Search

With the advance of industrialisation and urbanisation, land use change and climate change have significant impacts on the global ecosystem. Focusing on Chongqing Municipality, a municipality that plays a central role in regional economic development and national strategies, this study explores the impacts of land use and climate change on the evolution of its NDVI spatial and temporal patterns between 2000 and 2020 and reveals the driving mechanisms behind them. By analysing remote sensing image data and climate data, it was found that Chongqing Municipality experienced significant land use changes during the study period, especially urban expansion and the reduction of agricultural land, which led to the reduction of vegetation cover. Meanwhile, precipitation in climate change positively affected vegetation growth and coverage, while the increase in surface temperature during urbanisation negatively affected vegetation cover and exacerbated the urban heat island effect. NDVI was positively correlated with precipitation and negatively correlated with air temperature, suggesting that moderate precipitation promotes vegetation growth, while high temperatures may adversely affect vegetation activities. The results of this study can provide a scientific basis for urban planning and ecological conservation, especially in formulating effective urban management and land management strategies to protect the ecological environment and rationally utilise land resources. [ABSTRACT FROM AUTHOR]

Rapid urban expansion and chaotic urban land-use patterns cause many socio-economic and environmental issues, e.g., traffic congestion and urban heat islands; thus, scientific planning considering land-use trade-offs and layout optimization is highly required for resolving these issues, especially in the urban renewal stage. However, previous spatial optimization methods were weak in processing land-use patches and ignored their neighborhood dependency, leading to fragmented and inapplicable optimization results. Accordingly, this study proposes a patch-level and neighborhood-dependency spatial optimization method (PNO) to adjust urban land-use patterns considering multiple optimization targets (i.e., improving population and economy but controlling land surface temperature). The PNO represents land-use patterns in a graph structure, quantifies land-use patterns' impacts on the population, economy, and land surface temperature, defines the spatiotemporal constraints of land-use optimization considering neighborhood-dependency and optimization sequences, and finally optimizes land uses and their spatial layouts based on a multi-objective genetic algorithm. Experiments were conducted in the urban area of Beijing, and the results suggested that, after optimization, the population and GDP can be improved by 667,323 people (4.72%) and USD 10.69 billion in products (2.75%) in the study area; meanwhile, the land surface temperature can be reduced by 0.12 °C (−0.32%). Through comparison, the proposed PNO outperforms previous spatial optimization methods, e.g., NSGA-II, in processing land-use patches as well as their neighborhoods. Taking the land-use map in 2022 as a reference, the PNO optimization results are more consistent with actual land-use changes (consistency of 25%), compared to the existing spatial optimization results (consistency of 10.6%). Thus, PNO is more applicable to land-use planning in urban renewal circumstances. [ABSTRACT FROM AUTHOR]

Urbanized riverine cities in southern Asian developing countries face significant challenges in understanding the spatiotemporal thermal impacts of land use/land cover (LULC) changes driven by rapid urbanization and climatic variability. While previous studies have investigated factors influencing land surface temperature (LST) variations, gaps persist in integrating Landsat imagery (7 and 8), meteorological data, and Geographic Information System (GIS) tools to evaluate the thermal effects of specific LULC types, including cooling and warming transitions, and their influence on air temperature under variable precipitation patterns. This study investigates LST variations in Islamabad, Pakistan, from 2000 to 2020 using quantile classification at three intervals (2000, 2010, 2020). The thermal contributions of each LULC type across the LST-based temperature classes were analyzed using the Land Contribution Index (LCI). Finally, Warming and Cooling Transition (WCT) maps were generated by intersecting LST classes with 2000 as the baseline. Results indicated a rise in LST from 32.39 °C in 2000 to 45.63 °C in 2020. The negative LCI values revealed that vegetation and water bodies in lower temperature zones (Ltc_1 to Ltc_3) contributed to cooling effects, while positive LCI values in built-up and bare land areas in higher temperature zones (Ltc_5–Ltc_7) exhibited warming effects. The WCT map showed a general warming trend (cold-to-hot type) from 2000 to 2020, particularly in newly urbanized areas due to a 49.63% population increase, while cooling effects (hot-to-cold type) emerged in the newly developed agricultural lands with a 46.46% rise in vegetation. The mean annual air temperature gap with LST narrowed from 11.55 °C in 2000 to 2.28 °C in 2020, reflecting increased precipitation due to increasing yearly rainfall from 982.88 mm in 2000 to 1365.47 mm in 2020. This change also coincided with an expansion of water bodies from 2.82 km2 in 2000 to 6.35 km2 in 2020, impacting the local climate and hydrology. These findings highlight the importance of green spaces and water management to mitigate urban heat and improve ecological health. [ABSTRACT FROM AUTHOR]

Land use and land cover (LULC) change, driven by environmental and human activities, significantly impacts ecosystems, climate, biodiversity, and socio-economic systems. This study focuses on Khyber Pakhtunkhwa (KPK), Pakistan, a region with sensitive ecosystems and diverse landscapes, to analyze LULC dynamics and their environmental consequences. Based on Landsat imagery from 2000, 2010, and 2020, we used the Random Forest algorithm on Google Earth Engine (GEE) to classify LULC, and the CA-ANN model to project future scenarios for 2030, 2050, and 2100. Additional simulations were conducted using the MOLUSCE Plugin in QGIS. The results revealed a 138.02% (4071.98 km2) increase in urban areas from 2000 to 2020, marking urbanization as a major driver of LULC change. Urban expansion strongly correlated with land surface temperature (LST) (R2 = 0.89), amplifying the urban heat island effect. Rising LST showed negative correlations with the key environmental indices NDVI (−0.88), MNDWI (−0.49), and NDMI (−0.62), signaling declining vegetation cover, water resources, and soil moisture, respectively. Projections for 2100 predict LST rising to 55.3 °C, with NDVI, MNDWI, and NDMI dropping to 0.36, 0.17, and 0.21, respectively. Vegetation health, as indicated by the Leaf Area Index (LAI), also declined, with maximum and minimum values falling from 4.66 and −5.75 in 2000 to 2.16 and −2.55 in 2020, reflecting increased barren land and reduced greenness. The spatial analysis highlights significant transitions from vegetated to barren or urban land, leading to declining moisture levels, water stress, soil erosion, and biodiversity. Projections show continued reductions in forests, vegetation, and agricultural lands, replaced by barren and built-up areas. Declines in key indices such as NDVI, MNDWI, and NDMI indicate deteriorating vegetation, water resources, and soil moisture levels. These findings emphasize the need for sustainable urban planning and environmental management. Expanding urban green spaces, using reflective materials, and preserving vegetation and water resources are vital to mitigating heat island effects and maintaining ecological balance. Anticipated declines in LST, NDVI, MNDWI, NDMI, and LAI stress the urgency for climate adaptation strategies to protect human health, ecosystem services, and economic stability in KPK. [ABSTRACT FROM AUTHOR]

Remote sensing of land surface temperature (LST) is a fundamental variable in analyzing temperature variability in urban areas. Geostationary sensors provide sufficient observations throughout the day for a diurnal analysis of temperature, however, lack the spatial resolution needed for highly heterogeneous areas such as cities. Polar orbiting sensors have the advantage of a higher spatial resolution, enabling a better characterization of the surface while only providing one to two observations per day. This work aims at using a multi-layer perceptron-based method to downscale geostationary-derived LST based on a polar-orbit-derived one. The model is trained on a pixel-by-pixel basis, which reduces the complexity of the model while requiring fewer auxiliary data to characterize the surface conditions. Results show that the model is able to successfully downscale LST for the city of Madrid, from approximately 4.5 km to 750 m. Performance metrics between training and validation datasets show no overfitting. The model was applied to a different time period and compared to data derived from three additional sensors, which were not used in any stage of the training process, yielding a R2 of 0.99, root mean square errors between 1.45 and 1.58 and mean absolute errors ranging from 1.07 to 1.15. The downscaled LST is shown to improve the representation of both the temporal variability and spatial heterogeneity of temperature, when compared to geostationary- and polar-orbit-derived LST individually. The resulting downscaled data take advantage of the high observation frequency of geostationary data, combined with the spatial resolution of polar orbiting sensors and may be of added value for the study of diurnal and seasonal patterns of LST in urban environments. [ABSTRACT FROM AUTHOR]

