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Urbanization and land cover change (LCC) drive carbon emission, which in turn contributes to a substantial rise of about 1 °C in the global average surface temperature. Conducting a crucial study of carbon emissions (CEs) and land surface temperature (LST) change is vital for reducing global warming below 1.5 °C to avoid extreme events on cities, resources, and ecosystems. This study examines connections between land cover (LC), CE, and LST. Hotspot mapping pinpoints CE hotspots on LC types with spatial clustering. LST depicts gradual temperature rise for each LC. Eventually, the notational rubric method compares real-time situations involving CE and LST in composite carbon–thermal emission and absorption map. The solutions provided in the rubric are situation-based and nature-centric. This study presents novel mapping method of composite CE and absorption map by combining both land cover emissions and absorptions and fossil fuel emissions. Development of relational rubric gives an integrated approach to understand complex relationship between LCC CE and surface temperature. [ABSTRACT FROM AUTHOR]

Urban blue-green space (UBGS) is considered to be an effective way to mitigate Urban Heat Island (UHI) effects. UBGS not only cools the actual space but also influences the surrounding areas; this phenomenon is termed as UBGS cooling effect. The present study tries to anatomize the UBGS of urban Tezpur with the help of geo-technology. Landsat satellite images of Thematic Mapper (TM) and Operational Land Imager (OLI) with 30 m spatial resolution were used to investigate the UBGS scenario for the years 1993 and 2023, respectively. Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI) were taken into consideration for the ascertainment of UBGS and UHI. The correlation between LST and NDVI was also determined with the aid of simple regression analysis. The NDVI values for the years 1993 and 2023 are − 0.32 to 0.70 and − 0.44 to 0.50 respectively. The LST values of the town for the year 1993 are 28.76 to 20.17 and for 2023, the LST value is 29.47 to 20.36. The NDWI value indicates that the water index increased in the water bodies from the year 1993 to 2023. Though sufficient data are not available on the website, the data used in the study are free from major environmental and geometric disturbances to establish the LST, NDVI, and NDWI. However, the present work is the pioneer that used geo-spatial technology which will also help the urban planners and designers to deal with UBGS and UHI effects. [ABSTRACT FROM AUTHOR]

The increasingly serious urban heat island (UHI) effect is unfavorable to urban development. This study utilized land cover data and land surface temperature (LST) data of China in 2020 by using correlation analysis and spatial regression models to analyze the relationships between LST and two influencing factors (land cover and digital elevation model (DEM)). The results showed the following: (1) The correlation between LST and forest was highest in the Northeast China Plain (NCP), Huang-Huai-Hai Plain (HHP), Qinghai Tibet Plateau (QTP), and Loess Plateau (LP). DEM mean displayed its highest correlation in the Northern arid and semiarid region (NAR), Sichuan Basin and surrounding regions (SCR), Yunnan-Guizhou Plateau (YGP), and Middle-lower Yangtze Plain (MYP). Southern China (SC) had the highest correlation between LST and construction land. (2) There was spatial heterogeneity between land cover and LST. Unused land in LP had larger impact on LST. For every 1 % increase in the proportion of unused land area, the LST increased by 0.250 °C. LST in some central and western regions of China (the NAR, the LP, the SCR, and the YGP) was mainly affected by local land cover; LST in eastern coastal regions (the HHP, MYP, NCP, SC) and QTP was not only affected by local land cover, but also by LST or land cover of neighboring regions. The warming effect of construction land on LST was more significant, with LST increasing by 0.079 °C to 0.338 °C for every 1 % increase in the proportion of construction land area. Coordination of land use planning and synergistic remediation in different regions and rational planning of construction land are essential to mitigate the UHI effect. (3) Water bodies in the NCP, NAR, and MYP had the greatest cooling impact on LST, with LST decreasing by 0.277 °C, 0.246 °C, and 0.079 °C, respectively, for every 1 % increase in the proportion of water bodies area. Forest in the QTP, LP, SC, and YGP had the greatest cooling impact on LST, and for every 1 % increase in the proportion of forest area, LST decreased by 0.144 °C, 0.089 °C, 0.086 °C, and 0.038 °C, respectively. Actively planting trees and increasing the area of forests and water bodies are of positive significance in alleviating the UHI effect and improving the ecological environment. [ABSTRACT FROM AUTHOR]

