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The response of vegetation canopy conductance (gc) to changes in moisture availability ((Formula presented.)) is a major source of uncertainty in climate projections. While vegetation typically reduces stomatal conductance during drought, accurately modeling how and to what degree stomata respond to changes in moisture availability at global scales is particularly challenging, because no global scale gc observations exist. Here, we leverage a collection of satellite, reanalysis and station-based near-surface air and surface temperature estimates, which are physically and statistically linked to (Formula presented.) due to the local cooling effect of gc through transpiration, to develop a novel emergent constraint of (Formula presented.) in an ensemble of Earth System Models (ESMs). We find that ESMs systematically underestimate (Formula presented.) by ∼33%, particularly in grasslands, croplands, and savannas in semi-arid and bordering regions of the Central United States, Central Europe, Southeastern South America, Southern Africa, Eastern Australia, and parts of East Asia. We show that this underestimation occurs because ESMs inadequately reduce gc when soil moisture decreases. As gc controls carbon, water and energy fluxes, the misrepresentation of modeled (Formula presented.) contributes to biases in ESM projections of gross primary production, transpiration, and temperature during droughts. Our results suggest that the severity and duration of droughts may be misrepresented in ESMs due to the impact of sustained gc on both soil moisture dynamics and the biosphere-atmosphere feedbacks that affect local temperatures and regional weather patterns.

Terrestrial processes influence the atmosphere by controlling land‐to‐atmosphere fluxes of energy, water, and carbon. Prior research has demonstrated that parameter uncertainty drives uncertainty in land surface fluxes. However, the influence of land process uncertainty on the climate system remains underexplored. Here, we quantify how assumptions about land processes impact climate using a perturbed parameter ensemble for 18 land parameters in the Community Earth System Model version 2 under preindustrial conditions. We find that an observationally‐informed range of land parameters generate biogeophysical feedbacks that significantly influence the mean climate state, largely by modifying evapotranspiration. Global mean land surface temperature ranges by 2.2°C across our ensemble (σ = 0.5°C) and precipitation changes were significant and spatially variable. Our analysis demonstrates that the impacts of land parameter uncertainty on surface fluxes propagate to the entire Earth system, and provides insights into where and how land process uncertainty influences climate. Plain Language Summary: Land processes can affect climate by controlling the transfer of energy and water from the land to the atmosphere. Previous research has shown that uncertainty surrounding land processes (e.g., photosynthesis and the movement of water through soils) can drive uncertainty in land‐to‐atmosphere fluxes. However, it remains unclear how much that land uncertainty can impact climate. Here, we quantify how climate is sensitive to assumptions about land processes by varying 18 land model parameters to create an ensemble of 36 possible worlds in a global climate model. Land temperature ranges by 2.2°C across this ensemble, mostly due to changes in how much water is evaporated from the land surface. Changing land parameters also drives regionally variable changes in mean precipitation. This study highlights a large and underappreciated impact of land processes in determining the mean climate state, and provides insights into how climate is influenced by land process uncertainty. Key Points: Land processes substantially impact the climatological mean state terrestrial temperature and precipitationLand parameters influence climate predominantly through changing evapotranspiration rather than through other mechanismsWarming driven by land processes activates different atmospheric feedbacks than radiatively‐driven warming [ABSTRACT FROM AUTHOR]

Unraveling plausible future rainfall change (ΔPr) patterns is crucial for tailoring societies' responses to climate change‐induced hazards. This study compares rainfall projections from the regionally coupled ocean model (ROM) and its atmospheric component, the regional atmospheric model REMO, over Central Equatorial Africa (CEA). Both models are forced by the Earth system model MPI‐ESM‐LR following the Representative Concentration Pathway 8.5. Results reveal increased rainfall across most of CEA, with ROM projecting more widespread and intensified wetting than REMO, although REMO produces more precipitation under future conditions, underscoring the influence of historical biases on REMO's projection. Examining processes underpinning changes unveils strong controls of sea and land surface temperature changes in ΔPr differences between the two models. Specifically, ROM mitigates warming more over the Atlantic than over CEA landmass compared to REMO, inducing enhancement of the Congo Basin cell and increased precipitable water content through specific humidity, affecting deep convection. Both models project enhanced Sahel and Kalahari thermal lows, with ROM better depicting the Kalahari low's warmer nature than the Sahel low. The resulting temperature gradients strengthen the northern and southern shallow meridional Hadley overturning circulation. ROM simulates the wetter conditions than REMO, attributed to its weaker northern Hadley Cell, which restricts the likelihood of northward moisture divergence toward the Sahel. Additionally, differences in mid‐tropospheric moisture convergence differentiate between ROM and REMO's wetness relative to the historical period and under future conditions. ROM projections are more plausible, in association with the reliability of its added value under the historical climate and mechanisms underlying Δpr. Plain Language Summary: Discriminating models that project plausible rainfall change signals become increasingly necessary in a context where climate information surrounding future rainfall patterns is fraught with important uncertainties. It is not always straightforward to differentiate plausible projected changes from imperfect simulations as climate models are approximated solutions to mathematical and physical differential equations often solved with limited orders. One of the suggested approaches involves process‐based assessments of projections. This approach requires that for example, rainfall change patterns must be underpinned by plausible mechanisms of change. A mechanism of change is plausible when it is identifiable in observational or reanalysis data sets. We applied such a method over Central Equatorial Africa using the regionally coupled ocean model (ROM) and its uncoupled version, the regional climate model REMO. The results show that projections based on ROM are more plausible thanks to a better depiction of the atmospheric baroclinicity associated with rainfall changes. Key Points: The coupled ROM and uncoupled REMO project increased precipitation for the future, with ROM projecting wetter conditions than REMOChanges in sea and land surface temperatures underpin rainfall changes with the interplay of changes in atmospheric baroclinicityROM shows more plausible precipitation change in association with its improved historical climatology and reliable mechanisms of change [ABSTRACT FROM AUTHOR]

The Southeast Asian Region is one of the world's regions that has historically had poor air quality conditions due to excess fossil fuel usage, coal combustion, and intensive deforestation activities. As the hub of economic activity, Indonesia's Jakarta and Palembang cities have drawn significant attention from rural areas, leading to unrestrained urbanization in the past decade. Therefore, this study aimed to integrate the two urban centers of Indonesia, Jakarta and Palembang. It analyzed the variation of nitrogen dioxide (NO 2) concentration, vegetation cover, and land surface temperature (LST) across all seasons from 2021 to 2023. We retrieved the tropospheric NO 2 concentration data from the ozone monitoring instrument (OMI) aboard Aura Satellite, and obtained LST and the normalized difference vegetation index (NDVI) from Landsat 8 OLI images. We then used a Pearson correlation analysis to determine the relationship between the NO 2 concentration, NDVI, and LST. The findings showed that Jakarta had higher levels of air pollution than Palembang, with rising LST and NO 2 levels. During the study period, the mean NO 2 concentration in Jakarta and Palembang was 3.25 × 1015 mol/cm2 and 0.87 × 1015 mol/cm2, respectively. While, the mean LST values in Jakarta and Palembang were 33.97 °C and 27.17 °C, respectively. The correlation results indicated a negative relationship between NO 2 level and NDVI (r = −0.378, p < 0.01), while it obtained a positive association with LST (r = 0.421, p < 0.01). These results can be a chance for the authority to formulate a new strategy for mitigating air pollution, especially for NO 2 pollutants in urban areas. [ABSTRACT FROM AUTHOR]

