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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]

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]

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]

In volcanic regions, the analysis of Thermal InfraRed (TIR) satellite imagery for Land Surface Temperature (LST) retrieval is a valid technique to detect ground thermal anomalies. This allows us to achieve rapid characterization of the shallow thermal field, supporting ground surveillance networks in monitoring volcanic activity. However, surface temperature can be influenced by processes of different natures, which interact and mutually interfere, making it challenging to interpret the spatio-temporal variations in the LST parameter. In this paper, we use a workflow to detect the main thermal patterns in active volcanic areas by analyzing the Independent Component Analysis (ICA) results applied to satellite nighttime TIR imagery time series. We employed the proposed approach to study the surface temperature distribution at the Campi Flegrei caldera volcanic site (Southern Italy, Naples) during the 2013–2022 time interval. The results revealed the contribution of four main distinctive thermal patterns, which reflect the endogenous processes occurring at the Solfatara crater, the environmental processes affecting the Agnano plain, the unique microclimate of the Astroni crater, and the morphoclimatic aspects of the entire volcanic area. [ABSTRACT FROM AUTHOR]

This research offers a fresh understanding of desertification in Turkmenistan by utilizing satellite remote sensing data and machine learning techniques. With 80% of its area covered by desert, Turkmenistan has particular difficulties as a result of the harsh effects of desertification, which are made worse by climate change and irresponsible land use. Despite the fact that desertification has been the subject of numerous studies conducted worldwide, this study is among the first to use a multi-index approach to specifically focus on Turkmenistan. It does this by integrating six important desertification indicators within machine learning models like random forest (RF), eXtreme Gradient Boosting (XGBoost), naïve Bayes (NB), and K-nearest neighbors (KNN). These indicators include the Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Moisture Index (NDMI), Bare Soil Index (BSI), Enhanced Vegetation Index (EVI), and land surface temperature (LST). Based on Sentinel-2 satellite data processed by the Google Earth Engine (GEE) platform, the findings show that the country's northern, central, and eastern regions are undergoing severe desertification. Moreover, RF and XGBoost performed better than the straightforward models like NB and KNN in terms of accuracy (96% and 96.33%), sensitivity (both 100%), and kappa (0.901 and 0.9095). By concentrating on Turkmenistan, this study fills a significant gap and provides a framework for tracking desertification in similar regions around the world. [ABSTRACT FROM AUTHOR]

Rapid urbanization can change local climate by increasing land surface temperature (LST), particularly in metropolitan regions. This study uses two decades of remote sensing data to investigate how urbanization-induced changes in land use/land cover (LULC) affect LST in the Beijing Region, China. By focusing on the key issue of LST and its contributing variables through buffer zones, we determined how variables influence LST across buffer zones—core, transit, and suburban areas. This approach is crucial for identifying and prioritizing key variables in each zone, enabling targeted, zone-specific measures that can more effectively mitigate LST rise. The main driving variables for the Beijing Region were determined, and the spatial-temporal relationship between LST and driving variables was investigated using a geographically weighted regression (GWR) model. The results demonstrate that the Beijing Region's LST climbed from 2002 to 2022, with increases of 0.904, 0.768, and 0.248 °C in core, transit, and suburban areas, respectively. The study found that human-induced variables contributed significantly to the increase in LST across core and transit areas. Meanwhile, natural variables in suburban areas predominated and contributed to stabilizing local climates and cooling. Over two decades and in all buffer zones, GWR models slightly outperformed ordinary least squares (OLS) models, suggesting that the LST is highly influenced by its local geographical location, incorporating natural and human-induced variables. The results of this study have substantial implications for designing methods to mitigate LST across the three buffer zones in the Beijing Region. [ABSTRACT FROM AUTHOR]

The urban heat island (UHI) effect, marked by higher temperatures in urban areas compared to rural ones, is a key indicator of human-driven environmental changes. This study aims to identify the key morphological parameters that primarily contribute to the development of surface urban heat island intensity (SUHII) and investigates the relationship between SUHII and urban morphology using land surface temperature (LST) data from the Sentinel-3 satellite. The research focuses on Milan and Lecce, analyzing how urban geometry affects SUHII. Factors such as building height, aspect ratio, sky visibility, and surface cover are examined using approximately 1000 satellite images from 2022 and 2023. The study highlights seasonal and diurnal variations in SUHII, with particular emphasis on HW periods. Through multicollinearity and multiple regression analyses, the study identifies the main morphological drivers influencing SUHII in the two cities, specifically the Impervious Surface Fraction (ISF) and Mean Building Height (HM). Milan consistently exhibits higher SUHII, particularly during HWs, while Lecce experiences a negative SUHII, especially during the summer, due to lower urban density, more vegetation, and the low soil moisture around the urban area. Both cities show positive SUHII values at night, which are slightly elevated during HWs. The heat wave analysis reveals the areas most susceptible to overheating, typically characterized by high urban density, with ISF and HM values in some cases above the 90th percentile (0.8 and 13.0 m, respectively) compared to the overall distribution, particularly for Milan. The research emphasizes the importance of urban morphology in influencing SUHII, suggesting that detailed morphological analysis is crucial for developing climate adaptation and urban planning strategies to reduce urban overheating and improve urban resilience to climate change. [ABSTRACT FROM AUTHOR]

The Great Green Wall (GGW) initiative, which started in 2007 and is still in development as of 2024, aims to combat desertification and enhance sustainability over 8000 km across Africa's Sahel-Sahara region, encompassing 11 key countries and 7 countries associated with the initiative. Because of limited ground measurements for the GGW project, the progress and impacts of the GGW initiative have been a challenging problem to monitor and assess. This study aims to utilize satellite remote sensing data to analyze changes in the key factors related to the sustainability of the GGW region, including land cover type, vegetation index, precipitation rate, land surface temperature (LST), surface soil moisture, etc. Results from temporal analysis of these factors indicate that the deserts along the GGW are retreating and the regional mean of the Normalized Difference Vegetation Index (NDVI) has an increasing trend, although the precipitation has a slightly decreasing trend, over the past two decades. Further analysis shows spatial heterogeneity of vegetation, precipitation, and soil moisture changes. Desertification is still a challenging issue in some GGW countries. These results are helpful in understanding climate change in the GGW regions and the impacts of the Great Green Wall initiative. [ABSTRACT FROM AUTHOR]

