Satellite-derived Land Surface Temperature Research Articles

A Modified Method for Reducing the Scale Effect in Land Surface Temperature Downscaling at 10 m Resolution

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 R2 improvement of 33.75%. The average MAE is 1.63 °C, and the average RMSE 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 MAE of 2.03 °C and an RMSE 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.

Satellite-based estimation of monthly mean hourly 1-km urban air temperature using a diurnal temperature cycle model

Cities worldwide face escalating climate change risks, underscoring the need for spatially and temporally resolved urban air temperature (Ta) data. While satellite-derived land surface temperature (LST) data have been widely used to estimate Ta, high-resolution hourly Ta estimation in urban areas remains underexplored. Traditional methods typically rely on LST data from geostationary satellites and continuous 24-h Ta observations from weather stations. To address these limitations, we introduce a method that combines a diurnal temperature cycle (DTC) model with a random forest model to estimate monthly mean hourly urban Ta at 1-km resolution. This approach leverages a limited number of diurnal Ta observations from weather stations, MODIS LST data, and ancillary information. The core idea of the proposed method is to transform the estimation of monthly mean hourly 1-km Ta into estimating 1-km DTC model parameters, primarily daily maximum and minimum Ta values. This method capitalizes on MODIS LST's ability to estimate daily Ta extremes and requires only four diurnal Ta observations within a daily cycle to estimate monthly mean hourly 1-km Ta. Station-based five-fold cross-validation yields overall RMSE values consistently below 1.0 °C across nine cities with diverse geographic and climatic contexts. The accuracy achieved with only four diurnal Ta observations rivals that obtained using continuous 24-h Ta observations. Even with a limited training set of ten stations, the overall RMSE remains below 1.0 °C for most cities. The proposed method proves effective for both single-city and multi-city modeling and can estimate daily hourly 1-km Ta under clear-sky conditions. In conclusion, this study offers a feasible, efficient, and versatile method for accurately estimating monthly mean hourly 1-km Ta, which can be readily applied to other cities and holds potential for various applications.

Unraveling the Influence of Satellite-Observed Land Surface Temperature on High-Resolution Mapping of Ground-Level Ozone Using Interpretable Machine Learning.

Accurately mapping ground-level ozone concentrations at high spatiotemporal resolution (daily, 1 km) is essential for evaluating human exposure and conducting public health assessments. This requires identifying and understanding a proxy that is well-correlated with ground-level ozone variation and available with spatiotemporal high-resolution data. This study introduces a high-resolution ozone modeling method utilizing the XGBoost algorithm with satellite-derived land surface temperature (LST) as the primary predictor. Focusing on China in 2019, our model achieved a cross-validation R2 of 0.91 and a root-mean-square error (RMSE) of 13.51 μg/m3. We provide detailed maps highlighting ground-level ozone concentrations in urban areas, uncovering spatial variations previously unresolved, along with time series aligning with established understandings of ozone dynamics. Our local interpretation of the machine learning model underscores the significant contribution of LST to spatiotemporal ozone variations, surpassing other meteorological, pollutant, and geographical predictors in its influence. Validation results indicate that model performance decreases as spatial resolution becomes coarser, with R2 decreasing from 0.91 for the 1 km model to 0.85 for the 25 km model. The methodology and data sets generated by this study offer new insights into ground-level ozone variability and mapping and can significantly aid in exposure assessment and epidemiological research related to this critical environmental challenge.

Predicting urban tomorrow: CA-Markov modeling and district evolution

The global population is experiencing exponential growth, resulting in a substantial increase in urbanization and subsequent urban expansion. This uncontrolled expansion, often termed urban sprawl, poses significant challenges to sustainable urban development. Understanding the importance of this concern, obtaining precise information about changes in Land Use/Land Cover (LULC) becomes crucial. This research examines the transformative processes of LULC and urban expansion over a 20-year period in two distinct study areas. For this purpose, built-up areas are first analysed using satellite-derived land surface temperature data, revealing temperature increase over time attributed to urban expansion. To project future trends using CA-Markov model, the suitability maps for each LULC class aggregated through the Multi Criteria Evaluation (MCE) method. Subsequently, the CA-Markov simulates the LULC maps for 2017 and 2018 for each study area, with a focus on the year 2050. Model calibrated by comparing the simulated maps with the actual maps in both study areas, and the reliability is affirmed by high Kappa coefficients (> 80%). Consequently, the study predicts LULC maps for 2050, revealing that both areas will experience a continued increase in built-up areas, a decrease in forested areas, and a relative stability in agricultural zones over the next 33 years.