Urban green spaces (UGS) regulate the urban microclimate through cooling the ambient air temperature (AAT), as an ecosystem service. Commonly, the cooling effect (CE) is quantified as the land surface temperature (LST) pixel value differences between UGS and their surroundings, using once-off measurements on thermal images acquired by morning overpass satellites. This multiple date study demonstrates the comparative reliability of ambient air temperature (AAT) measurements. In a remote, hot climate town, AAT measurements were taken from mid-morning to noon along transects towards the UGS, at the same sites on four dry season dates that encompassed winter, spring, and summer. Near-concurrent LST at the respective sites was retrieved from 30 m pixel Level-2 Science Product (L2SP) Landsat-9 Thermal Infrared Sensor-2 (TIRS-2) band 10 (B10) images. The town's peak dry season proportion of non-senescent vegetation was extracted from a 3 m resolution PlanetScope image using Normalized Difference Vegetation Index (NDVI) enhancement. It showed that the town had very little green vegetation and was, therefore, in acute need of the CE of UGS. At the UGS, a peak dry season L2SP image yielded negative NDVI-LST correlations, indicating land surface cooling by UGS. AAT differences along the transects showed that large, irrigated grass UGS had a CE of up to 4.2 °C and polynomial-modelled cooling distances of up to 778 m during hot summer weather, with slightly lower cooling and occasional warming effects during winter. AAT measurements within 15 min of the Landsat-9 overpass, spanning all four image dates, correlated highly (r = 0.675, p < 0.001) with corresponding B10 LST. Using them, a statistically significant regression model (F = 28.468, p < 0.0001) predicting AAT from B10 LST, potentially applicable in similar climate, was developed. However, primarily due to wind direction and UGS sizes, plotting LST against distance from the UGS did not always match the AAT trends. Additionally, LST values were nearly twice the corresponding AAT values, suggesting that LST on thermal images should not be equated to AAT. Therefore, using AAT measurements can detect the CE more accurately than the commonly employed approach of LST pixel value differencing. [ABSTRACT FROM AUTHOR]

Alteration zones in the Eastern Desert are promising for minerals and geological resources exploration. Remote sensing and geophysical techniques offer cost-effective tools for identifying new exploration sites. Additionally, their use in mapping potential alteration zones is crucial for enhancing exploration. Geophysical and remote sensing data are integrated to perform a comprehensive study of minerals alteration associated with radioactive materials under controlling geological structures. This study aims to explore the associated radiogenic heat and geothermal energy to expand the geothermal resources assessments. The study utilizes Spectral Angle Mapper classification, band algebra, principal component analysis (PCA), surface lineament density, and decorrelation stretch techniques and Multiple Criteria Decision Analysis to enhance the mapping of mineralized alterations in the study area. It uses VNIR-SWIR ASTER data to identify hydrothermal alteration minerals and dominant alteration zones, also Landsat 8 Thermal Infrared Sensor (TIRS) offers two adjacent thermal bands, benefiting land surface temperature (LST) radiation from space in the Gabal Abu Hashim area. The area contains minerals alteration indicators like chlorite, alunite, illite, kaolinite, sericite, with less abundant ferrous minerals and epidote. Airborne gamma-ray spectrometry (AGRS) was used to identify naturally occurring radioactive anomalous zones, including potassium (K), equivalent uranium (eU), and equivalent thorium (eTh), to estimate the radiogenic heat production (RHP) in selected areas of the study area. The examination of AGRS data indicated that the studied region has radioelement concentrations ranging from 2.8 to 148 ppm, 18 to 144 ppm, and 0.004 to 9% of (eU), (eTh), and (K), respectively, indicating the existence of various rock types. The Radioelements Composite Image (RCI) successfully highlighted the radioelements enriched zones associated with younger granite, older granite, and metamorphic rocks, particularly those with extensive hydrothermal alteration. The results successfully discriminated alteration zones associated with radioelements K, U, and Th potential parts in the regional shear oblique zone. The weighted overlay GIS model was used to produce the alteration zones potentiality map, and to identify five zones of significant variations in heat production across different geological formations. The northern and southeastern regions demonstrate high alterations and land surface temperature corresponding to areas of high fault density and shear zones. The results of this study reveal that the proposed methods of remote sensing and AGRS data are effective in detecting areas rich in K, eU, and eTh in alteration zones associated with high radiogenic heat production in younger granite, older granite, and metamorphic rocks. [ABSTRACT FROM AUTHOR]

Understanding the activity patterns of urban residents is crucial for urban community design and urban energy forecasting. Predicting the visitation rates of different types of locations is a important issue because urban residents can only be in one place at a time. Achieving accurate predictions of outdoor public space visitation rates is equivalent to predicting the upper limit of the number of visitors to other buildings. Previous similar studies usually rely on survey methods such as questionnaires and GPS positioning. These methods have some drawbacks, such as small sample sizes and inaccurate data. In this paper, we used mobile signaling to investigate the park visit rates of residents in different communities in Guangzhou. In accordance with Chinese tradition, we define a community as a residential community, which consists of multiple residential units and a set of basic service facilities and management structures that are sufficient to meet the daily needs of residents. We found that the park visit rate of a community is closely related to the characteristics of the community itself. The park visit rate is positively correlated with the average LST (land surface temperature) of the communities, the average distance between the community and the large park, and the building coverage rate, while it is negatively correlated with the vegetation coverage rate, the average surface reflection rate, the convenience of transportation, and the per capita green space area. Given the different travel habits of urban residents on weekdays and weekends, we also used multiple linear regression to establish prediction models for community park visitation rates on weekdays and weekends. [ABSTRACT FROM AUTHOR]

In this paper, we examined the elevation-dependent warming (EDW) patterns of MODIS LST across different seasons in the Qinling-Daba Mountains, further investigate the connections between the EDW patterns of Land surface temperature (LST) and land surface albedo (ALB) as well as aerosol optical depth (AOD). The key findings include: (1) Our study reveals a robust correlation between LST and air temperature in the Qinling-Daba Mountains, suggesting the feasibility of using MODIS LST to predict the temperature trends (2) During the period from 2001 to 2010, MODIS LST shows a significant EDW trend, primarily in the spring season. In contrast, a negative EDW is observed in the period during 2011–2021, which is contrary to the earlier decade, particularly during the autumn and winter seasons. (3) EDW of MODIS LST is affected by the combination of ALB and AOD. The former has a negative influence on the change of LST, particularly above 2500 m in elevation. However, the latter is negatively correlated with the trend of MODIS LST, primarily at lower and middle altitudes (0–2500 m). This study gives a comprehensive explanation for the EDW of the temporal variations of LST in the Qinling-Daba Mountains to improve our understanding of the complex interactions and potential future climate scenarios in the region. [ABSTRACT FROM AUTHOR]

Recent satellite maps have reported that India is experiencing extreme heat waves, surpassing even Middle Eastern countries. This study addresses a critical gap in understanding how land use land cover (LULC) changes impact land surface temperature (LST), urban heat intensity (UHI), and water spread area (WSA) in rapidly growing cities such as Vellore and Katpadi over three decades (1997–2024). We used Landsat thermal bands and the support vector machine (SVM) algorithm to investigate LULC and LST patterns, examining the effects of urbanization and water body reduction on local climate dynamics. The LULC results showed an increase in built-up lands from 5.89 to 25.89%, while zooming water areas shrank from 3.15 to 1.02%. LST showed a significant increasing trend, with temperatures for water bodies and vegetation ranging from 17.4°C to 26°C, and for barren and built-up areas from 28°C to 42.6°C. The results of the multivariate analysis revealed a positive correlation between LST and the Normalized Difference Built-up Index (NDBI) and negative correlations between LST and the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), and the Modified Normalized Difference Water Index (MNDWI). Moreover, spatial and time series analyses of WSAs indicated a significant increase in LST. Furthermore, a strong negative correlation was found between WSA and LST, with a 10% decrease in WSA potentially increasing LST by 0.12°C to 0.55°C in surrounding regions. This study offers important contributions to improving land use policy and water resource management in urban areas, while addressing environmental concerns related to rising temperatures. The findings underscore the urgency of mitigating heat impacts and managing water resources in rapidly expanding cities. Our results provide valuable insights for policymakers and practitioners aiming to develop more sustainable, resilient, and livable urban environments. [ABSTRACT FROM AUTHOR]

