Daytime Land Surface Temperature Research Articles

Land surface temperature and transboundary air pollution: a case of Bangkok Metropolitan Region

In a rapidly urbanizing world, heavy air pollution and increasing surface temperature pose significant threats to human health and lives, especially in densely populated cities. In this study, we took an information theory perspective to investigate the causal relationship between diel land surface temperature (LST) and transboundary air pollution (TAP) from 2003 to 2020 in the Bangkok Metropolitan Region (BMR), which includes Bangkok Metropolis and its five adjacent provinces. We found an overall increasing trend of LST over the study region, with the mean daytime LST rising faster than nighttime LST. Evident seasonal variations showed high aerosol optical depth (AOD) loadings during the dry period and low loadings at the beginning of the rainy season. Our study revealed that TAP affected diel surface temperature in Bangkok Metropolis significantly. Causality tests show that air pollutants of two adjacent provinces west of Bangkok, i.e., Nakhon Pathom and Samut Sakhon, have a greater influence on the LST of Bangkok than other provinces. Also, the bidirectional relationship indicates that air pollution has a greater impact on daytime LST than nighttime LST. While LST has an insignificant influence on AOD during the daytime, it influences AOD significantly at night. Our study offers a new approach to understanding the causal impact of TAP and can help policymakers to identify the most relevant locations that cause pollution, leading to appropriate planning and management.

Solar zenith angle-based calibration of Himawari-8 land surface temperature for correcting diurnal retrieval error characteristics

The geostationary Himawari-8 satellite offers a unique opportunity to monitor sub-daily thermal dynamics over Asia and Oceania, and several operational land surface temperature (LST) retrieval algorithms have been developed for this purpose. However, studies have reported inconsistency between LST data obtained from geostationary and polar-orbiting platforms, particularly for daytime LST, which can arise from variations in viewing geometries and inherent differences in sensor types and LST algorithms. Despite this, previous research has primarily focused on analysing the directionality of LST without thoroughly exploring systematic differences between platforms. Hence, we presented a Solar Zenith Angle-based Calibration (SZAC) method to harmonise the daytime component of a split-window retrieved Himawari-8 LST (referred to here as the baseline) with the MODerate-resolution Imaging Spectroradiometer (MODIS) LST. SZAC describes the spatial heterogeneity and magnitude of diurnal LST discrepancies from different platforms, which is anticipated to complement typical directionality analyses. We evaluated the harmonised LST data, referred to as the Australian National University LST with SZAC (ANUSZAC), against MODIS LST and the Visible Infrared Imaging Radiometer Suite (VIIRS) LST, as well as in-situ LST from the OzFlux network. Two peer Himawari-8 LST products from Chiba University and the Copernicus Global Land Service were also collected for comparison. The median daytime bias of ANUSZAC LST against Terra-MODIS LST, Aqua-MODIS LST and VIIRS LST was 1.52 K, 0.98 K and −0.63 K, respectively, which demonstrated improved performance compared to baseline (5.37 K, 4.85 K and 3.02 K, respectively) and Chiba LST (3.71 K, 2.90 K and 1.07 K, respectively). All four Himawari-8 LST products showed comparable performance of unbiased root mean squared error (ubRMSE), ranging from 2.47 to 3.07 K, compared to LST from polar-orbiting platforms. In the evaluation against in-situ LST, the mean values of bias (ubRMSE) of baseline, Chiba, Copernicus and ANUSZAC LST during daytime were 4.23 K (3.74 K), 2.16 K (3.62 K), 1.73 K (3.31 K) and 1.41 K (3.24 K), respectively, based on 171,289 hourly samples from 20 OzFlux sites across Australia between 01/Jan/2016 and 31/Dec/2020. In summary, the SZAC method offers a promising approach to enhance the reliability of geostationary LST retrievals by incorporating the spatiotemporal characteristics observed by accurate polar-orbiting LST data. SZAC can also reduce LST angular effects due to its capability of quantifying spatial inhomogeneity of surface heat dynamics. Furthermore, it is possible to implement SZAC using LST data acquired by geostationary satellites in other regions, e.g., Europe, Africa and Americas. This could improve our understanding of the error characteristics of coincident imageries from geostationary and polar-orbiting platforms, allowing for targeted refinements and global harmonisations to further enhance applicability.