Integrating Remote Sensing and Ground-Based Data for Enhanced Spatial–Temporal Analysis of Heatwaves: A Machine Learning Approach

In response to the urgent global threat posed by human-induced extreme climate hazards, heatwaves are still systematically under-reported and under-researched in Thailand. This region is confronting a significant rise in heat-related mortality, which has resulted in hundreds of deaths, underscoring a pressing issue that needs to be addressed. This research article is one of the first to present a solution for assessing heatwave dynamics, using machine learning (ML) algorithms and geospatial technologies in this country. It analyzes heatwave metrics like heatwave number (HWN), heatwave frequency (HWF), heatwave duration (HWD), heatwave magnitude (HWM), and heatwave amplitude (HWA), combining satellite-derived land surface temperature (LST) data with ground-based air temperature (Tair) observations from 1981 to 2019. The result reveals significant marked increases in both the frequency and intensity of daytime heatwaves in peri-urban areas, with the most pronounced changes being a 0.45-day/year in HWN, a 2.00-day/year in HWF, and a 0.27-day/year in HWD. This trend is notably less pronounced in urban areas. Conversely, rural regions are experiencing a significant escalation in nighttime heatwaves, with increases of 0.39 days/year in HWN, 1.44 days/year in HWF, and 0.14 days/year in HWD. Correlation analysis (p<0.05) reveals spatial heterogeneity in heatwave dynamics, with robust daytime correlations between Tair and LST in rural (HWN, HWF, HWD, r>0.90) and peri-urban (HWM, HWA, r>0.65) regions. This study emphasizes the importance of considering microclimatic variations in heatwave analysis, offering insights for targeted intervention strategies. It demonstrates how enhancing remote sensing with ML can facilitate the spatial–temporal analysis of heatwaves across diverse environments. This approach identifies critical risk areas in Thailand, guiding resilience efforts and serving as a model for managing similar microclimates, extending the applicability of this study. Overall, the study provides policymakers and stakeholders with potent tools for climate action and effective heatwave management. Furthermore, this research contributes to mitigating the impacts of extreme climate events, promoting resilience, and fostering environmental sustainability.

Modelling Europe-wide fine resolution daily ambient temperature for 2003–2020 using machine learning

To improve our understanding of the health impacts of high and low temperatures, epidemiological studies require spatiotemporally resolved ambient temperature (Ta) surfaces. Exposure assessment over various European cities for multi-cohort studies requires high resolution and harmonized exposures over larger spatiotemporal extents. Our aim was to develop daily mean, minimum and maximum ambient temperature surfaces with a 1 × 1 km resolution for Europe for the 2003–2020 period. We used a two-stage random forest modelling approach. Random forest was used to (1) impute missing satellite derived Land Surface Temperature (LST) using vegetation and weather variables and to (2) use the gap-filled LST together with land use and meteorological variables to model spatial and temporal variation in Ta measured at weather stations. To assess performance, we validated these models using random and block validation. In addition to global performance, and to assess model stability, we reported model performance at a higher granularity (local). Globally, our models explained on average more than 81 % and 93 % of the variability in the block validation sets for LST and Ta respectively. Average RMSE was 1.3, 1.9 and 1.7 °C for mean, min and max ambient temperature respectively, indicating a generally good performance. For Ta models, local performance was stable across most of the spatiotemporal extent, but showed lower performance in areas with low observation density. Overall, model stability and performance were lower when using block validation compared to random validation. The presented models will facilitate harmonized high-resolution exposure assignment for multi-cohort studies at a European scale.

