Land Surface Temperature Research Articles

Variation and Forecasting of Land Surface Temperature in Malaysia

Long-term variations in temperature and weather patterns provide evidence that the planet is experiencing global warming. The detrimental consequences of global warming on the ecosystem have affected people, plants, and animals. The rising Land Surface Temperature (LST) in a region has become a crucial indicator for determining specific climate change policies. Malaysia is divided into Peninsular Malaysia and Sabah Sarawak, located on Borneo Island, comprising four super-regions and 36 sub-regions. The distance between sub-regions, measured in latitudes and longitudes, is 150 pixels (equivalent to 95 kilometres), covering the entire country. This study uses data from NASA’s Terra satellites’ Moderate Resolution Imaging Spectroradiometers (MODIS) covering 2000–2022. Eight, four, and three knots were deployed on the cubic spline equation to analyse cyclical data, variation, and the LST forecast from 2022 to 2030. The global mean rise in LST variation per decade is 0.445°C, with a significance level of 5%, from a confidence interval of [0.377, 0.507]°C. The average predicted fluctuation in LST indicates a significant rise of 0.383°C per decade. Malaysia has not shown a significant decrease in LST acceleration at the 0.05 significance level, and a p-value of 0.06 suggests that LST variation is still increasing. Compared to the Sabah Sarawak group, which experiences LST deceleration, most Peninsular Malaysia group experiences LST acceleration.

Consecutive one-week model predictions of land surface temperature stay on track for a decade with chaotic behavior tracking

Temperature prediction over decades provides crucial information for quantifying the expected effects of future climate changes. However, such predictions are extremely challenging due to the chaotic nature of temperature variations. Here we devise a prediction method involving an information tracking mechanism that aims to track and adapt to changes in temperature dynamics during the prediction phase by providing probabilistic feedback on the prediction error of the next step based on the current prediction. We integrate this information tracking mechanism, which can be considered as a model calibrator, into the objective function of the proposed method to obtain the corrections needed to avoid error accumulation. Experimental results on the task of global weekly land surface temperature prediction over a decade validate the effectiveness of the proposed method.

A comparative study of the effects of urban morphology on land surface temperature in Chengdu and Chongqing, China

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

A cross-scale indicator framework for the study of annual stability of land surface temperature in different land uses

Urban Land Surface Temperature (LST) is crucial in surface urban heat island (SUHI) and microclimate studies. Currently, research has focused on seasonal LST differences across land uses, but annual LST fluctuations (ΔLST) within the same land use and their drivers remain underexplored. To explore the impact of land characteristics and urban elements on seasonal LST differences, we propose annual LST stability. We constructed a new indicator framework based on Land Use and Land Cover (LULC), supplemented by Land Morphology (LM) and Land Properties (LP), for cross-scale ΔLST research. We identified land use ratios and key characteristics of urban plots with high stability. The results show an interactive effect of the green land ratio to other land on ΔLST. For residential and office land, the green space ratio (GSR) is key to annual LST stability. Residential land needs a GSR of more than 24 %. The floor area ratio (FAR) for residential and office land has a significant nonlinear effect on annual LST stability, with FARs of 1.8 for residential land and 1.5 for office land being most detrimental to the LST stability. For practical implications, we conducted cluster analyses on residential, office, and green lands, providing strategies to improve stability. These conclusions help balance land economic benefits with urban climate resilience and guide urban planning and design to address the challenges of heat and cold waves.

Spatial Assessment of Urban Heat Island (UHI) in Kütahya using Landsat-8 Satellite Data

The Urban Heat Island (UHI) effect refers to the phenomenon where urban areas experience significantly higher temperatures than their rural surroundings due to human activities and modifications, such as replacing natural land cover with impervious surfaces, reducing vegetation, and increasing heat-absorbing materials. Kütahya central district has a dense population and building layout, which contributes to the intense UHI. Geographic Information System (GIS) and its tools are widely used in assessing UHI in urban areas, providing insights for both researchers and local authorities. Landsat-8 images acquired on August 8, 2023 (Path: 179, Row: 33, Cloud Cover: 0%) were utilized to create the Urban Heat Island (UHI) map for the study area. Through spatial analysis using Landsat-8 data for Kütahya city center, six different classes were defined, with the UHI map created using land surface temperature values. The highest and lowest UHI values vary between 4.245°C and -3.457°C. It has been observed that the average UHI increases by 12% in areas lacking dense building structures, characterized by rock surfaces and limited green areas. Conversely, in areas with parks, forests, and sparse settlements, the UHI decreases by 10%. With this study, a comprehensive evaluation of UHI was conducted for Kütahya city center. While the results obtained are expected to be an important resource for researchers and local authorities, they are also anticipated to be beneficial in the fields of engineering geology, urban geology, and urban planning.