In this era of rapid development, environmental quality is an essential aspect of sustainable development. A healthy urban environment supports, regulates, and provides livable conditions. In urban areas, environmental quality is considerably affected by socioeconomic factors such as population expansion and economic development. For decision‐making, it is also significant for stakeholders and policymakers to understand the impact of urban expansion on environmental quality. While previous studies have examined urban environmental quality, they often focused on single cities or limited environmental parameters. This research addresses these limitations by conducting a comparative analysis of two major Asian cities with similar demographic features, utilizing a comprehensive set of socioeconomic and environmental variables. Our innovative approach combines open‐source datasets with advanced remote sensing techniques to provide a more holistic assessment of urban environmental quality over two decades. We analyzed urban environmental quality for last two decades with the selected urban environmental quality parameters: surface greenness, surface moisture, and land surface temperature. Lahore (Pakistan) and Wuhan (China) cities were selected for having approximately same demographic features. Correlation matrix has been used to assess the relationship between these environmental variables and social‐economic variables: urban expansion and carbon emission. Correlation coefficient indicated that urban expansion correlates negatively with surface greenness and surface moisture for both Lahore (−0.67 and −0.71) and Wuhan District (−0.5 and −0.75), respectively, while it had a positive relation with land surface temperature: 0.65 and 0.57 for Lahore and Wuhan District, respectively. These effects are more prominent within 10 km distance from the city center, where a substantial urban expansion is observed during the time window. [ABSTRACT FROM AUTHOR]

The COVID‐19 pandemic, which originated in China at the end of 2019, escalated into a global crisis by March 2020. To mitigate its spread, governments worldwide implemented strict lockdown measures. While these lockdowns had adverse social, economic, and health impacts, they also led to significant environmental improvements in many regions. India's urban environment also significantly improved during lockdown. This study investigates the changes in Land Surface Temperature (LST) across eight major Indian cities, each representing diverse climatic and physiographic zones: Delhi, Dehradun, Lucknow, Kolkata, Bhopal, Bhubaneshwar, Mumbai, and Hyderabad. It aims to enhance the understanding of how sudden reductions in anthropogenic activities influence urban temperatures. The LST was computed for the lockdown period of April to May 2020 and was compared with the pre‐lockdown years of 2018 and 2019 and the post‐lockdown year of 2021, utilizing Landsat thermal data processed through the mono‐window algorithm. The results exhibit significant reductions in LST during the lockdown period. Cities like Delhi, Dehradun, and Lucknow experienced a reduction of 6°C, 5°C, and 4°C, respectively, in LST from pre‐lockdown to lockdown periods. In contrast, cities like Bhopal, Bhubaneswar, Mumbai, and Hyderabad experienced a reduction of around 2°C–3°C. However, the city of Kolkata showed an increase of 3°C from 2019 to 2020. These results highlight the substantial influence of human activities on urban thermal environments and underline the potential benefits of reducing anthropogenic impacts to improve urban thermal well‐being. [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]

Land surface temperature (LST) is a key parameter reflecting the interaction between land and atmosphere. Currently, thermal infrared (TIR) quantitative remote sensing technology is the only means to obtain large-scale, high spatial resolution LST. Accurately retrieving high spatial resolution mountainous LST (MLST) plays an important role in the study of mountain climate change. The complex terrain and strong spatial heterogeneity in mountainous areas change the geometric relationship between the surface and satellite sensors, affecting the radiation received by the sensors, and rendering the assumption of planar parallelism invalid. In this study, considering the influence of complex terrain in mountainous areas on atmospheric downward radiation and the thermal radiation contribution of adjacent pixels, a mountainous TIR radiative transfer model based on the sky view factor was developed. Combining with the atmospheric radiative transfer model MODTRAN 5.2, a nonlinear generalized split-window algorithm suitable for high spatial resolution MLST retrieval was constructed and applied to Landsat-9 TIRS-2 satellite TIR remote sensing data. The analysis results indicate that neglecting the topographic and adjacency effects would lead to significant discrepancies in LST retrieval, with simulated data showing LST differences of up to 2.5 K. Furthermore, due to the lack of measured MLST in the field, the MLST accuracy obtained by this retrieval method was indirectly validated using the currently recognized highest-accuracy forward 3D radiative transfer model DART. The MLST and emissivity were input into the DART model to simulate the brightness temperature at the top of the atmosphere (TOA) of Landsat-9 band 10, and compared with the brightness temperature at TOA of Landsat-9 band 10. The RMSE (Root Mean Square Error) for the two subregions was 0.50 and 0.61 K, respectively, indicating that the method proposed can retrieve high-precision MLST. [ABSTRACT FROM AUTHOR]

The Local Climate Zone (LCZ) scheme representing urban structure and land use pattern is essential for urban heat island (UHI) research. Fine-grained LCZ mapping considering spatial and temporal heterogeneity can provide a more precise characterization of surface thermal properties, thereby enabling a comprehensive analysis and understanding of spatiotemporal trends in climate change research. However, data-driven deep learning-based methods have limitations in coping with the complex urban landscapes of the real-world scenarios, including the spectral similarity of different LCZ and the geospatial heterogeneity of urban LCZ categories. In this study, we constructed a geographic knowledge base for enhanced LCZ characterization with the consideration of prior surface spatial information, including a set of spectral indices and urban morphological parameters (UMPs). Then, we integrated the explicable geographic knowledge base into a learnable deep learning framework in an end-to-end manner for accurate LCZ mapping by fusing multi-source heterogeneous data with a multi-level fusion strategy. The constructed Chinese Climate Zone Time Series LCZ (CClimate-TLCZ) dataset derived from Landsat-8 data with a 30 m spatial resolution, covering 18 representative cities in China, were used to evaluate the proposed framework. The experimental results demonstrate that the proposed framework achieved optimal outcomes across 18 cities, with an average overall accuracy exceeding 94 %, which is more than 20 % higher than that obtained by the standard WUDAPT method. Furthermore, the analysis of LCZ mapping-driven land surface temperature (LST) and surface UHI (SUHI) applications shows that cities within the same climate zone have similar LST distribution patterns, while significant heterogeneity exist between different zones. The annual consistency of LST patterns within each climate zone supports the validity of the LCZ classification scheme and accurate LCZ mapping for urban thermal environment studies. From 2016 to 2022, SUHI intensity initially increases and then decreases, indicating improvements in the urban thermal environment. These findings underscore the critical role of precise LCZ mapping in urban climate resilience and sustainable urban planning. [ABSTRACT FROM AUTHOR]