Urban woodland composition and configuration have strong associations with land surface temperatures (LST), but the evidence is contradictory due to different spatial scales, regional climate zones, woodland types and urban contexts. In this study, we analyse associations between urban woodland and LST within and between five cities in different Köppen climate zones. Our consistent methodology is framed around local climate zones and conducted at a fine spatial scale. We find that urban woodland fragmentation, connectedness, and shape complexity all influence LST, though much less than overall cover. The importance of cover holds for all climates except for hot-desert (Cairo). Otherwise, every 1% increase in woodland cover corresponds to a reduction of LST of around 0.07 °C to 0.02 °C (London-Cfb > Toronto-Dfa > Nanjing-Cfa > Shenyang-Dwa). Within cities, increasing urban woodland cover generally reduces LST more in built-up compared to vegetated zones. Nevertheless, associations between local LST and urban woodland composition and configuration are highly heterogeneous across cities, especially in cooler climates. Thus, to unravel the complexities of urban woodland cooling, systematic analysis of contemporaneous local and regional factors is required. [ABSTRACT FROM AUTHOR]

The increasing population density and impervious surface area have exacerbated the urban heat island effect, posing significant challenges to urban environments and sustainable development. Urban spatial morphology is crucial in mitigating the urban heat island effect. This study investigated the impact of urban spatial morphology on land surface temperature (LST) at the township scale. We proposed a six-dimensional factor system to describe urban spatial morphology, comprising Atmospheric Quality, Remote Sensing Indicators, Terrain, Land Use/Land Cover, Building Scale, and Socioeconomic Factors. Spatial autocorrelation and spatial regression methods were used to analyze the impact. To this end, the township-scale data of Linyi City from 2013 to 2022 were collected. The results showed that LST are significantly influenced by urban spatial morphology, with the strongest correlations found in the factors of land use types, landscape metrics, and remote sensing indices. The global Moran's I value of LST exceeds 0.7, indicating a strong positive spatial correlation. The High-High LISA values are distributed in the central and western areas, and the Low-Low LISA values are found in the northern regions and some scattered counties. The Geographically Weighted Regression (GWR) model outperforms the Spatial Error Model (SEM) and Ordinary Least Squares (OLS) model, making it more suitable for exploring these relationships. The findings aim to provide valuable references for town planning, resource allocation, and sustainable development. [ABSTRACT FROM AUTHOR]

The increasing trend in land surface temperature (LST) and the formation of urban heat islands (UHIs) has emerged as a persistent challenge for urban planners and decision-makers. The current research was carried out to study the land use and land cover (LULC) changes and associated LST patterns in the planned city (Kabul) and the unplanned city (Jalalabad), Afghanistan, using Support Vector Machine (SVM) and Landsat data from 1998 to 2018. Future changes in LULC and LST were predicted for 2028 and 2038 using Cellular Automata-Markov (CA-Markov) and Artificial Neural Network (ANN) models. The results clearly emphasize different LULC changes and LST patterns between Kabul and Jalalabad. Between 1998 and 2018, the built-up areas in Kabul and Jalalabad increased by 16% and 30%, respectively, while bare soil and vegetation decreased by 15% and 1% in Kabul and 4% and 30% in Jalalabad. The built-up areas showed the highest seasonal and annual LST, followed by bare soil and vegetation. The maximum seasonal LST occurred during the summer for both cities from 1998 to 2018. Future predictions showed that the built-up areas (48% and 55% in 2018) will increase to approximately 59% and 68% by 2028 and to 68% and 79% by 2038 in Kabul and Jalalabad, respectively. Similarly, LST simulations showed that the percentage of areas with higher LST (> 35°C) would increase from (0% and 5% in 2018) to 4% and 5% and 22% and 43% in Kabul and Jalalabad by 2028 and 2038, respectively. Kabul's planned city shows lower LST than Jalalabad's unplanned city, primarily due to planned urbanization and greater vegetation cover in the city center. Urban planners should limit unplanned development and increase vegetation in Jalalabad to reduce the potential impacts of high temperatures. [ABSTRACT FROM AUTHOR]