Wind energy is widely considered a clean and renewable resource, yet the environmental impacts of wind farm (WFs) installations, particularly on local climate and ecosystems, remain underexplored on a large scale. This study presents a comprehensive assessment of the long-term effects of 250 WFs across China on land surface temperature (LST) and vegetation using remote sensing data. By comparing inside and outside LST and peak normalized difference vegetation index (NDVI) trends before and after five years of construction, we identified key environmental changes. Results indicated that the WFs significantly increased nighttime LST by 0.20 °C and decreased daytime LST by 0.11 °C, with pronounced seasonal variability during daytime. A total of 75.20% of the WFs negatively impacted vegetation, with no discernible seasonality in this effect. Geographical factors such as latitude, longitude, and elevation showed weak correlations with these impacts. Our findings provide valuable insights into the environmental consequences of wind power development and contribute to more informed planning for sustainable energy generation and climate adaptation strategies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Urban areas experience the impact of natural disasters, such as heatwaves and flash floods, disparately in different neighbourhoods across a city. The demand for precise urban hydrometeorological and hydroclimatological modelling to examine this disparity, and the interacting challenges posed by climate change and urbanisation, has thus surged. The Weather Research and Forecasting (WRF) model has served such operational and research purposes for decades. Recent advancements in WRF, including enhanced numerical schemes and sophisticated urban atmospheric‐hydrological parameterizations, have empowered the simulation of urban geophysical processes at high resolution (~1 km), but even this resolution misses significant urban microclimate variability. This study applies the large‐eddy simulations (LES) mode within WRF, coupled with single‐layer urban canopy models (SLUCM), to enable even finer‐scale modelling (150 m) of the Urban Heat Island (UHI) effect in the Baltimore metropolitan area. We run nine scenarios to evaluate various methods of initializing soil moisture and various spinup lead times, and to assess the impact of WRF's Mosaic approach in depicting subgrid‐scale processes. We evaluate the scenarios by comparing the WRF simulated land surface temperature (LST) against Landsat LST and the WRF simulated hourly 2‐m air temperatures (AT) with observations from eight weather stations across the domain. Results underscore the paramount influence of the lead spinup time on the spatiotemporal distribution of simulated soil moisture, consequently shaping WRF's efficacy in predicting the UHI. Furthermore, interpolating soil moisture‐related parameters from the parent for child domain initialization yields a notable reduction in mean and root‐mean‐squared errors. This improvement was particularly evident in simulations with the longest spinup time, affirming the importance of carefully designing the initialization of soil moisture for improved urban temperature predictions. [ABSTRACT FROM AUTHOR]

The land surface temperature (LST) changes influence on many factors such as land use and land cover (LULC), vegetation and water resources etc. Now a day's urban population growth, industrial development and human activity are increased due to human migration and urban expansion. The surface temperature is very harmful to vegetation, groundwater level, and ecosystem health. Urban pollution growth and air pollution factor is also playing important role for increase and decrease the LST with climate change impact. Demand of drinking water are increases particular in the urban city due to the population growth and urban expansion, which all of this direct effect on surface and groundwater sources and resources. Hence we have been chosen the latest technologies such as remote sensing (RS), Geographic information System (GIS) and Google Earth Engine (GEE) are very effective for mapping, monitoring and assessment of spatial maps of LST, LULC, and NDVI (Normalised differential vegetation index) estimation. In this study, main focus on the LULC of 2011 and 2021 were classified using random forest (RF) algorithm, ML and remote sensing datasets. LULC were divided into four categories i. e. agricultural land, built up land, waterbody and waste land. For better understanding about the growth of LST, this study examines the association between LST and NDVI change, what vegetation condition impact on the LST. The main and important results have been found the built-up land and LST positive correlation due to increases both the factors in the urban area. LST increases gradually and its bad effect on the green land, water level and expansion of barren land. The range of LST is 43.60 °C to 45.30 °C. Less vegetation cover showing highest LST and vice versa. All of the LST, NDVI, and LULC direct effect on the groundwater availability in the area. This paper important result found the built-up land and LST both are increased in 2021, NDVI and LST having negative correlation. The results of study can very much useful for understanding the LST, NDVI and LULC, these factors what impact on groundwater availability in the study area. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is crucial parameter in urban heat island studies, agricultural water management and drought monitoring. Study introduces a novel and efficient approach for retrieving LST from sea and land surface temperature radiometer onboard Sentinel-3A. A hybrid method has been developed using radiative transfer (RT) simulations, an explicit emissivity approach and advanced random forest machine learning (RF-ML) algorithm. The amalgamation of RT-modelling and ML offers a significant advantage in LST retrieval, where ML learns complex relationships directly from RT simulations bypassing the complex task of fitting numerous interrelated parameters. A total of 3,035,259 RT simulations encompassing globally representative conditions were generated with the MODTRAN 5.3 RT-model and used to train and validate the RF-ML model. The RF-ML model is demonstrably accurate, yielding less than 0.64 K root mean square error (RMSE) over RT simulations. The robustness of the method was further assessed through sensitivity analysis, highlighting its ability to handle higher uncertainties in water vapor and surface emissivity. The accuracy of a retrieved LST was validated using in-situ LST observations and obtained an overall systematic RMSE of 1.47 K with bias of -0.02 K over a 457-day period across diverse environments. These results demonstrate the efficacy of the proposed method in achieving accurate LST retrieval under diverse atmospheric and surface conditions. Furthermore, the method’s robustness in handling uncertainties associated with real-world applications, thereby making it a promising alternative for LST retrieval. [ABSTRACT FROM AUTHOR]

Land Surface Temperature (LST) is considered a critical variable for assessing heat stress in urban environments. Understanding LST and its spatial variation is essential to comprehending the interactions between human activity and urban areas. This study investigates the impact of geospatially derived factors—namely built-up density, Normalized Difference Built-up Index (NDBI), road density, Normalized Difference Vegetation Index (NDVI), Bare Soil Index, distance to water bodies, elevation, slope, and aspect— on LST in Hue City, Vietnam, a region with limited prior documentation on this subject. Landsat 8 imagery data, collected in early 2024 during an exceptional heatwave, is utilized for this purpose. Advanced machine learning techniques, including deep neural networks, random forests, and XGBoost, are employed to model the relationship between LST and these explanatory variables. To deepen the understanding of the factors contributing to LST, the study uses the state-of-the-art Shapley Additive Explanations (SHAP) method. Experimental results show that the machine learning approach can accurately estimate the spatial variation of LST. The coefficient of determinations (R2) achieved by deep neural networks, random forests, and XGBoost are 0.83, 0.83, and 0.85, respectively. Sensitivity analysis based on SHAP reveals that built-up density, road density, and the Bare Soil Index are the most crucial variables that positively affect the LST. The factors of distance to water and slope negatively influence the LST. The established data-driven approach, coupled with SHAP, provides a valuable tool for understanding the spatial distribution of LST as well as mapping hot spots that experienced the highest level of urban heat stress. This tool also supports the analysis of mitigation measures for regulating temperature and reducing the impacts of the urban heat island effect. [ABSTRACT FROM AUTHOR]

The purpose of this work is to determine land-use and land-cover (LULC) patterns, land surface temperature (LST), and normalized difference vegetation index (NDVI) changes in Thatta district using Landsat data from 1991 to 2021 and evaluate the relationship between LST and NDVI. The research process employed the selection of the study area, data acquisition, preprocessing, and classification of remotely sensed images for the estimation of the land use land cover change (LULC), vegetation index (NDVI), and evaluation of LST using thermal bands in the Landsat dataset. The study revealed the area under built-up structures has increased from 1991 to 2021. Although the vegetation cover showed an increase, the bare soil showed a decreasing pattern, indicating a constant change in the LULC patterns in the region. The confusion matrix method for accuracy valuation of LULC data of 2021 revealed an overall accuracy of 88.24%, with a Kappa coefficient of 84.22%, while the Artificial Neural Network Multilayer Perceptron (ANN-MLP) model had a Kappa validation of 0.95 for 2021. The highest maximum temperature is observed for 2021, indicating a positive relationship between LST and built-up structures, while regression analysis found a negative correlation between LST and NDVI. This study provides a valuable monitoring framework to help resource managers develop strategies to manage land resources. [ABSTRACT FROM AUTHOR]

Urban heat islands (UHIs) are a phenomenon of temperature rising within urban areas relative to their rural counterparts. UHIs are becoming an increasingly common problem in large cities, which appear due to excessive urbanization and reductions in natural cover and vegetation. This phenomenon negatively affects the quality of life of the residents of the affected city, causing discomfort, reducing air quality, and increasing energy demand. In this study, UHIs were detected and analyzed in the city of Split, Croatia, using data from the Landsat 8 and 9 satellite missions and ground-based measurements of air and land surface temperatures, which were conducted in July, August, and September 2024. This research compares ground-based and sensor-based temperatures, and their analysis results in the proposal of a new index: the Combined Thermal Index. The main feature of this new index, which combines measured and perceived temperature, is to improve the understanding of the impact of UHIs in the city (Split), compared to existing indices. So far, LST and air temperature have not been combined, nor have they been combined with human perception of temperature, which is important in the case of UHIs because it is these people who will ultimately feel a rise in temperature. [ABSTRACT FROM AUTHOR]

Dynamic development of tourism activities and rapid urbanisation in Special Economic Zones (SEZs) can lead to significant land use and land cover changes (LULCCs) and environmental degradation, particularly in ecologically sensitive areas. This study examines the transformation of land use and its associated impacts on habitat quality and thermal environment in Phu Quoc Island (Vietnam) over a 20-year period (2003–2023). Using multi-temporal Landsat satellite imagery and random forest classification, we quantify LULCCs and assess the environmental consequences of urban expansion on habitat degradation and intensification of the island's thermal environment, focusing on land surface temperature (LST) changes. Our analysis reveals that rapid urbanisation, driven by large-scale tourism and infrastructure developments, has led to a significant loss of forest and farmland, leading to a 5.6% decline in habitat quality and a marked increase in LST. The study also highlights the uneven distribution of urban growth, with the majority of expansion occurring in the southern and central regions of the island. By applying the InVEST Habitat Quality Model, we identify key zones of habitat degradation and offer insights into the spatial patterns of environmental sensitivity and changes. Our findings underscore the need for integrated land use planning and sustainable development strategies to mitigate the negative environmental impacts of SEZ-driven urbanisation on island ecosystems. This research provides critical guidance for policymakers, planners, and environmental managers to balance economic growth with environmental conservation in fragile island environments. [ABSTRACT FROM AUTHOR]