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

Urban micro-climate plays an important role in human activities and in ensuring public health. For instance, the urban heat island effect is crucial to the thermal comfort of citizens and tourists, similar to the urban cool island effect's importance on human and infrastructure resilience. Approximately 35% of global big cities are located in drylands. While existing research has focused on the spatial and temporal changes of surface urban cooling island intensity (SUCII) in drylands in the past, there is a gap in predicting the future spatiotemporal changes in SUCII for cities within these dryland regions. This study aims to forecast the spatiotemporal dynamics of daytime SUCII of representative growing cities with a dry and cold climate. Kerman and Zahedan cities, which are undergoing large urbanization and have harsh hot summer climates, were selected as the study area. Landsat 5 and 8 images and products were utilized for six timestamps within the timeframe of 1986–2023. Various methods, including a random forest algorithm, spectral indices, Cellular Automata-Markov (CA-Markov) model, the cross-tabulation model, and spatial overlay and zonal statistics, were employed to assess and model the spatiotemporal changes in SUCII. Initially, historical land cover maps, land surface temperature (LST), surface biophysical characteristics, and SUCII data were prepared, and their spatiotemporal changes were evaluated. Then, projected maps for these variables for the year 2045 were produced. The results indicated that the built-up areas, bare lands, and green spaces of Kerman (Zahedan) city in 1986 were 26.6 km2 (17.6 km2), 103 km2 (92.5 km2), and 44.4 km2 (5.6 km2), respectively, and these values reached 99.3 km2 (41.9 km2), 61.2 km2 (70.7 km2), and 13.5 km2 (3.2 km2) in 2023. The built-up lands area of Kerman (Zahedan) city is expected to increase by approximately 26% (36%) by 2045, while bare land and green space are expected to decrease by about 32% (20%) and 39% (31%), respectively. The greatest rise in average LST of Kerman (Zahedan) city is associated with the conversion of green spaces to barren land, resulting in a notable increase of 5.5 °C (4.3 °C) in 1986–2023. The conversion of barren land to built-up land in Kerman (Zahedan) city has led to a decrease of 4.6 °C (3.8 °C) in LST. The SUCII of Kerman (Zahedan) city for 1986, 1994, 2001, 2008, 2015, and 2023 were −0.3 °C (0.9 °C), −0.8 °C (0.4 °C), −1.4 °C (−0.5 °C), −1.9 °C (−1.5 °C), −2.6 °C (−2.5 °C), and −3.2 °C (−3.4 °C), respectively. The projected SUCII in Kerman (Zahedan) city for 2045 is about −4.3 °C (−4.5 °C), indicating an increasing trend in SUCII in the future. The area of zones without SUCII in Kerman (Zahedan) city decreased by 44.8 Km2 (54.8 Km2) from 1986 to 2023, while the areas of low, medium, and high SUCII classes increased by 9.1 Km2 (9.9 Km2), 10.9 Km2 (11.9 Km2), and 24.8 Km2 (33.1 Km2), respectively. The area of non-SUCII and high SUCII classes of Kerman (Zahedan) city in 2045 is expected to decrease by 31.5 Km2 (12.0 Km2) and increase by 51.2 Km2 (9.5 Km2) compared with 2023. The findings of this research indicate that the physical growth of cities in drylands can lead to the moderation of LST, contrary to mechanisms in humid and wet regions. [ABSTRACT FROM AUTHOR]

The local or overall mass balance of a glacier is significantly influenced by the spatial heterogeneity of its overlying debris thickness. Accurately estimating the debris thickness of glaciers is essential for understanding their hydrological processes and the impact of climate change. This study focuses on the Koxkar Glacier in the Tian Shan Mountains, using debris thickness data to compare the accuracy of three commonly used approaches for estimating the spatial distribution of debris thickness. The three measurement approaches include two empirical relationships between the land surface temperature (LST) and debris thickness approaches, empirical relationship approach 1 and empirical relationship approach 2, and the energy balance of debris approach. The analysis also explores the potential influence of topographic factors on the debris distribution. By incorporating temperature data from the debris profiles, this study examines the applicability of each approach and identifies areas for possible improvement. The results indicate that (1) all three debris thickness estimation approaches effectively capture the distribution characteristics of glacial debris, although empirical relationship approach 2 outperforms the others in describing the spatial patterns; (2) the accuracy of each approach varies depending on the debris thickness, with the energy balance of debris approach being most accurate for debris less than 50 cm thick, while empirical relationship approach 1 performs better for debris thicker than 50 cm and empirical relationship approach 2 demonstrates the highest overall accuracy; and (3) topographic factors, particularly the elevation, significantly influence the accuracy of debris thickness estimates. Furthermore, the empirical relationships between the LST and debris thickness require field data and focus solely on the surface temperature, neglecting other influencing factors. The energy balance of debris approach is constrained by its linear assumption of the temperature profile, which is only valid within a specific range of debris thickness; beyond this range, it significantly underestimates the values. These findings provide evidence-based support for improving remote-sensing methods for debris thickness estimation. [ABSTRACT FROM AUTHOR]

Urban green spaces, such as parks, cemeteries, and allotment gardens provide important cooling functions for mitigating the urban heat island (UHI) effect. In the densely populated Ruhr Area (Germany), rehabilitated tailing piles (TPs), as relicts of the coal-mining history, are widespread hill-shaped landscape forms mainly used for local recreation. Their potential role as cooling islands has never been analyzed systematically. Therefore, this study aimed at investigating the TP surface cooling potential compared to other urban green spaces (UGSs). We analyzed the factors controlling the piles' summer land surface temperature (LST) patterns using k-mean clustering and random forest regression modeling. Generally, mean LST values of the TPs were comparable to those of other UGSs in the region. Indices describing vegetation moisture (NDMI), vitality (NDVI), and height (VH) were found to control the LST pattern of the piles during summer. The index for soil moisture (TVDI) was directly related to VH, with the highest values on the north and northeast-facing slopes and lowest on slopes with south and southeast expositions. Terrain attributes such as altitude, slope, aspect, and curvature were of minor relevance in that context, except on TPs exceeding heights of 125 m. In conclusion, we advise urban planners to maintain and improve the benefit of tailing piles as green cooling islands for UHI mitigation. As one measure, the soil's water-holding capacity could be increased through thicker soil covers or soil additives during mine tailing rehabilitation, especially on the piles' south and southeast expositions. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) plays a significant role in the surface water cycle, particularly within the unique geographical context of Southwest China. The region's different topography, driven by mountain uplift and variations in slope direction, alters regional hydrothermal conditions, thereby affecting local ecoclimatic patterns. ET characteristics, shaped by slope orientation, can also serve as important indicators of climate variability in the study area. While most existing ET research on shady and sunny slopes has been conducted at the point scale, this study employed Global Land Surface Satellite (GLASS) ET products to estimate the average ET for shady and sunny slopes across five provinces in Southwest China between 2003 and 2018. The driving factors behind the variation in ET across different regions were also explored. Key results include the following: (1) Annual ET in Southwest China ranges between 200 mm and 800 mm, with Tibet exhibiting the lowest values and Yunnan the highest. (2) ET decreases gradually with increasing altitude in the altitude range of 0 m to 5000 m. The ET is higher on the sunny slopes than on the shady slopes. Notably, when the altitude is higher than 5000 m, ET on shady slopes in Tibet is greater than that on sunny slopes as the altitude increases. (3) ET initially increases with slope inclination before decreasing. Notably, in areas with slopes exceeding 35° in Yunnan, the ET value is found to be significantly higher on shady slopes compared to sunny slopes. (4) The effects of soil moisture, the Normalized Difference Vegetation Index, relative humidity, and land surface temperature on ET are more substantial on shady slopes than sunny slopes, whereas air temperature has a stronger impact on ET on sunny slopes. These results provide valuable data for research on regional climate change and contribute to strategies for ecological and environmental protection. [ABSTRACT FROM AUTHOR]

