Land Surface Temperature Difference Research Articles

Comparative Analysis of Two Methods for Valuing Local Cooling Effect of Forests in Inner Mongolia Plateau

Studies have extensively examined the cooling effects of forests. Various methods exist for evaluating climate regulation at regional and global levels. Local-scale cooling effects and their valuing methods, however, remain poorly understood. In this study, the temperature difference and energy balance methods were compared to assess the value of cooling services of three forest types at a local scale. Using the window searching strategy, land surface temperature and sensible heat flux differences between forest and open land were compared. The average cooling temperature of broad-leaved forests was found to be 0.229 °C, significantly higher than that of coniferous forests, at 0.205 °C, while mixed coniferous–broad-leaved forests were not significantly different to the other two types. The average sensible heat flux differences in broad-leaved, coniferous, and coniferous–broad-leaved forests were found to be 0.23, 0.079, and 0.11 MJ/m2/day, respectively. According to the correlation analysis, the sensible heat flux was significantly correlated with the cooling degree (R = 0.33, p = 0.05), suggesting consistency between the two approaches. However, the total cooling value calculated with the energy balance method was CNY 0.51 billion, significantly higher than the temperature difference method at CNY 0.11 billion. The main reason for the differences between the two approaches is the uncertainty in cooling volume and cooling time for the temperature difference method and energy balance method, respectively. The impact of vegetation on the microclimate depends on the vegetation type, topography, local climate, and other factors. It is also important to note that cooling services are not required at all times of the day, and energy differences can hardly be calculated based on the hour. However, surface radiation and evapotranspiration generally occur during the daytime, which is also when the surface temperature is high. Therefore, there is a certain coincidence with the time when cooling is needed. The energy balance method presented herein provides a novel alternative approach to assessing the cooling services of local-scale forests, offering advantages over the commonly used temperature difference approach, which is associated with large uncertainty.

A cross-scale indicator framework for the study of annual stability of land surface temperature in different land uses

Urban Land Surface Temperature (LST) is crucial in surface urban heat island (SUHI) and microclimate studies. Currently, research has focused on seasonal LST differences across land uses, but annual LST fluctuations (ΔLST) within the same land use and their drivers remain underexplored. To explore the impact of land characteristics and urban elements on seasonal LST differences, we propose annual LST stability. We constructed a new indicator framework based on Land Use and Land Cover (LULC), supplemented by Land Morphology (LM) and Land Properties (LP), for cross-scale ΔLST research. We identified land use ratios and key characteristics of urban plots with high stability. The results show an interactive effect of the green land ratio to other land on ΔLST. For residential and office land, the green space ratio (GSR) is key to annual LST stability. Residential land needs a GSR of more than 24 %. The floor area ratio (FAR) for residential and office land has a significant nonlinear effect on annual LST stability, with FARs of 1.8 for residential land and 1.5 for office land being most detrimental to the LST stability. For practical implications, we conducted cluster analyses on residential, office, and green lands, providing strategies to improve stability. These conclusions help balance land economic benefits with urban climate resilience and guide urban planning and design to address the challenges of heat and cold waves.

Assessing diurnal land surface temperature variations across landcover and local climate zones: Implications for urban planning and mitigation strategies on socio-economic factors

Rising temperatures and rapid urbanization globally reinforce the need to understand urban climates. We investigated the influence of land cover and local climate zones (LCZs) on diurnal land surface temperature (LST) in various seasons in greater Delhi region, India, and their implications on socio-economic factors. Day LST was the highest in the summer and night LST in the monsoon, which also had the lowest diurnal differences in LST. Higher height and density of built-up features contributed to greater heat at night. During the day, open built-up and vegetated areas experienced relatively less heat than their compact equivalents. The lowest diurnal difference was in medium height compact urban zones and tall vegetation. Social inequity in access to urban cooling was indicated by large low-income and heat-vulnerable populations inhabiting the hottest LCZs. This research highlighted that even in semi-arid and subtropical climates, spatial planning policy should consider both the seasonality and diurnal differences in temperature as much as appropriate morphologies for design of thermally comfortable and climate resilient urban spaces. These policies should address the evidenced social inequities in heat exposure to reduce the adverse health impacts on vulnerable groups and therefore contribute to wider societal and economic benefits of healthier populations.

