Land Surface Temperature Dynamics Research Articles

Multi-Scale Effects of LCZ and Urban Green Infrastructure on Diurnal Land Surface Temperature Dynamics

The impact of urban green infrastructure (UGI) on land surface temperature (LST) has been widely discussed as a strategy to improve urban thermal environments. However, most studies have focused primarily on daytime conditions. Due to the limitations of polar-orbiting satellites like the Landsat series, the influence of UGI on LST over a 24-hour cycle remains unclear. To assess the relative influence and interdependence of UGI characteristics on LST across four different grid scales over the diurnal cycle, this study, conducted within Beijing's Fifth Ring Road during the summer, utilized a random forest (RF) regression model. The results indicate that: (1) UGI's impact on LST follows two distinct patterns: daytime (morning and afternoon) and non-daytime (night and dawn), which indicates the intensity of sunlight as a key driving factor; (2) tree landscape pattern indices are the most significant factors affecting LST; (3) during non-daytime periods (night and dawn), cropland's cooling effect is equally important as that of trees. These findings are valuable for prioritizing and strategically placing different types of UGI in urban planning. However, as this study focuses on a specific area, future research should include comparative studies in cities with different climatic conditions.

Data-driven approach for land surface temperature retrieval with machine learning and sentinel-2 data

This research endeavors to advance land surface temperature (LST) prediction accuracy through the development of a sophisticated machine learning model. Leveraging the potential of Sentinel 2 data and atmospheric parameters, we augment Landsat-based LST with MODIS-based LST, enriching the temporal dimensions of our dataset. A distinctive feature of our study is the pioneering use of Sentinel 2 data as inputs for LST prediction, a facet scarcely explored in the existing literature. Our investigation delves into the correlation dynamics between LST and atmospheric parameters. Notably, the study employs a diverse set of machine learning models, including Extra Trees, Random Forests, LightGBM, XGBoost, and Support Vector Regressor. These models collectively exhibit superior performance, with Extra Trees emerging as a standout performer, with a minimal mean absolute error (MAE) of 0.423, a root mean square error (RMSE) of 1.340 °C, and an impressive coefficient of determination (R2) of 0.984. The exploration of Sentinel 2 data as an input source for LST prediction not only refines predictive accuracy but also opens novel research avenues in the realm of LST dynamics. This study contributes to the existing body of knowledge by introducing innovative methodologies and providing a comprehensive understanding of the intricate correlations influencing LST.

Seasonal dynamics of land surface temperature and urban thermal comfort with land use land cover pattern in semi-arid Indian cities: Insights for sustainable Urban Management

The cities in semi-arid and arid regions generally exhibit distinctive land use land cover (LULC) pattern and seasonal variation in urban thermal comfort (UTC) and land surface temperature (LST). This study examines the seasonal variation in LST and UTC and quantify the importance of LULC pattern in influencing LST in eight Indian semi-arid cities using Landsat 8 datasets. Random forest regression (RFR) model has been applied to quantify the influence of LULC in determining LST. The study shows that bare soil and open spaces in outskirts of selected cities have comparatively high LST than the core of cities. The mean LST varies seasonally from 12.84 °C in Udaipur to 26.18 °C in Jaipur in winter and summer. Analysis of UTC shows that autumn and winter seasons have better UTC than spring and summer seasons. RFR analysis shows a robust correlation (R2 > 0.85) between LULC and LST across all cities. Notably, built-up areas, open land, and vegetation cover exert the greatest influence on LST. Study suggests that targeted LULC planning may effectively address UTC problem in semi-arid cities and enhance sustainability. Study may help in understanding the seasonality thermal environment and assist in mitigating UHI and maintain UTC in these cities.

