Land Surface Temperature Research Articles

Modeling the spatially varying effects of biophysical factors on land surface temperature

A growing number of studies have investigated how land surface temperature (LST) is influenced by a variety of driving factors; however, little effort has been made to identify the dominant ones. The suggested method used the Upper Awash Basin (UAB), Ethiopia, as an example to explore the spatial heterogeneity and factors affecting LST, which is critical for selecting effective mitigation strategies to manage the thermal environment. The study employed two models: ordinary least squares (OLS) and geographically weighted regression (GWR). The OLS model was first used to capture the overall relationship between LST and some biophysical factors. The GWR was then utilized to investigate the spatial non-stationary relationships between LST and its influencing biophysical factors. Although the method was tested in UAB, Ethiopia, it can be applied in similar agroecosystems, to identify the dominant factors that influence LST and develop site-specific LST mitigation strategies.•The OLS and GWR models investigated the spatial heterogeneities of the influencing factors and LST.•Biophysical parameters such as enhanced vegetation index (EVI), modified normalized difference water index (MNDWI), normalized difference built-up index (NDBI), normalized difference bareness index (NDBaI), albedo and elevation were used as potential driving environmental factors of LST•The models performance was computed using the adjusted coefficient of determination (adj. R2), Akaike Information Criterion (AICc), and residual sum of squares (RSS).

Surface temperature change amplifies social inequality in heat exposure? Evidence from Shanghai, China

Heat-related environmental justice has gained increasing attention, with many studies focusing on social inequalities in exposure to extreme surface temperatures. However, changes in land surface temperature (LST) also contribute to residents’ thermal discomfort, underscoring the need to explore inequalities in exposure to these changes. This study addresses this gap by analyzing spatial and social disparities in exposure to both LST changes and summer LST in Shanghai. We employed a boosted regression tree model to quantify LST changes, offering better predictive performance than traditional linear regression, and used geographically weighted regression with data from China's sixth population census to assess the city-wide and local scale inequality differences in exposure to LST changes and summer LST. Our findings reveal that 57.83% of Shanghai's subdistricts experienced above-average LST changes and summer LST. Specific groups—males (22.17%), children (22.61%), the elderly (13.04%), and residents in older housing (60.87%)—were disproportionately exposed to local-scale LST changes. In contrast, those employed in agriculture, forestry, and fisheries faced consistently lower exposure. Furthermore, exposure to LST changes was 3.47% higher among the elderly and 28.26% higher for residents in older housing compared to exposure based on summer LST alone. To mitigate these inequalities, we recommend increasing water coverage and green roofs, enhancing green infrastructure in aging neighborhoods, and providing financial subsidies for outdoor workers. These findings emphasize the importance of addressing social inequalities in exposure to temperature changes to enhance urban resilience and promote sustainable urban planning in the face of climate change.

Incorporating changes in land surface temperature improves BESS evapotranspiration estimates under water-deficit conditions: A case study for US Midwest and Great Plains grasslands

Evapotranspiration (ET) is a critical climate and ecosystem variable that interconnects water, energy, and carbon cycles. Breathing Earth System Simulator (BESS) is one of the state-of-the-art biophysical models capable of producing spatio-temporal continuous ET results. However, we found that since the BESS model does not use an explicit constraint on soil moisture (SM), it has a relatively lower performance under drier conditions. Given that changes in land surface temperature (LST) are closely associated with surface water status and sensible heat energy, we hypothesize that integrating LST changes could explicitly add the soil moisture constraints and thus enhance BESS’s ability to estimate ET. Here we used the morning rise rate of LST (Trate) as a proxy of LST change because of the low noise level in Trate as well as Trate’s close relationship with daily mean sensible heat. To test the hypothesis, this study first assessed whether the performance of BESS ET can be explained by the LST change, targeting grassland sites of the AmeriFlux network in the US Midwest and Great Plains. Specifically, the ET deviation (i.e., the difference between BESS-modeled ET and field-measured ET) and Trate deviation, as well as their relationships, were investigated under different conditions of precipitation, SM, and vapor pressure deficit (VPD) at the AmeriFlux sites. Results indicated that BESS ET exhibited consistently higher performance under well-watered conditions than water-deficit conditions. Also, the deviations of ET and Trate became more negatively correlated under water-deficit conditions. Leveraging the empirical relationship between ET and Trate deviations, this study developed a new way to calibrate BESS ET based on Trate calculated from LST diurnal observations, particularly under soil or atmospheric water-deficit conditions. After calibrating BESS ET, the statistical indicators between the calibrated ET and the ground measurements showed meaningful improvements relative to those before calibration. Specifically, in the Midwest (Great Plains), R2 increased from 0.42 to 0.51 (from 0.45 to 0.46), and RMSE and absolute bias decreased by 12% and 42% (11% and 45%), respectively. This study highlights that the morning rise rate of LST can effectively constrain the ET models that have no SM constraints under water-deficit conditions.