Spatio-temporal tendencies of urban land surface temperature on the Andean piedmont under climate change: A case study of Metropolitan Lima, Peru (1986–2024)

The increase in land surface temperature (LST) in megacities has contributed to global warming due to poor urban planning and high population concentration. This study analyzes spatio-temporal trend patterns of LST in the city of Metropolitan Lima, Peru, during the summers of 1986–2024. The Mann-Kendall tests and the Theil-Sen Slope method were used to analyze the spatio-temporal trends of LST, relating R2 and the Mann-Kendall p-value of the annual average LST in each district. A hierarchical cluster analysis was performed to group districts according to their average yearly LST. Urban heat islands were identified based on basin configuration and distance to the sea at 1 km intervals. The results reveal a significant increase in LST (p < 0.001) related to El Niño-Southern Oscillation phenomena, in addition to urban growth and land cover change. The significant positive trend of LST showed a heterogeneous distribution in all districts. The districts were grouped into three clusters with statistically significant differences in LST (p < 0.001), spatially configured radially from the sea to the foot of the Andes, up to 1179 m a.s.l. Urban heat islands did not correlate with significant positive trends in basins. Still, they showed a considerable increase in LST in areas of high economic activity, including dense commercial, industrial, and residential areas. This information is crucial to managing climate change adaptation and mitigation measures and contributing to sustainable urban planning focused on the population's well-being.

Persistent heat islands monitoring of Tehran metropolis using temporal analysis of Landsat-8 satellite images.

The management and design of urban areas in metropolises pose significant challenges. Balancing diverse land uses within a metropolitan structure and addressing spatial and environmental constraints are just some of these challenges. Urban heat islands, which stem from factors such as inappropriate construction materials, inadequate building insulation, improper land use locations, and unsuitable built-up density, reflect environmental imbalances within urban infrastructure. Effectively addressing these temperature discrepancies can lead to energy preservation, reduced environmental hazards, and enhanced comfort for urban residents. This study employed Landsat-8 satellite images to identify and monitor positive and convex temperature disparities across various districts of Tehran over 3years (2018 to 2020) using three different strategies. These disparities are estimated through the differences in land surface temperature from the thermal trend of each district and their persistence has been assessed in seasonal, semi-annual, and annual strategies. The study found that industrial areas, including warehouses, were the significant contributors to the persistent presence of urban heat islands in summer and winter. Open areas with impervious surfaces or bare soil, particularly those lacking sufficient green cover, also significantly contributed to the heat island effect. Certain large shopping centers, often due to their air conditioning systems, were also consistently identified as persistent heat islands. Evaluations demonstrated that over 78.8% of the identified persistent heat islands were meaningful, with most located in the northern and western parts of Tehran.

ASSESSING URBAN HEAT ISLAND IMPACT AND IDENTIFYING VULNERABILITY ZONES THROUGH GEOSPATIAL AND GEO-STATISTICAL TECHNIQUES

An urban heat island emerged due to micro urban temperature variations are also referred to as urban heat islands or urban hot spots. The high-rise buildings along the roads form "Urban Canyons" that inhibit reflected radiation from the built-up surface. Urban heat island develops over the cities due to man-made activity and the landscape. An understanding of the urban heat island and its formation is not only helpful in understanding urban thermal characteristics but also helps in understanding human comfort. A geospatial technique has the ability to acquire updated and cost-effective data over large regions. For urban climatology studies, remote sensing and geographic information systems are an important source of information and an effective methodology. Since 1971, the city of Krishnanagar and its vicinity have been witnessing rapid urban growth. Due to its dense population, urban climate and rapid urban expansion, they cause environmental degradation. Appraisal and Impact of urbanization on micro-climate in the Krishnanagar city complex based on satellite derived parameters. For the years 1995, 2007 and 2018, several satellite image analysis approaches such as NDVI, NDWI and NDVI were computed. Significant differences in land surface temperature were observed between 1995 and 2007, as compared to 2007 and 2018.