Land Surface Temperature Dynamics during COVID‐19 Lockdown in Diverse Climatic and Physiographic Zones—A Study of Indian Mega Cities

Land Surface Temperature Dynamics during COVID‐19 Lockdown in Diverse Climatic and Physiographic Zones—A Study of Indian Mega Cities

Exploring Summer Variations of Driving Factors Affecting Land Use Zoning Based on the Surface Urban Heat Island in Chiang Mai, Thailand

Numerous studies have examined land surface temperature (LST) changes in Thailand using remote sensing, but there has been little research on LST variations within urban land use zones. This study addressed this gap by analyzing summer LST changes in land use zoning (LUZ) blocks in the 2012 Chiang Mai Comprehensive Plan and their relationship with surface biophysical parameters (NDVI, NDBI, MNDWI). The approach integrated detailed zoning data with remote sensing for granular LST analysis. Correlation and stepwise regression analyses (SRA) revealed that NDBI significantly impacted LST in most block types, while NDVI and MNDWI also influenced LST, particularly in 2023. The findings demonstrated the complexity of LST dynamics across various LUZs in Chiang Mai, with SRA results explaining 45.7% to 53.2% of summer LST variations over three years. To enhance the urban environment, adaptive planning strategies for different block categories were developed and will be considered in the upcoming revision of the Chiang Mai Comprehensive Plan. This research offers a new method to monitor the urban heat island phenomenon at the block level, providing valuable insights for adaptive urban planning.

Assessment of U.S. Urban Surface Temperature Using GOES-16 and GOES-17 Data: Urban Heat Island and Temperature Inequality

Abstract This study utilizes hourly land surface temperature (LST) data from the Geostationary Operational Environmental Satellite (GOES) to analyze the seasonal and diurnal characteristics of surface urban heat island intensity (SUHII) across 120 largest U.S. cities and their surroundings. Distinct patterns emerge in the classification of seasonal daytime SUHII and nighttime SUHII. Specifically, the enhanced vegetation index (EVI) and albedo (ALB) play pivotal roles in influencing these temperature variations. The diurnal cycle of SUHII further reveals different trends, suggesting that climate conditions, urban and nonurban land covers, and anthropogenic activities during nighttime hours affect SUHII peaks. Exploring intracity LST dynamics, the study reveals a significant correlation between urban intensity (UI) and LST, with LST rising as UI increases. Notably, populations identified as more vulnerable by the social vulnerability index (SVI) are found in high UI regions. This results in discernible LST inequality, where the more vulnerable communities are under higher LST conditions, possibly leading to higher heat exposure. This comprehensive study accentuates the significance of tailoring city-specific climate change mitigation strategies, illuminating LST variations and their intertwined societal implications.

Land Surface Temperature Changes in Different Urbanization Increments in China since 2000

In the rapidly urbanizing world, as one of the distinct anthropogenic alterations of global climate change, global warming has attracted rising concerns due to its negative effects on human well-being and biodiversity. However, existing studies mostly focused on the difference in temperature elevation among urbanized areas and non-urbanized areas, i.e., rural or suburban areas. The allometric urban warming at intra-urban scales was overlooked. This research aimed to expand our understanding of urbanization–temperature relationships by applying a concept of a “previous-new” dichotomy of urbanized areas. To quantify the land surface temperature (LST) dynamics of 340 cities in China, we analyzed the LST of different land use types through trend analysis and absolute change calculation models. The urban heat island (UHI) effect of two spatial units, i.e., newly expanded urbanized area (“new UA” hereinafter) during 2000–2015 and previously existing urbanized area (“previous UA” hereinafter) in 2000, were compared and discussed. Our findings reveal that urban growth in China coincided with an LST increase of approximately 0.68 °C across the entire administrative boundary, with higher increases observed in regions between the Yellow River and Yangtze River and lower increases in other areas. Moreover, the new UA exhibited significantly greater LST increases and urban heat island intensity (HUII) compared to the previous UA. The dynamics of LST corresponded to the speed and scale of urban growth, with cities experiencing higher growth rates and percentages exhibiting more pronounced LST increases. This study reveals the impact of the underlying surface on human settlements on a large scale.

