenzhen had the strongest summer surface urban heat island (UHI) intensity and the largest UHI region area. However, the proportion of urban cooling island areas was still the highest in Shenzhen. (3) Different LCZs showed significant LST differences. The largest LST difference between the LCZs reached 5.57 °C, 4.50 °C, and 12.08 °C in Beijing, Shanghai, and Shenzhen, respectively. Built-up LCZs had higher LSTs than other LCZ types. (4) The dominant driving LCZs on LST were different among these cities. The LST in Beijing was easily influenced by built-up LCZ types, while the cooling effects generated by LCZ G(water) were much stronger than built-up LCZs’ warming effects in Shanghai. These results indicated that the effect of the LCZ on LST had significant differences among LCZ types and across cities, and the dominant LCZs should be given more priority in future urban planning.

Land surface temperature responses to land use dynamics in urban areas of Doha, Qatar

Rapid urbanization primarily converts naturally vegetated areas and pervious surfaces into impervious built-up areas, significantly transforming microclimates and ecological dynamics. The impervious surfaces, marked by their higher thermal conductivity, disrupt surface energy balance and accumulate solar heat, subsequently elevating the land surface temperatures (LSTs). This study investigates the impact of land use and land cover changes on summer and winter LSTs in Doha and Al Dayeen municipalities of Qatar, spanning from the years 2000 to 2023, using remote sensing techniques and Geographic Information Systems (GIS). The analysis of land use and land cover changes reveals a remarkable 343.16 % increase in the built-up area from 2000 to 2023, at the expense of previously existing desert lands and water bodies. While Qatar's desert land has high land surface temperature, substituting such areas with built-up exhibits a notable rise in temperatures. Additionally, land reclamation also results in elevated LSTs. The LST data derived from remote sensing sources demonstrates an upward trend for summer and a contrasting trend for winter. Specifically, the mean summer LST increases by 7.64 °C (0.34 °C annually), and the mean winter LST decreases by 4.87 °C (0.22 °C annually). Notably, built-up areas and desert lands consistently recorded the highest mean LST in both seasons in all observed years. A strong correlation was observed between summer and winter LST with land use and land cover patterns using Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Built-up index (NDBI) and Normalized Difference Barrenness Index (NDBal). The results imply the negative influence of climate change and the urgent need for urban planning mitigation measures to counteract the adverse effects of increasing LSTs, particularly in summer months, to ensure the human well-being and resilience of the urban environments.

Evaluation of climate change impact on plants and hydrology

Climate change (CC) is the menace of the hour impacting every facet of human existence. Regional CC and its impact studies are crucial in that they contribute to global change. The current study aims to investigate the prevalence of CC in Charsadda, Pakistan and its impact on vegetation and hydrology of the region to understand microclimate variability contribution to global CC. Utilizing local climate data for 20 years (2001–2020), Modified Mann-Kendall and Sen’s Slope statistics were employed to determine monthly and seasonal trends in climate variables. Significant changing climate variables were regressed on Moderate resolution Imaging Spectroradiometer (MODIS) satellite dataset viz. normalized difference vegetation index (NDVI). Due to the prominent climate factor impacting vegetation, NDVI was further correlated to MODIS land surface temperature (LST). Floods being the conspicuous climate calamity were mapped for 2005 and 2010 using satellites Landsat 5 and 7 dataset viz. normalized difference water index (NDWI) with flood risk assessment by watershed delineation. The findings revealed significant (p < 0.05) variability in climate variables (average monthly and summer maximum temperature, and average monthly and summer precipitation) that are driving CC and impacting vegetation and hydrology in the region. Temperature and solar radiation affect NDVI adversely while precipitation and relative humidity has positive impact on vegetation. NDVI varied greatly spatiotemporally, often increasing but worsening in some areas (Shabqadar, Abazai, Palai and Charsadda city with NDVI = 0.1–0.3) of the study region as a result of extreme weather events. Temporally, NDVI improved with an overall positive trend with a stage (2007–2016) of noticeable zigzag fluctuation. Spatial grids with higher LST (>40°C) were either devoid of or with sparse NDVI (<0.3) presenting global warming as peril to vegetation. NDWI maps (2005, 2010 floods) indicate that after floods wreaked havoc on the region altering the vegetation pattern revealing heavy irregular precipitation as the next to temperature in jeopardizing vegetation of the region. Lower elevation regions along the Swat and Kabul Rivers with a greater risk of flooding were identified by watershed delineation. The study suggests that local governments and stakeholders implement CC mitigation strategies and plans for vegetation restoration, flood alerts with post-flood management for regional sustainable development.