Rapid urban growth in an unplanned way, decreased forest cover and consequential changes in land use in urban and rural areas have become pivotal obstacles for policymakers and urban planners to a great extent. Specifically, in a developing country like India, the repercussions of these changes have generated a substantial alteration in climate in the form of increased land surface temperature (LST), heat islands and associated human health risks. This research was initiated to assess the interrelationship between LST and LULC dynamics using spatial techniques over the Barasat Sub-Division in West Bengal (India). This study encompasses the extraction of LST using satellite images, seasonal variations of LST, the relationship of different land indices (i.e. NDVI, NDBI, NDMI and NDWI) with LST, and hotspot analysis of the landscape to get a comprehensive understanding of LST changes from 1988 to 2023, seasonal patterns, and relationships with LULC dynamics in the study area. The results of this research signify a substantial percentage increase in built-up spaces (51%), a decline in fallow areas (− 73%), relatively higher LST changes (3.41 °C) in high-density built-up areas (e.g. Madhyamgram), a positive relationship (e.g. r2 = 0.59) between NDBI and LST, a transition from a cold spot to a hot spot share of 20% and a persisting hot spot of 42% throughout the summer season (1988 to 2023) in the selected landscape. The findings of this study will improve our present knowledge of seasonal fluctuations in land surface temperature (LST) and the variables that influence them. These findings will also draw attention to the growing worries about the fast variations in LST and the climatic and health hazards they pose. It is advised to establish planned urban expansion, efficient land use and land cover (LULC) management laws and encourage green areas that support sustainable development in India's urban surroundings in order to reduce these dangers. [ABSTRACT FROM AUTHOR]

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

Drought is a prevalent complex natural disaster due to its environmental extent and can severely impact global ecosystems. For the purpose of monitoring droughts and assessing their effects on a regional and global level, usually remote sensing data with an appropriate temporal and spatial resolution can be accessed. This research utilized the Moderate Resolution Imaging Spectroradiometer (MODIS)-based normalized difference vegetation index (NDVI), land surface temperature (LST), gross primary productivity (GPP) and vegetation health index (VHI) to investigate the historical duration, severity and recovery period for drought in selected districts of Balochistan. The Pearson correlation was used to determine the local link between the duration and severity of the drought between 2001 and 2021. The results showed that 2001, 2002, and 2004 were the driest years in which extreme to mild drought occurred with severity of 36%, 48% and 48% respectively. On the other hand, the drought duration result revealed 80–275 days, 160–275 days, and 176–275 days for 2001, 2002, and 2004 respectively. The result also indicated that crop land, water bodies, grass land and forest land, were positive correlation while shrub land was negative correlation with drought severity. On the other hand, crop land, water bodies, grass land and forest land, were negative correlation while shrub land was the positive correlation with drought duration. The drought recovery period analysis resulted in 16–66 days, 18–67 days, and 17–66 days for the years 2001, 2002, and 2004 respectively. With every aspect considered, the study offers insightful information on drought resistance for improved management. [ABSTRACT FROM AUTHOR]

The urban functional zone, serving as a bridge to understanding the complex interactions between human spatial activities and surface thermal environmental changes, explores the driving force information of its internal temperature changes, which is crucial for improving the urban thermal environment. However, the impacts of the current urban functional zones on the thermal environment, based on the delineation of human activities, have yet to be sufficiently investigated. To address the issue, we constructed a two-factor weighted dominant function vector model of "population heat--land use scale" to identify urban functional zones. This model is based on multisource data and considers the perspective of urban functional supply and demand matching. We then analyzed the spatial differentiation and driving factors of the relationship between urban functional zones and the surface thermal environment using the random forest algorithm, bivariate spatial autocorrelation, geographical detectors, and geographically weighted regression models. The results showed that there are significant differences in the Land Surface Temperature among different urban functional zones in the central urban area of Lanzhou. Among these, the life service zone has the greatest impact on the surface thermal environment, followed by the industrial zone and catering service zone, while the green space zone has the least impact. The surface thermal environment exhibits high-high clusters in localized spatial clustering patterns with life service, industrial, catering service, and residential zones. In contrast, it tends to exhibit low-high clusters with green spaces. Significant spatial clustering and dependence exist between various functional zones and the surface thermal environment. The land cover types characterized by the Normalized Difference Bare Land and Building Index, the vegetation coverage represented by the Fraction of Vegetation Cover, and the density of industrial activities indicated by the Industrial POI Kernel Density Index are the main drivers of the surface thermal environment in the various functional zones of the central urban area of Lanzhou, and all exhibit significant spatial heterogeneity. [ABSTRACT FROM AUTHOR]

Urbanization combined with global climate change, exacerbates the urban thermal environment and hinders sustainable urban development. However, the complex relationships between land surface temperature (LST) and urban morphology are being further understood, particularly in relation to different urban development patterns, distinct topography, and 3D building morphology. Thus, this study conducted a comparative study in Chengdu and Chongqing, Southwest China. We explored the impact of comprehensive factors (including socio-economic factos, topography, land use composition, and building morphology) on LST by employing the methods of linear regression, geographical detector model, and the boosted regression trees. Our results suggest that (1) high LST was mainly observed in the central part of Chengdu but it presented multicenter aggregation trend in Chongqing; (2) Socio-economic factors were the dominant variables affecting LST in both cities; (3) land use composition and building morphology showed distinct contributions to LST among the two cities; and (4) 3D building management was more effective in Chengdu than in Chongqing. A better understanding of the impact of various influencing factors on LST will enable policy makers and planners to develop appropriate strategies for constructing climate-adaptive cities and mitigating urban heat. [ABSTRACT FROM AUTHOR]

2023 was the warmest year on record with a global mean temperature of 1.45 ± 0.12 degC above pre‐industrial levels, surpassing the previous record (from 2016) by 0.17 degC. The onset of El Niño in early July was accompanied by record‐breaking land and sea‐surface temperatures, with June to September all exceeding previous monthly temperature records, and July and August being the hottest months on record. Sea‐surface temperatures have attained record highs for all months since March 2023 and Antarctic sea ice reached record lows throughout the year. [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]

This study integrates various remote sensing datasets to analyze environmental changes and their impacts on ecosystems across Pakhtunkhwa Province in Pakistan. Precipitation data from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) dataset, along with vegetation health assessments using Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data from the Landsat dataset, were used to comprehensively analyze the impact of vegetation dynamics on environmental footprints (i.e., temperature, precipitation, and LST). Land use maps, generated through supervised classification of Landsat images from 1985 to 2023, highlight significant changes in different land use classes, including vegetation and forest cover. Bayesian Network Modelling (BNM) and Dummy Variable Regression (DVR) methods were employed to assess the impact of vegetation (using NDVI time series) on environmental footprint and forest cover in particular. The results suggest that the NDVI generally increase the cooling effect across most of the study area, indicating that higher vegetation density is linked to a decrease in temperatures. This inverse relationship is also apparent in the connection between the NDVI and the LST, depicting a negative trend in surface temperature over most of the pixels/districts. The regression coefficients for the NDVI and the LST vary across different pixels, ranging from −5.3839 °C to 5.2697 °C, with standard deviations from 2.057 °C to 5.138 °C, reflecting a variability in the strength of this cooling effect. Similarly, for the relationship between the NDVI and the LST, coefficients range from −7.1513 °C to 6.6322 °C, with standard deviations between 1.612 °C and 4.155 °C. In contrast, NDVI and precipitation show a positive relationship, with regression coefficients ranging from 4.1686 °C to 44.3932 °C and standard deviations between 2.242 °C and 8.224 °C, suggesting greater variability in precipitation corresponding to vegetation dynamics. Additionally, forest cover generally correlates positively with precipitation in most pixels, but the variability across pixels emphasizes the complex nature of these relationships. The study identified substantial fluctuations in land use categories over the decades, indicating environmental shifts driven by both natural and human factors. BNM demonstrated a positive impact of vegetation dynamics on precipitation and a negative impact on both temperature and LST. On the other hand, the increase in forest cover, particularly due to the Billion Tree Tsunami Project, has a significant impact on the environmental footprint identified through DVR. By combining high-resolution datasets with advanced statistical techniques, this study offers key insights into the dynamic interactions between land cover, vegetation, and climate in the study region, providing valuable information for sustainable environmental management. [ABSTRACT FROM AUTHOR]