Chasing the heat: Unraveling urban hyperlocal air temperature mapping with mobile sensing and machine learning

Many cities face unprecedented high temperatures with increasing extreme events. Heatwaves pose significant health risks, including cardiovascular diseases, heatstroke, and dehydration. Mapping urban near-surface air temperature (Tair) is crucial for understanding thermal exposure and addressing climate change. Previous studies relied on satellite-derived land surface temperature (LST) and stationary monitoring, but high spaio-temporal Tair mapping is still a challenge. This study optimized a mobile sensing scheme using an electric bicycle platform with environmental and image sensors, and deep learning captured local-scale urban factors. A spatio-temporal data fusion model that consisted of three parts, temporal trend extraction, locality analysis, and neighborhood effect analysis, generated hyperlocal Tair maps. The Results from Beijing demonstrated the effectiveness of the framework, achieving the lowest MAE of 0.02 °C. Optimized data collection and the new model achieved accurate temperature predictions and thermal exposure assessment. Efficiency enhanced sensing strategy was also proposed. The study highlights local-scale factors and spatio-temporal dependencies in addressing heatwaves and climate change impacts in urban areas.

Urban heat load assessment in Zagreb, Croatia: a multi-scale analysis using mobile measurement and satellite imagery.

A limited number of meteorological stations and sparse data challenge microclimate assessment in urban areas. Therefore, it is necessary to complement these data with additional measurements to achieve a denser spatial coverage, enabling a detailed representation of the city's microclimatic features. In this study, conducted in Zagreb, Croatia, mobile air temperature measurements were utilized and compared with satellite-derived land surface temperature (LST). Here, air temperature measurements were carried out using bicycles and an instrument with a GPS receiver and temperature probe during a heat wave in June 2021, capturing the spatial pattern of air temperature to highlight the city's microclimate characteristics (i.e. urban heat load; UHL) in extremely hot weather conditions. Simultaneously, remotely sensed LST was retrieved from the Landsat-8 satellite. Air temperature measurements were compared to city-specific street type classification, while neighbourhood heat load characteristics were analysed based on local climate zones (LCZ) and LST. Results indicated significant thermal differences between surface types and urban forms and between street types and LCZs. Air temperatures reached up to 35 °C, while LST exceeded 40 °C. City parks, tree-lined streets and areas near blue infrastructure were 1.5-3 °C cooler than densely built areas. Temperature contrasts between LCZs in terms of median LST were more emphasised and reached 9 °C between some classes. These findings highlight the importance of preserving green areas to reduce UHL and enhance urban resilience. Here, exemplified by the city of Zagreb, it has been demonstrated that the use of multiple datasets allows a comprehensive understanding of temperature patterns and their implications for urban climate research.

Assessing Methane Emissions from Rice Fields in Large Irrigation Projects Using Satellite-Derived Land Surface Temperature and Agronomic Flooding: A Spatial Analysis

Synthetic aperture radar (SAR) imagery, notably Sentinel-1A’s C-band, VV, and VH polarized SAR, has emerged as a crucial tool for mapping rice fields, especially in regions where cloud cover hinders optical imagery. Employing multi-temporal characteristics, SAR data were regularly collected and parameterized using MAPscape-Rice software, which integrates a fully automated processing chain to convert the data into terrain-geocoded σ° values. This facilitated the generation of rice area maps through a rule-based classifier approach, with classification accuracies ranging from 88.5 to 91.5 and 87.5 percent in 2017, 2018, and 2022, respectively. To estimate methane emissions, IPCC (37.13 kg/ha/season, 42.10 kg/ha/season, 43.19 kg/ha/season) and LST (36.05 kg/ha/season, 41.44 kg/ha/season, 38.07 kg/ha/season) factors were utilized in 2017, 2018 and 2022. Total methane emissions were recorded as 19.813 Gg, 20.661 Gg, and 25.72 Gg using IPCC and 19.155 Gg, 20.373 Gg, and 22.76 Gg using LST factors in 2017, 2018 and 2022. Overall accuracy in methane emission estimation, assessed against field observations, ranged from (IPCC) 85.71, 91.32, and 80.25 percent to (LST) 83.69, 91.43, and 84.69 percent for the years 2017, 2018 and 2022, respectively, confirming the efficacy of remote sensing in greenhouse gas monitoring and its potential for evaluating the impact of large-scale water management strategies on methane emissions and carbon credit-based ecosystem services at regional or national levels.