Exploring the Dynamics of Land Surface Temperature in Jordan’s Local Climate Zones: A Comprehensive Assessment through Landsat Entire Archive and Google Earth Engine

Exploring the Dynamics of Land Surface Temperature in Jordan’s Local Climate Zones: A Comprehensive Assessment through Landsat Entire Archive and Google Earth Engine

Assessing the Relationship between Land Surface Temperature and Composition Elements of Urban Green Spaces during Heat Waves Episodes in Mediterranean Cities

In the context of escalating global temperatures and intensified heat waves, the Mediterranean region emerges as a noteworthy hotspot, experiencing a surge in the frequency and intensity of these extreme heat events. Nature-based solutions, particularly management of urban green infrastructure (UGI) areas, have shown promising outcomes in adapting urban areas to the challenges posed by heat waves. The objective of the current study is twofold: firstly, to identify the compositional patterns of strategically distributed small public green spaces, demonstrating their enhanced capacity to mitigate the impact of heat waves in the Mediterranean region; secondly, to assess the association, direction, and explanatory strength of the relationship between the composition elements of the UGI areas and area typology, specifically focusing on the variation in land surface temperature (LST) values during heat wave episodes spanning from 2017 to 2023. The methodology involved obtaining land surface temperature (LST) values from satellite images and classifying green areas based on composition, orientation, and typology. Ordinal multiple regressions were conducted to analyze the relationship between the considered variables and LST ranges during heat wave episodes that occurred from 2017 to 2023. The findings indicate an increase in LST ranges across many areas, emphasizing heightened thermal stress in a Mediterranean medium-sized compact city, Granada (in the southeast of the Iberian Peninsula). Traditional squares, pocket parks and gardens, and pedestrian areas with trees and impervious surfaces performed better in reducing the probability of exceeding LST values above 41 °C compared to other vegetated patches mainly occupied by herbaceous vegetation and grass. The study concludes by advocating for the strategic incorporation of vegetation, especially trees, along with traditional squares featuring semipermeable pavement with trees and shrubbery, as a potential effective strategy for enhancing resilience against extreme heat events. Overall, this research enhances our understanding of LST dynamics during heat waves and offers guidance for bolstering the resilience of urban green spaces in the Mediterranean region.

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.

Index-based Approach in Relation to Built-up and LST Dynamics; A Study of Lahore, Pakistan

In 21st century, cities outpaced in size and also in density due to development of economic sector.Consequently, the wide spread expansion of urban areas is resulting in the loss of productive green cover and waterbodies. Therefore, realizing this alarming situation, the present study is aimed to investigate and evaluate the pattern ofurban expansion by considering two major land cover types (i) built-up area (ii) other classes (vegetation, waterbody,soil etc.) during the last six years (2015-2020). For this study Sentinel imagery was acquired from USGS EarthExplorer, while Modis Terra images were acquired from World View NASA. New built-up area index (NBUI),normalized difference vegetation index (NDVI), worldview water index (WV-WI) and land surface temperature (LST)were calculated in order to analyze variations in Lahore’s major land cover types and its varying temperature patterns.Spatial analysis presented the obvious impacts of land development on Lahore. NBUI indicated that the built-up areahas increased drastically from 34.0% in 2015 to 84.2% in 2020; NDVI analysis depicted a decline from 0.76% to0.73%, in the green spaces of Lahore during the study period; WV-WI portrayed inconsistent values of water bodies, agift of massive rise in the built-up area in Lahore. LST results presented that the temperature was 42.21°C in 2015,which simultaneously increased and recorded at 49.51°C in 2020. The increase in LST exhibited the alarming situationfor urban environment and can become threat to increase the air pollution level in Lahore. Therefore, this study willserve as a snapshot for policy makers to control the menace of unplanned urbanization by formulating stringent policiesto protect environment.

Quantifying Spatio-temporal dynamics of land surface temperature for the rapidly growing city of Bahir Dar, Northwest Ethiopia using multidated Landsat data