Despite the increasing volume of research exploring the impact of various characteristics of urban green spaces (UGS) on land surface temperature (LST), findings remain inconsistent, particularly in arid climatic zones. In this research, we examined UGS change and their temperature and analyzed the relationship between pertinent variables of vegetation biomass, area, and shape of green patches and LST in Karaj city, an Iranian semi-arid urban area in 2000 and 2020. Linear regressions were used to model the relationship between green patches' variables and LST. The results showed that vegetation biomass of green patches was more effective in reducing temperature in comparison with area and shape complexity. Moreover, larger patches with more vegetation biomass and higher shape complexity showed lower temperatures. These results can guide urban landscape optimization by providing a clear understanding of which factors contribute most significantly to temperature mitigation in arid and semi-arid urban areas. For instance, areas identified as green but thermally not significantly cold need to be prioritized for improvements such as planting denser vegetation or introducing more heat resilient species. [ABSTRACT FROM AUTHOR]

Urbanization transforms landscapes from natural ecosystems to configurations of impervious surfaces and green spaces, leading to urban heat island effects that impact health and ecosystem sustainability. This study in Xiamen City, China, categorizes urban areas into functional zones, employs Random Forest and Stepwise Regression models to assess thermal differences, and proposes optimization measures for the building–green space landscape. The optimization involves altering the characterization of the building–green space landscape pattern. Results indicate: (1) due to the spatial heterogeneity of the building–green space landscape pattern in different functional zones, the surface temperature also shows strong spatial heterogeneity in different functional zones; (2) different optimization measures for the building–green space pattern are needed for different functional zones; taking the urban residential zone as an example, the Normalized Difference Vegetation Index (NDVI) in the hot spot area can be adjusted according to the value range of the cold spot area; (3) considering the solar radiation process, Sun View Factor (SunVF) plays an important role in indicating the change in surface temperature in the commercial service area, and as SunVF increases, the surface temperature of the functional zone tends to rise. This research offers insights into urban thermal environment improvement and landscape pattern optimization. [ABSTRACT FROM AUTHOR]

Changing precipitation patterns in the Arctic is a key indicator of climate change, in addition to increasing land and ocean temperatures, but these patterns are not uniform across the circumpolar region. This regional analysis focuses on the Yukon–Kuskokwim Delta in southwestern Alaska and addresses the following questions: (1) What is the baseline hydroclimatology during the growing season on the Yukon–Kuskokwim Delta? (2) What are the seasonal and intraseasonal trends of the hydroclimate variables in the YKD? (3) What are the implications of documented trends for the study region? Utilizing ECMWF's ERA5 reanalysis dataset, we conducted a seasonal analysis for May through September for the years 1982–2022. While no strong trend emerged for total precipitation over the 41-year study period, differing trends were observed for large-scale and convective precipitation. The decline in large-scale precipitation is supported by a decrease in storm counts in the Bering Sea, as well as declining vertically integrated moisture convergence and moisture flux. By contrast, the increase in convective precipitation underscores the growing importance of the local hydrologic cycle, further supported by a significant rise in evaporation. These enhanced local hydroclimatological cycles have significant implications for wildfires and subsistence activities. [ABSTRACT FROM AUTHOR]

Urban water bodies play a critical role in regulating urban climate, mitigating the urban heat island effect, and enhancing ecological environments. This study focuses on five typical heat island cities in Zhejiang Province, systematically analyzing the cooling effects of urban water bodies. Specifically, the study divides urban buffer zones into basic analytical units based on the urban road network and performs land surface temperature inversion and land use classification using the Google Earth Engine platform. Six representative morphology indicators of water bodies are selected, and the contributions of these indicators to the cooling effects of urban water bodies are evaluated using a Gradient Boosting Decision Tree regression model. Additionally, optimization strategies for water bodies in different cities are proposed. The results show the following: (1) Water bodies in central urban areas generally exhibit significant cooling effects, with the average land surface temperature reduction in water bodies exceeding 5.13 °C compared to built-up areas in all cities. (2) The average land surface temperature in urban buffer zones is generally higher than that in central urban areas, with a temperature difference of at least 0.63 °C. (3) In Huzhou and Jiaxing, the high-temperature and low-temperature zones are relatively concentrated, while in Jinhua, Quzhou, and Shaoxing, a more interspersed distribution of high-temperature and low-temperature zones is observed, reflecting a higher spatial heterogeneity. (4) Among the water body morphology indicators, the water edge density, the proportion of landscape area occupied by water patches, the largest patch index of water, and the water landscape shape index exert a relatively larger impact on cooling effects. These findings provide scientific guidance for optimizing the spatial layout of water bodies in urban buffer zones and improving urban thermal environments. [ABSTRACT FROM AUTHOR]

The research initiatives envisaged by the PNRR (Italian National Recovery and Resilience Plan) include the creation of innovation ecosystems to promote collaboration between universities, research centers, and local institutions with a focus on territorial integration and sustainability. The NODES Project (Nord-Ovest Digitale E Sostenibile) is part of this research. In this context, the Laboratory of Geomatics of the University of Pavia, in collaboration with other partners, deals with the study of the suitability maps for the renowned Pinot Noir wine. To achieve this, we considered different thematic input layers: elevation, slope, aspect, soil depth and type, Land Use Land Cover maps, NDVI, and current and forecast climatic aspects. An important thematic layer is concerned with the air temperature, which requires high spatial and temporal resolution. In the selected study area, the Lombardy Region has some accurate and reliable weather stations with high temporal resolution but low spatial resolution (7 stations in 648.5 square kilometers, i.e., one every 92 square kilometers). In addition, we considered Land Surface Temperature (LST) MODIS maps: these maps have good spatial resolution but present some voids and low temporal resolution. From the first evaluations made, the temperatures reported by MODIS are not always in excellent agreement with the ones from monitoring stations. To evaluate MODIS as a data source, we decided to use Kriging spatio-temporal interpolation. Starting from multitemporal MODIS data matrices, we interpolate them to estimate the temperature of the weather stations, in order to compare the estimation with the real weather station data, thus allowing the validation of MODIS data. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) plays an essential role in water and energy exchanges between the Earth's surface and atmosphere. Recent advancements in high-quality satellite-derived LST data and land data assimilation systems present a unique opportunity to bridge the gap between global observational data and land surface models (LSMs) to better constrain the water and energy budgets in a changing climate. In this vein, this study focuses on the assimilation of the ESA CCI-LST product into the ORCHIDEE LSM (the continental part of the Institut Pierre-Simon Laplace Earth system model) with the aim of optimizing key parameters to improve the simulation of LST and surface energy fluxes. We use the land data assimilation system for the ORCHIDEE model (ORCHIDAS) to conduct a series of synthetic twin data assimilation experiments accounting for actual data availability and uncertainty from ESA CCI-LST to find an optimal strategy for assimilating LST. Here, we test different strategies of assimilation, notably investigating (i) two optimization methods (a random search technique and a gradient-based technique) and (ii) different ways to assimilate LST using the only raw data and/or different characteristics of the LST diurnal cycle (e.g. mean daily, daily amplitude, maximum and minimum temperatures, and morning and afternoon gradients). Upon identifying the optimal approach, we use ORCHIDAS to assimilate ESA CCI-LST data across 34 European sites provided by the Warm Winter database. Our results demonstrate the effectiveness of assimilating 3 h CCI-LST data in ORCHIDEE over a single year in 2018, thereby improving the accuracy of simulated LST and fluxes. This improvement, assessed against CCI-LST and in situ observations, reaches up to a 60 % reduction in the root-mean-square deviation, with a median decrease of 20 % over the entire validation period (2009–2020). Furthermore, we evaluate the effectiveness of optimized parameters for application at larger scales using the median of optimized parameters per vegetation type across sites. Notably, the performance for both LST and fluxes exhibits consistent stability over the years, comparable to using site-specific parameters, and indicates a significant improvement in the modelled fluxes. Future work will be focused on refining the utilization of the observation uncertainties provided by the ESA CCI-LST product (e.g. decomposed uncertainties and spatio-temporal variability) in the assimilation process. [ABSTRACT FROM AUTHOR]

This study examines the interrelationship between land use, urban growth patterns, and the urban heat island effect in Istanbul, Turkey, utilising Landsat images spanning the period from 1990 to 2018. The land surface temperatures are derived from Landsat images, and the urban growth patterns are obtained using the Corine Land Cover and Global Human Settlement databases. Urban growth patterns are classified into four categories: high-rise high-density, high-rise low-density, low-rise high-density, and low-rise low-density. It is observed that the urban built-up areas in Istanbul have more than doubled during the study period, while the agricultural and forest areas have undergone a significant decrease. In consequence, there has been a notable increase in land surface temperatures (LST). The findings of the study indicate that artificial surfaces, particularly continuous urban fabric, industrial and commercial units, and airports, have exhibited the highest LST over time. A statistical analysis reveals a relationship between the growth pattern and surface temperature changes. The development patterns of high-rise low-density and low-rise low-density do not significantly contribute to the formation of urban heat islands. In contrast, high-rise high-density development and low-rise high-density development exert a pronounced influence on the formation of urban heat islands. Furthermore, a negative correlation was observed between vegetation coverage and LST, whereas a positive correlation was noted between building density and imperviousness and LST. The urban heat island effect in Istanbul, a major global metropolitan area with a population of approximately 16 million, is exhibiting a continuous increase due to the dynamics of urban growth. The findings of this study can inform the formulation of urban growth strategies for the forthcoming years, thereby facilitating thermal comfort. [ABSTRACT FROM AUTHOR]