Highways and their surrounding areas in mountainous and plateau regions are particularly susceptible to environmental changes, which can significantly impact their safety. In the context of global warming, the magnitude of environmental changes around highways has been further amplified. These environmental disturbances pose substantial risks to highway infrastructure in mountainous regions. By using satellite data and remote sensing techniques, this study focused on the environmental variations of the Du-Ku Highway (DKH) in the Tianshan Mountains and the preliminary revealed shifts in surface water, land surface temperature (LST), normalized difference vegetation index (NDVI), and temperature vegetation dryness index (TVDI) since 2000. The quantitative results showed that the water bodies with area between 0.1 and 0.5 ha showing the most significant growth around the DKH. The LST values are primarily distributed between 280 and 285 K, while the NDVI values are mostly below 0.4, and the TVDI is mainly concentrated at the two extremes. In the context of global warming and its amplified impact on mountainous and plateau regions, these findings offer critical insights that can directly support mountainous highway construction and maintenance strategies by identifying environmental indicators, providing a scientific foundation for making data-driven decisions. [ABSTRACT FROM AUTHOR]

Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) products are essential data sources for global and regional climate change research. Currently, several versions of the MODIS LST product have been released, yet the performance differences and uncertainties they introduce in land surface studies remain insufficiently addressed. To bridge this gap, this study focuses on four distinct versions of the LST product: MxD11A1 Collection 5 (C5), Collection 6 (C6), Collection 6.1 (C6.1), and MxD21A1 Collection 6.1 (MxD21). The spatial resolution of all product generations is 1 km, and the temporal resolution is 0.5 days. This study provides a comprehensive analysis of the errors arising from different generations of these products in various land surface process studies. The error assessment includes cross-comparisons between product versions and evaluations of the absolute errors generated. Absolute errors in evaluation data were collected from 13 surface sites within the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project during the period 2013–2018. Cross-validation results show that the largest difference between C5 and C6.1 occurs over bare land, with an RMSE of approximately 1.45 K, while there is no significant change between C6 and C6.1. MOD21 shows considerable variation compared to C6.1 at night across different land cover types, with RMSE over cropland exceeding 2 K. The temperature difference between MOD21 and C6.1 is more pronounced at night (2.01 K) than during the day (0.30 K). Validation results based on temperature indicate that C5 has greater uncertainty compared to C6, especially over bare land, where errors are 2.06 K and 1.06 K, respectively. Furthermore, MxD21 demonstrates significant day–night performance discrepancies, with an average bias of 0.10 K at night, while daytime errors over bare land can reach 2 K, potentially influenced by atmospheric conditions. Based on the research in this paper, it is possible to clarify the performance of different versions of MODIS products, reflecting the appropriateness of their past applications; on the other hand, it is recommended to prioritize the use of the MxD11A1 C6 and C6.1 products for monitoring and applications in bare soil areas to ensure higher accuracy. Furthermore, for day and night monitoring, it may be beneficial to alternate between the MxD11A1 and MxD21A1 products to fully leverage their respective advantages and enhance overall monitoring effectiveness. [ABSTRACT FROM AUTHOR]

The urban heat island effect (UHI) is among the major challenges of urban climate, which is continuously intensifying its impact on urban life and functioning. Against the backdrop of increasingly prolonged heatwaves observed in recent years, practical questions about adaptation measures in cities are growing—questions that traditional meteorological monitoring can hardly answer adequately. On the other hand, UHI has long been the focus of research interest, but due to the technological complexity of providing accurate spatially referenced data at high spatial resolution and the requirement to survey at strictly defined parts of the day, information provision is becoming a major challenge. This is one of the main reasons why UHI research results are less often used directly in urban spatial planning. However, advances in geospatial technologies, including unmanned aerial systems (UASs), are providing more and more reliable tools that can be applied to achieve better and higher-quality information resources that adequately characterize the UHI phenomenon. This paper presents a developed and tested methodology for the rapid and efficient assessment and mapping of the effects of surface urban heat island (SUHI). It is entirely based on the integrated use of data from unmanned aerial systems (UAS)-based remote sensing methods, including thermal photogrammetry and GIS-based analysis methods. The study follows the understanding that correct SUHI research depends on a proper understanding of the urban geosystem, its spatial and structural heterogeneity, and its functional systems, which in turn can only be achieved by supporting the research process with accurate and reliable information resources. In this regard, the possibilities offered by the proposed methodological scheme for efficient geospatial registration of SUHI variations at the microscale, including the calculation of intra-urban SUHI intensity, are discussed in detail. The methodology builds on classical approaches for using local climate zoning (LCZ), adding capabilities for precise delineation of individual zone types and for geostatistical characterization of the urban surface heat island (SUHI). Finally, the proposed scheme is based on state-of-the-art technological tools that provide flexible and automated capabilities to investigate the phenomenon at microscales, including by enabling flexible observation of its dynamics in terms of heat wave manifestation and evolution. Results are presented from a series of sequential tests conducted on the largest residential area in Bulgaria's capital city, Sofia, in terms of area and population, over a relatively long period from 2021 to 2024. [ABSTRACT FROM AUTHOR]

A novel method to derive surface temperatures from aerial thermography is proposed. Its theoretical foundation, details regarding the implementation, relevant sensitivities, and its application on a day and night survey are presented here. The method differs from existing approaches particularly in two aspects: first, a three-dimensional sampling approach is used to determine the reflected thermal radiation component. Different surface classes based on hyperspectral classification with specific properties regarding the reflection and emission of thermal radiation are considered in this sampling process. Second, the method relies on a detailed, altitude-dependent, directionally and spectrally resolved modelling of the atmospheric radiation transfer and considers the spectral sensitivity of the sensor used. In order to accurately consider atmospheric influences, the atmosphere is modelled as a function of altitude regarding temperature, pressure and greenhouse gas concentrations. The atmospheric profiles are generated specifically for the time of the survey based on measurements, meteorological forecasts and generic models. The method was initially developed for application in urban contexts, as it is able to capture the pronounced three-dimensional character of such environments. However, due to the detailed consideration of elevation and atmospheric conditions, the method is also valuable for the analysis of rural areas. The included case studies covering two thermographic surveys of city area of Graz during daytime and nighttime demonstrate the capabilities and feasibility of the method. In relation to the detected brightness temperatures apparent to the sensor, the determined surface temperatures vary considerably and generally cover an increased temperature range. The two processed surface temperature maps of the city area of Graz are finally used to validate the method based on available temperature recordings. [ABSTRACT FROM AUTHOR]

Satellite-derived Land Surface Temperature (LST) plays an important role in research on natural energy balance and water cycle. Considering the tradeoff between spatial and temporal resolutions, accurate fine-resolution LST must be obtained through the use of LST downscaling (DLST) technology. Various methods have been proposed for DLST at fine resolutions (e.g., 10 m) and small scales. However, the scale effect of these methods, which is inherent to DLST processes at different extents, has rarely been addressed, thus limiting their application. In this study, a modified daily 10 m resolution DLST method based on Google Earth Engine, called mDTSG, is proposed in order to reduce the scale effect at fine spatial resolutions. The proposed method introduces a convolution-based moving window into the DLST process for the fusion of different remote sensing data. The performance of the modified method is compared with the original method in six regions characterized by various extents and landscape heterogeneity. The results show that the scale effect is significant in the DLST process at fine resolutions across extents ranging from 100 km2 to 22,500 km2. Compared with the original method, mDTSG can effectively reduce the LST value differences between tile edges, especially when considering large extents (>22,500 km2) with an average R 2 improvement of 33.75%. The average M A E is 1.63 °C, and the average R M S E is 2.3 °C in the mDTSG results, when compared with independent remote sensing products across the six regions. A comparison with in situ observations also shows promising results, with an M A E of 2.03 °C and an R M S E of 2.63 °C. These findings highlight the robustness and scalability of the mDTSG method, making it a valuable tool for fine-resolution LST applications in diverse and extensive landscapes. [ABSTRACT FROM AUTHOR]