Optimizing Local Climate Zones through Clustering for Surface Urban Heat Island Analysis in Building Height-Scarce Cities: A Cape Town Case Study

Studying air Urban Heat Islands (AUHI) in African cities is limited by building height data scarcity and sparse air temperature (Tair) networks, leading to classification confusion and gaps in Tair data. Satellite imagery used in surface UHI (SUHI) applications overcomes the gaps which befall AUHI, thus making it the primary focus of UHI studies in areas with limited Tair stations. Consequently, we used Landsat 30 m imagery to analyse SUHI patterns using Land Surface Temperature (LST) data. Local climate zones (LCZ) as a UHI study tool have been documented to not result in distinct thermal environments at the surface level per LCZ class. The goal in this study was thus to explore relationships between LCZs and LST patterns, aiming to create a building height (BH)-independent LCZ framework capable of creating distinct thermal environments to study SUHI in African cities where LiDAR data are scarce. Random forests (RF) classified LCZ in R, and the Single Channel Algorithm (SCA) extracted LST via the Google Earth Engine. Statistical analyses, including ANOVA and Tukey’s HSD, assessed thermal distinctiveness, using a 95% confidence interval and 1 °C threshold for practical significance. Semi-Automated Agglomerative Clustering (SAAC) and Automated Divisive Clustering (ADC) grouped LCZs into thermally distinct clusters based on physical characteristics and LST data internal patterns. Built LCZs (1–9) had higher mean LSTs; LCZ 8 reached 37.6 °C in Spring, with a smaller interquartile range (IQR) (34–36 °C) and standard deviation (SD) (1.85 °C), compared to natural classes (A–G) with LCZ 11 (A–B) at 14.9 °C/LST, 17–25 °C/IQR, and 4.2 °C SD. Compact LCZs (2, 3) and open LCZs (5, 6), as well as similar LCZs in composition and density, did not show distinct thermal environments even with building height included. The SAAC and ADC clustered the 14 LCZs into six thermally distinct clusters, with the smallest LST difference being 1.19 °C, above the 1 °C threshold. This clustering approach provides an optimal LCZ framework for SUHI studies, transferable to different urban areas without relying on BH, making it more suitable than the full LCZ typology, particularly for the African context. This clustered framework ensures a thermal distinction between clusters large enough to have practical significance, which is more useful in urban planning than statistical significance.

Geo-environmental monitoring of coastal and land resources for Coatzacoalcos coastal region

Coatzacoalcos is one of the notable Mexico coastal areas regarding location, activities, and resources. The purpose of the current study is to use geographic techniques to assess the spatial and temporal changes in Land Use/Cover (LULC), Land Surface Temperature (LST), and Shoreline as a result of anthropogenic and natural factors. Four calibrated multi-temporal Landsat images, dated 1993, 2003, 2013, and 2023, were combined and processed to accomplish this goal. The six primary LULC classes forest, built-up land, mining, water bodies, and agriculture were mapped using RF Machine learning algorithms. The last period (2003–2023) shows the highest rate of built-up land expansion, while the years 1993–2013 show the lowest rate of agricultural land reduction. Forest cover in the southeast decreased steadily from 339.25, 308.49, 287.43, and 211.83 km2 in 1993, 2003, 2013, and 2023. There is a slight difference in mean LST between built-up areas that recorded 26, 29, 32, and 33 °C in 1993, 2003, 2013, and 2023, respectively. The following is an order of LULC from lowest to highest LST during 2023 water body 12 °C, Forest 19 °C, Agriculture Land 22 °C, Mining 26 °C, Barren Land 34 °C and Built-up Land 33 °C. In Zone B near Coatzacoalcos showed particularly high accretion as a result of building artificial structures. Maximum rates of accretion and erosion were recorded at 2.0 m/year and −9.63 m/year, respectively. Fortunately, Zone C has minimal urban development and is home to forests and agricultural areas, so the potential negative effects are reduced. This study aids in understanding spatial and temporal LULC changes, LST variations, and shoreline dynamics, crucial for the sustainable management of Coatzacoalcos coastal resources in Mexico.