ASSESSMENT OF THE RELATIONSHIP BETWEEN LAND SURFACE TEMPERATURE AND VEGETATION USING MODIS NDVI AND LST TIMESERIES DATA IN KADUNA METROPOLIS, NIGERIA

This study assessed the relationship between land surface temperature (LST) and vegetation using MODIS NDVI and LST timeseries data in Kaduna Metropolis. MOD13Q1 and MOD11A2 datasets were accessed using Google Earth Engine. Mann-Kendall trend test was used to analyse the trends in LST and NDVI. Pearson Moment Correlation Coefficient and Linear Regression were used to examine the relationship between LST and NDVI. Mann-Kendall trend test revealed monotonic downward trend in NDVI with a Z-statistics of -1.2758, but upward trend in daytime and nighttime LST, with a Z-statistics of 0.567 and 2.107 respectively. For the relationship, vegetation showed strong negative relationship with daytime LST with -0.704. Vegetation also showed weak positive relationship with nighttime LST. The linear regression analysis revealed that vegetation was able to predict 49.5% of LST in Kaduna Metropolis, with R2 value of 0.495 and a standard error of estimate is 2.459. The study concluded that loss of vegetation is responsible for the increase in land surface temperature. The study therefore recommended regulatory agencies should ensure that trees are planted whenever they are removed due to infrastructural development in order to prevent UHI phenomenon and planting of trees should be encouraged in order to regulate the urban climate.

Satellite Time-Series Analysis for Thermal Anomaly Detection in the Naples Urban Area, Italy

Naples is the most densely populated Italian city (7744 inhabitants per km2). It is located in a particular geological context: the presence of Mt Vesuvius characterizes the eastern part, and the western part is characterized by the presence of the Phlegrean Fields, making Naples a high-geothermal-gradient region. This endogenous heat, combined with the anthropogenic heat due to intense urbanization, has defined Naples as an ideal location for Surface Urban Heat Island (SUHI) analysis. SUHI analysis was effectuated by acquiring the Land Surface Temperature (LST) over Naples municipality by processing Landsat 8 (L8) Thermal Infrared Sensor (TIRS) images in the 2013–2023 time series by employing Google Earth Engine (GEE). In GEE, two different approaches have been followed to analyze thermal images, starting from the Statistical Mono Window (SMW) algorithm, which computes the LST based on the brightness temperature (Tb), the emissivity value, and the atmospheric correction coefficients. The first one is used for the LST retrieval from daytime images; here, the emissivity component is derived using, firstly, the Normalized Difference Vegetation Index (NDVI) and then the Vegetation Cover Method (VCM), defining the Land Surface Emissivity (LSɛ), which considers solar radiation as the main source of energy. The second approach is used for the LST retrieval from nighttime images, where the emissivity is directly estimated from the Advance Spaceborne Thermal Emission Radiometer database (ASTER-GED), as, during nighttime without solar radiation, the main source of energy is the energy emitted by the Earth’s surface. From these two different algorithms, 123 usable daytime and nighttime LST images were downloaded from GEE and analyzed in Quantum GIS (QGIS). The results show that the SUHI is more concentrated in the eastern part, characterized by intense urbanization, as shown by the Corine Land Cover (CLC). At the same time, lower SUHI intensity is detected in the western part, defined by the Land Cover (LC) vegetated class. Also, in the analysis, we highlighted 40 spots (10 hotspots and 10 coldspots, both for daytime and nighttime collection) that present positive or negative temperature peaks for all the time series. Due to the huge amount of data, this work considered only the five representative spots that were most representative for SUHI analysis and determination of thermal anomalies in the urban environment.