Using satellite-derived land surface temperatures to clarify the spatiotemporal warming trends of the Alborz Mountains in northern Iran

The Alborz Mountains are some of the highest in Iran, and they play an important role in controlling the climate of the country’s northern regions. The land surface temperature (LST) is an important variable that affects the ecosystem of this area. This study investigated the spatiotemporal changes and trends of the nighttime LST in the western region of the Central Alborz Mountains at elevations of 1500–4000 m above sea level. MODIS data were extracted for the period of 2000–2021, and the Mann–Kendall nonparametric test was applied to evaluating the changes in the LST. The results indicated a significant increasing trend for the monthly average LST in May–August along the southern aspect. Both the northern and southern aspects showed decreasing trends for the monthly average LST in October, November, and March and an increasing trend in other months. At all elevations, the average decadal change in the monthly average LST was more severe along the southern aspect (0.60°C) than along the northern aspect (0.37°C). The LST difference between the northern and southern aspects decreased in the cold months but increased in the hot months. At the same elevation, the difference in the lapse rate between the northern and southern aspects was greater in the hot months than in the cold months. With increasing elevation, the lapse rate between the northern and southern aspects disappeared. Climate change was concluded to greatly decrease the difference in LST at different elevations for April–July.

An Improved Integrated Model for Temporal Normalization of Satellite-derived Land Surface Temperature

An Improved Integrated Model for Temporal Normalization of Satellite-derived Land Surface Temperature

Differential Urban Heat Vulnerability: The Tale of Three Alabama Cities

Urban heat vulnerability varies within and across cities, necessitating detailed studies to understand diverse populations’ specific vulnerabilities. This research assessed urban heat vulnerability at block group level in three Alabama cities: Birmingham, Montgomery, and Auburn-Opelika. The vulnerability index combines exposure, sensitivity, and adaptive capacity subindices, incorporating Landsat 8 satellite-derived Land Surface Temperature (LST), demographic, and socioeconomic data using factor analysis and geospatial techniques. Results showed strong positive correlations between LST and impervious surfaces in Auburn-Opelika and Montgomery, with a moderate correlation in Birmingham. An inverse correlation between LST and Normalized Difference Vegetation Index was observed in all cities. High LST correlated with high population density, varying across cities. Birmingham and Montgomery’s central areas exhibited the highest heat exposure, influenced by imperviousness, population density, and socioeconomic factors. Auburn-Opelika had limited high heat exposure block groups, and high sensitivity did not always align with exposure. Correlations and cluster analysis were used to dissect the heat vulnerability index, revealing variations in contributing factors within and across cities. This study underscores the complex interplay of physical, social, and economic factors in urban heat vulnerability and emphasizes the need for location-specific research. Local governance, community engagement, and tailored interventions are crucial for addressing unique vulnerabilities in each urban context.

Antarctic daily mesoscale air temperature dataset derived from MODIS land and ice surface temperature

Knowledge about local air temperature variations and extremes in Antarctica is of large interest to many polar disciplines such as climatology, glaciology, hydrology, and ecology and it is a key variable to understand climate change. Due to the remote and harsh conditions of Antarctica’s environment, the distribution of air temperature observations from Automatic Weather Stations is notably sparse across the region. Previous studies have shown that satellite-derived land and ice surface temperatures can be used as a suitable proxy for air temperature. Here, we developed a daily near-surface air temperature dataset, AntAir ICE for terrestrial Antarctica and the surrounding ice shelves by modelling air temperature from MODIS skin temperature for the period 2003 to 2021 using a linear model. AntAir ICE has a daily temporal resolution and a gridded spatial resolution of 1 km2. AntAir ICE has a higher accuracy in reproducing in-situ measured air temperature when compared with the well-established climate re-analysis model ERA5 and a higher spatial resolution which highlights its potential for monitoring temperature patterns in Antarctica.