Southern California land surface temperature differences under different landscape composition

AbstractIn the last decade, due to prolonged and persisting drought conditions, California initially restricted water for outdoor landscape irrigation, and subsequently offered turf removal rebates to homeowners. Nevertheless, the effects of turf removal on land surface temperature (LST) in the region have not been investigated. Temperature differences between artificial and natural turf were assessed over time across four counties in Southern California using MODIS/ASTER airborne simulator (MASTER) with a spatial resolution of 5–50 m. Moreover, airborne thermal imagery (submeter spatial resolution) was acquired over selected areas of the city of Riverside, CA, during the summers of 2018 and 2019, including neighborhoods with different income levels, and temperatures for natural turf, artificial turf, and xeriscape were recorded. Environmental and socioeconomic data were compared to the LST in different neighborhoods. Results showed that the LST difference between artificial and natural turf increases in Southern California moving from coastal region to inland. Airborne thermal imagery in Riverside confirmed that University of California artificial turf fields are irrigated during the summer to allow athletes to use the pitch. No correlation between socioeconomic factors and LST of turf and artificial turf fields (and paired differences) was found. On a city scale, natural turf lawns were consistently cooler than xeriscape and artificial turf lawns, closely mirroring mean air temperature. Socioeconomic factors do not describe LST in residential lawns, as warmest and coolest lawns regardless of vegetation are found evenly distributed among different income neighborhoods. Turfgrass removal for water conservation may have unforeseen environmental side effects.

Multifactorial influences on land surface temperature within local climate zones of typical global cities

The land surface temperature (LST) of most cities is rising steadily due to both human activities and global climate change, affecting the thermal comfort of cities and threatening the physical health of their inhabitants. Investigating surface temperature features and their affecting elements is therefore essential to improve the thermal environment of cities. Due to differences in surface temperature characteristics in different climatic belts. However, there is still a lack of research on how the local climate zones (LCZs) of different climate belts are affected by natural and social causes. According to the Koppen-Geiger climate classification system, we selected three representative cities from each of the four macroclimatic belts and adopted multiple linear stepwise regression and boosted regression trees (BRT) to systematically explore the linear relationships, relative impacts and marginal effects of normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), population density, and road nuclear density within urban LCZs on LST. The results indicate that (1) LCZs with significant differences in LST among the four global climatic belts account for more than 95 % (P < 0.05), demonstrating that LCZs can effectively differentiate LST based on different land surface cover types. This study can delve into the relationships between the four influencing factors and LST based on LCZs. (2) Primary control factors regulating LST differ in different climate belts. The relative effects of NDVI and MNDWI on LST are greater in the arid and temperate belts, with each 0.1 increase in NDVI and MNDWI producing a cooling effect of more than 0.40 °C and 0.92 °C, respectively. Ventilation corridors created by increased road core density produced a cooling effect of 1 °C or more in the cold belt, with the most pronounced cooling effect. (3) When natural factors are higher and social factors are lower, LST cannot be minimized. It was found that LST was minimized when NDVI, MNDWI, population density and road nuclear density were controlled above 0.4, 0.1–0.2, 10,000–50,000 and 1500–2000 respectively. Our study will provide targeted measures for LCZ-based mitigation of urban thermal environments in various macroclimatic belts.

Small unmanned aerial vehicle (UAV)-based detection of seasonal micro-urban heat islands for diverse land uses

ABSTRACT Metropolitan areas have diverse land uses (LUs), which can also cause significant differences in land surface temperature (LST), leading to the formation of micro-urban heat islands (MUHIs). Measuring the MUHIs is significant for heat mitiga-tion and adaptation measures and requires high spatial-temporal resolution, which is not feasible through coarser satellite observations (CSOs). Thermal cameras onboard unmanned aerial vehicles (UAVs) can detect such MUHIs because of their high spatial and desired temporal resolution. This study used the Zenmuse H20T onboard a UAV providing LST at ∼8 cm resolution to evaluate MUHIs in an area with diverse and contiguous LUs including three urban built-up LUs: 1) residential high cost (RHC), 2) residential low cost (RLC), 3) industrial area (IA) and one natural area (i.e. park area (PA)). The LST and MUHI were estimated in two seasons: fall (October 2022) and summer (June-July 2023). In each season, six flights were conducted at similar times of day. The findings were compared with Landsat in each season to examine the loss of information between coarser and finer spatial resolution. Using UAV, a maximum MUHI of 25.54 ◦ C and 15.85 ◦ C was identified in the summer and fall seasons, respectively, between 15:30 and 16:20. The maximum LST was observed in RHC, and PA showed the minimum LST in both seasons. Notably, dark-coloured roofs with asphalt shingle coating reported up to 25.78 ◦ C and 27.37 ◦ C higher LST (UAV-estimated) than light-coloured roofs in the fall and summer, respectively. Landsat significantly underestimated MUHI hotspots in the summer and fall seasons. The on-ground validation of the UAV showed better results in the summer season. The study shows the pragmatic use of UAVs to detect localized MUHIs. The findings are useful to devise strategies to mitigate MUHIs utilizing UAVs in the face of climatic and environmental changes.