Geothermal target detection integrating multi-source and multi-temporal thermal infrared data

Thermal infrared remote sensing (TIRS) technology is intriguing for geothermal anomaly detection due to its time efficiency and cost-effectiveness. Land surface temperature (LST) derived from TIRS indicates potential geothermal anomalies. However, LST on different natural features, exhibiting relatively cold/hot anomalies in daytime and nighttime, affected the identification of abnormal areas. The aim of this study was to highlight geothermal anomalous areas by integrating multi-view daytime and nighttime LST data. Specifically, the winter LST time series of drillings on 2013–2023 was processed based on Landsat-8 TIRS in diurnal scenarios. An information aggregation classification method about Dempster-Shafer evidence theory was presented to target recognition at nighttime by the ASTER LST product. This day-night information fusion analysis effectively highlighted potential areas with geothermal anomalies. The results demonstrated that the geothermal anomalies were mainly distributed along the Ruili-Longling Fault and Wanding Fault in a Northeast (NE) – Southwest (SW) direction, and the NE side was more significant than the SW side. These findings suggested that the east side was closer to the magma heat source, and the presumed magma heat source location aligned closely with the detected geothermal anomalies. Geological data were employed for geological interpretation of the LST anomaly area, eliminating non-geothermal influences. Along with drilling data verification, this method was successfully used to identify geothermal anomaly areas in Ruili City, Yunnan Province. Overall, this study provided valuable insights into the detection of geothermal anomalies through TIRS, contributing to the successful development of geothermal resources.

Land surface temperature prediction with dual satellites and BPNN: a comparative transfer function analysis

ABSTRACT Driven by global sustainability goals, understanding land surface temperature (LST) in urban thermal environments (UTE) is crucial. This study analyses remotely sensed LST data from Aqua and Terra satellites in a rapidly growing non-tier Indian city. It uniquely integrates remote sensing and soft computational analysis to predict daytime LST using artificial neural networks (ANN). The study is categorized based on transfer functions and hyper-parameters to enhance the comprehension of LST prediction and simulation scenarios by employing Back Propagation Neural Network (BPNN) model. Results indicate that the tansig transfer function yields the best performance overall in Aqua and Terra LST prediction, while purelin performs the least effectively. This study provides evidence that soft computing techniques effectively estimate LST data, offering a valuable solution for data scarcity issues. However, the findings are specific to the study area and may vary elsewhere. Exploring alternative methods and incorporating data from other areas could potentially improve accuracy and overall performance. Future research could focus on investigating the mechanisms behind different methods and their interactions with various factors. Additionally, integrating other variables like vegetation index, soil moisture, and others could contribute to a more holistic understanding of LST dynamics and enhance predictive capabilities.

Wind farms increase land surface temperature and reduce vegetation productivity in the Inner Mongolia

Wind power has been developing rapidly as a key measure to mitigate human-driven global warming. The understanding of the development and impacts of wind farms on local climate and vegetation is of great importance for their rational use but is still limited. In this study, we combined remote sensing and on-site investigations to identify wind farm locations in Inner Mongolia and performed landscape pattern analyses using Fragstats. We explored the impacts of wind farms on land surface temperature (LST) and vegetation net primary productivity (NPP) between 1990 and 2020 by contrasting these metrics in wind farms with those in non-wind farm areas. The results showed that the area of wind farms increased rapidly from 1.2 km2 in 1990 to 10,755 km2 in 2020. Spatially, wind farms are mainly clustered in three aggregation areas in the center. Further, wind farms increased nighttime LST, with a mean value of 0.23 °C, but had minor impacts on the daytime LST. Moreover, wind farms caused a decline in NPP, especially over forest areas, with an average reduction of 12.37 GC/m². Given the impact of wind farms on LST and NPP, we suggest that the development of wind farms should fully consider their direct and potential impacts. This study provides scientific guidance on the spatial pattern of future wind farms.