Flood susceptibility prediction is complex due to the multifaceted interactions among hydrological, meteorological, and urbanisation factors, further exacerbated by climate change. This study addresses these complexities by investigating flood susceptibility in rapidly urbanising regions prone to extreme weather events, focusing on Gdańsk, Poland. Three popular ML techniques, Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Networks (ANN), were evaluated for handling complex, nonlinear data using a dataset of 265 urban flood episodes. An ensemble filter feature selection (EFFS) approach was introduced to overcome the single-method feature selection limitations, optimising the selection of factors contributing to flood susceptibility. Additionally, the study incorporates explainable artificial intelligence (XAI), namely, the Shapley Additive exPlanations (SHAP) model, to enhance the transparency and interpretability of the modelling results. The models' performance was evaluated using various statistical measures on a testing dataset. The ANN model demonstrated a superior performance, outperforming the RF and the SVM. SHAP analysis identified rainwater collectors, land surface temperature (LST), digital elevation model (DEM), soil, river buffers, and normalized difference vegetation index (NDVI) as contributors to flood susceptibility, making them more understandable and actionable for stakeholders. The findings highlight the need for tailored flood management strategies, offering a novel approach to urban flood forecasting that emphasises predictive power and model explainability. [ABSTRACT FROM AUTHOR]

Numerous challenges are associated with the classification of satellite images of coffee plantations. The spectral similarity with other types of land use, variations in altitude, topography, production system (shaded and sun), and the change in spectral signature throughout the phenological cycle are examples that affect the process. This research investigates the influence of biennial Arabica coffee productivity on the accuracy of Landsat-8 image classification. The Google Earth Engine (GEE) platform and the Random Forest algorithm were used to process the annual and biennial mosaics of the Média Mogiana Region, São Paulo (Brazil), from 2017 to 2023. The parameters evaluated were the general hits of the seven classes of land use and coffee errors of commission and omission. It was found that the seasonality of the plant and its development phases were fundamental in the quality of coffee classification. The use of biennial mosaics, with Landsat-8 images, Brightness, Greenness, Wetness, SRTM data (elevation, aspect, slope), and LST data (Land Surface Temperature) also contributed to improving the process, generating a classification accuracy of 88.8% and reducing coffee omission errors to 22%. [ABSTRACT FROM AUTHOR]

Remote sensing satellite data have been a crucial tool in understanding urban climates. The variety of sensors with different spatiotemporal characteristics and retrieval methodologies gave rise to a multitude of approaches when analyzing the surface urban heat island effect (SUHI). Although there are considerable advantages that arise from these different characteristics (spatiotemporal resolution, time of observation, etc.), it also means that there is a need for understanding the ability of sensors in capturing spatial and temporal SUHI patterns. For this, several land surface temperature products are compared for the cities of Madrid and Paris, retrieved from five sensors: the Spinning Enhanced Visible and InfraRed Imager onboard Meteosat Second Generation, the Advanced Very-High-Resolution Radiometer onboard Metop, the Moderate-resolution Imaging Spectroradiometer onboard both Aqua and Terra, and the Thermal Infrared Sensor onboard Landsat 8 and 9. These products span a wide range of LST algorithms, including split-window, single-channel, and temperature–emissivity separation methods. Results show that the diurnal amplitude of SUHI may not be well represented when considering daytime and nighttime polar orbiting platforms. Also, significant differences arise in SUHI intensity and spatial and temporal variability due to the different methods implemented for LST retrieval. [ABSTRACT FROM AUTHOR]

The estimation of spatially resolved near-surface air temperature (NSAT) has been extensively performed in previous studies using satellite-derived land surface temperature (LST) from MODIS. However, there remains a need for estimating daily NSAT based on LST data from other satellites, which has important implications for integrating multi-source LST in estimating NSAT and ensuring the continuity of satellite-derived estimates of NSAT over long-term periods. In this study, we conducted a comprehensive comparison of LST derived from Metop with MODIS LST in the modeling and mapping of daily NSAT. The results show that Metop LST achieves consistent predictive performance with MODIS LST in estimating daily NSAT, and models based on Metop LST or MODIS LST have overall predictive performance of about 1.2–1.4 K, 1.5–2.0 K, and 1.8–1.9 K in RMSE for estimating Tavg, Tmax, and Tmin, respectively. Compared to models based on nighttime LST, daytime LST can improve the predictive performance of Tmax by about 0.26–0.28 K, while performance for estimating Tavg or Tmin using different schemes of LST is comparable. Models based on Metop LST also exhibit high consistency with models utilizing MODIS LST in terms of the variability in predictive performance across months, with RMSE of 1.03–1.82 K, 1.3–2.49 K, and 1.26–2.66 K for Tavg, Tmin, and Tmax, respectively. This temporal variability in performance is not due to sampling imbalance across months, which is confirmed by comparing models trained using bootstrapped samples in balance, and our results imply that sampling representativeness, complicated by retrieval gaps in LST, is an important issue when analyzing the variability in predictive performance for estimating NSAT. To fully assess the predictive capability of Metop LST in estimating daily NSAT, more studies need to be performed using different methods across areas with a range of scales and geographical environments. [ABSTRACT FROM AUTHOR]

The bright band (BB) is an important symbol of the ice–water transition zone in stratiform precipitation, and the presence or absence of BB will lead to different microphysical processes. In this paper, the characteristics of BB and precipitation characteristics with and without BB in summer at Tibetan Plateau (TP) as well as Central-eastern China (CEC) are analyzed by using Global Precipitation Measurement (GPM) and the fifth generation ECMWF atmospheric reanalysis of the global climates (ERA5) datasets. The results show the freezing level height and BB height in TP are 0.5 km higher than those in CEC. With the increase in rain rate, the BB height decreases in TP but increases in CEC. The BB width becomes wider with the increase in maximum radar reflectivity. Secondly, the maximum reflectivity factor and particle diameter of stratiform precipitation with BB appear at 5 km, while the maximum reflectivity factor of stratiform precipitation without BB and convective precipitation appear near the ground. The particle diameter first decreases and then increases from the cloud top to the ground. Thirdly, the land surface temperature of convective precipitation is about 2.5 °C higher than stratiform precipitation with BB, indicating higher land surface temperatures are more likely to trigger convection. Lastly, BB can lead to a decrease in brightness temperature and an increase in polarized difference at 89 GHZ and 166 GHZ in CEC, likely due to the increasing ice particles in stratiform precipitation with BB. [ABSTRACT FROM AUTHOR]