Climate change-induced extreme meteorological phenomena, such as sudden temperature fluctuations, are raising global concerns. Increasing their adaptation capacity against temperature changes between seasons is becoming a more critical issue. The aim of this paper is to explore the association between surface temperature (ST) during the four seasons and urban geography in the city center of Izmir. The study identifies built environment parameters that impact seasonal land surface temperatures (LST) through spatial and statistical analysis at the local scale. At the same time, QUADRANT analysis revealed the spatial distinction between neighborhoods showing high LST compliance on a local scale (cooler summers and warm winters as well as neighborhoods showing LST sensitivity (hotter summers and colder winters. A spatial distinction is also revealed, showing significant differences in vegetation, number of buildings, building heights, and land use/cover change types. Geographic regression simulations show that the density of vegetation is vital in mitigating seasonal LST values, especially in making summers cooler and winters warmer. At the same time, population and built-up area density also play a role in increasing LST. Findings are consistent for neighborhood-scale analyses. Variables explain 26% in autumn, 31% in winter, 34% in spring, and 43% in summer. The study, which reveals the pressure of population and urbanization on temperatures, also highlights the adaptive character of the environment in adjusting the temperatures. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is critical to surface water dynamics. Effective water resource management necessitates an accurate ET estimation. In the Yellow River Basin China, a study area, cutting-edge technologies are needed to improve large-scale ET estimates. This study estimates ET using GSEBAL, an advanced ET estimation algorithm. Google Earth Engine integrates the surface energy balance model-based GSEBAL. The technique includes the collection, preparation, and calculation of ET using Landsat imagery and ERA5-Land meteorological data from 1990 to 2020. The study examined satellite LST, albedo, and NDVI data. The GSEBAL model calculates soil heat flow, net radiation, and sensible heat flux. The study tested the GSEBAL model utilizing essential ET datasets such as ECOSTRESS, MOD16, and SSEBop. The study showed that the model effectively predicted daily and seasonal ET variations in different climates. Root mean squared error, bias, and Pearson's correlation coefficient verified the model's reliability. The study also analyzed land use and land cover (LULC) over 30 years using Random Forest classifiers. In the 1990–2020 YRBC ET, land use changes affect ET rates annually and seasonally. The study area experiences changes in LST, NDVI, and LULC. Maximum ET values rose from 214.217 mm in 1990 to 234.891 mm in 2000. The pattern flipped in 2020, decreasing to 221.456 mm. In 2010, Summer had the highest ET, 484.455 mm. 2020 spring ET is 314.727 mm. Low ET decreased from 24.652 mm in 1990 to 18.2 mm in 2020, reducing water loss. Fall ET peaks at 24.9 mm in 2020; winter ET is 18.75 mm. [ABSTRACT FROM AUTHOR]

High-altitude areas are thought to be more sensitive to climate change, but long-term series of land surface temperature (LST) observations are still inadequate in low-latitude high-altitude mountainous areas. We investigated spatiotemporal variations in the LST and its dominant driving factors at different time scales based on the long-term series (2001 − 2020) of MODIS data over the Yunnan Province (YNP) in southwest China, with a special focus on elevation-dependent warming (EDW). The results indicated that annual LST generally increased at a rate of 0.18 °C decade−1 over the past 20 years, and the increase was stronger in summer (0.47 °C decade−1). Moreover, the nighttime warming rate (0.43 °C decade−1) was much faster than that during the daytime (− 0.08 °C decade−1), indicating that there was an asymmetric diurnal warming. We also confirmed the presence of EDW, which behaves more greatly above 3500 m. Spatially, the warming trend in high-cold mountains, hot-dry river valleys and the tropics was obvious, while the trend in the northeastern YNP and western side of the Ailao Mountains was opposite. On the timescales of annual, autumn and winter, more than 60% of the LST in the study area was mainly affected by temperature, and 20% ~ 30% was affected by precipitation. LST and warming trend largely differenced with respect to land cover types, with the highest values occurring in built-up lands. This research is expected to contribute to a better understanding of climate change processes in the YNP. [ABSTRACT FROM AUTHOR]

The intensity and frequency of tropical cyclones (TC) are on the rise due to climate change, resulting in severe damage to coastal regions. Hence, the mitigation of socioeconomic and environmental consequences of cyclones has attained paramount importance in the recent years. In this study, the rapid impact of a very severe cyclonic storm "Titli" on land cover (LC) changes were evaluated using Moderate Resolution Imaging Spectroradiometer (MODIS) and high-resolution Sentinel-2 data. The cyclonic event caused substantial modifications in land use and land cover with nearly 46% decrease in dense vegetation, 129% increase in fallow land, and 111% increase in water body, over the study region. Widespread damage (dense to less dense vegetation) was evident on the left side of the cyclone track as compared to the right. The analysis revealed a 98.3% decrease in dense vegetation, marked by a decrease in the normalized difference vegetation index (NDVI) from 0.73 to 0.44 over the landfall region. This NDVI decrease continued for nearly 3 months before the onset of vegetation regrowth. Change in vegetation into other LCs over the landfall region resulted in an increase of the mean daytime land surface temperature by ~ 6 °C. The analysis highlights the magnitude of spatiotemporal scale damages to LULC and consequent loss in seasonality that can be ushered in by a single short-duration extreme weather event like TC and thus emphasizes the need for well-formulated mitigation strategies. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) serves as a crucial indicator for evaluating the effects of different environmental factors on the ecosystem, including alterations in land use, climate variations, and emissions of greenhouse gases. This comprehensive study used remote sensing data to analyze changes and effects of land use/land cover (LULC) on LST in Tay Ninh province, Vietnam, from 1988–2023. Landsat satellite images in 1988, 2004, and 2023 were preprocessed and supervised classification on ArcGIS 10.8 software. The Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and LST in the study area were determined using the Landsat image data. The classification results showed a decrease in the area of agricultural land, barren land, and forest classes by 8.30%, 8.82%, and 15.93%, respectively, from 1988 to 2023. Conversely, the area of built-up and waterbodies classes indicated an increase of 33.00% and 0.06%, respectively, during the same period. In terms of LST, the study area exhibited temperature ranges of approximately 19.75 °C—35.28 °C, 26.26 °C—46.33 °C, and 21.05 °C—40.60 °C in 1988, 2004, and 2023, respectively. Contribution Index (CI) and multiple regression analysis evaluated the relationship between land cover, LST, NDVI, and NDBI. The regression analysis preliminary showed a negative correlation between NDVI and LST, while a positive correlation was observed between NDBI and LST. The CI of built-up areas has increased from 0.01 in 1988 to 0.77 in 2023, which shows that this coating has contributed to rising temperatures in the study area. Meanwhile, the forest and water body classes have consistently negative CI throughout the period 1988–2023, which has contributed to the decrease in temperature. This comprehensive study provides policymakers with valuable information regarding LULC and LST, instilling confidence in developing effective policies for land resource management. [ABSTRACT FROM AUTHOR]

The increasing surface heat in metropolitan areas is one of the biggest issues, especially as natural surfaces are being replaced by impermeable concrete surfaces. This study uses Landsat data (1991–2022) to examine the spatio-temporal dynamics of LST and LULC in Jaipur, highlighting the impact of urban expansion and the city's semi-arid nature on the thermal landscape. We have used the maximum likelihood classifier for supervised LULC classification and the mono-window algorithm for retrieving LST. The evaluation is done using buffer analysis. Furthermore, to assess the interrelationship between LST and LULC indices (NDVI & NDBI), regression analysis is used. The CA-ANN model is employed to project LSTs of 2032 and 2042. The findings indicate that the built-up land in the study area grew by 52.80% from 1991–2022. Most of this expansion has come at the expense of agriculture/open land, and vegetation cover. The mean LST in the city has risen by 5.9 °C, with the inner zone (B1) increasing from 35.44 °C to 41.93 °C, indicating urbanisation-induced heat stress. In the outer zones (B5-B6), dry sandy and rocky soils contribute to elevated temperatures. Water bodies show the lowest LST, while open and barren lands have the highest. LST exhibit a positive correlation with NDBI and a weak negative correlation with NDVI. Predictions indicate that by 2042, about 99% of the urban landscape will encounter surface temperatures above 40 °C, with 28.79% exceeding 45 °C. Raised temperatures could exacerbate the UHI effect, leading to serious health and environmental concerns. [ABSTRACT FROM AUTHOR]