Urban heat island (UHI) refers to the occurrence of very high temperature in urban areas as compared to the surrounding rural areas. Land surface temperature (LST) is a key parameter in urban environmental analysis and varies with different land use/covers. The present study aims to explore the spatiotemporal dynamics of land surface temperature of Bahir Dar City and its surroundings using multidated Landsat 5 TM and Landsat 8 OLI/TIRS data of 1984, 1994, 2009, and 2019. The thermal infrared data has been corrected and converted to top of the atmospheric radiance and later to temperature in °C from Kelvin scale Using ArcMap software. The time series data pertaining to 1984, 1994, 2009, and 2019, showed a gradual increase in maximum LST and its expanse. Out of the total area of 213.55 km2, low (‹17 °C) to medium (‹23 °C) LSTprevailed in 65.88% (140.7 km2) of the area, whereas high (›29 °C) LST existed in 34.12% (72.85 km2). Though the low, medium, and high LST classes increased through 1984, 1994, 2009, and 2019, and medium and high-temperature classes became dominant in the expanse. The temporal variation of the Normalized Difference Vegetation Index (NDVI), as an indicator of urban heat intensity, showed the highest NDVI of 0.68 in 1994 and the lowest NDVI value of 0.54 in 2019. LST has a negative statistical correlation with NDVI with a coefficient of determination R2 = 0.99, 0.99, 0,94 & 0.98 for 1984, 1994, 2009, and 2019, respectively. NDVI and NDBI have very strong correlation with LST (R2>94%), which is quite different from previous studies in other study areas.

Dynamics of land surface temperature: Insights into vegetation, elevation, and air pollution in Bengaluru

Bengaluru, among the five fastest-growing cities of India, is facing the challenge of urban heat islands (UHI) and temperature variations across its landscape. Monitoring the temperature fluctuations is crucial for understanding the city's microclimate and the impact of urbanization on its environment. Land Surface Temperature (LST) is a significant metric for this purpose. The present study examined the relationship of LST with vegetation, elevation, and air pollutants during the summer and winter seasons for 4-years (2019–2022). Air pollutants and weather parameters data from air quality monitoring stations was used. The study found negative relationship of LST with NDVI and Digital Elevation Model (DEM) during both seasons. The relationship between LST and individual air pollutants was variable during the summer and winter seasons and R2 ranged between 0.52 and 0.79. The relationship became stronger (R2 between 0.69 and 0.82 during summer and R2 between 0.74 and 0.80 during winter season) when all the parameters were considered simultaneously. The result from the Taguchi method showed that different air pollutants had variable impact over change in LST and the order of their effect was found to be PM2.5 > SO2 > O3 > CO > NO2. The results indicate that O3 (summer) and PM2.5 (winter) were the major contributors to rising LST. The present study has applications in future scientific research as well as it can be utilized to integrate mitigation strategies for increasing temperatures.

Integrating random forest and morphological spatial pattern analysis for predicting future land surface temperature dynamics: insights from urbanizing Saudi Arabia

In the face of rapid urbanization in Saudi Arabia, understanding the impact of landscape changes on land surface temperature (LST) is crucial for sustainable urban planning. This study assesses the influence of landscape morphology on LST and predicts future LST changes. A multi-temporal land use and land cover (LULC) analysis using random forest (RF) quantified urban expansion and its ecological impacts. Accuracy assessment using the kappa coefficient illustrated the precision of the classification techniques. Morphological Spatial Pattern Analysis (MSPA), developed in Python, analyzed the structural evolution of urban areas, vegetation and exposed rocks. Polynomial regression models established the relationship between landscape morphology and LST and predicted future temperature trends. Results for Asir show a significant improvement in accuracy of the LULC models over three decades, with overall accuracy increasing from 89.99% in 1990 to 91.72% in 2020. The bootstrapping trend analysis showed an urban expansion with a positive slope of 9.27 and a decline in water bodies with a negative slope of −0.03. The MSPA analysis reflected a significant urban expansion, with the core area growing from 45.19 km2 in 2001 to 230.33 km2 in 2021. The vegetated areas showed resilience and dynamic connectivity despite slight reduction and fragmentation. The polynomial regression predicted an increase in future average LST by 2030, with urban core areas reaching 58.47 °C, vegetated cores 42.19 °C and exposed rock cores 55.79 °C. These results highlight the link between urban expansion and LST rise and make the case for integrating green infrastructure and cooling strategies into urban development.