Accurate land surface temperature (LST) retrieval depends on precise knowledge of the land surface emissivity (LSE). Neglecting or inaccurately estimating the emissivity introduces substantial errors and uncertainty in LST measurements. The emissivity, which varies across different surfaces and land uses, reflects material composition and surface roughness. Satellite data offer a robust means to determine LSE at large scales. This study utilises the Normalised Difference Vegetation Index Threshold Method (NDVITHM) to produce a novel emissivity dataset spanning the last 40 years, specifically tailored for the Fennoscandian region, including Norway, Sweden, and Finland. Leveraging the long and continuous data series from the Advanced Very High Resolution Radiometer (AVHRR) sensors aboard the NOAA and MetOp satellites, an emissivity dataset is generated for 1981–2022. This dataset incorporates snow-cover information, enabling the creation of annual emissivity time series that account for winter conditions. LSE time series were extracted for six 15 × 15 km study sites and compared against the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD11A2 LSE product. The intercomparison reveals that, while both datasets generally align, significant seasonal differences exist. These disparities are attributable to differences in spectral response functions and temporal resolutions, as well as the method considering fixed values employed to calculate the emissivity. This study presents, for the first time, a 40-year time series of the emissivity for AVHRR channels 4 and 5 in Fennoscandia, highlighting the seasonal variability, land-cover influences, and wavelength-dependent emissivity differences. This dataset provides a valuable resource for future research on long-term land surface temperature and emissivity (LST&E) trends, as well as land-cover changes in the region, particularly with the use of Sentinel-3 data and upcoming missions such as EUMETSAT's MetOp Second Generation, scheduled for launch in 2025. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is important in a variety of applications, such as urban thermal environment monitoring and water resource management. In this paper, eleven candidate split-window (SW) algorithms were adapted to Thermal Infrared Sensor-2 (TIRS-2) data of the Landsat 9 satellite for estimating the LST. The simulated dataset produced by extensive radiative transfer modeling and five global atmospheric profile databases was used to determine the SW algorithm coefficients. Ground measurements gathered at Surface Radiation Budget Network sites were used to confirm the efficiency of the SW algorithms after their performance was initially examined using the independent simulation dataset. Five atmospheric profile databases perform similarly in training accuracy under various subranges of total water vapor. The candidate SW algorithms demonstrate superior performance compared to the radiative transfer equation algorithm, exhibiting a reduction in overall bias and RMSE by 1.30 K and 1.0 K, respectively. It is expected to provide guidance for the generation of the Landsat 9 LST using the SW algorithms. [ABSTRACT FROM AUTHOR]

Managing the amount of greenspace (i.e., increasing or decreasing greenspace coverage) and optimizing greenspace configuration (i.e., increasing or decreasing greenspace fragmentation) are cost-effective approaches to cooling the environment. The spatial variations in their impacts on the thermal environment, as well as their relative importance, are of great importance for greenspace planning and management but are far from thoroughly understood. Taking Changsha, China as an example, this study investigated the spatial variations of the impacts of greenspace amount (measured as a percent of greenspace) and greenspace fragmentation (measured by edge density of greenspace) on the Landsat-derived land surface temperature (LST) using geographically weighted regression (GWR), and also uncovered the spatial pattern of their relative importance. The results indicated that: (1) Greenspace amount showed significantly negative relationships with LST for 91.73% of the study area. (2) Both significantly positive and negative relationships were obtained between greenspace fragmentation and LST, covering 14.90% and 13.99% of the study area, respectively. (3) The negative relationship between greenspace fragmentation and LST is mainly located in the urban areas, while the positive relationship appeared in the rural areas. (4) Greenspace amount made a larger contribution to regulating LST than greenspace fragmentation in 93.31% of the study area, but the latter had stronger roles in about 6.69% of the study area, mainly in the city center. These findings suggest that spatially varied greenspace planning and management strategies should be adopted to improve the thermal environment. [ABSTRACT FROM AUTHOR]

The ecosystems in the mountainous region of Southwest China are exceptionally fragile and constitute one of the global hotspots for wildfire occurrences. Understanding the complex interactions between wildfires and their environmental and anthropogenic factors is crucial for effective wildfire modeling and management. Despite significant advancements in wildfire modeling using machine learning (ML) methods, their limited explainability remains a barrier to utilizing them for in-depth wildfire analysis. This paper employs Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) models along with the MODIS global fire atlas dataset (2004–2020) to study the influence of meteorological, topographic, vegetation, and human factors on wildfire occurrences in the mountainous region of Southwest China. It also utilizes Shapley Additive exPlanations (SHAP) values, a method within explainable artificial intelligence (XAI), to demonstrate the influence of key controlling factors on the frequency of fire occurrences. The results indicate that wildfires in this region are primarily influenced by meteorological conditions, particularly sunshine duration, relative humidity (seasonal and daily), seasonal precipitation, and daily land surface temperature. Among local variables, altitude, proximity to roads, railways, residential areas, and population density are significant factors. All models demonstrate strong predictive capabilities with AUC values over 0.8 and prediction accuracies ranging from 76.0% to 95.0%. XGBoost outperforms LR and RF in predictive accuracy across all factor groups (climatic, local, and combinations thereof). The inclusion of topographic factors and human activities enhances model optimization to some extent. SHAP results reveal critical features that significantly influence wildfire occurrences, and the thresholds of positive or negative changes, highlighting that relative humidity, rain-free days, and land use land cover changes (LULC) are primary contributors to frequent wildfires in this region. Based on regional differences in wildfire drivers, a wildfire-risk zoning map for the mountainous region of Southwest China is created. Areas identified as high risk are predominantly located in the Northwestern and Southern parts of the study area, particularly in Yanyuan and Miyi, while areas assessed as low risk are mainly distributed in the Northeastern region. [ABSTRACT FROM AUTHOR]

Forest change affects local and global climate by altering the physical properties of the land surface. Accurately assessing urban forest changes in local land surface temperature (LST) is a scientific and crucial strategy for mitigating regional climate change. Despite this, few studies have attempted to accurately characterize the spatial and temporal pattern of afforestation, reforestation, and deforestation to optimize their effects on surface temperature. We used the China Land Cover Dataset and knowledge criterion-based spatial analysis model to map urban forestation (e.g., afforestation and reforestation) and deforestation. We then analyzed the impacts of these activities on LST from 2010 to 2020 based on the moving window strategy and the spatial–temporal pattern change analysis method in the urban agglomerations of the Yangtze River Delta (YRD) and Pearl River Delta (PRD), China. The results showed that forest areas declined in both regions. Most years, the annual deforestation area is greater than the yearly afforestation areas. Afforestation and reforestation had cooling effects of −0.24 ± 0.19 °C and −0.47 ± 0.15 °C in YRD and −0.46 ± 0.10 °C and −0.86 ± 0.11 °C in PRD. Deforestation and conversion of afforestation to non-forests led to cooling effects in YRD and warming effects of 1.08 ± 0.08 °C and 0.43 ± 0.19 °C in PRD. The cooling effect of forests is more evident in PRD than in YRD, and it is predominantly caused by reforestation. Moreover, forests demonstrated a significant seasonal cooling effect, except for December in YRD. Two deforestation activities exhibited seasonal warming impacts in PRD, mainly induced by deforestation, while there were inconsistent effects in YRD. Overall, this study provides practical data and decision-making support for rational urban forest management and climate benefit maximization, empowering policymakers and urban planners to make informed decisions for the benefit of their communities. [ABSTRACT FROM AUTHOR]

