ABSTRACT Currently, a variety of land surface temperature (LST) products generated from thermal infrared bands have been already accumulated. Compared to the thermal infrared band, the mid-infrared band exhibits higher transmittance and greater robustness under humid atmospheric conditions, offering potential for further improving LST retrieval accuracy. However, the mid-infrared band presents larger variability in emissivity and higher estimation difficulty, limiting the effectiveness of LST retrieval using mid-infrared remote sensing data sources. This study proposed a MODIS night-time mid-infrared LST retrieval algorithm that integrates reflectance spectral characteristics to estimate emissivity. A few-shot machine learning model was established to build the correlation between MODIS optical band reflectance and mid-infrared band emissivity within a simulated dataset accounting for mixed spectral components, then applied to real observational data. Validation results from SURFRAD ground stations indicate an overall RMSE of 2.5521 K for this new algorithm, with values of 2.5558 K under dry atmospheric conditions and 2.5021 K under humid atmospheric conditions. The new algorithm can accurately retrieve night-time LST without significant error increasing as atmospheric water vapor content rises. Future work will further study on fields including eliminating daytime solar radiance effects, conducting multi-surface-type validation, and reducing dependence on external parameters.

• The 675 wind farms in China exhibited an overall LST effect of nighttime warming and daytime cooling. • The vegetation decreased by wind farm construction and recovered over time. • The LST and vegetation impacts of wind farms depended on land cover types. • Land cover distributions contributed to the latitudinal variations of LST impacts. The rapid development of wind energy in China since 2000 has raised concerns about its impacts on local climate and vegetation. Despite regional and local studies, a comprehensive national assessment is lacking. Here, we analyzed the effects of 675 onshore wind farms, representing >90,000 identified wind turbines in China, on land surface temperature (LST) and vegetation using Moderate-resolution Imaging Spectroradiometer (MODIS) satellite data from 2003 to 2022. We find a daytime cooling effect of -0.05 ± 0.48°C (mean ± STD) and a nighttime warming effect of 0.06 ± 0.28°C across all wind farms. The infrastructure construction of wind farms initially reduced peak normalized difference vegetation index (NDVI) by -0.006 ± 0.036, and this adverse impact weakened over time (-0.004 after 7 years), indicating vegetation recovery. The wind farm impacts varied by land cover type. The nighttime warming was largest for barren lands (0.19°C), followed by croplands (0.10°C), grasslands (0.07°C), and forests (0.01°C). These differences contributed to increasing warming from south to north China. The adverse vegetation impacts were largest for forests (-0.010), followed by grasslands (-0.008) and barren lands (-0.003), with croplands (0.001) being almost unaffected. Correlation analysis identified precipitation and mean LST as significant factors influencing spatial variations in nighttime LST impact, with greater vegetation decline reinforcing night warming. Our large-scale analysis provides comprehensive evidence of the heterogeneous environmental impacts of wind farms across China, informing the sustainable development of wind energy.

ABSTRACT Michoacán possesses extensive forest cover yet has undergone rapid and sustained land use transformations driven by illegal logging and the expansion of the ‘avocado strip’. This study utilized Random Forest (RF) classification of Landsat imagery (1986–2025) to analyze landscape dynamics, identifying Near-Infrared (NIR) and Shortwave Infrared 2 (SWIR 2) bands as the most critical predictors. Change detection analysis revealed severe deforestation: dry forest declined by 4,344 km2 and evergreen forest by 1,698 km2, fueling a corresponding increase in exposed soils (3,270 km2) and croplands (2,158 km2). To assess ecological impacts, MaxEnt was employed to model the distribution of 35 vulnerable species (16 Near Threatened, 10 Vulnerable, and 9 Endangered) with Nighttime Land Surface Temperature in December emerging as the most influential variable. The biodiversity assessment (2000–2025) indicated that the Reddish Egret (Egretta rufescens) suffered the greatest loss of potential distribution area (exceeding 50%), while the Grey Plover (Pluvialis squatarola) and the Mexican Ground Pit Viper (Agkistrodon bilineatus) exhibited the greatest gains (approximately 50% increase). Given that these threatened ecosystems are poorly represented within current protected areas, these findings underscore an urgent need to integrate conservation strategies into regions undergoing intense agricultural conversion to mitigate further biodiversity loss.