Analysis of Long Time Series of Summer Surface Urban Heat Island under the Missing-Filled Satellite Data Scenario.

Surface urban heat islands (SUHIs) are mostly an urban ecological issue. There is a growing demand for the quantification of the SUHI effect, and for its optimization to mitigate the increasing possible hazards caused by SUHI. Satellite-derived land surface temperature (LST) is an important indicator for quantifying SUHIs with frequent coverage. Current LST data with high spatiotemporal resolution is still lacking due to no single satellite sensor that can resolve the trade-off between spatial and temporal resolutions and this greatly limits its applications. To address this issue, we propose a multiscale geographically weighted regression (MGWR) coupling the comprehensive, flexible, spatiotemporal data fusion (CFSDAF) method to generate a high-spatiotemporal-resolution LST dataset. We then analyzed the SUHI intensity (SUHII) in Chengdu City, a typical cloudy and rainy city in China, from 2002 to 2022. Finally, we selected thirteen potential driving factors of SUHIs and analyzed the relation between these thirteen influential drivers and SUHIIs. Results show that: (1) an MGWR outperforms classic methods for downscaling LST, namely geographically weighted regression (GWR) and thermal image sharpening (TsHARP); (2) compared to classic spatiotemporal fusion methods, our method produces more accurate predicted LST images (R2, RMSE, AAD values were in the range of 0.8103 to 0.9476, 1.0601 to 1.4974, 0.8455 to 1.3380); (3) the average summer daytime SUHII increased form 2.08 °C (suburban area as 50% of the urban area) and 2.32 °C (suburban area as 100% of the urban area) in 2002 to 4.93 °C and 5.07 °C, respectively, in 2022 over Chengdu City; and (4) the anthropogenic activity drivers have a higher relative influence on SUHII than other drivers. Therefore, anthropogenic activity driving factors should be considered with CO2 emissions and land use changes for urban planning to mitigate the SUHI effect.

Improving process-consistency of an ecohydrological model through inclusion of spatial patterns of satellite-derived land surface temperature

Distributed hydrological models can correctly simulate spatial patterns if the model parameters adequately represent the spatial heterogeneity of the basin. However, determining realistic values of these parameters is often difficult. Considering remote sensing-derived land-surface states and fluxes in combination with streamflow observations is a suitable strategy to better constrain parameters and improve process-consistency. For ecohydrological modelling, an accurate representation of vegetation characteristics is important to capture the vegetation response to varying moisture availability. Land surface temperature (Ts) may serve as a valuable diagnostic because it is pivotal to the surface energy and water balance, and conveys information about ecosystem stress and water use. This study aims at assessing the benefits of integrating spatial patterns of Landsat-derived Ts into calibration of a process-based ecohydrological model to improve process representation of catchment-scale energy fluxes and vegetation response to moisture deficits. We explicitly analyze the trade-off between streamflow and Ts performance, and explore the value of adding an increasing number of Ts images in the calibration process. The study is performed in a mixed land cover catchment in Germany using the ecohydrological model EcH2O. Our results demonstrate the value of satellite-derived Ts data for reducing uncertainties of energy-balance related vegetation parameters, which are hardly constrained in streamflow-only calibration. Including satellite-derived Ts for model calibration reduced the mean absolute error of spatial anomalies in simulated Ts patterns by 15 % with negligible deterioration of streamflow performance. Inclusion of spatial Ts patterns improved capturing the differences in Ts between vegetation types, and affected simulated evapotranspiration fluxes. Improvements in simulated Ts could already be achieved by including only few (4–5) images of satellite-derived Ts in calibration. Based on our results, we advocate a wider use of satellite-based Ts data for multivariate calibration to improve model parameter identifiability and process consistency of highly parameterised, process-based distributed ecohydrological models, especially when capturing hydrology-vegetation interactions is crucial.