Investigation of Surface Urban Heat Island by High-Rise Buildings: Istanbul Levent Region

Although there are studies on urban climate, studies on the effects of skyscrapers in these areas are quite limited. This study aims to determine the impact of skyscrapers on the formation of surface urban heat islands (SUHIs). Accordingly, an area within a radius of one kilometer, centered on the skyscraper region in Levent, Istanbul, was selected as the study area. This area was chosen because it shows three different urban textures. Daytime land surface temperature (LST) maps were created from Landsat TM 5 images from 1990 and 2009, and Landsat 8 (OLI-TIRS) images from 2021. Then, the differences in LST between the three years were calculated to investigate the changes in LST in different urban textures. The results of the study show a low increase in LST in the skyscraper area (2.7 °C) when looking at the 1990-2021 LST difference, while an increase in LST is observed toward the region where construction is low in density (6.9 °C) and the area with unplanned development (10.1 °C). The factors causing the formation of SUHI in different urban textures will be discussed with the findings of this study.

Urban land use, land cover change and urban microclimate dynamics in Addis Ababa, Ethiopia

Land surface temperature (LST) increases and urban heat island (UHI) variability are the major urban climatology problems arising in urban development. This study attempts to assess the effects of urban land use and land cover change on microclimate dynamics in Addis Ababa city. Three different sets of remotely sensed data from Landsat 5 TM (1990), Landsat 7 ETM+ (2005) and Landsat 8 OLI/TIRS (2021) were used for the study. LSTs were retrieved from Landsat5 TM and Landsat7 ETM+ using a mono window,and the thermal infrared band (TR-10) of Landsat–8 was used to retrieve LST. Regression and correlation analyses of the LST, normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) were performed in SPSS V23. The study also examined the different residential urban morphology types (UMTs) of the LST and NDVI. The selected built-up blocks of UMTs included apartments, villas and mud houses. These UMTs are extracted by digitizing them from the Google Earth explorer. The results from this study showed that the proportion of urban green space (UGS) to other LULC types decreased from 120.4 km2 in 1990 to 76.26 km2 in 2021. However, the built-up area increased at a rate of 216.5 km2 (39.03%) from 1990 to 2021. The rapid expansion of built-up land in the study area was the main factor influencing the increase in LST. The residential UMTs exhibited significant differences in mean LSTs and NDVIs. The results indicate that UMT inhibited by Villia had the highest mean NDVI value and that the highest mean LST was observed in Apartment. The results of multiple linear regression analysis clearly indicate that built-up and green vegetation contributed 92.2% of the LST variations with R2 = 0.92 and VIF ≤ 10 in Addis Ababa city. The results of the study indicate that strengthening public participation in urban greening and optimizing the NDVI and NDBI are important strategies for mitigating the effects of microclimate change and that sustaining urban development and providing better quality of life for the urban population are important.

Coupling effects of building-vegetation-land on seasonal land surface temperature on street-level: A study from a campus in Beijing

The land surface temperature (LST) of urban streets is significantly influenced by the surrounding urban environment. In the cold areas of northern China, this influence exhibits a seasonal pattern. Currently, in areas with noticeable seasonal variations, the coupling effects of urban elements on the annual stability of street LST remain unclear. This study developed a new metric, Normalized Winter-Summer Land Surface Temperature Difference, to measure the year-round LST stability of campus streets. Three key urban elements affecting street-level LST in winter and summer were identified: building, vegetation, and land. The influencing characteristics for winter and summer LST were quantified and ranked respectively. The study reveals that the green space ratio dominates summer LST, while fraction vegetation coverage, leaf area index and average building height dominate winter LST. We found that morphological characteristics of buildings and vegetation cannot fully explain the impact of the urban physical environment on street-level LST. For winter street LST studies, greater emphasis should be placed on non-morphological characteristics and land. Based on the impact mechanisms and critical thresholds of key features in winter and summer, this study establishes an interpretable spatial classification method. By assigning seasonal weights to influencing features, it provides comprehensive design strategies that consider both winter and summer features to enhance annual LST stability and climate resilience in urban spaces facing extreme meteorological conditions.