Exploring El Nino effects on agricultural area using Landsat images analysis: A case study in Bondowoso Regency, Indonesia

El Nino, which hit Indonesia in 2023, poses severe food security threats due to the high dry season with no rainfall and minimal cloud cover and can trigger serious drought problems if it happen for a long time. This study aimed to explore the impact on agricultural land in Bondowoso Regency during El Nino events. The analysis in this study primarily uses land surface temperature (LST) and normalized difference vegetative index (NDVI) map distribution. The Landsat data from USGS are collected and processed to become LST and NDVI distribution maps. Data analysis focused on the agricultural area layers based on data from the Indonesia geospatial portal. Referring to the LST and NDVI map distribution, the notable rise of LST starts in August 2023, and the peak is in October 2023. Around 46% of areas in the Bondowoso regency are detected as hotspot areas, which had LST above 30<sup>o</sup>C in October 2023. El Nino affects the irrigated lands and rain-fed fields more than the plantations. The NDVI alteration data does not show that the Bondowoso Regency is experiencing extraordinary drought due to the short-term impact of El Nino. However, the emergence of numerous areas in the moderate NDVI category warns that stress affecting vegetation is starting to occur. Mitigation plans should be prepared for the long-term impact of El Nino, particularly in the hotspot areas. This study could be a comprehension tool for the government and farmers to prepare mitigation plans.

Diminishing control of evaporation on rising land surface temperature of the Earth

Evaporation rates and land surface temperatures can be modified by planned water availability as well as land use and land cover changes. In general, a higher evaporation rate via its associated latent heat flux yields a cooler surface. Here we demonstrate that increasing energy at the land surface necessitates more intense latent heat fluxes for the same unit degree of surface cooling. When the wet-surface temperature is around 25 °C, a unit drop in land surface temperature requires about twice as much water to evaporate than when it is only 10 °C. As a consequence, today an estimated 5 ± 3% of extra water may be needed to evaporate globally for the same cooling effect as before the industrial era when near surface air temperature over land was about 1.5 °C cooler on average. This increase is a magnitude larger than what the thermal properties of water explain.

Toward an advanced physics-based scheme for retrieving land surface emissivity and temperature based on FengYun-3D MERSI-II daytime mid-infrared data

Toward an advanced physics-based scheme for retrieving land surface emissivity and temperature based on FengYun-3D MERSI-II daytime mid-infrared data

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.

A Copernicus-based evapotranspiration dataset at 100 m spatial resolution over four Mediterranean basins

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

From Data to Decision: Interpretable Machine Learning for Predicting Flood Susceptibility in Gdańsk, Poland

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

Study on the spatiotemporal pattern evolution of surface urban heat island in shrinking cities: Fushun and Tieling

Under rapid urbanization, the urban heat island (UHI) problem impacts not only large cities, but also poses severe challenges to shrinking cities with rapidly declining population. In China, most shrinking cities are characterized by population loss alongside the expansion of built-up areas due to policy. Urban warming exacerbates the human settlement environment, with UHI intensifying due to urban expansion, while population loss simultaneously alleviates it. This raises a question: will the UHI problem be mitigated in shrinking cities? In this study, we analyze the spatiotemporal pattern evolution of surface urban heat island (SUHI) in Fushun and Tieling from 2000 to 2020 using Landsat series products. We combine landscape pattern indices and SUHI indicators, and perform correlation analyses of the factors influencing SUHI with multiscale geographically weighted regression (MGWR). The findings reveal that in Fushun, mining activities significantly impact SUHI, while in Tieling, extremely Land Surface Temperature (LST) zones are expanding and dispersing. SUHI patterns are notably shaped by subsurface conditions, and spatial configurations play key roles in regulating SUHI. However, population loss has not significantly influenced SUHI, even in shrinking cities. This study offers a new perspective for SUHI research and provides further insights into urban planning strategies.