Rapid urbanization in Gulf cities has driven significant land cover changes, influencing outdoor thermal comfort and land surface temperatures (LST). This study investigates land cover dynamics from 1998 to 2023 across six cities – Doha, Dubai, Kuwait City, Manama, Muscat, and Riyadh – using Landsat imagery to assess LST, Normalized Difference Vegetation Index (NDVI) and approximated wet-bulb globe temperature (AWGBT). Results reveal an increase in urban areas, with Manama and Kuwait City experiencing the largest expansions (47.50% and 47.02%). Vegetation patterns varied, with cities like Dubai and Riyadh showing consistent increases, while Doha stagnated from 2013 to 2023. LST ranged from 42°C to 55°C, with desert areas showing the highest temperatures. Built-up areas had LST comparable to desert land, highlighting a reverse urban heat island effect. Dubai’s LST decreased between 2013 and 2023 due to successful green initiatives, contrasting with rising temperatures in other cities. The mean LST difference between the desert and urban areas was 2.5°C, and vegetation displayed a cooling effect, with a 3.5°C difference between vegetated and desert areas. Thermal comfort maps aligned with LST data, showing increasing heat stress, particularly in Doha and Kuwait City, while Dubai maintained stable comfort levels. This study underscores the critical role of vegetation and sustainable urban planning in mitigating heat stress and enhancing outdoor thermal comfort across Gulf cities. [ABSTRACT FROM AUTHOR]

Climate change poses significant global challenges, especially in the West African sub-region, with high temperature and precipitation patterns variability, threatening socio-economic stability and ecosystem health. While global factors such as greenhouse gases and oceanic circulations shape regional climates, this study focuses on the understudied role of local climatic variables in influencing near-surface air temperature (NST) in Ghana from 1981 to 2020. Based on ground observations, our findings reveal significant correlations between land surface temperature (LST) and NST before and after the identified breakpoint year of 2001. Additionally, we observe a reduction in precipitation post-2001. We also identify LST as the primary driver of NST and precipitation changes based on cause-effect analysis of multiple factors. Specifically, higher LST leads to decreased precipitation and increased NST, contributing to the increasing trend of NST over the last two decades. The insights are vital for developing targeted adaptation strategies, including integrated land and water management, sustainable agriculture, and effective interventions, directly supporting the United Nations Sustainable Development Goals (SDG) 13 (Climate Action) and SDG 15 (Life on Land). Moreover, the study provides evidence for promoting climate-smart agriculture to ensure food security (SDG 2). By integrating these findings into climate adaptation frameworks, policymakers and stakeholders can better address the unique challenges posed by climate variability in Ghana, ensuring more resilient and sustainable environmental management. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a crucial factor for reflecting climate change. High spatial resolution LST is particularly significant for environmental monitoring in plateau and mountainous areas, which are characterized by rugged landscapes, diverse ecosystems, and high spatial variability in LST. Typical plateau mountainous areas in Diqing Tibetan Autonomous Prefecture and Dali Bai Autonomous Prefecture were selected as study areas. Three machine learning models, including Back Propagation (BP) Neural Network, random forest (RF), and extreme gradient boosting (XGBoost), and two classic single-factor linear regression models (DisTrad and TsHARP) were compared. Particle Swarm Optimization (PSO) was introduced to optimize hyperparameters of three machine learning methods. Regression factors suitable for plateau mountainous areas, including normalized vegetation index (NDVI), normalized multi-band drought index (NMDI), bare soil index (BSI), normalized difference snow index (NDSI), elevation, surface roughness (SR), and Hillshade were selected. The performance of five models was analyzed from the perspective of different spatial resolutions and land cover types. The results revealed that the performance of machine learning models is better than traditional linear models in both study areas. Based on the coefficient of determination (R 2), root mean square error (RMSE), and mean absolute error (MAE), XGBoost demonstrated the best performance. For study area A, the results were R 2 = 0.891, RMSE = 2.67 K, and MAE = 1.83 K, while for study area B, the values were R 2 = 0.832, RMSE = 1.98 K, and MAE = 1.54 K. In addition, among different land cover types, the XGBoost model has the best performance in both study areas. Moreover, the larger the ratio of initial resolution to target resolution, the lower the accuracy of downscaled LST (DLST). In summary, the XGBoost model is more suitable for downscaling LST in plateau mountainous areas. [ABSTRACT FROM AUTHOR]