The cooling and warming effects of potential forest transition on local land surface temperature in Northeast China

Forest cover change directly affects the surface energy balance by altering the radiative and non-radiative properties of the surface. These competing processes may yield considerable biophysical impacts on local and regional climate that depend on background climate and specific forest type. Here we compared the differences in land surface temperature (LST) between temperate forests and adjacent croplands/grasslands to quantify the potential biophysical effects of transitions from croplands/grasslands to forests in Northeast China, based on multiple satellite observation products from 2001 to 2016. Forests were found to show a slight lower daytime LST and strong higher nighttime LST than adjacent croplands or grasslands. As a result, the daily LST of forests was 0.15 ± 0.06 ℃ and 0.32 ± 0.04 ℃ higher than that of croplands and grasslands, respectively. On the seasonal scale, the warming effect of cropland/grassland-to-forest transition during non-growing season outweighed the cooling effect during growing season, and dominated the annual positive LST response. Moreover, the cooling effect of cropland-to-forest transition during growing season was stronger in the early growth stage than late growth stage (−1.65 ± 0.9 ℃ in May to June, −0.39 ± 0.5 ℃ in July to September). The net cooling effect of warm temperate broadleaved forests was found to shift toward net warming effect of cold temperate coniferous forests with increasing latitude. The daytime cooling effect of forests was mainly driven by increased non-radiative evapotranspiration. Overall, the radiative process related to the solar energy dominated the net warming effect of forest transitions over Northeast China. Estimating the climate feedback potential of forests could be useful to provide theoretical references for the adaptation and mitigation strategies for climate change.

The Improved U-STFM: A Deep Learning-Based Nonlinear Spatial-Temporal Fusion Model for Land Surface Temperature Downscaling

The thermal band of a satellite platform enables the measurement of land surface temperature (LST), which captures the spatial-temporal distribution of energy exchange between the Earth and the atmosphere. LST plays a critical role in simulation models, enhancing our understanding of physical and biochemical processes in nature. However, the limitations in swath width and orbit altitude prevent a single sensor from providing LST data with both high spatial and high temporal resolution. To tackle this challenge, the unmixing-based spatiotemporal fusion model (STFM) offers a promising solution by integrating data from multiple sensors. In these models, the surface reflectance is decomposed from coarse pixels to fine pixels using the linear unmixing function combined with fractional coverage. However, when downsizing LST through STFM, the linear mixing hypothesis fails to adequately represent the nonlinear energy mixing process of LST. Additionally, the original weighting function is sensitive to noise, leading to unreliable predictions of the final LST due to small errors in the unmixing function. To overcome these issues, we selected the U-STFM as the baseline model and introduced an updated version called the nonlinear U-STFM. This new model incorporates two deep learning components: the Dynamic Net (DyNet) and the Chang Ratio Net (RatioNet). The utilization of these components enables easy training with a small dataset while maintaining a high generalization capability over time. The MODIS Terra daytime LST products were employed to downscale from 1000 m to 30 m, in comparison with the Landsat7 LST products. Our results demonstrate that the new model surpasses STARFM, ESTARFM, and the original U-STFM in terms of prediction accuracy and anti-noise capability. To further enhance other STFMs, these two deep-learning components can replace the linear unmixing and weighting functions with minor modifications. As a deep learning-based model, it can be pretrained and deployed for online prediction.

A global assessment of the effects of solar farms on albedo, vegetation, and land surface temperature using remote sensing

The rapid development of solar energy worldwide has attracted increasing attention due to its climatic and environmental impacts. Using MODIS data, we quantified the effects of solar farms (SFs) on albedo, vegetation (using enhanced vegetation index (EVI) as a proxy), and land surface temperature (LST) based on 116 large SFs across the world. The results show that the installation of SFs decreased the annual mean surface shortwave albedo by 0.016 ± 0.009 (mean ± 1 STD) and reduced the EVI by 0.015 ± 0.019 relative to the surrounding areas. SFs produced a strong cooling effect of −0.49 ± 0.43 K in the annual mean land surface temperature during the daytime and a weaker cooling effect of −0.21 ± 0.25 K during the nighttime. The greatest impacts on albedo and daytime LST were observed in barren land, followed by grassland and cropland, while the opposite order applied for vegetation impact. In terms of seasonal and latitudinal variations, the largest impact was observed at high latitudes in winter on albedo, at mid-latitudes in summer on vegetation, and at low latitudes in spring–summer transitions on daytime LST. Correlation analysis showed that the albedo and LST impacts were enhanced over large SFs with high capacity. The vegetation and LST impacts were both correlated with geographic and climatic factors and dependent on the type of SF (photovoltaic or concentrating solar power). Our global assessment provides observational evidence for the effects of SF construction on the environment and local climate, which can help the sustainable development of solar energy.