The Impact of Urban Green Space on The Urban Heat Island Phenomenon – A Study Case in East Jakarta, Indonesia

The urban heat island (UHI) phenomenon has grown disastrous in many major cities across the world, including Jakarta. The more cities worldwide that suffer this scenario, the faster global warming will occur, making it a global concern that has to be given priority. The goal of this study was to determine the distribution of UHI in each region and how land use planning may mitigate it. East Jakarta was found to have the highest UHI in Jakarta. The method of this study used spatial analysis, which consists of land surface temperature (LST) and normalized vegetation difference index (NDVI) using GIS. The analysis results showed that the area with the highest LST and the widest coverage of UHI in East Jakarta is in Cakung where the temperature has increased to 4oC, while the area with the lowest LST and the narrowest coverage of the UHI area is Cipayung. Meanwhile, Cipayung has the most urban green space and urban forest, making up roughly 5.6% and 1.96% of its area, while Matraman has the least, making up 1.09% of its area without any urban forest. Therefore, there are no areas in East Jakarta that meet the requirements of 10% urban forest and 30% green space. However, the region with the lowest UHI distribution is Cipayung, which has the greenest urban area. It shows how the presence of urban forests, affects the distribution of UHI in metropolitan areas.

Quantifying Spatio-temporal dynamics of land surface temperature for the rapidly growing city of Bahir Dar, Northwest Ethiopia using multidated Landsat data

Urban heat island (UHI) refers to the occurrence of very high temperature in urban areas as compared to the surrounding rural areas. Land surface temperature (LST) is a key parameter in urban environmental analysis and varies with different land use/covers. The present study aims to explore the spatiotemporal dynamics of land surface temperature of Bahir Dar City and its surroundings using multidated Landsat 5 TM and Landsat 8 OLI/TIRS data of 1984, 1994, 2009, and 2019. The thermal infrared data has been corrected and converted to top of the atmospheric radiance and later to temperature in °C from Kelvin scale Using ArcMap software. The time series data pertaining to 1984, 1994, 2009, and 2019, showed a gradual increase in maximum LST and its expanse. Out of the total area of 213.55 km2, low (‹17 °C) to medium (‹23 °C) LSTprevailed in 65.88% (140.7 km2) of the area, whereas high (›29 °C) LST existed in 34.12% (72.85 km2). Though the low, medium, and high LST classes increased through 1984, 1994, 2009, and 2019, and medium and high-temperature classes became dominant in the expanse. The temporal variation of the Normalized Difference Vegetation Index (NDVI), as an indicator of urban heat intensity, showed the highest NDVI of 0.68 in 1994 and the lowest NDVI value of 0.54 in 2019. LST has a negative statistical correlation with NDVI with a coefficient of determination R2 = 0.99, 0.99, 0,94 & 0.98 for 1984, 1994, 2009, and 2019, respectively. NDVI and NDBI have very strong correlation with LST (R2>94%), which is quite different from previous studies in other study areas.

Improved Surface Urban Heat Impact Assessment Using GOES Satellite Data: A Comparative Study With ERA‐5

AbstractWe compare high‐resolution land‐surface temperature (LST) estimates from the GOES‐16/17 (GOES) satellites to ERA‐5 Land (ERA‐5) reanalysis data across nine large US cities. We quantify the offset and find that ERA‐5 generally overestimates LST compared to GOES by 1.63°C. However, this overestimation is less pronounced in urban areas, underscoring the limitations of ERA‐5 in capturing the LST gradient between urban and non‐urban areas. We then examine three quantities: Surface Urban Heat Island Intensity (SUHII), extreme LST events, and LST exposure by population. We find that ERA‐5 does not accurately represent the diurnal variation and magnitude of SUHII in GOES. Furthermore, while ERA‐5 was on average too warm, ERA‐5 underestimates extreme heat by an average of 2.40°C. Our analysis reveals higher population exposure to high LST in the GOES data set across the cities studied. This discrepancy is especially pronounced when estimating the population fraction that are most exposed to heat.

Comparison of multi-factor spatial downscaling models for high-resolution LST estimation in mountainous and hilly open-pit mines