The Olympic Games are a sporting event and a catalyst for urban development in their host city. In this study, we utilized remote sensing and GIS techniques to examine the impact of the Olympic infrastructure on the surface temperature of urban areas. Using Landsat Series Collection 2 Tier 1 Level 2 data and cloud computing provided by Google Earth Engine (GEE), this study examines the effects of various forms of Olympic Games facility urban planning in different historical moments and location typologies, as follows: monocentric, polycentric, peripheric and clustered Olympic ring. The GEE code applies to the Olympic Games that occurred from Paris 2024 to Montreal 1976. However, this paper focuses specifically on the representative cases of Paris 2024, Tokyo 2020, Rio 2016, Beijing 2008, Sydney 2000, Barcelona 1992, Seoul 1988, and Montreal 1976. The study is not only concerned with obtaining absolute land surface temperatures (LST), but rather the relative influence of mega-event infrastructures on mitigating or increasing the urban heat. As such, the locally normalized land surface temperature (NLST) was utilized for this purpose. In some cities (Paris, Tokyo, Beijing, and Barcelona), it has been determined that Olympic planning has resulted in the development of green spaces, creating "green spots" that contribute to lower-than-average temperatures. However, it should be noted that there is a significant variation in temperature within intensely built-up areas, such as Olympic villages and the surrounding areas of the Olympic stadium, which can become "hotspots." Therefore, it is important to acknowledge that different planning typologies of Olympic infrastructure can have varying impacts on city heat islands, with the polycentric and clustered Olympic ring typologies displaying a mitigating effect. This research contributes to a cloud computing method that can be updated for future Olympic Games or adapted for other mega-events and utilizes a widely available remote sensing data source to study a specific urban planning context. [ABSTRACT FROM AUTHOR]

Land Surface Temperature (LST) is integral to our understanding of the radiative energy budget of the Earth's surface since it provides the best approximation to the thermodynamic temperature that drives the outgoing longwave flux from surface to atmosphere. Since 5 July 2017, an operational LST product has been available from the Sentinel-3A mission, with the corresponding product being available from Sentinel-3B since 17 November 2018. Here, we present the first paper describing formal products, including algorithms, for the Sea and Land Surface Temperature Radiometer (SLSTR) instruments onboard Sentinel-3A and 3B (SLSTR-A and SLSTR-B, respectively). We evaluate the quality of both the Land Surface Temperature Climate Change Initiative (LST_cci) product and the Copernicus operational LST product (SL_2_LST) for the years 2018 to 2021. The evaluation takes the form of a validation against ground-based observations of LST across eleven well-established in situ stations. For the validation, the mean absolute daytime and night-time difference against the in situ measurements for the LST_cci product is 0.77 K and 0.50 K, respectively, for SLSTR-A, and 0.91 K and 0.54 K, respectively, for SLSTR-B. These are an improvement on the corresponding statistics for the SL_2_LST product, which are 1.45 K (daytime) and 0.76 (night-time) for SLSTR-A, and 1.29 K (daytime) and 0.77 (night-time) for SLSTR-B. The key influencing factors in this improvement include an upgraded database of reference states for the generation of retrieval coefficients, higher stratification of the auxiliary data for the biome and fractional vegetation, and enhanced cloud masking. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) data from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard the Sentinel-3 satellites have been used in the Met Office's Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) since 2019 (Sentinel-3A SST data since March 2019 and Sentinel-3B data since December 2019). The impacts of using SLSTR SSTs and the SLSTR as the reference sensor for the bias correction of other satellite data have been assessed using independent Argo float data. Combining Sentinel-3A and -3B SLSTRs with two Visible Infrared Imaging Radiometer Suite (VIIRS) sensors (onboard the joint NASA/NOAA Suomi National Polar-orbiting Partnership and National Oceanic and Atmospheric Administration-20 satellites) in the reference dataset has also been investigated. The results indicate that when using the SLSTR as the only reference satellite sensor, the OSTIA system becomes warmer overall, although there are mixed impacts in different parts of the global ocean. Using both the VIIRS and the SLSTR in the reference dataset leads to moderate but more consistent improvements globally. Numerical weather prediction (NWP) results also indicate a better performance when using both the VIIRS and the SLSTR in the reference dataset compared to only using the SLSTR at night. Combining the VIIRS and the SLSTR with latitudinal weighting shows the best validation results against Argo, but further investigation is required to refine this method. [ABSTRACT FROM AUTHOR]

Growth in urban areas contributes to environmental degradation through increased land surface temperature (LST), exacerbating the urban heat island (UHI) effect. This study examined how land use and land cover (LULC) characteristics of Shillong City are linked to the UHI phenomenon. The LULC was classified into five broad categories: agricultural land, barren land, settlement, vegetation, and water bodies. The results show that the study area experienced notable changes in the LULC pattern from 1993 to 2023, with settlement areas increasing by 10.96%, transforming previously barren lands. The emergence and growth of settlements (and/or built-up areas) and impervious surfaces have led to a steady increase in LST. The settlement land use class had an average LST of 17.45 °C in 1993, 21.56 °C in 2003, 21.37 °C in 2013, and 21.75 °C in 2023. From 1993 to 2023, surface temperatures in settlement areas rose by a maximum of 4.3 °C, while barren land and vegetated areas also saw an increase of 4.9 °C and 4.0 °C, respectively. The relationship between LULC and the LST has been evaluated to identify hotspot areas. The highest temperatures are found in crowded and dense built-up areas, while the lowest temperatures are found in vegetated areas and water bodies. The findings also reveal a clear warming trend over the 30-year period, marked by a substantial decrease in areas with LST below 12 °C and between 12–17 °C, highlighting a shift towards warmer temperatures. Projected LULC changes indicate that urban areas will experience significant growth, increasing from 17.36% of the total area in 2023 to 21.39% in 2030, and further to 28.56% by 2050. The results suggest that the settlement land use class will increase by 11.2%, accompanied by a decrease in agricultural lands, vegetation, and water bodies. [ABSTRACT FROM AUTHOR]

Global warming and climate change are significantly impacting local climates, causing more intense heat during the summer season, which poses risks to individuals with pre-existing health conditions and negatively affects overall human health. While various studies have examined the Surface Urban Heat Island (SUHI) phenomenon, these studies often focus on small to large geographic regions using low-to-moderate-resolution data, highlighting general thermal trends across large administrative areas. However, there is a growing need for methods that can detect microscale thermal patterns in environments familiar to urban residents, such as streets and alleys. The temperature-humidity index (THI), which incorporates both temperature and humidity data, serves as a critical measure of human-perceived heat. However, few studies have explored microscale THI variations within urban settings and identified potential THI hotspots at a local level where SUHI effects are pronounced. This research aims to address this gap by estimating THI at a finer resolution scale using data from multiple sensor platforms. We developed a model with the random forest algorithm to assess THI trends at a resolution of 0.5 m, utilizing various variables from different sources, including Landsat 8 land surface temperature (LST), unmanned aerial system (UAS)-derived LST, Sentinel-2 NDVI and NDMI, a wind exposure index, solar radiation modeled from aircraft and UAS-derived Digital Surface Models, and vehicle density and building floor area from social big data. Two models were constructed with different variables: Modelnatural, which includes variables related to only natural factors, and Modelmix, which includes all variables, including anthropogenic factors. The two models were compared to reveal how each source contributes to the model development and SUHI effects. The results show significant improvements, as Modelnatural had a fitting R2 = 0.5846, a root mean square error (RMSE) = 0.5936 and a mean absolute error (MAE) = 0.4294. Moreover, when anthropogenic factors were introduced, Modelmix performed even better, with R2 = 0.9638, RMSE = 0.1751, and MAE = 0.1065 (n = 923). This study contributes to the future of microscale SUHI analysis and offers important insights into urban planning and smart city development. [ABSTRACT FROM AUTHOR]