Solar radiation (Rs) is a major renewable energy source and also a crucial factor in designing solar panels, determining water requirement, and irrigation scheduling. In this study, meteorological parameters (air temperature, average air temperature, and relative humidity; Scenario 1), satellite image–based indices (normalized difference vegetation index: NDVI and land surface temperature: LST; Scenario 2), and their combination (Scenario 3) were used as predictors of Rs simulator models in Mashhad watershed (2005–2015). To this end, three different transfer function algorithms of the multi-layer perceptron (MLP), namely Levenberg–Marquardt backpropagation (MLP-LVM), gradient descend backpropagation (MLP-GDB), and batch training with weight and bias learning rules (MLP-BTWB), as well as two other machine learning models, M5 Tree and XGB (eXtreme Gradient Boosting), were employed. In addition, hybrid models obtained through coupling the best MLP transfer function with genetic algorithm (MLP-LVM-GA) and coupling Variational Mode Decomposition with M5 Tree and XGB (VMD-M5 Tree and VMD-XGB, respectively) were also investigated. In the case of MLP-based models, the third scenario proved to be more efficient, with RMSEs equal to 404.2, 424.5, and 423.8 J.cm−2.day−1 for the aforementioned transfer functions, respectively. The results showed that GA coupled with the best transfer function algorithm of MLP improved Rs estimates, decreasing estimation error by 14.7, 30.65, and 32.67% in the three defined scenarios, respectively. In training set, VMD-M5 Tree and VMD-XGB hybrid models outperformed corresponding base models under all three scenarios; but in testing set, estimation error was decreased only in the third scenario (by 37 and 42%, respectively). Overall, all models (base and hybrid) had the least Rs estimation errors in the third scenario (application of both meteorological parameters and satellite image–based data). [ABSTRACT FROM AUTHOR]

Converting natural vegetation to croplands alters the local land surface energy budget. Here, we use two decades of satellite data and a physics-based framework to analyse the biophysical mechanisms by which croplands influence daily mean land surface temperature (LST). Globally, 60% of croplands exhibit an annual warming effect, while 40% have a cooling effect compared to their surrounding natural ecosystems. Aerodynamic resistance is identified as the dominant biophysical factor impacting LST by adjusting latent heat flux. The magnitude of cropland-induced LST change is negatively correlated with the difference in leaf area index between croplands and their surrounding biome types. The strongest warming occurs in temperate dry regions where croplands are surrounded by savannas. However, a lower-than-expected LST disturbance is seen in hot and wet regions where croplands are surrounded by rainforests, attributed to lower cropland fraction and energy limitations. These findings highlight the complex interplay of land use, vegetation, and regional climate, providing valuable insights into sustainable agriculture and land-based climate change mitigation. Croplands alter land surface temperature, with 60% warming and 40% cooling effects globally. Temperature changes are driven by convection, which is influenced by leaf area index differences. The warming is strongest in temperate dry regions surrounded by trees. [ABSTRACT FROM AUTHOR]

Urbanization is transforming landscapes globally, altering environmental conditions that affect ecosystem functioning, particularly in urban areas where trees are crucial for regulating microclimates, improving air quality, and sustaining biodiversity. This study investigates the environmental differences and tree leaf structure and morphology in urban and suburban sites in the Chicago Metropolitan Region. The leaf functional traits of Norway Maple and Little − leaved Linden were studied in three locations in the summer of 2023: an urban park (University of Illinois Chicago, Chicago, IL), a suburban park (Morton Arboretum, Lisle, IL), and a suburban residential site (Lombard, IL). The urban site had higher daytime and nighttime air, and land surface temperatures compared to the suburban sites with significant fluctuations observed across the sites. Cumulative growing degree days, a measure of potential photosynthetically active days, were also higher in the urban park than in the suburban sites between March and August. Norway Maple trees growing in the urban site displayed higher specific leaf area (SLA) and lower leaf dry matter content (LDMC) than in the suburban sites, resulting in thinner leaves. Similarly, Little−leaved Linden trees in the suburban residential site displayed higher SLA and lower LDMC than those in the suburban park. The values of gas exchange traits − namely photosynthetic assimilation, transpiration rates, and stomatal conductance − of Norway Maple were higher at the urban site compared to suburban sites as temperatures increased during the summer. Norway Maple gas exchange values decreased as the growing season progressed, as expected by ontogeny. In contrast, Little−leaved Linden maintained similar leaf gas exchange values throughout the growing season. Both species in the urban site exhibited lower instantaneous water use efficiency and reduced LDMC, suggesting greater water loss in response to elevated temperatures compared to suburban park and residential sites. Comparisons with existing global trait databases emphasize the need for localized data to accurately capture site−specific responses. Although some traits aligned with database values, others deviated significantly, underscoring the importance of comprehensive, site−specific datasets for robust ecosystem modeling and management strategies. [ABSTRACT FROM AUTHOR]

The sub‐grid terrain solar radiative effect (STSRE) significantly affects the heterogeneity of the surface downward solar radiation (SDSR) over mountainous areas, which further exerts remarkable influences on simulations of surface energy budgets and hydrological processes. Given that two thirds of China is mountainous, we have systematically revealed the noticeable STSRE impacts on the performance of the Common Land Model (CoLM) in simulating land surface thermal and hydrological processes over China. Validations indicate that adopting the three dimensional (3D) STSRE scheme clearly improves the CoLM's ability in simulating the land thermal and moist characteristics over China in almost all seasons, and the improvements increase with the terrain complexity increasing. Over the regions with the most rugged terrain, adopting the 3D STSRE scheme can remarkably reduce the overestimated SDSR and thereafter land surface temperature (LST) warm biases in the CoLM with the plane‐parallel radiative scheme. The modeled snow cover increases corresponding to the reduced LST with the Taylor score improved the most by 52.84% in summer, and the modeled evapotranspiration (ET) over southeastern Tibet and the Hengduan Mountains is also notably improved in summer. Further analysis indicates that the surface net radiation well corrected by the 3D STSRE scheme firstly reduces the warm biases of LST simulation using the plane‐parallel radiative scheme over strong terrain shaded areas, directly leading to the depressed sensible heat flux and ET and more snow accumulation, then the inhibited ET and increased snow accumulation jointly affect the runoff and soil moisture simulations and thereafter latent heat simulation. Plain Language Summary: The landform produces considerable affects on the surface downward solar radiation (SDSR) over regions with rugged terrains, which controls heat and water exchanges between atmosphere and land. Considering that China especially the Tibetan Plateau (TP) owns the most rugged terrains in the world, we introduce a newly developed scheme that much more realistically describes the sub‐grid terrain solar radiative effect (STSRE) relative to the plane‐parallel radiative scheme in the Common Land Model (CoLM) and indicate its impacts on the model performance. Results show that the CoLM adopting the 3D STSRE scheme could remarkably advance the simulations of land surface energy balance and land thermal and hydrological features. Findings of this study emphasize the necessity of considering the STSRE in simulations of land surface process and hopefully contribute to deepen our understanding of the surface energy budget and improve the land thermal and hydrological simulations over the rugged terrains in China. Key Points: The 3D sub‐grid terrain solar radiative effect (STSRE) scheme can much more realistically describe the surface solar radiation over mountainous relative to plane‐parallel schemeAdopting the 3D STSRE scheme clearly improves the CoLM ability in simulating the land surface thermal and hydrological processes over ChinaThe improvements of CoLM performance induced by the 3D STSRE scheme increase with the terrain complexity increasing [ABSTRACT FROM AUTHOR]

Background: Malaria remains the leading cause of under-five morbidity and mortality in low- and middle-income countries. Sleeping under mosquito nets, especially insecticide-treated nets (ITNs), is one of the best ways to prevent malaria as they form a physical and chemical barrier against mosquitoes. Therefore, the present study aimed to assess not only mosquito net use, but also how environmental factors, specifically land surface temperature, contribute to malaria prevention among households with children under 5 years of age in Lao PDR. Methods: The most recent Multiple Indicator Cluster Survey datasets of Lao PDR 2017 and the Japan Aerospace Exploration Agency (JAXA) Public Health Monitoring and Analysis Platform (JPMAP) were used. Data from 51,948 households were used in the analysis. A bivariate logistic regression analysis was followed by a multivariate logistic regression analysis to determine the factors influencing mosquito net use with children under five. Results: In this study, 77.8% of all households with children under 5 years of age slept under mosquito nets. Of these, 80.5% were ITNs (Olyset, Permanent, and other brands). Multivariate logistic regression analysis revealed that mosquito net use was significantly associated with the land surface temperature, ethno-linguistic group (Lao-Tai, Mon-Khmer, Hmong-Mien, Chinese-Tibetan, and other), education level of the household head, and wealth index quintile. Conclusions: The analysis of the present study suggested measures to intensify the use of mosquito nets with an emphasis on ethnic minorities living in hot areas to bring Lao PDR closer to the day that mosquito-borne infections such as malaria can be eliminated. [ABSTRACT FROM AUTHOR]

Climate change has intensified urban heat risks through extreme heat and heat island effects. Using Fuzhou as a case study, we conducted assessments of heat risk and cool island quality to identify core heat risk sources (CHRSs) and core cold sources (CCSs). Based on the degree of resistance to surface heat transfer, we constructed a comprehensive resistance surface. This was followed by the construction of a composite cooling network using the minimal cumulative resistance and circuit theory models, along with the identification of key nodes to enhance the protection of cool island resources and ensure network stability. Our findings revealed that the central urban area had the highest heat risk, followed by the eastern coastal areas, showing a trend of further expansion towards the southeastern coast. Relatively high-quality cool island resources were distributed in the western mountainous area. We identified 21 CHRSs and 32 CCSs. The composite cooling network included 94 heat transport corridors and 96 cool island synergy corridors, with 148 cooling nodes and 78 barrier nodes. The average land surface temperature of transport and synergy corridors was 27.89°C and 25.34°C, respectively, significantly lower than the high-risk areas (31.14°C). Transport corridors enable heat transfer from CHRSs to CCSs, while synergy corridors can achieve further cooling by enhancing the synergy among cool islands. [ABSTRACT FROM AUTHOR]