Association of neighborhood-level socioeconomic status and urban heat in China: Evidence from Hangzhou

The escalating contradiction between global urban development and thermal environments has become increasingly apparent, underscoring the imperative to address social inequality in heat exposure and advocate for environmental justice (EJ) in the pursuit of sustainable urban development. To bridge the research gap in this domain, a comprehensive study was conducted to examine the correlation mechanism linking the thermal environment with the socioeconomic status (SES) of Chinese cities, employing Hangzhou as a representative case—a pivotal city among China's "four fire stoves". The investigation involved analyzing the spatial distribution pattern of diurnal Land Surface Temperature (LST) during the summer months spanning 2016 to 2018 (July to September). For SES characterization, a holistic indicator was established. Community-level LST variables were derived from LST surfaces obtained through the Terra and Aqua satellite MODIS sensors, with the community serving as the fundamental unit of analysis. The relationship between SES and LST was explored using random forest regression (RF), eXtreme Gradient Boosting (XGBoost), and support vector regression (SVR) to assess socioeconomic inequality in urban heat. The findings reveal that (1) RF exhibits the highest fitting accuracy and adeptly elucidates the nonlinear relationship and marginal effects between LST variables and SES. (2) Community SES in the Hangzhou metropolitan area exhibits spatial clustering. (3) Residents of low and middle SES communities experience heightened heat inequality. (4) A complex nonlinear relationship exists between daytime and nighttime LST and SES, with significant social disparities in urban heat within specific temperature thresholds. When deciding on measures to advance thermal environmental justice, it is crucial to prioritize both relatively disadvantaged groups and specific temperature intervals. This study departs from conventional approaches, exploring the nonlinear relationship between SES and urban heat at a fine scale, thereby assisting urban planners in developing effective strategies.

Creating a spatially continuous air temperature dataset for Taiwan using thermal remote-sensing data and machine learning algorithms

Weather stations can provide accurate and high temporal resolution air temperature (Ta) measurements, but their limited spatial coverage due to sparse distribution poses an issue and challenge. However, satellite data can offer land surface temperature (LST) observations with high spatial coverage, which have a strong relationship with Ta, making them ideal for enhancing Ta estimation. This study uses satellite-derived and auxiliary data to create a monthly mean Ta dataset with a 1 km resolution over Taiwan from 2003 to 2020. We employed three machine learning (ML) algorithms and seven different datasets comprising 12 explanatory variables with LST obtained from the MODIS to find the optimal combination of algorithm and dataset for Ta estimation in Taiwan. We applied recursive feature elimination (RFE) to reduce the model complexity and overfitting issues. For model assessment, we used five-fold cross-validation to evaluate the ML models, and indicators such as the coefficient of determination (R2), mean absolute error (MAE), and root mean square of error (RMSE) were employed. The results show that the XGB regressor performed the best among the three models with the highest accuracy. The RFE using the XGB model suggested eight selected variables, including nighttime LST, daytime LST, elevation, longitude, latitude, distance to the sea, month, and year. Based on the variance importance analysis, nighttime LST was the most crucial variable, followed by daytime LST and month. We found that the final monthly Ta dataset using the XGB model had an excellent five-fold cross-validated performance (R2 = 0.986, MAE = 0.477 °C, and RMSE = 0.639 °C). Furthermore, the XGB model not only performed well throughout all four seasons but also had high and consistent accuracy across months, years, and subsets, indicating its potential for accurately estimating Ta in Taiwan's complex topographic features with varying climate conditions. The resulting monthly Ta dataset created by our model can be an essential input for environmental studies.