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.

Ilha de calor de superfície e indicadores geoespaciais: avaliação decadal comparativa por sensoriamento remoto

Abstract Surface Urban Heat Islands (SUHI) are formed through processes that include decreasing vegetation and increasing materials that are more conductive to heat. To investigate this phenomenon, the objective of this work is to comparatively verify the surface temperature and geospatial indicators to analyze environmental variation in the city of São Paulo, Brazil. The study method is remote sensing, with data from the Landsat 8 satellite over a ten-year timeframe. The variables include land cover, the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Built-up Index (NDBI), and land surface temperature (LST), thereby obtaining the Urban Thermal Field Variation Index (UTFVI) and estimating SUHI formation. The results allowed for the presentation of a progression of variables, highlighting the recurrent formation of SUHI in the central and eastern regions of the city of São Paulo, Brazil. Conversely, the southern and extreme northern districts exhibited the best NDVI indices and low LST values.

Unraveling the Complexities of Land-Use–Land Cover Transformation and Its Impact on Land Surface Temperature and Urban Heat Island Effect: A Study of Nowshera District, Pakistan

Unraveling the Complexities of Land-Use–Land Cover Transformation and Its Impact on Land Surface Temperature and Urban Heat Island Effect: A Study of Nowshera District, Pakistan

A method to map land use impacts on microclimate regulation supply in urban environments

Land use impacts land surface temperature (LST), especially in urban areas where anthropogenic materials have a high capacity to store energy. Nevertheless, cities have many other land uses (e.g., forests, lawns) that can reduce LST and contribute to high microclimate regulation. In this work, we develop a method to map land use impacts on microclimate regulation supply using an Unmanned Aerial Vehicle (UAV). A detailed methodology was developed for 1) UAV's mission planning, 2) field data collection for method validation, 3) RGB and thermal mission reconstruction, 4) land use classification, 5) data extraction and 6) spatial and statistical analysis. The method developed can be beneficial to local authorities and transferable to other realms. It will allow us to understand the impacts of different land uses on microclimate regulation. For this, an area with heterogeneous land uses was used as a test site.• A novel methodology was created to map land use impacts on microclimate regulation supply in urban areas;• High-resolution UAV RGB and thermal imagery for land-use classification and surface temperature analysis;• The method can help understand the capacity of the different land uses on microclimate regulation;

Elevation-Dependent Snow Cover Dynamics and Associated Topo-Climate Impacts in Upper Indus River Basin