Mangroves are an integral part of the coastal ecosystems, demonstrating resiliency against adverse anthropogenic and environmental effects. They provide blue carbon stock and security against coastal erosion and act as a nutrient source for many aquatic inhabitants along with providing raw materials for human consumption. However, mangroves continue to perish due to several climate factors. Past research has indicated some environmental factors influencing the growth and sustenance of mangrove forests. This paper aims to study the impact of the climate changes on the UAE mangrove forests during the period 1990–2020. The climate change related factors studied include surface temperature, sea-level rise, salinity, and coastal inundation. Here, remote sensing techniques and supervised machine learning methods were utilized to derive the climatic factors. The correlations between mangrove and these factors were investigated as well. It was revealed that Land Surface Temperature is closely related to mangrove and that mangrove biomass was highest in the land surface temperature range 30–35 °C. Results have not shown a clear correlation between tidal inundation and mangroves. Furthermore, outputs also showed that mangrove biomass was negatively affected by sea-level rise. The analysis showed a general positive relationship between mangrove and salinity corresponding to an increase in mangrove biomass which was only found when conducting spatial analysis, however site-specific analysis (i.e., temporal) showed an increase in mangrove density with decrease in salinity. These findings could potentially aid in facilitating the identification of suitable new sites for plantation or existing sites for rehabilitation of mangroves in UAE in addition to formulating policies at the highest level for mangrove conservation. [ABSTRACT FROM AUTHOR]

In the face of excessive urban growth, urban green spaces face the challenge of efficiently providing ecosystem and environmental services benefits. While public green spaces (PGS) stand out for their different environmental and social benefits, their efficiency depends on the vegetated cover, which can be evaluated in relation to the area, type of polygon, and degree of equipment. In this study, the effective green cover (EGC) assessed from a geographic information system, and the level of equipment–infrastructure were evaluated in different green spaces in the metropolitan area of San Luis Potosí, Mexico. The PGS categories included park with hydrological potential, urban park, linear park, neighborhood park, local garden, residual green space, and sports area. In our results, the urban park and the park with hydrological potential indicated 69.5 and 79.5% of EGC, respectively, and a value of 0.3 of Normalized Difference Vegetation Index (NDVI). Specifically, only in urban park, the land surface temperature (LST) decreased with the increase in the NDVI. The total green coverage of PGS was 6.7 m2 and the EGC was 5.8 m2, which is largely due to the large-sized parks. Furthermore, the provision of parks is insufficient compared to other metropolitan areas on national and international scales. In the spaces with the highest score of equipment, the outstanding elements were urban furniture, children's playgrounds, and exercise areas, which varied according to the type of green space, indicating a differentiated social use. This study suggests that EGC can be a parameter to assess the green proportion of PGS in cities. Integrating this variable in PGS planning and design can enhance ecosystem services provision in metropolitan areas. [ABSTRACT FROM AUTHOR]

ABSTRACT The integrated use of remote sensing (RS) techniques, vertical magnetic gradient (VMG) and electrical resistivity tomography (ERT) measurements, and, in particular, combined analysis of 2D and 3D data, can provide a viable option for the identification of targets of interest at complicated archaeological sites. In this regard, a case study is Kom C at the archaeological site of Buto (Tell El Fara'in) in the northern Nile Delta (Egypt), where satellite data (Google Earth, Landsat 8 and OrbView‐3), VMG and ERT measurements were collected prior to site excavation. In this particular case, soil salinity in the buried structures, a lack of contrast in magnetic susceptibility and electrical resistivity, as well as the orientation, complex spatial distribution and overlapping of the architectural elements, all contributed to a number of anomalies that were difficult to interpret using only 2D results. Initially, the archaeological remains were identified as being made of mud‐brick based on land surface temperature (LST) estimated from thermal bands (Bands 10 and 11) in Landsat 8. Then, the high‐resolution satellite data, as well as the VMG and ERT (2D, quasi‐3D and full 3D resistivity models), were integrated to produce a comprehensive map of buried archaeological features. Excavations by Kafrelsheikh University in collaboration with the Ministry of Tourism and Antiquities recovered archaeological remains, including architectural elements that were perhaps used for official or administrative purposes or pottery‐making workshops during the Late Roman period (between the 4th and 7th century ce). The direct comparison of geophysical results to archaeological evidence from the excavation enabled a robust interpretation of geophysical anomalies visible in the horizontal resistivity depth slice and magnetic maps. As a whole, this case study highlights the value of combining satellite data with the analysis of 2D data and 3D views of geophysical surveys to better understand the real distribution of buried archaeological remains at similar complex sites. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) serves as an important climate variable which is relevant to a number of studies related to energy and water exchanges, vegetation growth and urban heat island effects. Although LST can be derived from satellite observations, these approaches rely on cloud‐free acquisitions. This represents a significant obstacle in regions which are prone to cloud cover. In this paper, a graph‐based propagation method, referred to as GraphProp, is introduced. This method can accurately obtain LST values which would otherwise have been missing due to cloud cover. To validate this approach, a series of experiments are presented using synthetically obscured Landsat acquisitions. The validation takes place over scenarios ranging from between 10% and 90% cloud cover across six urban locations. In presented experiments, GraphProp recovers missing LST values with a mean absolute error of less than 1.1°C, 1.0°C and 1.8°C in 90% cloud cover scenarios across the studied locations respectively. Plain Language Summary: Although it is possible to work out what the ground temperature is from satellite images, when there are clouds which cover part of the image we cannot use existing methods. In parts of the world which are often cloudy, this means you might only occasionally have access to information about the surface temperature. Land surface temperature relates to many physical processes, therefore, we propose a method which aims to provide accurate surface temperature values even when it is cloudy, in order to assist the study of these systems. To do so, the method we propose uses a satellite image of the same region which was captured on a different day when there were not clouds. This earlier image is used to describe the region in a graph structure, where similar areas are grouped together. This representation of the region is then used to work out what the region might have looked like if there were not clouds in the later image. From this, we can apply existing methods to compute the land surface temperature. In our experiments we show that this approach is effective and the recovered temperature values are more accurate than can be recovered by other existing approaches. Key Points: Gaps in satellite‐derived land surface temperature (LST) measurements caused due to clouds can tackled using graph‐based propagationThe proposed approach, GraphProp, recovers missing LST values more accurately than existing tensor completion methods from literatureThe presented results show the approach to be robust in even highly challenging settings including up to 90% cloud cover [ABSTRACT FROM AUTHOR]

Monitoring the dynamic distribution of irrigated cropland and assessing its cooling effects are essential for advancing sustainable agriculture amid climate change. This study presents an integrated framework for irrigated cropland monitoring and cooling effect assessment. Leveraging dense time series vegetation indices with Google Earth Engine (GEE), we evaluated multiple machine learning algorithms within to identify the most robust approach (random forest algorithm) for mapping irrigated cropland in Inner Mongolia from 2010 to 2020. Furthermore, we developed an effective method to quantify the diurnal, seasonal, and interannual cooling effects of irrigation. Our generated irrigated cropland maps demonstrate high accuracy, with overall accuracy ranging from 0.85 to 0.89. This framework effectively captures regional cropland expansion patterns, revealing a substantial increase in irrigated cropland across Inner Mongolia by 27,466.09 km2 (about +64%) between 2010 and 2020, with particularly pronounced growth occurring after 2014. Analysis reveals that irrigated cropland lowered average daily land surface temperature (LST) by 0.25 °C compared to rain-fed cropland, with the strongest cooling effect observed between July and August by approximately 0.64 °C, closely associated with increased evapotranspiration. Our work highlights the potential of satellite-based irrigation monitoring and climate impact analysis, offering a valuable tool for supporting climate-resilient agriculture practices. [ABSTRACT FROM AUTHOR]

Over 30 million people rely on Lake Victoria for survival in Northeast African countries, including Ethiopia, Eritrea, Somalia, and Djibout. The lake faces significant challenges due to changes in land use and climate. This study used multi-source remote sensing data in the Google Earth Engine (GEE) platform to create Land Use and Land Cover (LULC), land surface temperature (LST), and Normalized Difference Water Index (NDWI) layers in the period 2000–2023 to understand the impact of LULC and climate change on Lake Victoria Basin. The land use/land cover trends before 2020 indicated an increase in the urban areas from 0.13% in 2000 to 0.16% in 2020. Croplands increased from 6.51% in 2000 to 7.88% in 2020. The water surface area averaged 61,559 square km, which has increased since 2000 with an average rate of 1.3%. The "Permanent Wetland" size change from 2000 to 2020 varied from 1.70% to 1.83%. Cropland/Natural Vegetation Mosaics rose from 12.77% to 15.01%, through 2000 to 2020. However, more than 29,000 residents were displaced in mid-2020 as the water increased by 1.21 m from the fall of 2019 to the middle of 2020. Furthermore, land-surface temperature averaged 23.98 degrees in 2000 and 23.49 in 2024. [ABSTRACT FROM AUTHOR]