MONITORING SUMMER DAYTIME AND NIGHTTIME DECADAL TRENDS OF LAND SURFACE TEMPERATURE OVER THE NATIONAL CAPITAL REGION DELHI

Abstract. Urbanization-related growth and development have been recognized as substantial factors in increasing surface temperatures. India's rapid urbanization has exacerbated climate change challenges, particularly the rising Land Surface Temperature (LST) and Urban Heat Island (UHI) phenomena. The study used LST data from the MODerate Resolution Imaging Spectroradiometer (MODIS) to examine decadal variances in LST and diurnal LST variations over the summer months in a study region. We compared the LST values in 2010, 2020, and 2022 with the reference year 2000 by analyzing LST data collected from 2000 to 2022 using geospatial analysis. The study found a significant increase in the LST over a two-decade period, particularly in May and June, with temperature jumps of up to 10 (°C) both during the day and at night. The study also revealed differences in LST patterns between rural and urban regions, with rural areas having a higher daytime LST than urban areas and urban areas witnessing an elevated nighttime LST owing to the UHI impact. Analysis revealed significant differences in LST patterns, with 2020 showing lower surface temperatures due to limited heat released from anthropogenic activities such as industrial operation and vehicular traffic due to the COVID-19 lockdown. However, in the years 2010 and 2022, the study observed a significant increase in LST during the summer months both during the day and at night. These findings provided useful insights for urban development and planning and contribute to the implementation of measures aimed at reducing heat-related risks and improving the well-being of urban residents.

Heterogeneous effects of the availability and spatial configuration of urban green spaces on their cooling effects in China

The impacts of the availability and spatial configuration of urban green spaces (UGS) on their cooling effects can vary with background climate conditions. However, large-scale studies that assess the potential heterogeneous relationships of UGS availability and spatial configuration with urban thermal environment are still lacking. In this study, we investigated the impacts of UGS availability and spatial configuration on urban land surface temperature (LST) taking 306 cities in China as a case study covering a multi-biome-scale. We first calculated the availability of surrounding UGS for urban built-up pixels in each city using a distance-weighted approach, and its spatial configuration was quantified through the Gini coefficient. Then, we employed various regression models to explore how the impacts of UGS availability and the Gini coefficient on LST varies across different LST quantiles and between day- and nighttime. The results revealed that UGS availability was negatively associated with both daytime and nighttime LST, while the Gini coefficient showed a positive impact solely on daytime LST, indicating that an adequate and equally distributed UGS contributes to lower environmental temperatures during the daytime. Furthermore, the impact of UGS availability on LST decreased during both day- and nighttime with increased background LST quantiles. Whereas the impact of the Gini coefficient increased only with daytime LST quantile levels, with its effect remaining almost insignificant during the night. Our findings provide new insights into the impacts of UGS on urban thermal environment, offering significant implications for urban green infrastructure planning aiming at lowering the urban heat island.

Spatiotemporal patterns of land surface temperature and their response to land cover change: A case study in Sichuan Basin

Land surface temperature (LST) is a critical geo-parameter in terrestrial environmental interaction processes, directly related to land cover change (LCC) which modifies surface energy balance. In this study, LST data from 2003 to 2019 were reconstructed in the Sichuan Basin with average R2 of 0.85 (daytime) and 0.91 (nighttime), effectively filling in the missing pixels and reducing the noise components. Emerging hot spot analysis (EHSA) and land cover transfer matrix were utilized to analyze the multi-patterns of LST spatiotemporal evolution and responses to LCC. Results indicate that LST hot spots are concentrated in low-altitude basin floor and are dominated by sporadic hot spots. Cold spots are mainly in marginal high-elevation mountains, but the dominant pattern varies with time scale. The largest proportions of hot and cold spots are found in summer (>46 %) and autumn (>29 %), respectively. Moreover, the significant upward and downward trends of LST cold and hot spots are most prominent in western plain and marginal mountains, respectively, and have the largest coverage in summer and autumn, respectively. In total LCC area, cropland-to-forest (CF), cropland-to-impervious (CI), and forest-to-cropland (FC) account for 93.55 %. Among them, CI significantly promotes the aggregation and upward trend of daytime LST hot spots. CF and FC have the strongest effect of aggregating LST cold spots and cooling LST in daytime, with CF being more effective. The information can serve as a reference for regional planning and climate change mitigation measures.