In the present study, Improved Moderate Resolution Imaging Spectro-radiometer (MODIS) snow cover product (MOYDGL06*) has been used to evaluate the snow cover area (SCA) in Kabul, Jhelum, and Indus river basins for the time period of 2003 – 2020 with available MODIS land surface temperature (LST), and CHIRPS (precipitation) with objectives to evaluate the spatio-temporal SCA, and climate variables with respect to different elevations analyzed from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model Version 3 (GDEM v3) and also to correlate the climatic variables with SCA. The results presented average annual SCA is around 50.7% – 64.7% in sub-basins of UIB, further it has been observed that SCA is decreasing on annual and seasonal timescale in all three basins. Elevation-dependent SCA, temperature, and precipitation presented a mix of trend on annual, seasonal, and monthly timescale at lower and higher altitude in all selected basins. Moreover, it was noticed that topography (slope, & aspect) also influences the SCA in the region. Furthermore, it has been examined that temperature has significant inverse relationship with SCA at middle and higher altitude in Indus, while in Kabul, and Jhelum no significant relationship observed at extreme lower and higher altitudes. It is also evident from relationship between SCA and climate variable that temperature is significantly responsible for decreasing trend of SCA rather than intense precipitation in all three river basins. Thus, all these elevation-dependent changes can improve our hydrological understanding which can have a considerable implication for hydrology, climate science, water resource management and socio-economic activities.

Analytical Extrapolation of Land Surface Temperatures in Clairvoyantly Perceived Regions Using Remote Sensing: A Case Study of Moscow, Taif, Amman, and Abu Dhabi.

Analytical Extrapolation of Land Surface Temperatures in Clairvoyantly Perceived Regions Using Remote Sensing: A Case Study of Moscow, Taif, Amman, and Abu Dhabi.

Estimation of land surface temperature in relation to land-use using remote sensing technology and geographic information system (Case study: Tabriz city(

Estimation of land surface temperature in relation to land-use using remote sensing technology and geographic information system (Case study: Tabriz city(

Summer and Winter Anomalies of Day and Night Cloud Parameters over Western Siberia Using MODIS Data and ERA5 Reanalysis During 2001–2022

We present the results of analysis of multiyear variability of cloud parameters (day and night) over Western Siberia in summer and winter during 2001-2022 based on MODIS data and ERA5 reanalysis. Three latitude zones of the target region are considered: northern (66-72° N, 68-82° E), transitional (60-65° N, 62-88° E) and southern (54-59° N, 62-88° E). We have plotted time series and on their basis we have identified trends of the following cloud parameters: fraction, top height, top pressure, top temperature and effective emissivity. It was found that in 2010, 2012, 2014 and 2016, the largest number of anomalies in the time series of the above cloud features was observed. The comparison results for the considered cloud parameters with the variability of the land surface temperature and geopotential heights at the baric levels of 500, 700, 850 and 1000 hPa are presented. We discuss hypotheses about the causes of anomalous values in time series of investigated cloud parameters at different times of day in summer and winter, related to the specific features of atmospheric circulation over Western Siberia in different years.

Unveiling Ghana's urban tapestry: satellite-based analysis of Tamale city's spatial expansion and land surface temperature (LST) dynamics

Unveiling Ghana's urban tapestry: satellite-based analysis of Tamale city's spatial expansion and land surface temperature (LST) dynamics

Analysis of Land Surface Performance Differences and Uncertainty in Multiple Versions of MODIS LST Products

Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) products are essential data sources for global and regional climate change research. Currently, several versions of the MODIS LST product have been released, yet the performance differences and uncertainties they introduce in land surface studies remain insufficiently addressed. To bridge this gap, this study focuses on four distinct versions of the LST product: MxD11A1 Collection 5 (C5), Collection 6 (C6), Collection 6.1 (C6.1), and MxD21A1 Collection 6.1 (MxD21). The spatial resolution of all product generations is 1 km, and the temporal resolution is 0.5 days. This study provides a comprehensive analysis of the errors arising from different generations of these products in various land surface process studies. The error assessment includes cross-comparisons between product versions and evaluations of the absolute errors generated. Absolute errors in evaluation data were collected from 13 surface sites within the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project during the period 2013–2018. Cross-validation results show that the largest difference between C5 and C6.1 occurs over bare land, with an RMSE of approximately 1.45 K, while there is no significant change between C6 and C6.1. MOD21 shows considerable variation compared to C6.1 at night across different land cover types, with RMSE over cropland exceeding 2 K. The temperature difference between MOD21 and C6.1 is more pronounced at night (2.01 K) than during the day (0.30 K). Validation results based on temperature indicate that C5 has greater uncertainty compared to C6, especially over bare land, where errors are 2.06 K and 1.06 K, respectively. Furthermore, MxD21 demonstrates significant day–night performance discrepancies, with an average bias of 0.10 K at night, while daytime errors over bare land can reach 2 K, potentially influenced by atmospheric conditions. Based on the research in this paper, it is possible to clarify the performance of different versions of MODIS products, reflecting the appropriateness of their past applications; on the other hand, it is recommended to prioritize the use of the MxD11A1 C6 and C6.1 products for monitoring and applications in bare soil areas to ensure higher accuracy. Furthermore, for day and night monitoring, it may be beneficial to alternate between the MxD11A1 and MxD21A1 products to fully leverage their respective advantages and enhance overall monitoring effectiveness.