Temporal Analysis of Land Surface Temperature Dynamics and Urban Heat Islands in Chennai using Geo-Spatial Technology

Temporal Analysis of Land Surface Temperature Dynamics and Urban Heat Islands in Chennai using Geo-Spatial Technology

Combined modelling of annual and diurnal land surface temperature cycles

The land surface's thermal dynamics follows annual and diurnal cycles that are to a large extent controlled by solar geometry. Therefore, annual and diurnal variations of land surface temperature (LST) can be modelled with relatively simple functions controlled by a small number of parameters, typically from three to six. The parameter values of the models can be determined by fitting the respective functions to time series of LST observations. Commonly either annual or diurnal LST variations are modelled or they are modelled sequentially in a two-step process. Here, we combine an annual temperature cycle (ATC) model controlled by the solar zenith angle (ATCsza) with a four-parameter version of the diurnal temperature cycle (DTC) model GOT09: this yields a new annual-diurnal temperature cycle (ADTC) model that simultaneously describes the annual and diurnal surface temperature dynamics. The proposed ADTC model is controlled by physically meaningful parameters: annual minimum temperature, annual temperature amplitude, annual maximum of daily temperature amplitude, mean time of thermal noon and time lag of maximum temperature with respect to summer solstice. Thus, the entire annual-diurnal LST dynamics is described with only five parameters. The new model was tested by fitting it to one year of LST observations obtained for five globally representative tiles of the Moderate Resolution Imaging Spectroradiometer (MODIS), onboard EOS – TERRA and EOS – AQUA satellites. For these tiles, the mean of the root mean square error (RMSE) was 4.2 K. ADTC modelled LSTs were also compared against those obtained with the standard ATC model for the four MODIS overpass times at five representative sites: for these, an overall RMSE of 1.2 K between the two models was obtained. The ADTC derived LSTs were validated against in-situ measurements from three different sites, which yielded an overall RMSE of 3.4 K. Additional investigations over five areas with different land covers (i.e. urban, lake, forest, mountain area and desert) revealed the potential of the ADTC parameters to describe the corresponding surface and climate properties. Since it is driven by solar geometry, the ADTC model reproduces double LST peaks in the tropics naturally. Furthermore, all available observations are modelled simultaneously, which means that a single set of parameters is obtained for each pixel and year.

An analysis of the dynamics of land surface temperature on land use/ land cover in Kano Metropolis, Kano State, Nigeria

The rapid urbanisation and associated land use changes have profound impacts on the local climate and environmental conditions in urban areas. This study aims to analyse the dynamics of land surface temperature (LST) and its relationship with land use/land cover (LULC) patterns in Kano Metropolis, Kano State, Nigeria. The research utilizes remote sensing data and geospatial techniques from multiple sensors, such as Landsat MSS, ETM + and OLI/TIRS, spanning a period of 38years (1984 – 2022), to processed, analysed and investigate the spatiotemporal variations in LST and their drivers. Statistical analyses, such as correlation and regression models, are employed to quantify the associations between LST and LULC variables. Findings show that urban area increased from 7% in 1984 to 32% in 2022, while bare land decreased from 82% in 1984 to 49% in 2022. Vegetation also increased slightly from 11% in 1984 to 19% in 2022. The LST increased with a mean value of 16°C in 1984, 25°C in 2003, and 30.5°C in 2022. Results still revealed a negative correlation between vegetation health and land surface temperature, indicating that as vegetation health declines, land surface temperature increases due to the lack of cooling effects from transpiration while a positive correlation exist between the built-up index and land surface temperature, signifying that as urban areas expand, land surface temperature rises due to the urban heat island effect. The research emphasises the significance of implementing land use planning and management strategies to address the adverse effects of urban heat and improve the urban microclimate. The findings offer valuable guidance for policymakers, urban planners, and environmental practitioners, assisting them in making informed decisions for sustainable urban development and enhancing the residents' quality of life in Kano Metropolis.

