Mean Land Surface Temperature Research Articles

Spatio-temporal Variation Analysis of Thermal Climate in Response to Urban Cover Changes: A Case Study of Delhi Using Landsat Data

ABSTRACT Urbanization and changes in Land Use Land Cover (LULC) influence the formation of urban microclimate (UMC). The current study analysed the urbanization and microclimatic changes in the National Capital Territory (NCT) of Delhi utilizing the Landsat-derived LULC map, Land Surface Temperature (LST), and a variety of spectral indices viz., Heat Effect Contribution Index (HECI), Surface Urban Heat Island (SUHI), Thermal Index (TI) and Environment Criticality Index (ECI). The results indicate that Delhi has undergone a substantial increase in urbanization and concomitant urban warming (1999–2021). The mean LST had risen across all LULC classes, with vegetation exhibiting the most significant variance in mean LST. Largely, water bodies and vegetation lands converted to other LULC classes yielded the highest rise in LST. Notably, the contribution of the build-up to the heat effect rose by more than 40%. A rise in LST and its association with different LULC classes led to the widespread development and spatiotemporal shifting of SUHI and a subsequent decline in thermal comfort. This research uniquely quantifies LST changes associated with shifts in LULC categories and utilizes multiple heat indices to study the UMC of Delhi, offering crucial insights for future planning and policy formulation.

Pattern of cooling benefits from ecospaces during urbanization: A case study of the Yangtze River Economic Belt

Urban ecological spaces are effective thermoregulators under global warming. However, the cooling efficiency of urban ecological spaces during the urbanization has not been studied comprehensively. Here, we investigate the spatio-temporal dynamics of Urban Cold Island (UCI) intensity in 11 typical cities of the Yangtze River Economic Belt (YREB). We determined the impact of ecological landscape trends on these dynamics by using GlobalLand and MODIS 8 d mean land surface temperature (LST) data for three periods (2000, 2010, and 2020), and the landscape pattern index and diversity index. We found that in the past 20 years, the built-up area has increased by sixfold; 62.53 % and 37.47 % of YREB were warming or cooling, with 71.22 % of the daytime cooling and 93 % of the nighttime warming. The average UCI intensity of YREB has increased from 0.518 to 0.847 and is negatively correlated with LST with a decreasing slope. As the UCI intensity of green spaces increased, that of blue spaces decreased. Surface area and landscape pattern are the key determinants of UCI intensity in blue and green spaces, respectively, especially the landscape shape index (LSI). Therefore, maintaining ecological spaces, enriching the structural integrity of green spaces, and improving blue space connectivity can help cities at different development levels cope with heat stress during regional urbanization.

Characterizing Land Surface Temperature (LST) through Remote Sensing Data for Small-Scale Urban Development Projects in the Gulf Cooperation Council (GCC)

Characterizing Land Surface Temperature (LST) through Remote Sensing Data for Small-Scale Urban Development Projects in the Gulf Cooperation Council (GCC)

Examining thermal inequities: Land surface temperature, social vulnerability, and historical redlining in San Antonio, TX

Rising urban temperatures significantly impact human health, with their effects varying across socio-economic groups. With a focus on San Antonio, Texas, during its notably intense summer heat in 2023, this research explores the intricate relationship between urban heat and social vulnerability. Through the analysis of Land Surface Temperature (LST) data from Landsat 8 data, captured during three exceptionally hot days, a positive correlation between LST and the Social Vulnerability Index (SVI) was revealed. This relationship shows that areas with higher SVI, indicative of greater vulnerability, consistently experience higher LSTs. The study further investigated two distinct census tracts: one in a previously redlined area with a high SVI and another in a ‘A-best’ HOLC area with a low SVI. The comparative study between these two tracts revealed significant Normalized Difference Vegetation Index (NDVI) and subsequent thermal variances. It was found that the tract in the historically redlined area consistently experiences higher minimum and mean LST, indicating how past housing policies continue to impact present-day environmental and social conditions. Findings reveal the critical connection between urban heat and social vulnerability, contributing to a broader understanding of the interplay between historical and social factors in shaping thermal inequities in a historically redlined city.