Addressing global warming and adapting to the impacts of climate change is a primary focus of climate change adaptation strategies at both European and national levels. Land surface temperature (LST) is a widely used proxy for investigating climate-change-induced phenomena, providing insights into the surface radiative properties of different land cover types and the impact of urbanization on local climate characteristics. Accurate and continuous estimation across large spatial regions is crucial for the implementation of LST as an essential parameter in climate change mitigation strategies. Here, we propose a deep-learning-based methodology for LST estimation using multi-source data including Sentinel-2 imagery, land cover, and meteorological data. Our approach addresses common challenges in satellite-derived LST data, such as gaps caused by cloud cover, image border limitations, grid-pattern sensor artifacts, and temporal discontinuities due to infrequent sensor overpasses. We develop a regression-based convolutional neural network model, trained on ECOSTRESS (ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station) mission data, which performs pixelwise LST predictions using 5 × 5 image patches, capturing contextual information around each pixel. This method not only preserves ECOSTRESS's native resolution but also fills data gaps and enhances spatial and temporal coverage. In non-gap areas validated against ground truth ECOSTRESS data, the model achieves LST predictions with at least 80% of all pixel errors falling within a ±3 °C range. Unlike traditional satellite-based techniques, our model leverages high-temporal-resolution meteorological data to capture diurnal variations, allowing for more robust LST predictions across different regions and time periods. The model's performance demonstrates the potential for integrating LST into urban planning, climate resilience strategies, and near-real-time heat stress monitoring, providing a valuable resource to assess and visualize the impact of urban development and land use and land cover changes. [ABSTRACT FROM AUTHOR]

One of the most significant urban challenges focuses on addressing the effects of urban overheating as a consequence of climate change. Several methods have been developed to characterize urban heat islands (UHIs); however, the most widely used involve complex planning, huge time consumption, and substantial human and technical resources on field monitoring campaigns. Therefore, this study aims to provide an easily accessible and affordable remote sensing method for locating urban hotspots and addresses a multi-criteria assessment of urban heat-related parameters, allowing for a comprehensive city-wide evaluation. The novelty is based on leveraging the potential of the last Landsat 9 satellite, the application of kernel spatial interpolation, and GIS open access data, providing very high-resolution land surface temperature images over urban spaces. Within GIS workflow, the city is divided into LCZs, thermal hotspots are detected, and finally, it is analyzed to understand how urban factors, such as urban boundaries, building density, and vegetation, affect urban scale LST, all using graphical and analytical cross-assessment. The methodology has been tested in Seville, a representative warm Mediterranean city, where variations of up to 10 °C have been found between homogeneous residential areas. Thermal hotspots have been located, representing 11% of the total residential fabric, while results indicate a clear connection between the urban factors studied and overheating. The conclusions support the possibility of generating a powerful affordable tool for future research and the design of public policy renewal actions in vulnerable areas. [ABSTRACT FROM AUTHOR]

The microwave polarization index (PI), defined as the difference between vertically polarized (V-pol) and horizontally polarized (H-pol) brightness temperature divided by their average, is independent of land surface temperature. Since soil emission is stronger at V-pol than H-pol and vegetation attenuates this polarized soil signal primarily because of liquid water stored in vegetation tissue, a lower PI will be indicative of more water in vegetation if vegetation emits a mostly unpolarized signal and changes in soil moisture within the emitting depth are small (like during periods of drought) or accommodated by averaging over long periods. We hypothesize that the L-band PI will reveal diurnal changes in vegetation water related to whether plants have adequate soil water. We compare 6 a.m. and 6 p.m. L-band PI from NASA's Soil Moisture Active Passive (SMAP) satellite to the evaporative stress index (ESI) in the U.S. Corn Belt during the growing season. When ESI < 0 (there is not adequate plant-available water, also called plant water stress), the L-band PI is not significantly different at 6 a.m. vs. 6 p.m. On the other hand, when ESI ≥ 0 (no plant water stress), the L-band PI is greater in the evening than in the morning. This diurnal behavior can be explained by transpiration outpacing root water uptake during daylight hours (resulting in a decrease in vegetation water from 6 a.m. to 6 p.m.) and continued root water uptake overnight (that recharges vegetation water) only when plants have adequate soil water. Consequently, it may be possible to use L-band PI to identify plant water stress in the Corn Belt. [ABSTRACT FROM AUTHOR]