Development and Evaluation of Machine Learning Models for Air-to-Land Temperature Conversion Using the Newly Established Kunlun Mountain Gradient Observation System

Mountainous land types are characterized by a scarcity of observational data, particularly in remote areas such as the Kunlun Mountains, where conventional Automatic Weather Stations (AWSs) typically do not record land surface temperature (LST) data. This study aims to develop and evaluate models for converting air temperature (TA) to LST using newly established meteorological station data from the Kunlun Mountain Gradient Observation System, thereby providing time-continuous LST data for AWSs. We constructed a conceptual model to explore the relationship between 1.5 m TA and LST and instantiated it using three machine learning algorithms: Support Vector Machine (SVR), Convolutional Neural Network (CNN), and CatBoost. The results demonstrated that the CatBoost algorithm outperformed the others under complex terrain and climatic conditions, achieving a coefficient of determination (R2) of 0.997 and the lowest root mean square error (RMSE) of 0.627 °C, indicating superior robustness and accuracy. Consequently, CatBoost was selected as the optimal model. Additionally, this study analyzed the spatiotemporal distribution characteristics of cloud cover in the Kunlun Mountain region using the MOD11A1 product and assessed the uncertainties introduced by the 8-day average compositing method of the MOD11A2 product. The results revealed significant discrepancies between the monthly average LST derived from polar-orbiting satellites and the hourly composite monthly LST measured on-site or under ideal cloud-free conditions. These differences were particularly pronounced in high-altitude regions (4000 m and above), with the greatest differences occurring in winter, reaching up to 10.2 °C. These findings emphasize the importance of hourly LST calculations based on AWSs for accurately assessing the spatiotemporal characteristics of LST in the Kunlun Mountains, thus providing more precise spatiotemporal support for remote sensing applications in high-altitude regions.

Unlocking the mysteries of drought: integrating snowmelt dynamics into drought analysis at the Narayani River Basin, Nepal

AbstractDrought is among the most impactful natural hazards, undermining water security, agriculture, and livelihoods worldwide. Analysing droughts in large catchments presents several unique challenges, primarily related to the complexity of land surface characteristics and data availability limitations. Conducting drought analysis in the Narayani River Basin, which encompasses a vast area within the Himalayan region of Nepal, is extremely challenging but crucial for maintaining the river basin's social, economic, and environmental balance. In response, this study develops a new combined drought index (CDI), integrating satellite-based reanalysis parameters [i.e., Land Surface Temperature (LST), Snow Cover (SC), and Normalised Difference Vegetation Index (NDVI)] with a meteorological parameter [i.e., Standardised Precipitation (std_prec)]. The novel CDI was applied at the Narayani Basin to assess the droughts over the 2004–2013 period, and the results were independently evaluated using streamflow observations to validate the accuracy of the novel drought index. The principal component analysis (PCA) technique was used to determine the contribution of input parameters to the multivariate drought index. The PCA results show a strong positive correlation (0.78) between the CDI and standardised streamflow, indicating the effectiveness of the novel index in monitoring drought conditions. Accordingly, it can be concluded that surface water availability is interdependent on landscape characteristics, such as LST, SC, and NDVI, in addition to the effects of precipitation. Also, the novel CDI can identify the specific drought-affected areas in the Narayani River Basin, offering insights into its drought characteristics beyond traditional drought assessment techniques.