Assessing the Dynamics of Land Surface Temperature, Vegetation Health and Urban Heat Islands in Lucknow City: A Comprehensive Analysis from 1993 to 2022

Assessing the Dynamics of Land Surface Temperature, Vegetation Health and Urban Heat Islands in Lucknow City: A Comprehensive Analysis from 1993 to 2022

Satellite image analysis of thermal comfort for a sustainable urban ecology of Winneba, Ghana

To mitigate thermal heat effects for a sustainable urban living environment through planning and interventions, studies on land surface temperature (LST) in urban settings are crucial. Thus, the relationship between LST and surface landscape must be established to accurately assess the impacts of surface temperature changes for planning purposes. This will help gain knowledge about the spatial concentration of the heat effects and to design practical needs of urban planning. The study aimed to analyse and reveal the spatial dynamics of LST and its statistical relationship with two normalised satellite indices using Landsat satellite data. The Landsat-derived LST is validated using the MODIS surface temperature data. It also mapped the heat spots (HS) and evaluated the ecological evaluation index and thermal comfort using the thermal field variance index (TFVI). The results showed spatial variation in LST even within the built-up areas due to heterogeneity in the surface characteristics. Thus, localised heat islands (HIs) exist within short distances. The analysis established an inverse correlation for LST-NDVI and a direct correlation for LST-NDBI. The vegetation and water surfaces served as important heat reduction sinks while heavily built-up areas and the hardened-clayey bare surfaces served as heat-enhancing sources. More than half (52.80%) of the study area enjoys an excellent (48.57%) and good (4.23%) ecological evaluation index, indicating excellent to good thermal comfort. Much of these areas are in the urban periphery. Also, more than one-third (38.96%) enjoy the worst (34.85%) and worse (4.11%) ecological evaluation index and thus high thermal discomfort. Identifying these HS areas and evaluating the thermal comfort will help the planners and decision-makers to fully evaluate the thermal consequences of different landuse or land development scenarios and therefore improve the scientific basis of future planning and regulations.

Generating daily 100 m resolution land surface temperature estimates continentally using an unbiased spatiotemporal fusion approach

Fine spatial resolution (i.e., ≤ 100 m) land surface temperature (LST) data are crucial to study heterogeneous landscapes (e.g., agricultural and urban). Some well-known spatiotemporal fusion methods like the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM), which were originally developed to fuse surface reflectance data, may not be suitable for direct application in LST studies due to the high sub-diurnal dynamics of LST. Furthermore, the effectiveness of spatiotemporal fusion methods for LST data has not been thoroughly evaluated in previous studies that only focused on relatively small spatiotemporal extents. To address these limitations, we proposed a variant of ESTARFM, referred to as the unbiased ESTARFM (ubESTARFM), specifically designed to accommodate the high temporal dynamics of LST to generate fine-resolution LST estimates. We evaluated ubESTARFM and ESTARFM against in-situ LST and the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) LST across 12 regions throughout Australia, encompassing various land covers and environments. Independent validation showed that ubESTARFM had a bias of 2.55 K, unbiased root mean squared error (ubRMSE) of 2.57 K, and Pearson correlation coefficient (R) of 0.95 against the in-situ LST over 11,290 observations at the 12 sites, all of which were considerably better than those calculated for ESTARFM, being a bias of 4.73 K, ubRMSE of 3.80 K and R of 0.92. When compared to ECOSTRESS data, ubESTARFM LST had a bias of −1.69 K, ubRMSE of 2.00 K, and R of 0.70 over 43 near clear-sky scenes, while ESTARFM LST had a bias of 1.79 K, ubRMSE of 2.68 K, and R of 0.59. Overall, our results demonstrated that ubESTARFM can avoid systematic bias accumulation, substantially reduce uncertainty deviation, and maintain a good level of correlation with validation datasets when compared to ESTARFM. A further assessment underscored the potential of ubESTARFM for application using LST data acquired from geostationary platforms (e.g., Himawari-8), with a mean ubRMSE (R) of 2.22 K (0.97) against in-situ LST over 1327 observations at 3 sites from southeast Australia at the overpass time of MODIS/Terra. This promising method leverages reliable numeric values from coarse-resolution LST while borrowing spatial heterogeneity from fine-resolution LST and has the potential to be coupled with energy balance and/or radiative transfer models thus enabling better farm and/or regional-scale water management strategies to be implemented. Furthermore, both the input and generated LST data, encompassing a comprehensive spatial extent over diverse land covers and climatic conditions, are publicly available for benchmarking future algorithmic refinements.