All-sky 1 km land surface temperature (LST) data are urgently needed. Two widely applied approaches to derive such LST data are merging thermal infrared remote sensing (TIR)–passive microwave remote sensing (PMW) observations and merging TIR reanalysis data. However, as only the Moderate Resolution Imaging Spectroradiometer (MODIS) is adopted as the TIR source for merging, current 1 km all-sky LST products are limited to the MODIS observation time. Therefore, a gap still remains in terms of all-sky LST data with a higher temporal resolution or at other times (e.g., dawn–dusk time). Under this background, this study merged the observations of the Medium Resolution Spectrum Imager (MERSI-LL) on board the dusk–dawn-orbit Fengyun (FY)-3E satellite and Global Land Data Assimilation System (GLDAS) data to estimate dawn–dusk 1 km all-sky LST using a random forest-based method (RFRTM). The results showed that the model had good robustness, with an STD of 0.62–0.86 K of the RFRTM LST, compared with the original MERSI-LL LST. Validation against in situ LST showed that the estimated LST had an accuracy of 1.34–3.71 K under all-sky conditions. In addition, compared with the dawn–dusk LST merged from MERSI-LL and the Special Sensor Microwave Imager/Sounder (SSMI/S), the RFRTM LST showed better performance in accuracy and image quality. This study's findings are beneficial for filling the gap in all-sky LST at high spatiotemporal resolutions for associated applications. [ABSTRACT FROM AUTHOR]

Urbanisation and changes in land use and land cover (LULC) significantly impact Urban Heat Islands (UHI). Despite extensive research on UHI, regional variations demand localised studies. This research assesses the UHI effect in Damascus, Syria, where rapid urbanisation threatens environmental balance. Leveraging Google Earth Engine, Landsat 8, and MODIS data, LULC changes, temperature patterns, NDVI, and NDBI, have been analysed with a focus on the summers of 2013, 2017, and 2022. Results revealed a substantial 12 % increase in urban areas between 2013–2022, accompanied by an 11 % decrease in green spaces. Land surface temperatures (LST) rose from 47.1 °C in 2013 to 48 °C in 2022. NDBI analysis confirmed built-up area expansion, while NDVI indicated dwindling vegetation cover. The pronounced UHI effect (48.7 °C) in industrial and densely populated areas during summer were observed. A strong positive correlation existed between LST and NDBI 90 %, and a moderately negative correlation between LST and NDVI. These findings highlight the adverse impact of urbanisation on Damascus' climate. They underscore the need for urban planning strategies prioritising green space preservation and sustainable development to mitigate the UHI effect and ensure a healthier urban environment. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) data records longer than 30 years are critical for climate change research and various applications. However, only a few such long-term global SM datasets exist, and they often suffer from large biases, low spatial resolution, or spatiotemporal incompleteness. Here, we generated a consistent and seamless global SM product from 1982 to 2021 using deep learning (DL) by integrating four decades of Advanced Very High Resolution Radiometer (AVHRR) albedo and land surface temperature products with multi-source datasets. Considering the temporal autocorrelation of SM, we explored two types of DL models that are adept at processing sequential data, including three long short-term memory (LSTM)-based models, i.e., the basic LSTM, Bidirectional LSTM (Bi-LSTM), and Attention-based LSTM (AtLSTM), as well as a Transformer model. We also compared the performance of the DL models with the tree-based eXtreme Gradient Boosting (XGBoost) model, known for its high efficiency and accuracy. Our results show that all four DL models outperformed the benchmark XGBoost model, particularly at high SM levels (> 0.4 m3 m-3). The AtLSTM model achieved the highest accuracy on the test set, with a coefficient of determination (R2) of 0.987 and root mean square error (RMSE) of 0.011 m3 m-3. These results suggest that utilizing temporal information as well as adding an attention module can effectively enhance the estimation accuracy of SM. Subsequent analysis of attention weights revealed that the AtLSTM model could automatically learn the necessary temporal information from adjacent positions in the sequence, which is critical for accurate SM estimation. The best-performing AtLSTM model was then adopted to produce a four-decade seamless global SM dataset at 5 km spatial resolution, denoted as the GLASS-AVHRR SM product. Validation of the GLASS-AVHRR SM product using 45 independent International Soil Moisture Network (ISMN) stations prior to 2000 yielded a median correlation coefficient (R) of 0.73 and unbiased RMSE (ubRMSE) of 0.041 m3 m-3. When validated against SM datasets from three post-2000 field-scale COsmic-ray Soil Moisture Observing System (COSMOS) networks, the median R values ranged from 0.63 to 0.79, and the median ubRMSE values ranged from 0.044 to 0.065 m3 m-3. Further validation across 22 upscaled 9 km Soil Moisture Active Passive (SMAP) core validation sites indicated that it could well capture the temporal variations in measured SM and remained unaffected by the large wet biases present in the input European reanalysis (ERA5-Land) SM product. Moreover, characterized by complete spatial coverage and low biases, this four-decade, 5 km GLASS-AVHRR SM product exhibited high spatial and temporal consistency with the 1 km GLASS-MODIS SM product, and contained much richer spatial details than both the long-term ERA5-Land SM product (0.1°) and European Space Agency Climate Change Initiative combined SM product (0.25°). The annual average GLASS-AVHRR SM dataset from 1982 to 2021 is available at https://doi.org/10.5281/zenodo.14198201 (Zhang et al., 2024), and the complete product can be freely downloaded from https://glass.hku.hk/casual/GLASS_AVHRR_SM/. [ABSTRACT FROM AUTHOR]