Analyzing the Contribution of Urban Land Uses to the Formation of Urban Heat Islands in Urmia City

An Urban Heat Island (UHI) is an important variable in climate and environmental studies. Nowadays, population growth and urbanization development are the most important factors affecting the temperature increase in urban areas, which cause the creation of heat islands in urban areas. (1) Background: This study explores the influence of major land uses on the creation of Urban Heat Islands in Urmia city, Iran. (2) Methods: To achieve the aim of this study, Landsat satellite data including Landsat 5 and 8 imageries from the time periods of 1990 and 2023 were used. With the series of data processing and analyses on vegetation cover and land surface temperature, the impact of main land uses on the creation of Urban Heat Islands and the intensification of their effects have been investigated. (3) Results: The analysis reveals that barren lands consistently exhibit the highest temperature, while garden lands show the lowest temperature across both periods. In addition, the spatial distribution of Urban Heat Islands demonstrates a clustered pattern throughout the study period, with hot spots mainly located in the northwestern and southwestern areas. (4) Conclusions: This study’s findings can be helpful for urban policymakers and planners to develop practical strategies to mitigate UHIs and improve climate resilience in cities.

Using Google Earth Engine open-source code for land surface temperature estimation from Landsat data in Chuong My district, Hanoi city, Vietnam

Land surface temperature (LST) in association with urbanization has significantly influenced on local climates and terrestrial surface processes. By using multi-temporal Landsat data in the GEE platform, the study has found that there have been changes in land covers due to the main drivers of industrial development, urban and built-up expansion. This study estimated land surface temperature (LST) in Chuong My district from Landsat 5, 7, 8 and 9 thermal infrared sensors, using different surface emissivity sources. The Google Earth Engine (GEE), an advanced earth science data and analysis platform, offers the estimation of LST products, covering the time period from 2000 to 2024 in study site. RF algorithm for land cover classification and mapping is suggested to be applied in Chuong My district, with overall accuracy of land cover mapping in 2024 being 91.9% with Kappa coefficient of 0.89. The period 2000-2010 showed that 327.0 ha of land were converted to land for industrial development, urban and residential land, while the period 2010-2024 continued to witness a continued conversion of land to other land use purposes, with a decrease in agricultural land area (65.7 ha) and an increase in industrial land use (65.2 ha), but the rate of conversion was much lower than the previous period (2000-2024). The periods of 2000-2010 and 2010-2024 showed that there have been significant changes in land surface temperature. Most of the surface temperature in the entire region increased by over 20C, especially in the period 2003-2015 when a total area of 1424 ha had the temperature increased by above 30C. This is a consequence of the process of urbanization and industrialization, which began to take place in 2008, along with the process of changing the land cover status of the study area. Further studies are needed to better understand the thermal conductivity of surface materials and to plan how to reduce LST in such areas.

Estimating Cooling Energy Demand from Building Attributes and Environmental Parameters using 3D City Models

Abstract. Most of the global population have shifted to urbanization with advancements in technology. With this transition comes the responsibility of applying these technologies to promote sustainable development practice. Since energy demand is highest in the urbanized areas, it is important that proper assessment and management of energy resource is considered in policy-making and urban planning. This study investigated the estimation of cooling energy demand in Iloilo City Proper, Philippines using a 3D city model integrated with building attributes including building functions and year of construction, while also taking into consideration meteorological factors in the area. The proposed method used the application SimStadt2.0, following the German computation standard DINV18599. Building functions and year of construction were extracted from available building attributes data and satellite images respectively, using the free and open-source software QGIS. A CityGML Level of Detail 1 was generated from 3Dfier using building footprints and LiDAR point cloud data, along with the extracted building attributes of the 5,426 buildings. Meteorological data from INSEL 8 were also considered in the estimation of cooling energy demand in SimStadt2.0. Results showed a monthly energy demand of 12.33 kWh to 313,530.08 kWh in the study area. The estimated energy demand values were higher than the standard mean for different building functions in the country, but within the expected range of values for each season. Urban Heat Islands (UHIs), analyzed using Land Surface Temperature (LST) values, also have significant correlation to areas with higher cooling energy demands. However, inconsistencies can imply the need for further investigation.