Despite its significance, net primary productivity is rarely used as an indicator of climate change. The purpose of this study was to assess the spatiotemporal dynamics of vegetation net primary productivity and its response to climate variability in the Welmel watershed, southeastern Ethiopia. The investigation utilized data from the Moderate Resolution Imaging Spectroradiometer, and the Climate Hazards Group Infrared Precipitation with Stations accessed through the Google Earth Engine cloud computing platform. To evaluate the trend and response of net primary productivity to climatic factors, the Mann-Kendall and Theil Sen's tests, the Pearson correlation coefficient, and stability analysis through the coefficient of variation of net primary productivity were utilized. The analysis of the temporal trend of net primary productivity revealed that the entire watershed, forest, wood, and cultivated land areas exhibited decreasing trends with net primary productivity values of -0.681, -4.361, -1.41, and − 3.25 g C m− 2/year, respectively. On the other hand, shrubs and grazing land displayed increasing trends of 1.15 and 2.04 g C m− 2/year, respectively. The wood and shrubland trends were statistically significant (p < 0.05). In the spatial analysis, an area of 64.6% showed a decreasing trend, whereas 33.78% displayed an increasing trend in net primary productivity values. The areas with stable and unstable variations in net primary productivity accounted for 26.47% and 8.02%, respectively. The net primary productivity of the land cover types and entire watershed were positively correlated with rainfall, however, only forests and woodlands were statistically significantly correlated (p < 0.05). The net primary productivity and land surface temperature were negatively correlated, except for forests and cultivated land, which were positively correlated. The results of this study provide essential information for managing ecosystems, supporting agricultural practices, and enhancing community resilience in a changing climate. [ABSTRACT FROM AUTHOR]

In this study, we analyzed the surface temperature of Chuncheon from 2014 to 2020 using Landsat 8 images. The findings revealed that man-made structures such as asphalt roads and buildings cause significant increases in land surface temperatures in urbanized, arid, and residential areas. Industrial areas exhibited the highest relative land surface temperature (RLST) index, while densely populated residential areas showed relatively high RLST values. Conversely, areas with high-rise apartments had lower land surface temperatures due to shade and green space. Natural land cover areas demonstrated negative mean RLST, highlighting the usefulness of RLST in distinguishing temperature differences between land cover types. A 0 °C reference surface temperature was used in RLST to standardize land surface temperature measurements, which helped understand land cover status. As the urban heat island phenomenon continues, land surface temperatures are gradually rising. To build a sustainable urban ecosystem, policies and plans to expand and improve green spaces and waterfront environments are essential. To achieve these goals, a comprehensive strategy for expanding and improving green spaces is needed, as well as various policies to improve water quality. [ABSTRACT FROM AUTHOR]

Urbanization in the coastal region has increased socioeconomic losses from landfalling tropical cyclones (TCs). Although previous studies have explored the broad impacts of urbanization on TCs, the effect of heterogeneity caused by local intra‐urban variability has not been examined. To address this gap, this study utilized the urban Local Climate Zone (LCZ) to capture the urban landscape heterogeneity and the impacts of this representation on the simulation of the post‐landfall TCs characteristics in Houston. Taking the case of two recent TCs: Hurricane Harvey (occurred in 2017) and Tropical Storm Imelda (occurred in 2019), the study evaluated the impact of urban heterogeneity on 10‐m winds, 2‐m temperature, land surface temperature, and precipitation across Houston. The consideration of intra‐urban heterogeneity using LCZ can improve the 10‐m winds, 2‐m temperature, and spatial pattern of land surface temperature. Although the cumulative rainfall remained largely similar within the experiments (with and without LCZ), incorporating intra‐urban heterogeneity notably modified the spatial structure of urban rainfall, particularly in simulating heavy rainfall hot spots. These findings are consistent for both TCs and demonstrate the positive impact of incorporating intra‐urban heterogeneity on the landfalling TC simulations over the Houston area. Plain Language Summary: In coastal cities, more buildings and development have made tropical cyclones more costly. Previous studies have sxamined the general effects of cities on tropical cyclones, but the effect from intra‐urban variability has not been investigated. This study has looked into two recent tropical cyclones and show that capturing intra‐urban variability in model can enhance the simulations of temperature and winds and hence improve the spatial structure of urban rainfall. Key Points: Enhancing urban representation improves the accuracy of 10‐m wind, 2‐m temperature, and skin temperature in urban areas during tropical cyclonesHigher localized winds and temperatures along the street can be simulatedLocal heavy rain clusters can be simulated, even though the cumulative amount across the domain is not significantly altered [ABSTRACT FROM AUTHOR]

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

Evapotranspiration (ET) is responsible for regulating the hydrological cycle, with a relevant impact on air humidity and precipitation that is particularly important in the context of acute drought events in recent years. With the intensification of rainfall deficits and extreme heat events, the Mediterranean region requires regular monitoring to enhance water resource management. Even though remote sensing provides spatially continuous information for estimating ET on large scales, existing global products with spatial resolutions ≥ 0.5 km are insufficient for capturing spatial detail at a local level. In the framework of ESA's 4DMED-Hydrology project, we generate an ET dataset at both high spatial and high temporal resolutions using the Priestley–Taylor Two-Source Energy Balance (TSEB-PT) model driven by Copernicus satellite data. We build an automatic workflow to generate a 100 m ET product by combining data from Sentinel-2 (S2) MultiSpectral Instrument (MSI) and Sentinel-3 (S3) land surface temperature (LST) with ERA5 climate reanalysis derived within the period 2017–2021 over four Mediterranean basins in Italy, Spain, France, and Tunisia (Po, Ebro, Hérault, and Medjerda). First, original S2 data are pre-processed before deriving 100 m inputs for the ET estimation. Next, biophysical variables, like leaf area index and fractional vegetation cover, are generated, and then they are temporally composited within a 10 d window according to S3 acquisitions. Consequently, decadal S2 mosaics are used to derive the remaining TSEB-PT inputs. In parallel, we sharpen 1 km S3 by exploiting the dependency between coarse-resolution LST and 100 m S2 reflectances using a decision tree algorithm. Afterwards, climate forcings are utilized to model energy fluxes and then for daily ET retrieval. The daily ET composites demonstrate reasonable TSEB-PT estimates. Based on the validation results against eight eddy covariance (EC) towers between 2017 and 2021, the model predicts 100 m ET with an average RMSE of 1.38 mm d−1 and a Pearson coefficient equal to 0.60. Regardless of some constraints mostly related to the high complexity of EC sites, TSEB-PT can effectively estimate 100 m ET, which opens up new opportunities for monitoring the hydrological cycle on a regional scale. The full dataset is freely available at https://doi.org/10.48784/b90a02d6-5d13-4acd-b11c-99a0d381ca9a , https://doi.org/10.48784/fb631817-189f-4b57-af6a-38cef217bad3 , https://doi.org/10.48784/70cd192c-0d46-4811-ad1d-51a09734a2e9 , and https://doi.org/10.48784/7abdbd94-ddfe-48df-ab09-341ad2f52e47 for the Ebro, Hérault, Medjerda, and Po catchments, respectively (Bartkowiak et al., 2023a–d). [ABSTRACT FROM AUTHOR]