Simple yet efficient downscaling of land surface temperatures by suitably integrating kernel- and fusion-based methods

Kernel-based and fusion-based methods have been widely used to downscale satellite-derived land surface temperatures (LSTs) for obtaining LSTs with high spatiotemporal resolution. The integration of these two methods can potentially provide a more effective method for LST downscaling. However, a downscaling algorithm that appropriately integrates the advantages of these two methods in accuracy and stability, yet is characterized by adequate simplicity, effectiveness, and global applicability, is still lacking. To address this issue, here we proposed the Simple and Effective Downscaling (SED) algorithm, which integrates kernel- and fusion-based methods suitably and uses a single adjacent Landsat image to downscale MODIS LSTs by statistical regression. The SED was tested in 50 different study areas worldwide, and its performance was compared with four benchmark downscaling algorithms, including a kernel-based algorithm (the RFT21), a fusion-based algorithm (the FSDAF), and the simple average (the Hybrid 1) and weighted average (the Hybrid 2) of the RFT21 and the FSDAF. Our evaluations show that the SED performs better in almost all study areas and has finer global applicability when compared with these four benchmark algorithms. The mean RMSE of the SED is 1.29 K over these 50 study regions, with an accuracy improvement of over 13 % relative to the best benchmark algorithm. Particularly, the SED exhibits higher accuracies in tropical and temperate regions, over urban areas and savannas, and when using Landsat images closer to MODIS LSTs. We believe that the SED can contribute to the generation of high-quality and high spatiotemporal resolution LSTs over global lands.

Generating 60–100 m, hourly, all-weather land surface temperatures based on the Landsat, ECOSTRESS, and reanalysis temperature combination (LERC)

Satellite-derived land surface temperatures (LSTs) often encounter a tradeoff between spatial and temporal resolutions, as well as severe cloud contamination. While extensive efforts have focused on resolution enhancement and under-cloud reconstruction, generating fine-resolution (≤100 m) diurnal LSTs under all-weather conditions remains a challenge, which hampers fine-scale monitoring of climatological, hydrological, and ecological processes. The latest 70-m ECOSTRESS observations at varying times of day provide an unprecedented opportunity for detailed mapping of diurnal LST dynamics, and reanalysis products with spatiotemporal continuity offer promising references for all-weather thermal dynamics. However, these advantages have rarely been integrated to concurrently achieve high spatiotemporal resolution and completeness. Here, we present a simple yet effective framework for reconstructing 60–100 m, hourly, all-weather LSTs based on the Landsat, ECOSTRESS and Reanalysis temperature Combination (termed LERC). The framework involves three steps: (i) preliminary under-cloud estimations within annual cycles of several times by fitting an enhanced annual temperature cycle (EATC) model to clear Landsat/ECOSTRESS scenes and China Land Data Assimilation System (CLDAS) LST fluctuations; (ii) optimized daily estimations at each selected time by correcting biases of the preliminary under-cloud estimations and re-modeling the EATC with temporally densified samples; and (iii) hourly seamless estimations by interpolating the two nearest daily estimations with reference to the weighted diurnal changes in CLDAS. LERC was evaluated in an urban-dominated region throughout 2020, resulting in an average root-mean-squared-error of 2.0 K (3.0 K) against 50 Landsat and ECOSTRESS images (hourly ground measurements at 13 sites). Compared to the enhanced spatial and temporal adaptive reflectance fusion model, classic ATC model, diurnal temperature cycle model, and three sophisticated all-weather products, LERC demonstrates outperformance in terms of general accuracy, spatiotemporal variability, and robustness against sparse input. LERC has great potentials for generating long-term reliable all-weather LST records with a high spatiotemporal resolution to promote broad applications.