Rice is a primary food crop, and rice production ensures food security and maintains social stability with great significance. Flooding paddy rice fields as an important step in rice production affects the entire growth process of rice. The selection of flooding time is highly correlated with paddy rice yield and water resource utilization. In the background of global warming, early flooding in high-latitude paddy rice planting areas can ensure that rice has sufficient growing time to increase yield. However, overly early flooding may cause waste of water resources due to insufficient heat. Currently, research on flooding timing is relatively lacking, and monitoring of temperature during flooding is particularly deficient. To respond to climate change, it is necessary to explore whether the current flooding schedule meets the actual needs. Based on MODIS surface reflectivity data, we identified the First Flooding Day (FFD) and Peak Flooding Day (PFD) in the Sanjiang Plain. Using MODIS Land Surface Temperature (LST) data and meteorological station-provided air temperature data, we analyzed the corresponding LST and air temperature for PFD from 2008 to 2024. The main conclusions are as follows: (1) both FFD and PFD in the Sanjiang Plain have a trend of advancing year by year, with PFD showing stronger advancement than FFD; (2) the LST and air temperature during flooding in the Sanjiang Plain show a downward trend year by year; and (3) by 2024, the flooding temperature of paddy rice fields in the Sanjiang Plain has generally met the needs for the next step of production. This study first attempts to use high-temporal-resolution remote sensing images to identify the flooding time of paddy fields and achieve timely monitoring of flooding and changes in flooding temperature. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) plays a pivotal role in many environmental sectors. Unfortunately, thermal bands produced by instruments that are onboard satellites have limited spatial resolutions; this seriously impairs their potential usefulness. In this study, we propose an automatic procedure for the spatial downscaling of the two 100 m thermal infrared (TIR) bands of LandSat 8/9, captured by the TIR spectrometer (TIRS), by exploiting the bands of the optical instrument. The problem of fusion of heterogeneous data is approached as hypersharpening: each of the two sharpening images is synthesized following data assimilation concepts, with the linear combination of 30 m optical bands and the 15 m panchromatic (Pan) image that maximizes the correlation with each thermal channel at its native 100 m scale. The TIR bands resampled at 15 m are sharpened, each by its own synthetic Pan. On two different scenes of an OLI-TIRS image, the proposed approach is compared with 100 m to 15 m pansharpening, carried out uniquely by means of the Pan image of OLI and with the two high-resolution assimilated thermal images that are used for hypersharpening the two TIRS bands. Besides visual evaluations of the temperature maps, statistical indexes measuring radiometric and spatial consistencies are provided and discussed. The superiority of the proposed approach is highlighted: the classical pansharpening approach is radiometrically accurate but weak in the consistency of spatial enhancement. Conversely, the assimilated TIR bands, though adequately sharp, lose more than 20% of radiometric consistency. Our proposal trades off the benefits of its counterparts in a unique method. [ABSTRACT FROM AUTHOR]

Near-surface air temperature (Tair) is critical for addressing urban challenges in China, particularly in the context of rapid urbanization and climate change. While many studies estimate Tair at a national scale, they typically provide only daily data (e.g., maximum and minimum Tair), with few focusing on sub-daily urban Tair at high spatial resolution. In this study, we integrated MODIS-based land surface temperature (LST) data with 18 auxiliary data from 2013 to 2023 to develop a Tair estimation model for major Chinese cities, using random forest algorithms across four diurnal and seasonal conditions: warm daytime, warm nighttime, cold daytime, and cold nighttime. Four model schemes were constructed and compared by combining different auxiliary data (time-related and space-related) with LST. Cross-validation results were found to show that space-related and time-related variables significantly affected the model performance. When all auxiliary data were used, the model performed best, with an average RMSE of 1.6 °C (R2 = 0.96). The best performance was observed on warm nights with an RMSE of 1.47 °C (R2 = 0.97). The importance assessment indicated that LST was the most important variable across all conditions, followed by specific humidity, and convective available potential energy. Space-related variables were more important under cold conditions (or nighttime) compared with warm conditions (or daytime), while time-related variables exhibited the opposite trend and were key to improving model accuracy in summer. Finally, two samples of Tair patterns in Beijing and the Pearl River Delta region were effectively estimated. Our study offered a novel method for estimating sub-daily Tair patterns using open-source data and revealed the impacts of predictive variables on Tair estimation, which has important implications for urban thermal environment research. [ABSTRACT FROM AUTHOR]

The sub‐pixel terrain long‐wave radiative effect (STLRE) plays an important role in the satellite surface‐sensitive infrared brightness temperature (TB) simulation over rugged areas, however, it is absent in current radiative transfer models. This study incorporates a STLRE scheme in the Community Radiative Transfer Model (CRTM) for the FY‐4A/Advanced Geosynchronous Radiation Imager (AGRI). The total surface downwelling long‐wave radiance (SDLR) over rugged areas at sub‐pixel is composed of the radiance from the overlying atmosphere under plane‐parallel radiation condition and the radiance emitted from surrounding terrains, weighted by the sky view factor (SKV). Based on the homogenous assumption, a STLRE scheme is established through constructing terrain correction factors at AGRI pixel scale. The ExpCTL/ExpTOPO experiments with the CRTM adopting the plane‐parallel scheme/STLRE scheme are conducted to evaluate the simulations of AGRI clear‐sky TB over the rugged areas. Results show that considering the STLRE can significantly reduce the orography dependent simulation biases in the plane‐parallel scheme. The reductions of biases for the AGRI channel 11–13 regionally averaged over the rugged areas are 64.2%, 72.7% and 67.6%, respectively. Stable correction performance is found at different times of the day. Meanwhile, the corrections of SDLR are more obvious in winter than in summer, and in barren condition than in the other land cover types. This is theoretically attributed to the land‐air temperature difference and the land surface emissivity, respectively. This pilot study improves the observation operator for infrared TB data assimilation over complex terrain and exhibits broad promotion and application value. Plain Language Summary: Satellite observations are important for modern numerical weather predictions, but underutilized. Many valuable satellite observations near the surface are not used in numerical models, especially over complex terrain regions. The sub‐pixel terrain long‐wave radiative effect (STLRE) plays an important role in the satellite surface‐sensitive infrared TB simulation over rugged areas, however, it is absent in current radiative transfer models. Consequently, the TB simulation by default CRTM exhibits orography dependent biases over rugged areas. This study focuses on improving the data assimilation ability for infrared observations influenced by complex terrains. We incorporate a STLRE scheme into CRTM and explore the potential benefits for FY‐4A/AGRI TB simulation using the 1‐year TB simulation samples over eastern China during 2022–2023. The new scheme considers the obstruction of atmospheric downwelling radiance by terrain and also the emissive radiance from surrounding terrains. More accurate TB simulations are produced for FY‐4A/AGRI. The results of this study suggest a promising avenue for improving satellite data assimilation over complex terrain in the future. Key Points: Default Community Radiative Transfer Model (CRTM) leads to orography dependent simulation biases for FY‐4A /AGRI over rugged areasA sub‐pixel terrain long‐wave radiative effect parameterization scheme is developed in CRTM for FY‐4A/AGRIConsidering the sub‐pixel terrain long‐wave radiative effect can clearly improve the accuracy of infrared simulation over complex terrain [ABSTRACT FROM AUTHOR]

Impervious areas are one of the important indicators for evaluating the urbanization process, while surface temperature is one of the reference factors for evaluating the urban environment. In order to investigate whether the spatial distribution of an impervious surface has any influence on urban surface temperature, Xuzhou City was selected as the study area, and the impervious surface information was extracted based on the maximum likelihood classification method for Xuzhou City for the period of 2013–2022, and surface temperature inversion was performed using Landsat 8 remote sensing imagery and nighttime lighting data. In order to reduce the confusion between bare soil and impervious surfaces, the study area was divided into built-up and non-built-up areas for the selection of impervious and pervious surface samples using nighttime lighting data, and, finally, the maximum likelihood classification method was used to realize the extraction of impervious surfaces. The experimental results show that, by extracting the impervious surface of Xuzhou City, the impervious surface of Xuzhou City continued to increase from 2013 to 2022, in which the growth rate was faster in 2014–2016 and 2019–2021, and slower in 2017–2018 and 2021–2022, after performing surface temperature inversion as well as temperature grading. The results of impervious surface extraction and surface temperature inversion were subjected to overlay analysis and linear regression analysis. It was found that most of the impervious surface area is in high-temperature areas, and the density of the impervious surface is proportional to the surface temperature in the impervious surface and its surrounding area. Therefore, it can be concluded that the expansion of impervious surfaces is one of the reasons for the increase in urban surface temperature. [ABSTRACT FROM AUTHOR]