Local climate zones (LCZs) and urban functional zones (UFZs) can intricately depict the multidimensional spatial elements of cities, offering a comprehensive perspective for understanding the surface urban heat island (SUHI) effect. In this study, we retrieved two types of land surface temperature (LST) data and constructed 12 SUHI scenarios over the Guangdong–Hong Kong–Macao Greater Bay Area Central region using six SUHI identification methods. It compared the SUHI sensitivity differences among different types of LCZ and UFZ to analyze the global and local sensitivity differences of influencing factors in the 12 SUHI scenarios by utilizing the spatial gradient boosting trees, geographically weighted regression, and the coefficient of variation model. Results showed the following: (1) The sensitivity of different LCZ and UFZ types to multi-scenario SUHI was significantly affected by differences in SUHI identification methods and non-urban references. (2) In the morning, the shading effect of building clusters reduced the surface urban heat island intensity (SUHII) of some built environment types (such as LCZ 1 (compact high-rise zone) to LCZ 5 (open midrise zone)). The SUHIIs of LCZ E (bare rock or paved zone) and LCZ 10 (industry zone) were 4.22 °C and 3.87 °C, respectively, and both are classified as highly sensitive to SUHI. (3) The sensitivity of SUHI influencing factors exhibited regional variability, with importance differences in the sensitivity of importance for factors such as the impervious surface ratio, elevation, average building height, vegetation coverage, and average building volume between LCZs and UFZs. Amongst the 12 SUHI scenarios, an average of 87.43% and 89.97% of areas in LCZs and UFZs, respectively, were found to have low spatial sensitivity types. Overall, this study helps urban planners and managers gain a more comprehensive understanding of the complexity of the SUHI effect in high-density cities, providing a scientific basis for future urban climate adaptability planning. [ABSTRACT FROM AUTHOR]

Analyzing the impacts of urban landscape patterns on the thermal environment has become one of the key research areas in addressing urban heat islands (UHIs) and improving the living environment. A case study was carried out in Fuzhou, Fujian Province of China, and bi-temporal Landsat imagery was selected to calculate land surface temperature (LST), percent impervious surface area (ISA), and fractional vegetation cover (FVC). The urban area was further divided into three concentric urban zones, ranging from the city center to the urban periphery, based on urban development densities. The spatial pattern of LST and its variance were analyzed and compared between different zones and different dates. The thermal environment contribution of different zones was also quantified to indicate the change in urban landscape patterns resulting from urban expansion in different zones. Furthermore, Geodetector was used to explore the single factors and interaction factors controlling the spatial patterns of LST in each zone. The results showed that (i) urban expansion primarily increased in Zone 2 and Zone 3, and the areal proportion of high and sub-high LST areas increased from 56.11% and 21.08% to 62.03% and 32.49% in Zone 2 and Zone 3, respectively, from 2004 to 2021; (ii) the heat effect contribution of Zones 2 and 3 reached from 75.16% in 2004 to 89.40% in 2021, indicating that the increase in ISA with >LSTmean was more pronounced in Zone 3 and Zone 2 during the period; (iii) the driving factors of LST spatial distribution were regionally different because of the different landscape patterns, and the explanatory power for the heterogeneity of LST in Zone 1 was weaker than in Zone 2 and Zone 3 in the study area; (iv) the interaction of different factors had a higher explanatory power in the spatial distribution of LST than a single factor in each zone because the distributions of land cover types are heterogeneous in urban areas. The results of this study can be used to improve urban planning for urban ecology and UHI mitigation. [ABSTRACT FROM AUTHOR]

The widespread availability of Land Surface Temperature (LST) data from various sources presents a contemporary challenge for urban climate studies: how to efficiently compare these data with the results of traditional methods of temperature monitoring, which typically assume measurements at 2 m under sheltered conditions. In this line, the current study is based primarily on data extracted from a network of 31 points of hourly temperature monitoring at the 2 m level (Tair2m), in use between 2019 and 2021, in the city of Suceava in north-eastern Romania. These data allowed a detailed mapping for each hourly time step through multiple regression, adjusted by IDW, which was identified as the best interpolation method of Tair2m. These data were analyzed in parallel with LST data derived from Landsat imagery available in the analyzed period for 35 summer days with no or low cloud cover. The mapping results of both the Tair2m and LST data describe the main characteristics of the Suceava urban agglomeration (SvMA) heat island, which presents polynuclear features with intensities—as expressed by the temperature difference between the cores of the heat island and the surrounding rural areas—spanning during the summer noontime between 3.0 °C based on Tair2m and 7.1 °C on LST, respectively. The values of the Tair2m–LST differences were 0.68 °C on average, ranging from 5.33 to −19.17 °C, directly proportional to the imperviousness ratio (IMD) values, reaching the highest values in the local climate zones (LCZs) with a high built-up ratio (up to −19.17 °C) and the lowest (0.5 ÷ −0.5 °C) for those with bare soils, with isolated bushes and trees, with few or no buildings. The study results could serve as a tool to downscale the LST data to the level of Tair2m, which is useful for interpretation of the data derived from these commonly used tools in urban climate monitoring. [ABSTRACT FROM AUTHOR]

The global, near-real-time monitoring of volcano thermal activity has become feasible through thermal infrared sensors on various satellite platforms, which enable accurate estimations of volcanic emissions. Specifically, these sensors facilitate reliable estimation of Volcanic Radiative Power (VRP), representing the heat radiated during volcanic activity. A critical factor influencing VRP estimates is the identification of hotspots in satellite imagery, typically based on intensity. Different satellite sensors employ unique algorithms due to their distinct characteristics. Integrating data from multiple satellite sources, each with different spatial and spectral resolutions, offers a more comprehensive analysis than using individual data sources alone. We introduce an innovative Remote Sensing Data Fusion (RSDF) algorithm, developed within a Cloud Computing environment that provides scalable, on-demand computing resources and services via the internet, to monitor VRP locally using data from various multispectral satellite sensors: the polar-orbiting Moderate Resolution Imaging Spectroradiometer (MODIS), the Sea and Land Surface Temperature Radiometer (SLSTR), and the Visible Infrared Imaging Radiometer Suite (VIIRS), along with the geostationary Spinning Enhanced Visible and InfraRed Imager (SEVIRI). We describe and demonstrate the operation of this algorithm through the analysis of recent eruptive activities at the Etna and Stromboli volcanoes. The RSDF algorithm, leveraging both spatial and intensity features, demonstrates heightened sensitivity in detecting high-temperature volcanic features, thereby improving VRP monitoring compared to conventional pre-processed products available online. The overall accuracy increased significantly, with the omission rate dropping from 75.5% to 3.7% and the false detection rate decreasing from 11.0% to 4.3%. The proposed multi-sensor approach markedly enhances the ability to monitor and analyze volcanic activity. [ABSTRACT FROM AUTHOR]