A satellite view of the exceptionally warm summer of 2022 over Europe

Abstract. Summer heatwaves are becoming increasingly dangerous over Europe, and their close monitoring is essential for human activities. Typically, they are monitored using the 2 m temperature from meteorological weather stations or reanalysis datasets. In this study, the 2022 extremely warm summer over Europe is analysed using satellite land surface temperature (LST), specifically the LSA SAF (Land Surface Analysis Satellite Application Facility) all-sky LST product (available from 2004 onwards). Since climate applications of LST are still poorly explored, heatwave diagnostics derived from satellite observations are compared with those derived using ERA5/ERA5-Land reanalysis data. Results highlight the exceptionality of 2022 in different metrics such as the mean LST anomaly, area under extreme heat conditions, number of hot days and heatwave magnitude index. In all metrics, 2022 ranked first when compared with the remaining years. Compared to 2018 (next in all rankings), 2022 exceeded its LST anomaly by 0.7 °C and each pixel had on average 7 more hot days. Satellite LST complements reanalysis diagnostics, as higher LST anomalies occur over areas under severe drought, indicating a higher control and amplification of the heatwave by surface processes and vegetation stress. These cross-cutting diagnostics increase the confidence across satellite data records and reanalyses, fostering their usage in climate applications.

Analytical study of land surface temperature for evaluation of UHI and UHS in the city of Chandigarh India

The Land Surface Temperature (LST) plays a crucial role in the ecological well-being of cities worldwide, intricately tied to the presence of vegetation. This investigation aims to assess the relationship between seasonal LST fluctuations and the Normalized Difference Vegetation Index (NDVI), as well as the correlation between LST and the Normalized Difference Built-up Index (NDBI), utilizing Pearson's correlation coefficient technique with extensive Landsat-8 data spanning 2016 to 2020. Additionally, the study evaluates the ecological and thermal qualities of the city using the Urban Thermal Field Variance Index (UTFVI). The ecological evaluation based on UTFVI for 2020 indicates that 52.03% of the area falls under the excellent category and 31.82% under the worst category. Notably, there is a significant increase in summer mean LST from 32.42 °C in 2016 to 38.27 °C in 2020. Identified Urban Heat Islands (UHIs), indicative of the warmest areas within city limits due to human activities, show an increasing trend in the summer UHI temperature from 36.92 °C to 38.06 °C between 2016 and 2020, respectively. The study reveals a robust inverse correlation between LST and NDVI, as well as a direct relationship between LST and NDBI across the entire city. The differentiation between UHI and non-UHI areas is achieved using the LST range. Furthermore, LST maps pinpoint UHI hotspots in Chandigarh, highlighting areas unsuitable for human habitation due to extreme heat. Validation of LST values employs Moderate Resolution Imaging Spectroradiometer (MODIS) data as a baseline, where MOD11A1 data's thermal infrared band offers a spatial resolution of 1 km, contrasting with Landsat 8 (Band-10) 100 m resolution. Remarkably, a highly positive correlation is observed between approximated LST values derived from Landsat 8 and MOD11A1 data, without necessitating upscaling or downscaling.

GIS-BASED REGRESSION ANALYSIS OF AIR QUALITY AND LAND SURFACE TEMPERATURE IN METRO MANILA AIRSHEDS

Abstract. This study examines the spatial relationship of land surface temperature (LST) derived from MODIS, elevation, and air quality parameters (CO, NO2, and SO2) derived from Sentinel 5P in the Metro Manila airsheds from January 2019 to March 2023. Using Ordinary Least Squares (OLS) and Generalized Linear Regression (GLR), mean LST and heat index from the Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA) ground stations exhibit a strong positive correlation, allowing the use of LST for further analysis. Across different combinations between LST, elevation, and air quality parameters, a weak to low negative correlation was seen between DEM to LST, CO, and NO2. In addition, weak to low positive correlation was seen between LST to CO and NO2. Almost no correlation was found between DEM and SO2, and LST and SO2. These results may be unreliable due to overfitting, non-stationarity, bias, or misspecification as implied by their statistical parameters. To enhance the reliability, it is recommended to investigate additional air quality parameters such as Normalized Difference Built-Up Index (NDBI) as high LST, CO, and NO2 have shown clustering in urban areas of Metro Manila. Moreover, it suggests exploring other regression modeling and methodologies, such as training and test sets, to identify the best-fit model. In conclusion, this study provides an exploratory foundation for future research and comparative assessment on using different methods for modeling these variables. This comprehensive approach enhances understanding of the complex interplay between temperature, elevation, and air quality, aiding the development of informed urban climate adaptation strategies.