Field scale partitioning of Landsat land surface temperature into soil and canopy components for evapotranspiration assessment using a two-source energy balance model

Field scale partitioning of Landsat land surface temperature into soil and canopy components for evapotranspiration assessment using a two-source energy balance model

Exploring the LULC dynamics and its relation with land surface temperature variation using split window algorithm: A study of Barasat subdivision, West Bengal, India.

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

Spatial Analysis of the Impacts Caused by Changes in Land Use on the Estimation of Surface Temperature in the The City of Paracatu (MG)

The increase in built-up areas, coupled with the suppression of vegetation without the necessary mitigating measures to curb the effects of urbanization, causes changes in the microclimate and directly impacts the health of the local population. In this sense, the present study aims to analyze the influence of land cover type on the variation of Land Surface Temperature (LST) estimation in the city of Paracatu, Minas Gerais (MG), in the years 1990 and 2020.For this, the maps provided by the Annual Mapping of Land Cover and Land Use of Brazil (MapBiomas) and images from the LANDSAT-5 (1990) and LANDSAT-8 (2020) satellites were used. Four images, distributed across seasons, were utilized to estimate LST for these periods, aiming to obtain a representative LST estimate for that year. For the calculation of the LST with bands 6 and 10, respectively of the LANDSAT-5 and 8 satellites, the SCP plugin of the QGIS software was used, and the process of recovery of the LST in the scenes used occurred through conversion of the values of the Digital Number (DN) into radiance at the Top of the Atmosphere (ToA), conversion of radiance into brightness temperature in ToA, correction of atmospheric effects and obtaining LST in Kelvin and, subsequently, in degrees Celsius. After that, the average between them was performed by means of normalization of each of the scenes, obtaining a representative mosaic for the LST of each year,thus enabling the estimation of the spatial behavior of the minimum, mean, and maximum values.. Finally, in order to verify the influence of Changes in Use and Occupation (LULC) on the spatial variation of the LST, the classes found in Vegetation, Exposed Soil, Urban Area and Water Resources were segmented, and it was found that, while the urban area of Paracatu had its territorial extension practically doubled, the vegetation class decreased by almost 25%, a fact that implied in the average increase of the LST by more than 5º C. the minimum and maximum values of these classes varied around 4.5 and 3º C, and it is possible to conclude that, in this interval of 30 years, Paracatu had no urban growth occurring exponentially and without the proper mitigations for the transformation of the microclimate, implying the need for measures that can curb these changes in strategic points of the municipality, as in the Bom Pastor neighborhood, which had its use and occupation totally changed in this temporal hiatus. In addition, in view of the estimates of economic growth in the city, it is believed that these measures should be taken as a matter of urgency, so that the results presented here can support the decisions of the managing public agencies.

The Nexus between Land Use/Cover changes and Land Surface Temperature: Remote sensing based Two-Decadal Analysis

The use of Remote Sensing (RS) data is crucial for promptly detecting and monitoring changes in both short and long term, providing real time information on Land Use/Cover (LULC), Land Surface Temperature (LST), and Enhanced Vegetation Index (EVI), adapting spatio-temporal variations. The primary focus of this study is to assess the effect of LULC changes on LST in Tashk-Bakhtegan and Maharloo (TBM) lakes basin, Iran, within 2001, 2011, and 2021, using MODIS data. Specifically, five main LULC classes involving: water body, rangeland, cropland, urban area, and bareland were identified. Beside accuracy and transition of LULC maps using User Accuracy (UA), Producer Accuracy (PA), and Kappa Coefficient (KC), the analysis included changes in LULC, Enhanced Vegetation Index (EVI), and LST, as well as the relationship among them when vegetation cover was at its peak. Moreover, a one-way analysis of variance (ANOVA) test was performed to group these variables using Duncan's test. The results showed that the accuracy of LULC maps were more than 84% for all the years. Furthermore, the conversion of croplands to rangelands showed the most significant changes, with a total of 1311.38 km2 during 2001–2021. Average EVI remained almost stable across the total area, whereas average LST generally increased by 0.65 °C. Barelands consistently exhibited the highest temperatures in all the years, followed by urban areas. While no significant changes were observed in the EVI averages, significant changes were observed in the LST across all LULC classes in different years. The results also indicated a consistent negative correlation between LST and EVI, stronger in croplands than rangelands, with Spearman's correlation coefficient of −0.714, −0.674, and −0.623 over the total area in 2001, 2011, and 2021, respectively. The findings are crucial for land planners to comprehend the effects of LULC changes on LST to adopt appropriate strategies in the TBM lakes basin.