Assessment of Mutual Variation of Near-Surface Air Temperature, Land Surface Temperature and Driving Urban Parameters at Urban Microscale

The Urban Heat Island (UHI) effect is of critical concern for cities’ adaptation to climate change. The UHI effect shows substantial intra-urban variation at the city microscale, causing disparities in thermal comfort and energy consumption. Therefore, air temperature assessment should be prioritized for effective heat mitigation and climate adaptation. However, meteorological stations’ spatial distribution is far from meeting the scale that the UHI and its driving parameters operate. This limitation hampers demonstrating the intra-city variability of UHI and its origin of sources; for example, most studies employ Land Surface Temperature (LST), usually without demonstrating the relationship between UHI and LST. The current body of knowledge on urban climate implies a much better understanding and more detailed information on the spatial pattern of UHI and the driving factors to provide decision-makers with tools to develop effective UHI mitigation and adaptation strategies. In an attempt to address the adequacy of the use of LST and UPs in describing the intra-city variability of UHI, this study investigates the relationship between LST daytime and nighttime, and air temperature (Ta) daytime and nighttime, and driving urban parameters (UPs) of UHI together. Although it is well recognized that the intensity of the UHI is characterized by Ta, particularly at night, so-called nocturnal UHI, the use of remotely sensed LST is common, owing to the lack of spatially detailed Ta data in cities. Our findings showed that nocturnal UHI is weakly correlated with nighttime LST with a Pearson correlation (r) of 0.335 at p > 0.05 and that it is not correlated with daytime LST for the case study, highlighting the need for Ta observations for representing the intra-urban variation of nocturnal UHI. Among UPs, Sky View Factor (SVF), Building Volume Density (BVD), and Road Network Density (RND) explained 69% of the variability of Ta nighttime that characterizes nocturnal UHI. Therefore, UPs that performed well in estimating nocturnal UHI may be used in the absence of densely distributed Ta measurements. In a further investigation of the urban cooling phenomenon based on UHI diurnal changes, a particular region with high nighttime temperatures spoiled the Ta daytime and nighttime coherence. This region is characterized by high Mean Building Height (MBH), BFD, and BVD that re-emits heat, low SVF that prevents urban cooling, and high RND that releases extra heat at night. These particular UPs can be of prior interest for urban cooling. The present study, exploring the relationships of LST and Ta in a diurnal context, offers a further understanding of the preference of LST, Ta, or UPs to characterize UHI. Ta, in relation to major causative factors (UPs), provides insights into addressing the localities most vulnerable to the UHI effect and possible strategies targeting heat mitigation for sustainability and climate change resilience.

Study on the Impact of MODIS-derived NDVI and NDBI on Land Surface Temperature

With the development and expansion of cities, the increase in built-up area and the drop in vegetation coverage have significant influences on the climatic environment of urban areas. This study processed Moderate Resolution Imaging Spectroradiometer (MODIS) data from four months in 2001 to investigate the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), and Land Surface Temperature (LST) in the Chengdu region. This paper explored the correlation between NDVI and LST, as well as the correlation between NDBI and LST. The findings revealed that areas with higher NDBI, indicating a greater extent of urban built-up areas, exhibited higher daytime and nighttime LST, indicating a more pronounced urban heat island effect. Conversely, an increase in vegetation cover was found to lead to a decrease in surface temperature and a certain degree of mitigation of the urban heat island effect. Furthermore, a good linear relationship was observed between NDBI and LST, with a stronger correlation during the daytime compared to the nighttime.