Abstract With climate change accelerating, global temperatures continue to rise. Urbanization further compounds local and regional changes in climate and causes cities to be warmer than their surroundings. In this work, we quantify the urban warming rates as well as the contribution of urbanization across all densely populated climates, using a multi-level Bayesian model that accounts for the correlation among multi-city data and their uncertainty, and nighttime Land Surface Temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS). Our results show substantial latitudinal variation in the 2002–2021 trends, with continental cities warming the fastest (0.75 K decade-1) and tropical the slowest (0.44 K decade-1). In contrast to previous studies, we find no clear urbanization effect in the 2002–2021 MODIS trends. We demonstrate that this discrepancy arises from explicitly accounting for the trend standard errors and show that when these uncertainties are omitted, differences between the urban and rural LST trends appear spuriously significant. This finding remains robust across alternative datasets, methods, and urban boundaries, reinforcing our conclusion that any apparent differences between urban and rural LST trends derived from 19-year of MODIS data cannot be reliably attributed to urbanization at the climate zone level.

Rapid urbanization in the Yellow River Basin intensifies the conflict between urban expansion and the thermal environment, threatening ecological security and sustainable development. Utilizing multi-source data (2000–2023) including nighttime light (NTL) and land surface temperature (LST), this study applies spatial analysis and Geographically Weighted Regression (GWR) to explore the spatial associations between urban development and LST and its drivers across core cities. The results indicate significant spatiotemporal differentiation: mid-downstream cities exhibited contiguous urban expansion, whereas upstream growth remained constrained by local topography, with heat islands consistently concentrating in built-up areas. The warming rate decreased gradually from downstream (0.29–0.40 °C/year) to upstream (0.20–0.30 °C/year). The LST-NTL correlation strengthened notably in mid-downstream regions but remained moderate upstream. GWR analysis revealed that urban development intensity, represented by NTL, is the primary driver of LST increase downstream, while natural factors predominantly mitigate warming upstream. This long-term, multi-city comparison provides a scientific basis for precise urban heat island management and sustainable planning in the basin.

Assessment of daytime and nighttime land surface temperature behavior in an arid region: evidence of thermal trends asymmetry.

Threshold effects of urban built-up and green space morphology on seasonal daytime–night-time land surface temperature

Dense population, rapid urbanization, and industrialization make India a highly vulnerable country to the consequences of global warming. This study examines spatiotemporal trends of diurnal land surface temperature (LST) over the past 25years (2000-2024) and analyzes the surface urban heat island (SUHI) intensities across the country and for 50 major cities, respectively, including the influence of zonal biogeography. The significance of the LST trends is statistically confirmed by using the Mann-Kendall test and zonal heterogeneity is analyzed by using ANOVAtest. The study covers total span of 25years (2000-2024) which is classified in two periods, pre-COVID-19years (2000-2019) and including the post-COVID-19years (2000-2024). In the period from 2000 to 2019, the mean LST variability range (minimum to maximum) has substantially widened by 7.8°C and 2.3°C for daytime and nighttime, respectively. The LST change during the COVID-19 period was significantly hindered; the change in daytime and nighttime LST for May month was 0.18°C and 0.04°C, respectively, whereas during 2020-2024, it has become -1.24°C and -0.2°C, respectively. In general, the zones follow the country-level LST trends for 2000-2019 as well as for 2020-2024 periods, with variable LST change rates. The highest annual daytime LST growth (+ 0.15year-1) is observed for the Desert (DES) zone, whereas the highest nighttime LST rise (+ 0.07year-1) is observed for the Western Ghats (WG). Notably, the Himalaya and Trans-Himalaya (HTH) zones exhibit negative LST growth rate (-0.08 and -0.09 for daytime and nighttime, respectively). Further, SUHI analysis indicates that the cities within theIndo-Gangetic Plain (IGP), Semi-Arid Region (SAR), Deccan Plateau (DP), and Western Coastal Region (WCR) zones are found to be largely impacted by SUHI intensification, ranging between 1 and 5°Cfor daytime as well as nighttime. Interestingly, even trivial SUHI values of DES cities (1-3°C for daytime) could be consequential, as the zonal LST is extremely high. The study points out the requirement of urgent policy intervention and mitigation measures.