Prediction of Potential Urban Hot Spots in Urban Environments Using Convolutional Neural Networks and Remote Sensing Techniques

Abstract. With the increase in built-up areas and rising urban populations, Land Surface Temperature (LST) has significantly increased, leading to the proliferation of Urban Hot Spots (UHS) in urban environments. To mitigate UHS proactively, researchers have conducted studies using various models to predict LST. However, current predictions are primarily based on data samples from isolated stations, making them unfeasible for continuous LST prediction on larger scales, such as regional levels. Therefore, this research aims to use Singapore as a case study to predict UHS on a regional scale using machine learning based on essential variables. Specifically, this research proposes training a Convolutional Neural Network (CNN) model using identified independent variables, including elevation, Normalized Difference Built-up Index (NDBI), Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), population, and Land Use and Land Cover (LULC), along with the target variable, UHS. After training, the model achieves high test accuracy and is fed projected data, subsequently producing projected UHS locations. The findings indicate that new UHS primarily appear in southwestern areas, Marina Bay, and the northwest regions of the country. This research predicts that 2.24 percent of the site could be classified as UHS by 2025, compared to the current percentage of 0.95 percent. Based on these projections, the research proposes preventative measures to proactively mitigate UHS. This research fills the gap by constructing a prediction model that can predict UHS locations on a regional scale.

Using Local Entropy Mapping as an Approach to Quantify Surface Temperature Changes Induced by Urban Parks in Mexico City

Understanding the mechanisms whereby parks contribute to cooling urban settings is critical to effectively addressing the challenges posed by rising temperatures in densely populated cities and ultimately improving the quality of urban life. This study employs a spatial approach with advanced analytical techniques, including local entropy mapping, to quantify surface temperature changes induced by urban parks across different geographical areas. Using satellite imagery to estimate land surface temperature (LST) during a heat wave in Mexico City, the study provides a practical approach to understanding the complex relationship between urban park size and urban heat island intensity within 300 m. The study’s findings indicate that while parks exert a cooling influence on their immediate vicinity, the extent of this effect varies spatially and depends on factors such as the size and location of the park and the nature of the surrounding terrain. Specifically, the results indicate that this relationship is not randomly distributed across the urban landscape. Instead, there is a clear pattern of spatial clustering within the city. Consequently, this research underlines the complexity of the problem, emphasizing the indispensable role of urban design and planning strategies to harness the full potential of parks as cooling agents within cities.

Assessment of Fine-Scale Urban Heat Health Risk and Its Potential Driving Factors Based on Local Climate Zones in Shenzhen, China

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

Green space-building integration for Urban Heat Island mitigation: Insights from Beijing's fifth ring road district

In this research, we delve into the complex arrangement of urban landscapes, where green spaces and buildings are not merely co-existing but are interwoven into a cohesive fabric that shapes the thermal environment. Our approach transcends the conventional methods of analysis, which typically isolate the roles of greenery or built environments. Instead, we adopt a synergistic perspective that recognizes the collective influence of these landscape constituents on the urban thermal pattern. Key insights are: (1) A linear decrease in average land surface temperature with increasing green space coverage is observed. However, substantial temperature variations (up to 8 °C) within the same coverage interval highlight the significant impact of built-up pattern on thermal conditions; (2) High Building Height and Floor Area Ratio, and low Building Coverage Ratio and Sky View Factor, are linked to cooler temperatures in areas with up to 50 % green space; (3) The study suggests that low-temperature areas can inform the adjustment of built-up patterns in high-temperature areas, offering a strategy for thermal environment optimization within specific green space coverage intervals. This research contributes insights into the integrated planning of green spaces and buildings, with implications for urban development and renewal initiatives aiming to enhance the urban thermal environment.