Insights into heat islands at the regional scale using a data-driven approach

Urban heat island (UHI) phenomenon is crucial in the context of climate change. However, while substantial attention has been given to studying UHIs within cities, our understanding at the regional level still needs to be improved. This study delves into the intricate dynamics of the regional heat island (RHI) by examining its relationship with land use/land cover (LULC), vegetation, and elevation. The objective is to enhance our knowledge of RHI to inform effective mitigation strategies. The research employs a data-driven approach, leveraging satellite data and spatial modeling, examining surface and canopy-layer regional heat islands, and considering daytime and nighttime variations. To assess the impact of LULC, the study evaluates three main categories: anthropized (urbanized), agricultural, and wooded/semi-natural environments. Furthermore, it delves into the influence of vegetation on RHI and incorporates elevation data to understand its role in RHI intensity. The findings reveal meaningful variations in heat islands across different LULCs, providing essential insights. Although urbanized areas exhibit the highest RHI intensity, agricultural regions contribute notably to RHI due to land use changes and reduced vegetation cover. This emphasizes the significant impact of human activities. In contrast, wooded and semi-natural environments demonstrate potential for mitigating RHI, owing to their dense vegetation and shading effects. Elevation, while generally associated with reduced heat island, shows variations based on local conditions. Ultimately, this research underscores the complexity of the RHI phenomenon and the importance of considering factors such as different temperatures and their daily variation, landscape heterogeneity, and elevation. Additionally, the study emphasizes the significance of sustainable spatial planning and land management. Targeted efforts to increase vegetation in high daytime land surface temperature areas can reduce heat storage and mitigate RHI. Similarly, planning for agroforestry and green infrastructure in agricultural areas can significantly increase resilience to climate.

A dataset of MODIS-based monthly air temperature with a spatial resolution of 1 km on the Tibetan Plateau from 2001 to 2020

The Tibetan Plateau has experienced a rapid climate rise in recent years, which is a hot issue in the study of global change. However, the meteorological stations are sparsely and unevenly distributed on the Tibetan Plateau, which negatively influences the spatial representativeness of meteorological data on the climate change of the whole plateau. Satellite remote sensing provides a new approach to the large-scale research on the climate change. First, we extracted the monthly daytime land surface temperature, monthly nighttime land surface temperature, monthly clear sky days, monthly surface albedo, monthly NDVI, monthly NDSI, altitude, astronomical radiation radiance and CTI from MODIS remote sensing data, DEM data and CTI datasets. Then, we employed the Cubist algorithm to develop models for estimating monthly average, maximum and minimum air temperature through cross-validation and parameter tuning. Finally, we obtained dataset of MODIS-based monthly air temperature with a spatial resolution of 1 km on the Tibetan Plateau from 2001 to 2020. The dataset is helpful for further understanding the climate change on the Tibetan Plateau, and provides important database for the climate change research on the Tibetan Plateau.

The biophysical effects of potential changes in irrigated crops on diurnal land surface temperature in Northeast China

Irrigated crops have experienced a significant global expansion. The biophysical response of climate change to irrigated crop expansion in different regions, particularly in terms of monitoring the influence mechanism of nighttime land surface temperature (LST) change, however, remains insufficiently explored. Taking the three northeastern provinces of China as our study area, we apply window analysis, partial correlation analysis, and geographical detector to quantitatively characterize the spatial and temporal distribution pattern of daytime and nighttime LST (diurnal LST) and biophysical parameters, and the main driving mechanism of diurnal LST change. The results showed that irrigated crop expansion led to asymmetric changes in daytime (−2.11 ± 0.2°C, 97.4%) and nighttime (0.64 ± 0.2°C, 79.9%) LST. ΔLSTDT had a negative correlation with ΔLE (63%), but a positive correlation with ΔSSR and ΔH (91% and 77%). This revealed that the cooling effect caused by the superposition of the output latent heat flux and the absorbed solar shortwave radiation was greater than its heating effect. ΔLSTNT and ΔLE had a positive connection across 69% of the region. ΔLSTNT demonstrated a negative correlation with ΔSSR and ΔH in 82% and 75% of the regions, respectively. At this time, the superposition of latent heat flux and heating potential term produces a greater heating effect. The explanatory power of the single factor (the mean of q<0.50) of biophysical parameters for diurnal LST variation was significantly smaller than that of the interaction factor (the mean of q>0.50, p<0.01). This study shows more detailed dynamic information of diurnal LST and biophysical parameters from 8day scale. The findings highlighted the critical role of asymmetric changes in the diurnal surface thermal environment caused by irrigated crop expansion in the global climate from a land surface hydrothermal energy balance perspective.