Study on mapping method of irrigated cultivated land–taking Nebraska as an example

The distribution information of irrigated cultivated land is important to many studies, including grain yield estimation, water resources management, drought risk assessment, and so on. This study developed a feature variable, called the irrigation probability index (IPI), based on the principle that irrigation can slow down or inhibit the evolution from meteorological drought to agricultural drought. Nebraska, which has a good irrigation information database, was selected as the study area. Combining IPI with the irrigation statistical data and other selected feature variables, irrigated cultivated land in Nebraska was mapped in 2017 using both the spatialization method and remote sensing classification methods (random forest and support vector machine). We verified the effectiveness of IPI in identifying irrigated cultivated land, analyzed the importance of different feature variables for irrigated cultivated land identification, and compared the accuracy of different irrigation mapping methods. The results show that the IPI is significantly correlated with the actual irrigation area, the area of irrigation facilities, and the number of active irrigation wells. It has a better indicative ability of irrigation in areas with a suitable climate or arid areas, than in areas with a humid climate. The crop water stress index (CWSI), IPI, vegetation index (EVI and NDVI), and land surface temperature difference between day and night are the most important feature variables that are used to distinguish irrigated cultivated land from rain-fed cultivated land. There are differences between the recognition accuracies of the three mapping methods in different climatic regions. Overall, the spatialization method had the highest accuracy (overall accuracy = 86.92 %, kappa = 0.74) and the highest similarity with the existing high-resolution irrigation maps. This study provides a direct and useful reference for other researchers to select feature variables and mapping methods in irrigated cultivated land mapping research.

Exploring the spatiotemporal impacts of urban green space patterns on the core area of urban heat island

The key to managing urban heat island (UHI) is identifying and cooling the core areas of UHI. Studies have shown that spatial differences in urban green space (UGS) indicators have a significant impact on spatial differences in land surface temperature (LST). However, previous studies have mainly explored UGS and LST in the entire region, with few focusing on the urban core area. In this study, we extracted the urban core area based on the Point of Interest (POI) and kernel density methods. Correlation and regression analyses were then used to investigate the correlation between UGS indicators and seasonal LST in the urban core area. Our results confirm the significant cooling effect of UGS at the urban core scale. The effects of UGS landscape composition and configuration on LST varied across seasons and across urban green space ratio (UGSR) gradients. In the warm season, increasing the UGSR proved to be the most effective way to mitigate the urban thermal environment. In urban core areas where expanding UGS is not possible or in cores with high UGSR, designing UGS with complex shapes and clusters can better achieve the cooling effect. These results suggest that the influence of UGS landscape features on the urban surface thermal environment at the urban core area scale is very complex. Therefore, implementing urban greening to mitigate the UHI problem requires considering the premise of multifactorial analysis.

Assessing the role of urban green infrastructure in mitigating summertime Urban Heat Island (UHI) effect in metropolitan Shanghai, China

The importance of urban green infrastructure (UGI) in ameliorating the urban thermal environment and reinforcing urban climatic adaptation has attracted growing concerns. In this study, 36 representative urban thermal hotspots (UTHSs) in Metropolitan Shanghai, which vary in urban setting features and exposure to surface urban heat island (SUHI) effect, were used to quantitatively assess the UGI's role in mitigating the SUHI effect measured with the satellite-retrieved land surface temperatures (LSTs). We observed significantly contrasting LST differences between UTHSs ranging from highly to slightly urbanized areas. Moreover, we found the contrasting performances of forward stepwise regression (FSWR), partial least squares regressions (PLSR), and random forest regressions (RFR) models for quantitatively assessing the UGI's role in mitigating the SUHI effect. The FSWR models showed high r2 (averaged 0.816) but presented the worst performance for interpreting the results; PLSR models showed the highest r2 (averaged 0.820), presenting the best performance for interpreting the results; RFR models showed the lowest r2 (averaged 0.708), presenting an acceptably moderate performance for interpreting the results. Our findings provided an applicable methodology with repeatability for assessing UGI's role in mitigating the SUHI effect elsewhere, regarding the complexity of urban setting features.