The stripping of soil and vegetation by mining activities in mountainous and hilly areas of southern China has caused drastic surface disturbance and serious ecological and environmental problems. The spatial distribution of Land Surface Temperature (LST) in the mine site can better reflect the ecological disturbances caused by mining and is an important parameter for assessing the ecological environment of the mining sites. The classic Distrad thermal pixel decomposition model was improved by using different regression factors and linear and nonlinear fitting methods in this study, and three multi-factor downscaling models were proposed. Finally, four downscaling models were used to estimate the high spatial resolution LST for three three typical open-pit mining areas and explore the contribution of different surface parameters in the downscaling process from qualitative and quantitative perspectives. The results show that: (1) The MTVET (Extremely Randomized Trees regression model with multitype predictor variables) model exhibits the most accurate prediction accuracy in all three mining areas. The spatial distribution of errors shows that the area share of absolute errors between 0 and 1 K exist in 86.48 %, 81.38 %, and 84.51 % of the areas in Ganzhou rare earth mine, Dexing copper mine and Pingxiang coal mine, respectively. Based on the Root Mean Square Error (RMSE) assessment, the three mines are 0.699 K, 0.790 K, and 0.751 K, respectively, which are all controlled within 1 K; (2) The downscaled results of the MTVET model show rich and clear LST spatial texture features when typical mine sites are selected in each mining areas for detail comparison; (3) It was found that the topographic index has the greatest impact on accuracy enhancement, suggesting that topographic factors have an important influence in the study of LST downscaling in mountainous and hilly areas of southern China.

EXPLORING VERY HIGH-RESOLUTION REMOTE SENSING FOR ASSESSING LAND SURFACE TEMPERATURE OF DIFFERENT URBAN LAND COVER PATTERNS

Abstract. Very-high-resolution thermal infrared data has a good potential to detect and monitor LST variations in urban areas. In this work, an attempt was made to estimate Land Surface Temperature (LST) from very high resolution (VHR) data using machine learning technics in an area of Botanical garden of Yerevan, Armenia; UAV-derived and in-situ measured high precision LST of various land cover (LC) patterns were then compared for September and October. The main purpose of this study was to explore the capabilities of UAV imagery (multispectral/TIR) in assessing LST of the different LC patterns. For this purpose, a UAV survey was performed, and VHR data were collected using multispectral and TIR thermal camera while in-situ measurements of temperature and surveying of the different LC patterns were performed. A significant correlation was detected between LSTs in situ measured and detected by UAV in 06 September (r=0.758; p-value <0.01) and in 27 October (r=0.686; p-value <0.01). When comparing the LSTs of separate land-cover patterns the best results were received for Bare soils (r=0.591; p-value <0.05) and Concrete (r=0.927; p-value <0.01) when the survey and measurements was done in September. Despite its limitations, it can be stated that UAV thermal survey has a good potential to detect and monitor LST variations in urban areas.

Differential Urban Heat Vulnerability: The Tale of Three Alabama Cities

Urban heat vulnerability varies within and across cities, necessitating detailed studies to understand diverse populations’ specific vulnerabilities. This research assessed urban heat vulnerability at block group level in three Alabama cities: Birmingham, Montgomery, and Auburn-Opelika. The vulnerability index combines exposure, sensitivity, and adaptive capacity subindices, incorporating Landsat 8 satellite-derived Land Surface Temperature (LST), demographic, and socioeconomic data using factor analysis and geospatial techniques. Results showed strong positive correlations between LST and impervious surfaces in Auburn-Opelika and Montgomery, with a moderate correlation in Birmingham. An inverse correlation between LST and Normalized Difference Vegetation Index was observed in all cities. High LST correlated with high population density, varying across cities. Birmingham and Montgomery’s central areas exhibited the highest heat exposure, influenced by imperviousness, population density, and socioeconomic factors. Auburn-Opelika had limited high heat exposure block groups, and high sensitivity did not always align with exposure. Correlations and cluster analysis were used to dissect the heat vulnerability index, revealing variations in contributing factors within and across cities. This study underscores the complex interplay of physical, social, and economic factors in urban heat vulnerability and emphasizes the need for location-specific research. Local governance, community engagement, and tailored interventions are crucial for addressing unique vulnerabilities in each urban context.

Validation of FY-4A/AGRI LST and High Temporal–Spatial Resolution Analysis of Surface Heat Resources in Hunan Province, Central China

Land surface temperature (LST) is a crucial parameter in climate and ecology, exerting significant influence on meteorological conditions, ecosystems, and human life. LST data sources are diverse, with remote sensing being the prevailing means of acquisition. FY-4A/AGRI offers high-quality LST products for East Asia. We conducted a comprehensive evaluation and refined analysis of surface heat resources in Hunan Province, central China, over a two-year period using the 4 km/1 h resolution product in this study. The results demonstrate that the FY-4A LST product effectively captures surface temperature (R = 0.893), albeit with a relatively high error level (Bias = −6.295 °C; RMSE = 8.58 °C), particularly in capturing high LST values. The performance of this product is superior in the eastern flat terrain area of Hunan Province compared to its performance in the western mountainous region due to environmental conditions causing systematic errors that contribute to instability in detection deviation for this product. Surface heat resources are more abundant in eastern Hunan Province than in mountainous areas located west and southwardly, and the detailed distribution of them at finer scales is mainly influenced by terrain and climate conditions. There is no obvious seasonal difference in the distribution of heat resources except in winter, and rapid urbanization within Chang–Zhu–Tan urban agglomeration over two years has significantly altered the spatial distribution pattern of surface heat resources across Hunan Province. These findings provide a quantitative baseline for assessing FY-4A satellite’s detection capability while serving as a reference for further application of its LST products and establishing foundations for divisional classification and utilization strategies pertaining to surface heat resources within Hunan Province.