Abstract Green infrastructure (GI) effectively mitigates health effects associated with PM 2.5 and urban heat. However, the impact of GI's morphological characteristics on compound exposure remains unclear. With a large urban population, Chinese cities face significant compound exposure risks, yet national‐scale GI planning strategies addressing this issue are lacking. This study incorporates 374 Chinese cities, quantifying and mapping the spatial distribution and changes of four spatial morphological structures of GI, seasonal PM 2.5 pollution, and summer daytime and nighttime land surface temperature over the past 20 years. Results indicate that core areas are larger and have expanded significantly, failing to effectively mitigate compound exposure risks. Although connectors represent a smaller scale of GI, they play a significant and positive role in mitigating compound exposure risk, especially when their proportion exceeds 2% of urban space. We suggest that urban planners prioritize connectivity‐oriented GI strategies to effectively mitigate compound exposure risks.

Urban heat island (UHI) is a prevalent environmental hazard in modern cities, with higher surface and air temperatures than adjacent rural regions. The current study assessed the spatiotemporal distribution of land surface temperature (LST) in Iraq's Kurdistan region and the existence of urban heat islands during the daytime and at nighttime. The land surface temperature (LST) was composited from 2001 to 2024 using the historical Moderate-Resolution Imaging Spectroradiometer (MODIS) Terra satellite 8. The average LSTs of the rural and arid regions were contrasted with the average LSTs of the urban and suburban areas in three governorates of the study area, namely Erbil, Sulimaniyah, and Duhoke. Daytime and nighttime LST were also compared. The results revealed that the highest values of LST occurred in the urban region of the southern parts of the study area, where the mean value was 32.2 0C during the daytime. During the summer, Erbil had a higher temperature of 49.5 0C, while Sulimaniyah had the lowest (0.98 0C). According to annual data, almost 80% of the study region had an NLST score of 0.6 or 0.7. The biggest difference in LST mean value between urban and suburban regions was recorded in the summer daytime in Erbil city, with a value of 5.1 0C, while the smallest variances were reported in the fall season for all governorates in the study area, reaching 0.01 0C at night in Sulimaniyah city.

Abstract This study presents a comprehensive spatio-temporal analysis of nighttime Land Surface Temperature (LST) and Urban Heat Island Intensity (UHII) in Coimbatore from 2001 to 2022, highlighting statistically significant warming trends and intensifying urban heat island effects. Urban areas experienced a notable nighttime LST increase from 21.4 °C in 2001 to 23.7 °C in 2019, compared to a rural rise from 20.5 °C to 22.5 °C. The average urban–rural LST differential (~1 °C) widened post-2016, aligning with the recorded peak LST of 26.8 °C. The minimum LST dropped to 8.5 °C in 2001, indicating a reduction in cold extremes. Kendall’s tau analysis confirmed a stronger warming trend in urban areas (τ = 0.593) than rural zones (τ = 0.429). Seasonal UHII analysis showed progressive winter intensification post-2012, while summer UHII peaked in 2013 and 2015, then dipped post-2016 before rising again in 2022. Mann-Kendall tests confirmed statistically significant increasing trends in winter UHII, urban LST, and rural LST, with urban LST exhibiting the steepest rise. Spatial autocorrelation analysis using Moran’s Index revealed intensifying clustering of high LST zones: the annual Moran’s Index increased from 0.797 (2001) to 0.857 (2022), with z-scores rising from 42.253 to 45.445. Winter showed the most pronounced clustering, with Moran’s Index jumping from 0.812 to 0.903 and z-scores reaching 47.848 by 2022. Hotspots with 99 % confidence levels were primarily urban, expanding over time with temperatures between 24.8 °C and 26.7 °C, while cold spots (99 % CL) remained stable in rural areas. These findings confirm the persistent and intensifying nature of UHI in Coimbatore, driven by urban expansion, declining vegetation, and increased impervious surfaces. This study fills a critical research gap by providing one of the first long-term assessments of nighttime UHI intensity in a mid-sized Indian city, thereby contributing to the broader understanding of urban thermal dynamics beyond metropolitan regions. The study underscores the urgent need for spatially informed interventions, such as urban greening, reflective materials, and climate-sensitive planning, to mitigate urban thermal stress and enhance resilience in rapidly growing cities.