Cities are facing increased heat-related health risks (HHRs) due to the combined effects of global warming and rapid urbanization. However, few studies have focused on HHR assessment based on fine-scale information. Moreover, most studies only analyze spatial HHR patterns and do not explore the potential driving factors. In this study, we estimated the potential HHRs based on the "hazard–exposure–vulnerability" framework by using multisource data, including the modified thermal–humidity index (MTHI), population density, and land cover. Then, the variations in the HHRs among different local climate zones (LCZs) at the fine spatial scale were analyzed in detail. Finally, we compared the different contributions of the LCZs and types of land cover to the HHRs and their three components by using multiple linear regression models. The results indicate that the spatial pattern of the HHRs was different from those of the individual components, and high-hazard regions do not mean high HHRs. There were huge variations in the HHRs among the different LCZs. The built-up LCZs typically had much higher HHRs than the natural ones, with compact LCZs facing the most severe risk. LCZ 6 (open low-rise buildings) had a relatively low HHR and should be paid more attention in future urban planning. Compared to the LCZs, the land covers better explained the variations in the HHR. In contrast, the LCZs better predicted the land surface temperatures. However, both the LCZs and land covers made only slight contributions to the heat exposure and vulnerability. Furthermore, the manmade buildings and impervious surface areas contributed much more to the HHR than the natural land covers. Therefore, the arrangement of the warming LCZs and land cover types is worthy of further investigation from the perspective of HHR mitigation. [ABSTRACT FROM AUTHOR]

Atmospheric aerosols (aerosol optical depth, AOD) and green cover (normalized difference vegetation index, NDVI) significantly affect the radiation balance of a region and thereby modify the land surface temperature (LST). We have examined the long-term (2000–2017) temporal association between these variables using Wavelet Local Multiple Correlation (WLMC) analysis across six geographically separated areas representing different climatic zones of India. Spearman's correlation between the variables indicates a mix of positive and negative correlations for varying seasons across the climatic zones. The non-stationary co-movement of multivariate correlation structure among the variables has been resolved by applying Maximal Overlap Discrete Wavelet Transform and WLMC analyses. Results show that the multivariate correlation integrates well beyond quarterly and biannual scales (16–32 weeks) for all zones. Daytime and nighttime LST explain the correlation structure in the data in zones from almost all climatic regions, except from central India where AOD and NDVI are the dominant variables. To some extent, NDVI plays an important role in eastern Indian region. The WLMC analysis confirms that the most reliable information in the multivariate spatial-temporal data at the regional scale can be suitably investigated. Regional climate models in this regard can further investigate the dynamics of the dominant variable in affecting the regional energy budget based on the WLMC analysis. The study has potential applications in forecasting extreme climate disasters and planning preemptive mitigation strategies. [ABSTRACT FROM AUTHOR]

The Model for Prediction Across Scales–Atmosphere (MPAS-A) has been widely used for larger scale simulations, but its performance in mesoscale, particularly in tropical regions, is less evaluated. This study aimed to assess MPAS-A in simulating extreme surface air temperature in Jakarta during the hot spells of October 2023 with eight different simulation setups. Several validation metrics were applied to near-surface meteorological variables, land surface temperature (LST), and vertical atmospheric profile. From the eight simulations, MPAS-A captured diurnal patterns of the near-surface variables well, except for wind direction. The model also performed well in LST simulations. Moreover, the biases in the vertical profiles varied with height and were sensitive to the initial/boundary conditions used. Simulations with modified terrestrial datasets showed higher LST and air temperatures over the sprawling urban areas. MPAS-A successfully simulated the extreme event, showing higher air temperatures in southern Jakarta (over 36 °C) compared to the northern part. Negative temperature advection by sea breeze helped lower air temperature in the northern area. This study highlights the role of sea breezes as natural cooling mechanisms in coastal cities. Additionally, MPAS-A is feasible for several applications for urban climate studies and climate projection, although further development is needed. [ABSTRACT FROM AUTHOR]

The spatial heterogeneity of land surface temperature (LST) within cities is profoundly influenced by the built environment. Although significant progress has been made in the study of the urban thermal environment, there is still a lack of research on how the pattern and structural layout of the built environment affects the thermal environment. In this study, we take the Fifth Ring Road of Beijing as an example, invert the urban LST on the basis of multisource spatial data, characterize the built environment, and use k-means cluster analysis to investigate the main influencing factors of the LST in different functional areas and building patterns within the city, as well as the spatial relationship between the built environment and the urban LST. The results show the following: (1) The urban heat island (UHI) effect occurs to varying degrees over a large part of the study area, and these UHI areas are mainly concentrated in the southwestern part of the city, forming a large contiguous area between the second and fifth ring roads. (2) Class 1 is dominated by transport blocks, Class 3 is dominated by commercial blocks, and Class 5 is dominated by green space blocks, with a clustering index of 0.38. (3) The high-density, high-height class (HH-Class 2) has a greater number of blocks distributed in a ring shape around the periphery of the second ring road. The high-density, low-height class (HL-Class 2) has a relatively small number of blocks but a relatively large area, and the largest blocks are located in the western part of the study area. (4) In the HH and HL building patterns, extreme heat scenarios often occur; from the perspective of functional areas, the probability of extreme heat in the transport block is much higher than that of other functional areas, and except for the HH scenario, the green space functional area plays a very important role in reducing the temperature. This study explores the characteristics of the built environment that influence the urban LST from the perspective of different urban functional zones in cities to provide decision support for quantitative territorial spatial planning, optimization, and management. [ABSTRACT FROM AUTHOR]

This study reveals the temporal and spatial evolution characteristics of the winter nighttime urban heat island (UHI) effect in the case of Beijing, China. The land surface temperature (LST) is retrieved by radiative transfer equation by using the remote sensing data from Landsat ETM+/OLITIRS from 2007 to 2017 for the winter nighttime period, and LST is then divided by the mean-standard deviation method into different levels of thermal landscapes. A combination of the migration calculation of gravity center and multi-directional profile analysis is used to study the directional differentiation characteristics of LST and the migratory characteristics of the gravity center of UHI. Finally, the overall temporal and spatial evolution characteristics of winter nighttime surface urban heat island (SUHI) in Beijing are studied, and the possible reasons for the changes are discussed. Results show that Beijing's UHI effect first increased and subsequently decreased from 2007 to 2017. The winter heat island in the urban area developed from low-density agglomeration to high-density agglomeration to low-density diffusion. Additionally, the high-level thermal landscapes migrated to the southwest along with the city center of gravity, and the expansion rate is fastest in the southwest, which is directly linked to the changes in the urban construction land. Moreover, the overall spatial distribution of winter nighttime LST is high in the east and south and low in the west and north, and is influenced by topography, land cover, urbanization, anthropogenic heat, and other factors as well. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is an essential parameter in environmental monitoring. However, due to the cloud contamination and the limitations of sensors, existing thermal infrared LST products are challenging to provide all-weather LST with high spatiotemporal resolution. Therefore, this paper presents a framework for reconstructing hourly LST under clouds based on the moderate resolution imaging spectroradiometer (MODIS) LST products. This framework consists of two main steps: (1) Instantaneous LST estimation for MODIS using a modified annual temperature cycle (ATCM) model and (2) Hourly LST reconstruction under all-weather conditions from atmospheric reanalysis data and MODIS instantaneous LST with the linear model (LM). The proposed framework is evaluated with the full year data of 2019 with tile number H26V05. The results show that the spatial distribution of the estimated LST is similar with the MODIS LST. Additionally, it could provide an accurate indication of the spatial and temporal variability of LST. Comparing the MODIS LST of the selected area for tile number H26V05 in 2019 with the estimated LST of the ATCM, the root mean squared errors (RMSEs) of the model are around 1K ~ 3K. The hourly LST is then reconstructed based on the LM method, and the RMSE is around 2K during the daytime and around 1K during the night-time. Finally, to verify the robustness of the hourly LST reconstruction method, the accuracy of the hourly LST was evaluated by the MODIS LST at different situations in the day. The results show that the missing data at one moment will cause the accuracy of the model decreasing by 0.09 ~ 1.67K at the corresponding moment. In general, the proposed framework has the potential to reconstruct the 1 km all-weather hourly LST with high accuracy and a certain level of robustness. [ABSTRACT FROM AUTHOR]