Anthropogenic aerosols could weaken the East Asian summer monsoon (EASM). This study investigated the regional effects of varying aerosol optical depth (AOD) on the EASM through qualitative and quantitative analyses for three subregions in eastern China. After assessing 38 CMIP6 models, four models (ACCESS-CM2, CanESM5, MIROC6, and MRI-ESM2-0) were selected for detailed analysis. Results showed that the weakening of EASM was predominantly attributed to anthropogenic aerosols. Increased AOD reduced land-sea temperature and pressure differences, weakening the EASM as indicated by the EASMI. Higher aerosol levels decreased surface shortwave radiation, land surface temperature, and evaporation, weakening the land-sea thermal contrast. Enhanced aerosol-induced cooling increased atmospheric stability and downward flow, suppressing upper air water vapor flux and precipitation. These findings underscore the critical role of anthropogenic aerosols in altering regional climate patterns and the importance of emission control to mitigate their effects on the EASM. [ABSTRACT FROM AUTHOR]

The 'Third Pole', home to numerous glaciers, serves as vital water reserves for a significant portion of the Asian population and has garnered global attention within the context of climate change due to their highly vulnerable nature. While a general decline in global glacial extent has been observed in recent decades, the pronounced regional imbalances across the Third Pole present a perplexing anomaly. To assess the impact of glacier mass changes in the Gangotri basin, we conducted a comprehensive analysis using remote sensing data to estimate spatially resolved mass changes from 2000 to 2023. Our glacier mass balance estimates were based on empirical models and the digital elevation model difference method. We also examine the relationship between glacier retreat and the variability of albedo and aerosols in the glacier ice. Analysis of the geodetic mass balance indicates that the glacier surface has decreased by 8.12 m, a loss of 0.49 m of water equivalent per annum (m.w.e. a−1) between 2000 and 2014. The estimates and results revealed from the accumulation area ratio (AAR) mass balance, and ice velocity measurements indicate a negative mass balance of − 0.28 m.w.e. a−1 for Gangotri between 2000 and 2023. Our analyses highlight both climatological and anthropogenic factors responsible for the accelerated rate of mass loss. Regional mass loss during the ablation season is primarily influenced by land surface temperature, yet the role of other factors, such as changes in surface albedo and light-absorbing impurities (LAIs), remains uncertain. Our analysis investigated temporal variations in mass balance values, while also considering changes in surface albedo and LAIs like black carbon (BC), organic carbon (OC) and dust concentrations. This analysis reveals that LAIs have an inverse relationship with albedo, where an increase in LAI concentration results in reduced albedo over the glacier. Consequently, as albedo decreases, the surface mass balance of the glacier also declines, which is further validated by the findings of this study. While this study highlights the detrimental effects of light-absorbing pollutants on the health of glacier, further investigation is necessary to comprehensively establish their role in reducing the albedo of the glacier surface and influencing associated mass loss. [ABSTRACT FROM AUTHOR]