Land Surface Temperature and Thermal Comfort in the Cities of Punjab, India: Assessment Based on Remote Sensing Data

Objective: The present study aims to investigate thermal comfort in two cities in Punjab, India based on land surface temperature (LST), urban hot spots (UHS) and urban thermal field variance index (UTFVI). Method: Landsat 8 OLI/TIRS data are used to derive land surface temperature (LST), normalized difference vegetation index (NDVI) and enhanced built-up and bareness index (EBBI) for the year 2019. UTFVI reflects urban thermal conditions and demarcates comfort and discomfort zones in the cities. Findings: The results revealed that the mean LST (μ) of Ludhiana and Amritsar cities is 32.80 °C and 30.70 °C, respectively. LST shows a strong negative correlation with NDVI (-0.710 for Amritsar and -0.754 for Ludhiana) and a positive correlation with EBBI (0.531 for Amritsar and 0.541 for Ludhiana). About 57 and 52 per cent of geographical areas in Ludhiana and Amritsar city respectively are experiencing bad to worst ecological conditions. Novelty: (i) The study derived LST-based thermal comfort for the summer month in Amritsar and Ludhiana cities of Punjab which provide important information to urban planners and policymakers to design sustainable urban development policies to mitigate heat-related issues. (ii) Such information can be used to take steps to improve the situation in smart cities like Amritsar and Ludhiana. Keywords: Land surface temperature (LST), urban thermal field variance index (UTFVI), NDVI, Landsat 8, Punjab

An Analysis on the Impact of Normalized Difference Vegetation Index (NDVI) Changes on the Land Surface Temperature (LST) using Satellite Imagery in the West Bank, Palestine

Abstract
 This study investigated the impact of the Normalized Difference Vegetation Index (NDVI) on the Land Surface Temperature (LST) in the West Bank, Palestine, using multitemporal satellite imagery. Landsat 5 TM and Landsat 8 OLI data were used in the study. The Landsat 5 TM data was acquired on March 29, 2001, while the Landsat 8 OLI data was acquired on April 05, 2021. The NDVI and the LST for the two images were then calculated from the two images using ArcGIS 10.8 software. Results of the study showed that the NDVI of the 2021 image was much higher than that of the 2001 image. The mean NDVI of the 2001 image was 0.25, while that of the 2021 image was 0.32. As for the LST, the mean LST of the 2001 image was 29.85 °C, while that of the 2021 image was 26.98 °C. Around 74.7% of the West Bank lands recorded an inverse relationship between the NDVI change and the LST change between 2001 and 2021. Around 25.3% of the West Bank lands recorded a positive relationship. In other words, the study depicted the impact of the NDVI on the LST in the West Bank. The study recommends that policymakers need to increase the green areas in the West Bank to reduce the LST as this is seen to affect the climate conditions in the area.
 Keywords: LST, NDVI, West Bank, Palestine, Landsat data.

Impact of environmental factors on Biomphalaria pfeifferi vector capacity leading to human infection by Schistosoma mansoni in two regions of western Côte d'Ivoire

AbstractBackgroundIntestinal schistosomiasis remains a worrying health problem, particularly in western Côte d'Ivoire, despite control efforts. It is therefore necessary to understand all the factors involved in the development of the disease, including biotic and abiotic factors. The aim of this study was to examine the factors that could support the maintenance of the intermediate host and its vectorial capacity in western Côte d'Ivoire.MethodsData on river physicochemical, microbiological, and climatic parameters, the presence or absence of snails with Schistosoma mansoni, and human infections were collected between January 2020 and February 2021. Spearman rank correlation tests, Mann–Whitney, analysis of variance (ANOVA), and an appropriate model selection procedure were used to analyze the data.ResultsThe overall prevalence of infected snails was 56.05%, with infection reaching 100% in some collection sites and localities. Of 26 sites examined, 25 contained thermophilic coliforms and 22 contained Escherichia coli. Biomphalaria pfeifferi was observed in environments with lower land surface temperature (LST) and higher relative air humidity (RAH), and B. pfeifferi infection predominated in more acidic environments. Thermal coliforms and E. coli preferred higher pH levels. Lower maximum LST (LST_Max) and higher RAH and minimum LST (LST_Min) were favorable to E. coli, and lower LST_Max favored coliforms. The presence of B. pfeifferi was positively influenced by water temperature (T °C), LST_Min, RAH, and precipitation (Pp) (P < 0.05) and negatively influenced by pH, total dissolved solids (TDS), electrical conductivity (EC), LST_Max, and mean land surface temperature (LST). The parameters pH, TDS, EC, LST_Min, LST, and Pp had a positive impact on snail infection, while LST_Max had a negative impact on infection. Only pH had a positive effect on coliform and E. coli abundance. Of the 701 people examined for human schistosomiasis, 73.13% were positive for the point-of-care circulating cathodic antigen (POC-CCA) test and 12.01% for the Kato–Katz (KK) test. A positive correlation was established between human infections and the abundance of Biomphalaria (r2 = 0.879, P = 0.04959).ConclusionsThe results obtained reflect the environmental conditions that are conducive to the maintenance of S. mansoni infection in this part of the country. To combat this infection as effectively as possible, it will be necessary not only to redouble efforts but also to prioritize control according to the level of endemicity at the village level.Graphical