In the late twentieth century, nighttime land surface temperatures are observed to rise faster than daytime temperatures, resulting in high diurnal temperature ranges and considerable diurnal asymmetric warming. Nevertheless, the projected changes in diurnal temperature ranges under global warming remain with large uncertainties across statistical multi-model simulations. Here, we identify an emergent relationship between historical diurnal temperature ranges variability and future projections on both global and regional scales. By leveraging this spatially heterogeneous relationship, we present a framework to refine regional projections, reducing model uncertainties by 15–68% across the 27 IPCC AR6 reference regions. Our findings suggest a sustained influence of external forcing on diurnal temperature ranges from past to future, driven primarily by related widespread reductions in cloud cover, rather than internal variability. This study provides valuable insights into constraining regional extreme responses to a warming climate, laying the groundwork for informed regional climate policy decisions. The diurnal temperature range (DTR), which measures the difference between daily maximum and minimum temperatures, is a vital indicator of climate extremes. However, predicting future changes in DTR under global warming remains challenging due to significant uncertainties in model projections. This study uncovers a significant link between historical DTR trends and future projections on both global and regional scales. Leveraging this relationship, we introduce a constraining framework to enhance the accuracy of regional climate projections. The mechanism is that the changes in DTR are primarily driven by external forces such as greenhouse gas emissions, rather than internal climate variability. Increasing greenhouse gases reduces cloud cover, which migrates DTR decline by enhancing daytime shortwave radiation and reducing nighttime longwave radiation. While this relationship is generally robust across seasons, it weakens at high latitudes in winter due to minimal solar radiation. Our findings contribute to reducing uncertainties in model projections, offering valuable insights into future changes in DTR and their implications for regional climate responses. The identification of a strong link between historical diurnal temperature range trends and future projections can be used to constrain regional climate projections with greater accuracy, according to a study based on reanalysis data and CMIP6 simulations.

Abstract. The rapid urbanization of Pune, Maharashtra, over recent decades has intensified thermal impacts through the Urban Heat Island (UHI) effect, caused by replacing natural surfaces with impervious concrete and asphalt. This has led to higher nighttime land surface temperatures (LST), particularly in builtup and periurban areas compared to rural surroundings. While traditional UHI assessments correlate LST with land use alone, the InVEST Urban Cooling Model integrates remotely sensed biophysical parameters such as albedo, shading, evapotranspiration, along with proximity to green infrastructure into a pixel level framework that calculates Cooling Capacity (CC) and a Heat Mitigation Index (HMI). Seasonal LST composites for summer, monsoon, and winter were generated from Landsat 8 thermal bands (cloud cover <10 using the InVEST model). Per pixel Cooling Capacity values ranged from 0.116 to 0.943, while HMI values spanned from 0.116 to 0.943 across the metropolitan region. The highest values (>0.70) were found in heavily vegetated wards with dense forest cover and riverine zones, while the lowest values (<0.35) occurred in industrial and high density built-up areas with minimal vegetation cover. The resulting decision-ready maps provide a spatial blueprint not only for urban planners and local authorities but also for citizen groups, neighbourhood associations, and NGOs to collaboratively identify and prioritise greening interventions. By combining geospatial evidence with participatory planning, the study fosters community-led initiatives- such as urban forests, green roofs, and vegetative corridors- that are locally relevant, socially inclusive, and climate-resilient. This approach empowers citizens to be active co-creators of cooler, healthier, and more liveable urban environments across Pune’s urban- rural continuum.

A novel scheme for seamless global mapping of daily mean air temperature (SGM_DMAT) at 1-km spatial resolution using satellite and auxiliary data

Biogeophysical warming effects of vegetation growth in the temperate water-limited region

Intra-annual characterization of soil mean temperature at 5 and 10 cm depths based on remote sensing data, at country scale

Urbanization exerts profound influences on vegetation phenology, but the nature of these impacts can differ markedly between coastal and inland regions due to distinct climatic and geographic settings. However, most studies have treated urban areas as spatially homogeneous and relied primarily on linear models, which limits our understanding of region-specific, nonlinear, and threshold-driven phenological responses. To address this gap, we examined Shandong Province, China, as a representative region encompassing both coastal and inland urban–rural gradients. Using satellite-derived EVI time series, we extracted the Start (SOS) and End (EOS) of the growing season and applied an XGBoost–SHAP framework to disentangle the relative contributions of multiple environmental drivers. In addition, we analyzed the relationships between phenology and land surface temperature (LST) along the urban–rural gradient to identify thermal pathways through which urbanization influences vegetation cycles. The results showed that: (1) significant regional variation in SOS and EOS was observed across Shandong Province; (2) in the context of urbanization, SOS advanced by 0.48 days/km, and EOS was delayed by 0.4 days/km from rural to urban areas; (3) temperature and LST influenced phenology in a nonlinear manner, with relationships varying across seasons and regions, and seasonal as well as geographical differences significantly affecting the intensity and pattern of phenological changes; and (4) the effects of nighttime and daytime LST on phenology differed substantially between inland and coastal areas. This study investigates the complex nonlinear relationships between temperature and vegetation phenology, offering a deeper understanding of vegetation’s influence on the global carbon cycle.