Soil temperature (Ts) and vegetation temperature (Tv) provide significant information for various practical applications such as evapotranspiration estimation, water requirement analysis for vegetation, and drought monitoring during the crop growth. In this study, we estimated Ts and Tv with a land surface temperature (LST)-vegetation index (VI) trapezoidal space-based method (hereafter abbreviated as the trapezoidal method) and a dual-angle algorithm in which the estimation equations for Ts and Tv were established with thermal infrared observations from two angles and solved to using the least squares method. The Ts and Tv estimations were conducted using Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-3 data from the Google Earth Engine platform, together with auxiliary meteorological data from eight sites with various land cover types in the Heihe River Basin in 2019, and compared with corresponding in-situ measurements and temperatures from Landsat. The results showed that (1) both estimation methods outperformed the Ts retrievals compared to those of Tv; (2) the trapezoidal method overall had a similar but slightly better performance compared to the dual-angle algorithm, with the root mean square error for Ts retrievals of 3.7 K and 5.7 K, and for Tv retrievals of 6.5 K and 7.2 K, respectively, in comparison with temperatures from Landsat; (3) the trapezoidal method had higher accuracy over sites with grassland, desert, or meadow for Ts retrieval and sites with forests for Tv retrieval. This study provides advice about the method for Ts and Tv estimations and might be useful for rational utilization and more accurate monitoring of soil and vegetation. [ABSTRACT FROM AUTHOR]

Daily mean land surface temperature (LST) is a crucial indicator for investigating global long-term climate change. The polar-orbiting thermal infrared sensor (POTIRS) can provide limited instantaneous LSTs of a single day on a global scale. Several studies have developed algorithms to derive daily mean land surface temperature (DMLST) from limited instantaneous daily LST data, but these methods are restricted to using data from a single POTIRS after 2000. This study presents a generalized method for estimating global DMLST utilizing one daytime and one night-time observation from any POTIRS since 1981. The proposed method employs a simple linear regression of two instantaneous LST measurements from different observation times (once during the day and once at night) of POTIRSs based on in situ LST measurements from 227 flux stations operating in diverse climate regions globally. The results demonstrate that this simple linear regression model provides highly accurate estimates of DMLST under all-weather conditions, with a root mean square error (RMSE) value lower than 1.7 K. Additionally, the proposed method was employed to estimate DMLST from instantaneous LST products obtained from various sensors, including Moderate Resolution Imaging Spectroradiometer aboard the Terra satellite, Advanced Very High Resolution Radiometer aboard the polar-orbiting National Oceanic and Atmospheric Administration satellite, and Meteorological Operational satellite. Validation results indicate that the DMLSTs estimated from these POTIRS products are in close agreement with the daily mean in situ LST, with RMSE values varying from 2.2 to 2.4 K. We expect that this generalized method will be useful for generating long-term and high-quality DMLST datasets. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is an important parameter in many studies including hydrology, meteorology, and ecology. In addition to thermal infrared remote sensing, passive microwave remote sensing is an effective alternative for LST retrieval, particularly under cloudy conditions. In this study, we analysed the impact of cloud on the transfer of microwave radiation in simulated database and presented a parameterization method to compensate the impact. On this basis, we put forward an improved method for LST retrieval from Advanced Microwave Scanning Radiometer 2 (AMSR2) brightness temperature at the 18.7 and 23.8 GHz channels with precipitable water vapour (PWV) and cloud liquid water (CLW) as supplementary input. The method is built on the basis of microwave radiative transfer equation and the assumption that 1) surface emissivity at two adjacent channels is linear and 2) the main factor causing brightness temperature difference at two channels is the absorption difference of PWV and CLW. The LST retrieval method was tested in simulated database, with root mean square error (RMSE) of 3.1 K. The method was then applied to AMSR2 brightness temperature and GFS reanalysis data to estimate global LST in 2019–2020, and the accuracy was validated using in situ LST measurements collected from 52 sites. The RMSE is approximately 4.7 K and 3.7 K for daytime and night-time, respectively. The improved LST retrieval method is promising in long-temporal and large-area applications. [ABSTRACT FROM AUTHOR]

Wind power in China is experiencing accelerating growth to meet the need for structural reform of the energy supply, however its impact on the ecological environment urgently needs to be taken into account. In this study, the impact of a wind farm in Gonghe County, Qinghai Province, China, on local land surface temperature (LST) was explored by removing the temporal background using year-by-year LST averaging and filtering the spatial background using pixel-by-pixel LST averaging based on Moderate Resolution Imaging Spectroradiometer (MODIS) and the European Centre for Medium-Range Weather Forecast's ERA5 data. The main findings are as follows: (1) From a long-term series perspective, the LST on the wind farm and the adjacent areas shows an increasing trend, reaching 0.060K/19a in spring; (2) the wind farm exerts a warming effect on the local LST, with a maximum of 7.04 K and a minimum of 1.63 K, and there is a significant spatial matching with the wind farm boundaries, which is more obvious in spring and winter, and less pronounced in summer and autumn; (3) the construction of wind farms also has a potential upward impact on the LST in the adjacent areas, especially in autumn and winter, and the warming effect gradually decreases with increasing distance; (4) the warming effect is most remarkable when the predominant wind directions of the upwind and downwind are close to consistent and the differences in wind volumes are significant. Research findings on the effect of wind farms on local LST are vitally important for strengthening the establishment of wind farms, site selection of wind farms, and the scientific and reasonable utilization of wind energy resources. Key policy highlights: Impacts of wind farms on local land surface temperature. Using the MODIS and ERA5 data whose temporal and spatial backgrounds have been removed. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is an important physical parameter that reflects changes in surface processes. Remote sensing makes it possible to obtain global or regional LSTs with high spatial and temporal resolution. Feng-Yun 3E (FY-3E) is the world's first civilian dawn-dusk orbiting meteorological satellite on board the Medium Resolution Spectral Imager – Low-Light (MERSI-LL) sensor capable of acquiring LST at 250 m spatial resolution at dawn and dusk. By complementing with existing polar-orbiting satellites, FY-3E can fill the satellite observation gap within the 6-hour assimilation window and further enhance the observation capability of LST. To retrieve accurate LST from MERSI-LL, the generalized split-window (GSW) method was developed. The GSW coefficients were derived from the simulation dataset produced by Moderate Resolution Transmittance Code 5.2 with an atmospheric profile database. As a key input to the GSW algorithm, the land surface emissivity was dynamically estimated by the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Dataset in combination with the fractional vegetation cover. The water vapour content is calculated in real time by the split-window covariance/variance ratio method. The comparison between retrieved LSTs and in situ LSTs shows that the GSW algorithm can effectively retrieve the LSTs of vegetation cover surfaces. The root mean square error ranged from 0.79 to 1.79 K at dawn and from 1.32 to 1.92 K at dusk. [ABSTRACT FROM AUTHOR]