Urban overheating significantly affects thermal comfort and livability, making it essential to understand the relationship between urban form and land surface temperature (LST). While the horizontal dimensions of urban form have been widely studied, the vertical structures and their impact on LST remain underexplored. This study investigates the influence of three-dimensional urban form characteristics on LST, using ECOSTRESS sensor data and four machine learning models. Six urban morphology variables—building density (BD), mean building height (MH), building volume (BVD), gross floor area (GFA), floor area ratio (FAR), and sky view factor (SVF)—are analyzed across different seasons and times of day. The results reveal that MH, BD, and FAR are season-stable factors, with higher MH correlated with lower LST ((e.g., an observed reduction of approximately 3 °C in spring), while higher BD is associated with higher LST (e.g., an increase of about 3.5 °C in autumn). In contrast, BVD, GFA, and SVF are season-varying factors with variable impacts depending on the time of year. Higher BVD is generally associated with elevated LST, while GFA and SVF are linked to lower LST. These associations reflect absolute changes in LST, measured directly from ECOSTRESS data. These findings offer valuable insights into the complex interactions between urban morphology and LST, helping to inform strategies for urban heat mitigation and sustainable planning. [ABSTRACT FROM AUTHOR]

Greenhouse gas emissions are primary drivers of climate change, and the intensification of extreme heat and urban heat island effects poses serious threats to urban ecosystems, public health, and energy consumption. This study systematically evaluated the carbon reduction potential of 369 urban parks in Jinan during extreme heat events using land surface temperature (LST) retrieval, combined with CatBoost + SHAP machine learning methods. Results indicate that the LST in Jinan ranged from 1.77 °C to 59.44 °C, and 278 parks exhibited significant cooling effects, collectively saving 2943 tons of CO2 per day—offsetting 11.28% of the city's fossil fuel emissions. Small parks, such as community parks, demonstrated higher carbon-saving efficiency (CSE), while large ecological parks showed greater carbon-saving intensity (CSI). CSE was strongly correlated with vegetation coverage and surrounding population density, with efficiency increasing when the vegetation index was within 0.3–0.7 and population density ranged 0–5000 or 15,000–22,500 people. CSI was influenced by evapotranspiration and park geometric form, increasing significantly when the park area exceeded 250 hectares or evapotranspiration ranged 2.5–6.0. However, elevation and albedo negatively impacted both metrics, with the lowest CSI observed when elevation exceeded 150 m or albedo surpassed 18%. [ABSTRACT FROM AUTHOR]

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

In order to assess the spatial and temporal characteristics of the urban thermal environment in Zhengzhou City to supplement climate adaptation design work, based on the Landsat 8–9 OLI/TIRS C2 L2 data for 12 periods from 2019–2023, combined with the lLocal climate zone (LCZ) classification of the urban subsurface classification, in this study, we used the statistical mono-window (SMW) algorithm to invert the land surface temperature (LST) and to classify the urban heat island (UHI) effect, to analyze the differences in the spatial distribution of thermal environments in urban areas and the aggregation characteristics, and to explore the influence of LCZ landscape distribution pattern on surface temperature. The results show that the proportions of built and natural landscape types in Zhengzhou's main metropolitan area are 79.23% and 21.77%, respectively. The most common types of landscapes are wide mid-rise (LCZ 5) structures and large-ground-floor (LCZ 8) structures, which make up 21.92% and 20.04% of the study area's total area, respectively. The main urban area's heat island varies with the seasons, pooling in the urban area during the summer and peaking in the winter, with strong or extremely strong heat islands centered in the suburbs and a distribution of hot and cold spots aggregated with observable features. As building heights increase, the UHI of common built landscapes (LCZ 1–6) increases and then reduces in spring, summer, and autumn and then decreases in winter as building heights increase. Water bodies (LCZ G) and dense woods (LCZ A) have the lowest UHI effects among natural settings. Building size is no longer the primary element affecting LST as buildings become taller; instead, building connectivity and clustering take center stage. Seasonal variations, variations in LCZ types, and variations in the spatial distribution pattern of LCZ are responsible for the spatial differences in the thermal environment in the study area. In summer, urban areas should see an increase in vegetation cover, and in winter, building gaps must be appropriately increased. [ABSTRACT FROM AUTHOR]

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]