Rapid urbanization in China has exacerbated the dual challenges of urban heat islands (UHIs) and air pollution, threatening urban sustainability. We conducted a national-scale analysis of the spatiotemporal dynamics and synergy between the surface UHI intensity, distinguished as daytime (DUHI) and nighttime (NUHI), and major air pollutants (PM2.5, PM10, NO2) in 370 Chinese cities (2000–2019). Using multi-source remote sensing, ground-based monitoring, and urban data, we applied coupling coordination and correlation analyses to quantify these interactions. Key findings reveal distinct patterns: (1) The annual mean land surface temperature (LST) rose, with the nighttime LST (NLST) increasing faster than the daytime LST (DLST). Conversely, the UHI intensity showed an overall decline, with the DUHI decreasing more than the NUHI. (2) Air pollutants displayed strong seasonality; while PM10 concentrations decreased slightly over the long term, NO2 levels rose significantly. (3) Monthly, pollutants correlated negatively with LST (R2 > 0.92 for PM2.5), suppressing the DUHI but intensifying the NUHI. Long-term, the correlation trend revealed a strengthening synergy, particularly between particulate matter and NUHI (trend R2 = 0.50). (4) Spatially, over 90% of cities exhibited high UHI–particle coordination. Key associated factors include anthropogenic activities, urban morphology, and natural mitigation factors. We conclude that disrupting the heat–pollution synergy requires integrated strategies, namely reducing emissions at the source, optimizing the urban form, and enhancing ecological regulation. This is essential for advancing low-carbon, climate-resilient urban development.

Land use change is closely related to land surface temperature (LST). Based on remote sensing data from 2001 to 2020, this study analyzed the spatiotemporal variations and driving mechanisms of daytime and nighttime LST in the Urumqi Metropolitan Area (UMA) by combining traditional methods with the eXtreme Gradient Boosting (XGBoost)–SHAP coupled model. Although the average LST trend in the region was one of warming, the pixel-level significance analysis indicated that statistically significant warming (p < 0.05) is concentrated mainly in the urban core (2.65% of the area), while the majority of the region (70%) showed a non-significant warming trend. LST displayed significant spatial clustering, with Moran’s I remaining above 0.990, indicating a positive spatial autocorrelation in spatial distribution. With the advancement of urbanization, the proportion of impervious surfaces increased from 0.87% to 1.14%, while wastelands consistently accounted for approximately 50% of the total area. Different land use types showed distinct effects on the urban heat island (UHI) phenomenon: water bodies, grasslands, and forests played cooling roles, whereas barren land and impervious areas were the main heat contributors. The XGBoost-SHAP analysis further revealed that the importance ranking of driving factors has evolved over time. Among these factors, Elevation dominates, while the influence of population-related factors increased significantly in 2020. This study provides a scientific basis for regulating the thermal environment of cities in arid regions from the perspective of land use. This study provides a scientific basis for regulating the thermal environment of arid-region cities from the perspective of land use.

The urban heat island (UHI) effect not only impacts urban climates and residents’ quality of life but also poses challenges to energy consumption and sustainable development in cities. While many studies have explored the relative importance and marginal effects of two-dimensional (2D)/ three-dimensional (3D) urban morphology on land surface temperature (LST) to mitigate UHI, the interactive effects of these 2D/3D morphology metrics on daytime and nighttime LST at different grid scales have been largely overlooked. This study focuses on the area within the outer ring of Tianjin, and analyzes the relative importance, marginal effects, and particularly the interaction effects of 2D/3D urban morphology on LST. Our findings reveal the following: (1) The normalized difference vegetation index (NDVI) has the most significant cooling effect across all variables and at all three grid scales. (2) NDVI and bare land coverage have the greatest impact on daytime LST, while building height and tree height (TH) predominantly influence nighttime LST. (3) The relationships between key 2D/3D metrics and LST are nonlinear. Overall, NDVI is negatively correlated with LST across all three grid scales. (4) Interactions between 2D/3D metrics affect LST; LST decreases when TH exceeds 1.8 m and building density is below 62%, or when TH is below 1.8 m and building density exceeds 62%. These findings provide valuable insights and recommendations for sustainable urban development and effective heat adaptation strategies in specific locations.