Geospatial Analysis of Climate Change Impacts on Vegetation Dynamics in Owerri West, Imo State

Climate change is a global phenomenon with profound effects on the environment, economies, and societies. It refers to significant changes in global temperatures and weather patterns over time. This study explores the impact of climate change on vegetation in Owerri West, Imo State, Nigeria, using geospatial techniques over a 20-year period (2003–2023). By analyzing satellite imagery and Normalized Difference Vegetation Index (NDVI) values alongside key climatic variables such as precipitation and land surface temperature (LST), the research seeks to quantify the extent of vegetation changes due to climate variability. The study uses Landsat 8 satellite data to evaluate spatiotemporal trends in vegetation health. NDVI values in 2003 ranged from a high of 0.45, indicating healthy and dense vegetation, to a low of -0.04, representing barren areas. By 2013, NDVI values had drastically declined, with a maximum of 0.12 and a minimum of -0.33, despite an increase in precipitation from 3300 mm (2003) to 3900 mm (2013). This decline suggests that other factors, such as extreme weather events or urbanization, contributed to vegetation stress. In 2023, NDVI values showed partial recovery, with a maximum of 0.28 and a minimum of 0.05, although precipitation levels dropped to a range of 600 mm to 540 mm. The broader temperature range in 2023, with a maximum of 32°C and a minimum of 17°C, likely reduced heat stress, allowing for vegetation recovery, though still below the 2003 levels, The findings highlight the complex interplay between climatic variables and vegetation health, where precipitation and temperature changes significantly influence vegetation dynamics. However, the NDVI decline in 2013, despite high precipitation, suggests that anthropogenic factors like urbanization and land use changes also play a critical role. This research emphasizes the importance of integrating remote sensing and GIS for monitoring vegetation responses to climate change. The study calls for sustainable land management practices and climate adaptation strategies to enhance vegetation resilience in the region.

Thermal Footprint of the Urbanization Process: Analyzing the Heat Effects of the Urbanization Index (UI) on the Local Climate Zone (LCZ) and Land Surface Temperature (LST) over Two Decades in Seville

Urbanization is a multifaceted process characterized by changes in urban areas through various means, such as sprawl, ribbon development, or infill and compact growth. This phenomenon changes the pattern of the local climate zone (LCZ) and significantly affects the climate, vegetation dynamics, energy consumption, water resources, and public health. This study aims to discern the impacts of changes in urban growth on the LCZ and land surface temperature (LST) over a two-decade period. A comprehensive methodology that integrates statistical analysis, data visualization, machine learning, and advanced techniques, such as remote sensing technology and geospatial analysis systems, is employed. ENVI, GEE, and GIS tools are utilized to collect, process, and monitor satellite data and imagery of temporal and spatial variations in intensive or diffuse urbanization processes from 2003 to 2023 to analyze and simulate land use and land cover (LULC) changes, urbanization index (UI), LCZ patterns, and LST changes over the years and to make overlapping maps of changes to recognize the relation between LULC, LCZ, and LST. This study focuses on Seville’s urban area, which has experienced rapid urbanization and a significant increase in average temperature during the last few decades. The findings of this study will provide actionable recommendations into the interplay between urban growth and climate and highlight the pivotal role of urban growth in shaping resilience and vulnerable areas based on microclimate changes. Urban planners can leverage these insights to predict alternatives for the future development of urban areas and define practical climate mitigation strategies.