Continuous software development and widespread access to satellite imagery allow for obtaining increasingly accurate data on the natural environment. They play an important role in hydrosphere research, and one of the most frequently addressed issues in the era of climate change is the thermal dynamics of its components. Interesting research opportunities in this area are provided by the utilization of data obtained from the moderate resolution imaging spectroradiometer (MODIS). These data have been collected for over two decades and have already been used to study water temperature in lakes. In the case of Poland, there is a long history of studying the thermal regime of lakes based on in situ observations, but so far, MODIS data have not been used in these studies. In this study, the available products, such as 1-day and 8-day MODIS land surface temperature (LST), were validated. The obtained data were compared with in situ measurements, and the reliability of using these data to estimate long-term thermal changes in lake waters was also assessed. The analysis was conducted based on the example of two coastal lakes located in Poland. The results of 1-day LST MODIS generally showed a good fit compared to in situ measurements (average RMSE 1.9 °C). However, the analysis of long-term trends of water temperature changes revealed diverse results compared to such an approach based on field measurements. This situation is a result of the limited number of satellite data, which is dictated by environmental factors associated with high cloud cover reaching 60% during the analysis period. [ABSTRACT FROM AUTHOR]

The NOAA-AVHRR (National Oceanographic and Atmospheric Administration–Advanced Very High-Resolution Radiometer) archive includes data from 1981 onwards, which allow for estimating land surface temperature (LST), a key parameter for the study of global warming as well as surface characterization. However, algorithms for LST retrieval were developed before the latest sensors and were based on more reduced atmospheric datasets. Here, we present 50 novel sets of coefficients for an LST retrieval algorithm from NOAA-AVHRR sensors, to which we added one historical methodology, which we validate against historical in situ as well as independent satellite data. This validation shows that the historical algorithm performs surprisingly well, with an in situ RMSE below 1.5 K and a quasi-null bias when compared with independent satellite data. A couple of the novel algorithms also perform within expectations (errors below 1.5 K), so any of these could be used for the complete processing of the AVHRR dataset. In our case, considering consistency with previous works, we opt for the use of the historical algorithm, now also tested for more recent periods. [ABSTRACT FROM AUTHOR]

The Middle Route of the South-to-North Water Diversion Project is a critical infrastructure that ensures optimal water resource distribution across river basins and safeguards the livelihood of people in China. This study investigated its effects on the land surface temperature (LST) and fractional vegetation coverage (FVC) in the Danjiang River Basin. Moreover, it examined the spatial and temporal patterns of this project, providing a scientific basis for the safe supply of water and ecological preservation. We used the improved interpolation of mean anomaly (IMA) method based on the digital elevation model (DEM) to reconstruct LST while FVC was estimated using the image element dichotomous model. Our findings indicated a general increase in the average LST in the Danjiang River Basin post-project implementation. During both wet and dry seasons, the cooling effect was primarily observed in the south-central region during the daytime, with extreme values of 6.1 °C and 5.9 °C. Conversely, during the nighttime, the cooling effect was more prevalent in the northern region, with extreme values of 3.0 °C and 2.3 °C. In contrast, the warming effect during both seasons was predominantly located in the northern region during the daytime, with extreme values of 5.3 °C and 5.5 °C. At night, the warming effect was chiefly observed in the south-central region, with extreme values of 5.8 °C and 5.9 °C. FVC displayed a seasonal trend, with higher values in the wet season and overall improvement over time. Statistical analysis revealed a negative correlation between vegetation change and daytime temperature variations in both periods (r = −0.184, r = −0.195). Furthermore, a significant positive correlation existed between vegetation change and nighttime temperature changes (r = 0.315, r = 0.328). Overall, the project contributed to regulating LST, fostering FVC development, and enhancing ecological stability in the Danjiang River Basin. [ABSTRACT FROM AUTHOR]

As urbanization advances, the issue of urban heat islands (UHIs) grows increasingly serious, with UHIs gradually transitioning into regional urban heat islands. There is still a lack of research on the evolution and drivers of the thermal environment in urban agglomerations; therefore, in this study, we used trend analysis methods and spatial statistical analysis tools to investigate these issues in the Beijing–Tianjin–Hebei (BTH) urban agglomeration. The results demonstrated the following: (1) The land surface temperature (LST) exhibited low fluctuation, while the relative land surface temperature (RLST) fluctuated significantly. In Zhangjiakou and Chengde, the LST and RLST evolution trends were complex, and the results differed between daytime and nighttime, as well as between the annual and seasonal scales. In other regions, the trends of LST and RLST evolution were more obvious. (2) During the daytime, the high UHI clusters centered on "BJ–TJ–LF" and "SJZ–XT–HD" formed gradually; during the nighttime, the high UHI clusters were mainly observed in built-up areas. The distribution range and direction of UHIs showed greater degrees of evolution during the daytime in summer. (3) The total UHI area showed an increasing trend, and the intensity of heat stress suffered by the BTH agglomeration was increasing. (4) In BTH and Hebei, aerosol optical depth, surface solar radiation, population density, and gross domestic product were the dominant factors influencing UHIs; moreover, in Beijing and Tianjin, all factors showed an basically equal impact. The methodology and findings of this study hold significant implications for guiding urban construction, optimizing urban structure, and improving urban thermal comfort in the BTH urban agglomeration. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) has a wide application in Earth Science-related fields, and spatial downscaling is an important method to retrieve high-resolution LST data. However, existing LST downscaling methods have difficulties in simultaneously constructing and expressing spatial non-stationarity, spatial autocorrelation, and complex non-linearity during the LST downscaling process, which limits the performance of the models. Moreover, there is a lack of research on high-resolution nighttime land surface temperature (NLST) reconstruction based on spatial downscaling, which does not meet the data needs for urban-scale nighttime urban heat island (UHI) studies. Therefore, this study combined Geographically Neural Network Weighted Regression (GNNWR) with Area-to-Point Kriging interpolation (ATPK) to propose a Geographically Neural Network Weighted Regression Kriging (GNNWRK) model for NLST downscaling. To verify the model's generality and robustness, this study selected four study areas with different landform and climate type for NLST spatial downscaling experiments. The GNNWRK was compared with four benchmark downscaling methods, including TsHARP, Random Forest, Geographically Weighted Regression, and GNNWR. The results show that compared to these four benchmark methods, the GNNWRK method has higher accuracy in NLST downscaling, with a maximum Pearson's Correlation Coefficient (Pcc) of 0.930 and a minimum Root Mean Square Error (RMSE) of 0.886 K. Moreover, the validation based on MODIS NLST data and ground-measured NLST data also indicates that the GNNWRK model can obtain more accurate, high-resolution NLST with richer and more detailed texture. This enhances the potential of NLST in studying the effects of urban nighttime heat islands at a finer scale. [ABSTRACT FROM AUTHOR]