Impacts of Urban Morphology on Seasonal Land Surface Temperatures: Comparing Grid- and Block-Based Approaches

Climate change is expected to result in increased occurrences of extreme weather events such as heat waves and cold spells. Urban planning responses are crucial for improving the capacity of cities and communities to deal with significant temperature variations across seasons. This study aims to investigate the relationship between urban temperature fluctuations and urban morphology throughout the four seasons. Through quadrant and statistical analyses, built-environment factors are identified that moderate or exacerbate seasonal land surface temperatures (LSTs). The focus is on Seoul, South Korea, as a case study, and seasonal LST values are calculated at both the grid (100 m × 100 m) and street block levels, incorporating factors such as vegetation density, land use patterns, albedo, two- and three-dimensional building forms, and gravity indices for large forests and water bodies. The quadrant analysis reveals a spatial segregation between areas demonstrating high LST adaptability (cooler summers and warmer winters) and those displaying LST vulnerability (hotter summers and colder winters), with significant differences in vegetation and building forms. Spatial regression analyses demonstrate that higher vegetation density and proximity to water bodies play key roles in moderating LSTs, leading to cooler summers and warmer winters. Building characteristics have a constant impact on LSTs across all seasons: horizontal expansion increases the LST, while vertical expansion reduces the LST. These findings are consistent for both grid- and block-level analyses. This study emphasizes the flexible role of the natural environment in moderating temperatures.

Towards understanding climate change: Impact of land use indices and drainage on land surface temperature for valley drainage and non-drainage areas

The continuous increase of urbanization and industrialization brought various climatic changes, leading to global warming. The unavailability of meteorological data makes remotely sensed data important for understanding climate change. Therefore, the land surface temperature (LST) is critical in understanding global climate changes and related hydrological processes. The main objective of this work is to explore the dominant drivers of land use and hydrologic indices for LST in drainage and non-drainage areas. Specifically, the relationship between LST changes, land use, and hydrologic indices in Northeast Qena, Egypt, was investigated. The Landsat 5 and 8 imagery, Geographic Information System (GIS), and R-package were applied to identify the change detection during 2000–2021. The normalized difference between vegetation index (NDVI), bare soil index (BSI), normalized difference built-up, built-up index (BUI), modified normalized difference water index (MNDWI), and soil-adjusted vegetation index (SAVI) were employed. The non-drainage or mountain areas were found to be more susceptible to high LST values. The comprehensive analysis and assessment of the spatiotemporal changes of LST indicated that land use and hydrologic indices were driving factors for LST changes. Considerably, LST retrieved from the Landsat imaginary showed significant variation between the maximum LST during 2000 (44.82°C) and 2021 (50.74°C). However, NDBI has got less spread during the past (2000) with 10–13%. A high negative correlation was observed between the LST and NDVI, while the SAVI and LST positively correlated. The results of this study provide relevant information for environmental planning to local management authorities.

Evolution of urban morphological polycentricity and the thermal response in Wuhan from 2000 to 2020

The emergence of urban polycentric development patterns in response to the pressure of rapid urbanization can profoundly affect the urban heat island (UHI) effect. However, little research has been conducted on this issue at a high spatio-temporal resolution level. To investigate the evolution of urban polycentricity and the accompanying changes in the urban thermal environment, data for 2000 to 2020 from Wuhan in China obtained by multiple sensors were used to extract the urban morphological centers each year and generate annual summer mean land surface temperature (LST) with a 30 m resolution. Results show that the number and size of urban morphological centers in Wuhan increased significantly, from five centers covering 29.75 km2 in 2000 to 15 centers covering 171.72 km2 in 2020. The annual average growth rate was rapid (0.152 km2/a) from 2000 to 2012, then slowed down (0.026 km2/a). The location of newly emerged urban centers aligned with Wuhan's urban development planning. Meanwhile, the urban thermal environment also changed considerably. The summer mean LST have risen by around 1 K, whereas the area of high LST centers fluctuated in the recent two decades. The evolution of urban morphological centers and high LST centers was not always synchronized, with changes in the former showed hysteresis behind the latter, and overlapping areas between the two reaching the maximum at an interval of about nine years. These findings will further our understanding of the urban polycentricity, as well as the evolution of the urban thermal environment in this context.