Urbanization has significantly impacted the environment and urban life, with urban green spaces playing a crucial role in enhancing sustainability and public health. Urban Green Spaces (UGS) offer benefits such as temperature regulation, ecological services, and improved thermal comfort, while providing entertainment, culture, disaster avoidance, and ecological services. This study evaluates the cooling effects of UGS in mitigating the Urban Heat Islands (UHIs) phenomenon in Delhi, India, using satellite imagery to assess the Land Surface Temperature (LST) of 24 parks. We calculated the Park Cooling Intensity (PCI) by applying the mono-window algorithm. We found that parks larger than 1.55 hectares with dense vegetation and water bodies significantly reduced surrounding temperatures by up to 8.28 °C. The PCI effect was significantly influenced by park size, vegetation density, and the presence of water bodies, with larger parks and denser vegetation and water bodies demonstrating enhanced cooling capacity. These findings highlight the importance of integrating green spaces into urban planning as vital infrastructure for enhancing urban resilience, reducing heat-related health risks, and ensuring equitable access to public health benefits. The study also highlights the importance of addressing socio-economic disparities in park accessibility, which have significant implications for equitable urban development. This study emphasized the importance of formulating a strategic urban planning approach that focuses on green spaces' growth, conservation, and equitable allocation, thereby fostering eco-friendly, habitable, and robust urban environments. [ABSTRACT FROM AUTHOR]

The climate crisis seriously threatens Central European forests and their ecosystem functions. There are indications that old-growth forests are relatively resilient and efficient in micro-climatic regulation during extreme climatic conditions. This study evaluates five well-protected old beech forests in Germany, part of a UNESCO World Heritage Site. We examined temperature dynamics and vitality in core, buffer, and border zones during hot days from 2017 to 2023, using Landsat 8 and 9 imageries to assess Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI), alongside on-site Air Temperature (AT) measurements. Our findings reveal that all five forests were impacted by recent extreme heat events, with core zones remaining cooler and more vital, followed by buffer zones. Temperature-regulating patterns varied with landscape characteristics and the surrounding matrixes. We observed a site-dependent cooling effect of the forest interior that increased with higher LST. Our study highlights the value of old-growth forests and recommends increasing effective protection around mature forests, establishing corridors between isolated patches, and creating mosaics in managed landscapes that include unmanaged areas capable of developing into old-growth ecosystems. [ABSTRACT FROM AUTHOR]

Spatially continuous surface air temperature (SAT) is critically important for a wide range of fields such as eco-environmental assessments and hydrology. Remotely sensed estimation models based on satellite-derived thermal infrared data provides a structurally different approach for reconstructing SAT compared to spatial interpolation of ground observations of SAT and numerical modelling, which are mainly limited by the coverage of stations and coarse spatial resolutions, respectively. However, the data products of remotely sensed estimates of SAT developed in previous studies are only available at daily or monthly resolutions, and are primarily restricted for local regions. In this study, we generated the first hourly dataset (GHRSAT) of all-sky remotely sensed SAT estimates for the global land areas except Antarctica between 2011 and 2023. The hourly estimates in GHRSAT were reconstructed from land surface temperature using the hybrid estimation models that integrate random forest (RK) models and kriging techniques. The hybrid models were developed for different regions on a monthly basis. We adopted ordinary kriging (OK) and fixed rank kriging (FRK) in the modelling of the site residuals from the RF models for regions with low-density and high-density stations, respectively. Our results show that the hybrid models for generating GHRSAT have the predictive performance between 1.48 °C to 2.28 °C in overall cross-validation RMSE. The mean RMSE for estimating hourly SAT can be significantly reduced by 0.18–0.41 °C by the hybrid models compared to the RF models. We analyzed the variability in the predictive errors of estimating hourly SAT across regions, months and sites. The variability is apparently decreased when using the hybrid models. We found that the RF models are less sensitive to the parameter tuning of the RF models, which greatly impacts the hybrid models. Improving the RF models by parameter tuning can drastically improve the hybrid models based on the RF models. Additionally, we found the performance difference between OK and FRK in developing the hybrid models for regions with large amounts of stations is slight with the mean RMSE of 0.05 °C. In summary, the scheme of the hybrid models can result in satisfactorily higher performance for estimating SAT, and has the general practicability of applying to regions at various scales. The GHRSAT dataset is publicly available at http://doi.org/10.11888/RemoteSen.tpdc.301540 (Zhang, 2024). [ABSTRACT FROM AUTHOR]

We present a new gridded data set of air temperature change across global land and ocean extending back to the 1780s. This data set, called the GloSAT reference analysis, has two novel features: it uses marine air temperature observations rather than the sea surface temperature measurements typically used by pre-existing data sets, and it extends further into the past than existing merged land and ocean instrumental temperature records which typically estimate temperature changes from the mid-to-late 19th century onwards. New estimates of diurnal heating biases in marine air temperatures have enabled the use of daytime observations, extending the dataset further into the past compared to nighttime-only marine air temperature data. The data set uses an extended version of the CRUTEM5 station database over land areas, incorporating newly available bias adjustments for non-standard thermometer enclosures used prior to the adoption of Stevenson screens and new climatological normal estimates for stations with limited data in the 1961–1990 baseline period. Land and marine temperature anomalies are combined to produce a gridded data set following the methods developed for HadCRUT5. The GloSAT global and hemispheric temperature anomaly series show close agreement with those based on sea-surface temperature for much of the overlapping period of their records but with slightly less warming overall. [ABSTRACT FROM AUTHOR]

Undoubtedly, urbanization has improved human living conditions. However, it has also altered the natural landscape, leading to negative consequences such as increased Land Surface Temperature (LST) and Urban Heat Island (UHI) due to the expansion of Impervious Surface (IS). Much research has been conducted in other countries on the effects of changing urban landscape structures on LST and UHI formation. However, in Sri Lanka, only a few studies have been available on this topic, primarily concentrating on major cities like Colombo. Impervious Surface is absorbing high amounts of solar energy as well, which accelerates the magnitude of UHI in urbanized areas. Remote Sensing indices such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Buildup Index (NDBI), UHI, and Environmental Criticality Index (ECI) can effectively be used to quantify the intensity of the UHI phenomenon. This study aimed to investigate the effect of spatiotemporal variations in IS and Green Surface (GS) on UHI, LST, and the environmental criticality in Galle Municipal Council (MC), Sri Lanka employing multi-temporal Landsat-5 and 8 data from four different periods: 1996, 2005, 2014, and 2022. Different geospatial techniques including supervised image classification, Urban-Rural Gradient Zone (URGZ) analysis, grid-based analysis, UHI profiles, and regression analysis were used in the study. The findings revealed that Impervious Surface increased by 42.3% (7.34 km2) while Green Surface had a decline of 22.5% (3.91 km2) during the concerned period. This landscape transition led to a 2.74 C increase in mean surface temperature in the study area, along with a 9.5 C increase in the UHI index during 26 years. The results further revealed that Impervious Surface rapidly developed within 4 km (URGZ1-19) from city center, while Green Surface decreased. Newly built-up areas within the 1.5 km gradient (URGZ1-URGZ8) were more affected by increased LST. A positive correlation was identified between NDBI and LST, especially in the year 2022, with an R2 of 0.457, while NDVI and LST reported a negative R 2 of 0.257. The grid-based analysis demonstrated an increasingly positive relationship between mean LST and the fraction of Impervious Surface, highlighting the role of built-up areas in raising LST and UHI in the MC. As a result, very high environmental critical areas have been concentrated in and around high-density Impervious Surface. Thus, it can be predicted that the UHI effect and Environmental Criticality (EC) may increase further in the future. In this context, planning agencies should prioritize green urban planning strategies, such as implementing green belts and urban agriculture in the study area, particularly in and around areas with high LST and high environmental criticality. This approach may help protect the natural environment and sustainably ensure the health of the urban community. [ABSTRACT FROM AUTHOR]

Objective］ To investigate the spatial and temporal variations of land surface temperature (LST) in central Yunnan urban agglomeration and its relationship with land use/land cover (LULC) types. Methods］ Based on Google Earth Engine (GEE) platform, LST was inverted using Landsat satellite image data of central Yunnan urban agglomeration region from 199() to 2020, and was analyzed using Sen-MK trend analysis, mean-stand a rd deviation method, urban thermal field variance index (UTFVT) and correlation analysis were used to quantitatively analyze the spatial and temporal variations of LST and its response to LULC types in central Yunnan urban agglomeration from 199() to 202(). Results］ (1) There were significant spatial and temporal differences in interannual temperature classes in central Yunnan urban agglomeration. The mesothermal area always occupied an absolutely dominant position (40%), but its area share decreased year by year. (2) From 1 99() to 2020, the areas with UTFVT () showed an increasing trend year by year, and the areas with UTFVT〉0.02 showed a decreasing trend, and the ecological and thermal environments were gradually improved. (3) There were response differences in LST for different LULC types. Among them, LST was significantly positively correlated with the proportion of built-up area (r > 0,70). LST was always the lowest in the water area, with a multi-year average temperature of 17.38 °C, while the highest temperature was found in the area covered by built-up land and cropland, with multi-year average temperatures above 21 °C. (4) The rapid expansion of cities in the central Yunnan urban agglomeration had resulted in significant changes in LULQ The built-up land area had increased by more than 1 ()0.29%, and the rapid development of cities had increased LST from 199() to 2020. Conclusion] LULC is an important factor affecting the LST, and reasonable configuration of LULC structure can improve the thermal environment of urban agglomeration. The study's results can provide important insights for rational development planning and improvement of ecological conditions in the urban agglomeration of central Yunnan. [ABSTRACT FROM AUTHOR]