Land surface and air temperature dynamics: The role of urban form and seasonality

Due to the scarcity of air temperature (Ta) observations, urban heat studies often rely on satellite-derived Land Surface Temperature (LST) to characterise the near-surface thermal environment. However, there remains a lack of a quantitative understanding on how LST differs from Ta within urban areas and what are the controlling factors of their interaction. We use crowdsourced air temperature measurements in Sydney, Australia, combined with urban landscape data, Local Climate Zones (LCZ), high-resolution satellite imagery, and machine learning to explore the influence of urban form and fabric on the interaction between Ta and LST. Results show that LST and Ta have distinct spatiotemporal characteristics, and their relationship differs by season, ecological infrastructure, and building morphology. We found greater seasonal variability in LST compared to Ta, along with more pronounced intra-urban spatial variability in LST, particularly in warmer seasons. We also observed a greater temperature difference between LST and Ta in the built environment compared to the natural LCZs, especially during warm days. Natural LCZs (areas with mostly dense and scattered trees) showed stronger LST-Ta relationships compared to built areas. In particular, we observe that built areas with higher building density (where the heat vulnerability is likely more pronounced) show insignificant or negative relationships between LST- Ta in summer. Our results also indicate that surface cover, distance from the ocean, and seasonality significantly influence the distribution of hot and cold spots for LST and Ta. The spatial distribution for Ta hot spots does not always overlap with LST. We find that relying solely on LST as a direct proxy for the urban thermal environment is inappropriate, particularly in densely built-up areas and during warm seasons. These findings provide new perspectives on the relationship between surface and canopy temperatures and how these relate to urban form and fabric.

Comparing land surface temperature and mean radiant temperature for urban heat mapping in Philadelphia

Many cities are experiencing more frequent extreme heat during hot summers. With the rise of global temperature, the thermal comfort in urban areas become even worse. Quantitative information of the spatial distributions of urban heat has become increasingly important for resilience and adaptation to climate change in cities. This study compares satellite-derived land surface temperature (LST) and urban microclimate modeling-based mean radiant temperature (Tmrt) for mapping the urban heat distributions in Philadelphia, Pennsylvania, USA.The LST was estimated based on Landsat 8 thermal imagery with a spatial resolution of around 100 m, while the Tmrt was simulated based on high resolution LiDAR and national aerial imagery program multispectral aerial imageries with a spatial resolution of 1 m. Result shows that both LST and Tmrt show a similar general pattern of the urban heat across the study area, while the Tmrt presents much more details of the heat variations street by street and neighborhood by neighborhood. The LST tends to have a stronger relationship with the Tmrt on building roofs, which are usually not the place for human activities. This studyprovides evidence for choosing more appropriate metrics in urban heat-related studies.

Retrieval Consistency between LST CCI Satellite Data Products over Europe and Africa

The assessment of satellite-derived land surface temperature (LST) data is essential to ensure their high quality for climate applications and research. This study intercompared seven LST products (i.e., ATSR_3, MODISA, MODIST, SLSTRA, SLSTRB, SEVIR2 and SEVIR4) of the European Space Agency’s (ESA) LST Climate Change Initiative (LST_cci) project, which are retrieved for polar and geostationary orbit satellites, and three operational LST products: NASA’s MODIS MOD11/MYD11 LST and ESA’s AATSR LST. All data were re-gridded on to a common spatial grid of 0.05° and matched for concurrent overpasses within 5 min. The matched data were analysed over Europe and Africa for monthly and seasonally aggregated median differences and studied for their dependence on land cover class and satellite viewing geometry. For most of the data sets, the results showed an overall agreement within ±2 K for median differences and robust standard deviation (RSD). A seasonal variation of median differences between polar and geostationary orbit sensor data was observed over Europe, which showed higher differences in summer and lower in winter. Over all land cover classes, NASA’s operational MODIS LST products were about 2 K colder than the LST_cci data sets. No seasonal differences were observed for the different land covers, but larger median differences between data sets were seen over bare soil land cover classes. Regarding the viewing geometry, an asymmetric increase of differences with respect to nadir view was observed for day-time data, which is mainly caused by shadow effects. For night-time data, these differences were symmetric and considerably smaller. Overall, despite the differences in the LST retrieval algorithms of the intercompared data sets, a good consistency between the LST_cci data sets was determined.