Correcting an Off-Nadir to a Nadir Land Surface Temperature Using a Multitemporal Thermal Infrared Kernel-Driven Model during Daytime

Land surface temperature (LST) is a fundamental parameter in global climate, environmental, and geophysical studies. Remote sensing is an essential approach for obtaining large-scale and frequently updated LST data. However, due to the wide field of view of remote sensing sensors, the observed LST with diverse view geometries suffers from inconsistency caused by the thermal radiation directionality (TRD) effect, which results in LST products being incomparable, especially during daytime. To address this issue and correct current off-nadir LSTs to nadir LSTs, a semi-physical time-evolved kernel-driven model (TEKDM) is proposed, which depicts multitemporal TRD patterns during the daytime. In addition, we employ a Bayesian optimization method to calibrate seven unknown parameters in the TEKDM. Validation results using the U.S. Climate Reference Network (USCRN) sites show that the RMSE (MBE) for GOES-16 and MODIS off-nadir LST products is reduced from 3.29 K (−2.0 K) to 2.34 K (−0.02 K), with an RMSE reduction of 0.95 K (29%) and a significant reduction in systematic bias. Moreover, the proposed method successfully eliminates the angular and temporal dependence of the LST difference between the satellite off-nadir LST and in situ nadir LST. In summary, this study presents a feasible approach for estimating the high-accuracy nadir LST, which can enhance the applicability of LST products in various domains.

Simulating influences of land use/land cover composition and configuration on urban heat island using machine learning

Urban heat island (UHI) has become a worldwide concern under global warming and urbanization waves, which pose significant threats to human health and urban sustainability. Despite Land Use/Land Cover (LULC) being regarded as a crucial factor influencing UHI, few studies simulated the nonlinear influences from LULC compositions and configurations, especially considering the difference between monocentric and polycentric cities. Therefore, we measured the patterns of LULC and UHI from 2006 to 2022 using cases of a monocentric city (Chengdu) and a polycentric city (Chongqing). Further, we simulated future and land surface temperature (LST) using Machine Learning (ML) models, and further calculated the simulated UHI in 2030 by urban-rural LST differences. The results showed that Chengdu had prominent UHI intensity in cores and the surrounding satellite towns. Chongqing's main center and subcenters had high UHI intensity under clustered patterns. ML results indicated that built-up percentage was the most crucial factor driving LST. Configuration factors dominated in impacting Chengdu's LST, while composition factors were more critical in Chongqing. Moreover, future UHI is estimated to decrease in Chongqing's core, compared with the remaining high UHI in Chengdu's core. These results might provide insights for understanding and mitigating future UHI effects.

Evaluating the thermal environmental alterations due to photovoltaic installations in the kushida river basin, Japan

The construction of Photovoltaic (PV) provides renewable energy, but it is also impacting the basins environment. The study simulates the annual construction status of PV installations within the research area over a time series from 2013 to 2023, and to understanding of the trends in Land Surface Temperature (LST) changes due to the PVs. The results revealed that the number of PV installations in the study area increased most significantly between 2018 and 2019, with the newly installed area during this period being approximately 5.2 times larger than 2013. Although large-scale construction slowed after 2020, small and medium-sized PV installations continued. The study found that the LST around PV which built from 2013 to 2023 increased by an average of 2.85 °C. Also, LST-difference (LST-D) changes with the seasons. The analysis of the relationship between geographical conditions and LST difference revealed that in hilly and mountainous areas with higher elevations, the impact of PV construction on LST was lower. And found that the distance of the PV from the watercourse and the surrounding land cover also had an impact to LST changes, with spatial variability between the different impact variables as analysed by the Geographically Weighted Regression (GWR) model.