As the cloud covers the surface thermal radiation during the transmission process and the remote sensor in the air is difficult to detect, Thermal Infrared (TIR) remote sensing inversion of Land Surface Temperature (LST) must be performed under clear-sky and cloudless conditions. This study has established the functional relationship between LST and the vegetation index of cloudless vegetation pixels around the cloud coverage areas in the North China Plain based on the Normalized Difference Vegetation Index (NDVI) to address the issues of TIR remote sensing inversion for LST affected by cloud coverage. By acquiring NDVI and using short-term, relatively stable characteristics of vegetation, the LST of the cloud cover area is estimated. The findings show a linear negative correlation between LST and NDVI in vegetation pixels, with the vegetation type remaining essentially unchanged over time. When there are 20 pixels in each of the two thermal infrared channels of FY-3 D, the MAE value and RMSE value of the 24th thermal infrared channel are 0.77 and 0.88, respectively, and the MAE value and RMSE value of the 25th thermal infrared channel are 0.64 and 0.80, respectively. When the number of pixels is 200, the 24th thermal infrared channel's MAE value and RMSE values are 0.96 and 0.99, respectively, while the 25th thermal infrared channel's MAE value and RMSE values are 0.90 and 0.95, respectively. In other words, the estimation is more accurate and closer to the true value, and the land surface temperature retrieved by the 25th channel deviates from the true value to a lesser extent. The average absolute error and root mean square error are both less than 1, which may satisfy the accuracy demands of practical applications such as agricultural drought monitoring, ecological evaluation, and crop yield estimation. [ABSTRACT FROM AUTHOR]

Hyperspectral thermal infrared (HTIR) data offers enormous potential for land surface temperature (LST) and emissivity (LSE) inversion. Methods to fully use a large amount of spectral information, while avoiding data redundancy, is constantly being explored. Therefore, a new framework for LST and LSE inversion using HTIR data is proposed in this study. First, the stepwise iteration method based on information content was used to select channels that were more sensitive to land surface information. Principal component analysis (PCA) and a two-step machine learning method were then used to retrieve the LST and LSE based on the selected channels. The findings demonstrate that the LST and spectra of the LSE can be obtained simultaneously based on the proposed framework. An LST inversion accuracy of 1.5K and an emissivity inversion accuracy of 0.017 can be achieved. However, an increase in the number of channels does not result in superior outcomes. Satisfactory inversion results can be obtained by selecting only 10–35 channels with the highest information content. In conclusion, this framework can be used to guide channel selection and land surface parameters inversion for HTIR sensors. [ABSTRACT FROM AUTHOR]

Passive microwave has been used in land surface temperature (LST) inversion because of its all-weather availability. This paper proposed a LST retrieval method based on machine learning by integrating multiple datasets, including Advanced Microwave Scanning Radiometer 2 (AMSR2), Global Forecast System (GFS) reanalysis datasets, ERA5-land reanalysis products, Moderate Resolution Imaging Spectroradiometer (MODIS) products, Shuttle Radar Topography Mission (SRTM), and in situ measurements. Four algorithms, including linear regression (LR), random forests (RF), extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM), were explored and compared by ten-fold cross-validation. The best-performing LightGBM algorithm was applied to train the model, and day and night models were developed separately. The models were validated using in situ LST of training stations from 2017 to 2019. The day and night models root mean square error (RMSE) are 3.23 and 2.43 K, and that of bias are −0.24 and −0.36 K, respectively. The in situ LST from 2015 to 2019 that not used for model training were selected to further validate both day and night models, with RMSE = 5.42 and 2.91 K, and bias = −0.60 and 0.06 K, respectively. Validation results indicated that the temporal performance of the models is better than those of spatial performance and the night model performed better than the day model. Additionally, model performance in different seasons and land cover types demonstrated the robustness of the models over complicated surfaces. These results suggest that the LightGBM algorithm has good accuracy in LST estimation, making it possible to apply for the generation of LST at a global scale. [ABSTRACT FROM AUTHOR]

Urban Heat Islands are a major environmental and public health concern, causing temperature increase in urban areas. This study used satellite imagery and machine learning to analyze the spatial and temporal patterns of land surface temperature distribution in the Metropolitan Area of Merida (MAM), Mexico, from 2001 to 2021. The results show that land surface temperature has increased in the MAM over the study period, while the urban footprint has expanded. The study also found a high correlation ( r > 0.8) between changes in land surface temperature and land cover classes (urbanization/deforestation). If the current urbanization trend continues, the difference between the land surface temperature of the MAM and its surroundings is expected to reach 3.12 °C ± 1.11 °C by the year 2030. Hence, the findings of this study suggest that the Urban Heat Island effect is a growing problem in the MAM and highlight the importance of satellite imagery and machine learning for monitoring and developing mitigation strategies. [ABSTRACT FROM AUTHOR]

UAV thermal infrared remote sensing technology, with its high flexibility and high temporal and spatial resolution, is crucial for understanding surface microthermal environments. Despite DJI Drones' industry-leading position, the JPG format of their thermal images limits direct image stitching and further analysis, hindering their broad application. To address this, a format conversion system, ThermoSwitcher, was developed for DJI thermal JPG images, and this system was applied to surface microthermal environment analysis, taking two regions with various local zones in Nanjing as the research area. The results showed that ThermoSwitcher can quickly and losslessly convert thermal JPG images to the Geotiff format, which is further convenient for producing image mosaics and for local temperature extraction. The results also indicated significant heterogeneity in the study area's temperature distribution, with high temperatures concentrated on sunlit artificial surfaces, and low temperatures corresponding to building shadows, dense vegetation, and water areas. The temperature distribution and change rates in different local zones were significantly influenced by surface cover type, material thermal properties, vegetation coverage, and building layout. Higher temperature change rates were observed in high-rise building and subway station areas, while lower rates were noted in water and vegetation-covered areas. Additionally, comparing the temperature distribution before and after image stitching revealed that the stitching process affected the temperature uniformity to some extent. The described format conversion system significantly enhances preprocessing efficiency, promoting advancements in drone remote sensing and refined surface microthermal environment research. [ABSTRACT FROM AUTHOR]

The Standardized Precipitation Evapotranspiration Index (SPEI) is a widely recognized and effective tool for monitoring meteorological droughts. However, existing SPEI datasets suffer from spatial discontinuity or coarse spatial resolution problems, which limits their applications at the local level for drought monitoring research. Therefore, we calculated the SPEI index at meteorological stations, combined with the Global Precipitation Measurement (GPM) Precipitation (Pre), Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST), ERA5‐Land Shortwave Radiation (SR), Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) datasets and Random Forest Regression (RFR) model, developed a high spatial resolution (1 km) SPEI (HSPEI) datasets with multiple time scales in mainland China from 2001 to 2022. Compared to other SPEI datasets, the HSPEI datasets have higher spatial resolution and can effectively identify the detailed characteristics of drought in mainland China from 2001 to 2022. Overall, the HSPEI datasets can be effectively applied to the research of different droughts in China from 2001 to 2022. [ABSTRACT FROM AUTHOR]

To prepare for a hotter future, information on intra‐urban temperature distributions is crucial for cities worldwide. In recent years, different methods to compute high‐resolution temperature datasets have been developed. Such datasets commonly originate from downscaling techniques, which are applied to enhance the spatial resolution of existing data. In this study, we present an approach based on a fine‐scaled low‐cost urban temperature measurement network and a formerly developed land use regression approach. The dataset covers mean nocturnal temperatures of 16 summers (2007–2022) of a medium‐sized urban area with adapted land cover data for each year. It has a high spatial (50 m) and temporal (daily) resolution and performs well in validation (RMSEs of 0.70 and 0.69 K and mean biases of +0.41 and −0.19 K for two validation years). The dataset can be used to examine very detailed statistics in space and time, such as first heatwave per year, cumulative heat risks or inter‐annual variability. Here, we evaluate the dataset with two application cases regarding urban planning and heat risk assessment, which are of high interest for both researchers and practitioners. Due to potential biases of the low‐cost measurement devices during daytime, the dataset is currently limited to night‐time temperatures. With minor adaptions, the presented approach is transferable to cities worldwide in order to set a basis for researchers, city administrations and private stakeholders to address their heat mitigation and adaptation strategies. [ABSTRACT FROM AUTHOR]