Spatio-temporal urban land surface temperature variations and heat stress vulnerability index in Thiruvananthapuram city of Kerala, India

ABSTRACT Land use/Land cover (LU/LC) modifications leads to rise in surface temperature is a global concern. This study investigates the effects of LU/LC changes in Land Surface Temperature (LST) variations and effects of Urban Heat Island (UHI) in Thiruvananthapuram city. Furthermore, the study proposed a novel heat stress vulnerability index (HSVI) using Analytical Hierarchy Process (AHP) method. Results shows that transformation of all land uses is to built-up areas, which shows an increase of +118.46% where as others such as vegetation (-24.27%), open area (-64.315) and waterbody (-43.88%) shows drastic decrease over 34 years. The relationship between Normalized Difference Vegetation Index and LST shows a significant change with R2 values between 0.223 in 1988, 0.382 in 2000 and 0.253 in 2022. Moreover, this also contributed to the increase of the mean LST of 26.96 ºC in 1988 to 28.52 ºC in 2022. HSVI of the study indicates that 30.83% of the study area is categorized as very low heat stress vulnerable area followed by 37.22% area in low heat stress vulnerability, 17.88% in moderate heat stress vulnerability, 8.87% in high heat stress vulnerability and 5.19% of the area falling in very high heat stress Vulnerability class.

LCZ framework and landscape metrics: Exploration of urban and peri-urban thermal environment emphasizing 2/3D characteristics

Rapid urbanization has altered the environment and climate, necessitating a thorough grasp of urban thermal dynamics for sustainable development. The lack of detailed urban information poses a considerable challenge. This study employs the Local Climate Zone (LCZ) framework to delve into the intricate relationship between land surface temperature (LST) zones and both 2/3D urban characteristics. The LCZ map for Bojnourd city, Iran, was prepared, consisting of eleven classes categorized into five built types and six land cover types. The mean LST in the summer of 2021 was derived from Landsat8 using the split-window algorithm (SWA) and divided into seven thermal zones. The research reveals distinctive spatial patterns of LCZs across various Land LST zones. Notably, LCZ2 (Compact mid-rise) and LCZ 3 (Compact low-rise), despite differing heights, share the same thermal zone (Zone3), suggesting influences beyond height on thermal zoning. In contrast, LCZ5 (Open midrise), situated in a cooler thermal zone (Zone2), features more vegetation and taller trees, exhibiting a lower LST range compared to LCZ2 and LCZ3. Class-level metrics (AREA_MN and LPI) offer a similar description of LCZ spatial patterns, while landscape-level metrics like SHDI and IJI provide a more insightful depiction of spatial patterns. The study found that the most appropriate spatial pattern for improving urban thermal conditions involves considering a set of factors such as vegetation presence, land cover proximity, height, and density. This ensures sustainable and green urban development that is in tune with climate change and promotes balanced and harmonious thermal patterns in the urban environment.

Urban engineering insights: Spatiotemporal analysis of land surface temperature and land use in urban landscape

In the field of urban environment engineering, understanding the relationship between land surface temperature (LST) and land use land cover (LULC) is essential in rapidly growing and climatically unstable urban landscapes such as Chengdu. It helps alleviate the magnitude and intensity of Urban Heat Islands (UHIs). Toward this aim, summer and winter Landsat images were acquired in four years from 1992 to 2021 and were used to extract LULC classes, LST and three indices of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Modified Normalized Difference Water Index (MNDWI) and analyze their spatiotemporal associations. Results showed that built-up areas expanded approximately six times (820.82 Km2, 584.96%) from 1992 to 2021. Meanwhile, their mean LST increased in both seasons, by 9.94 °C in summer and by 0.95 °C in winter. The LST-NDBI correlation was significant and positive in both the summer and winter of the studied years (0.437< r <0.874, p=0.00) while a very high variability was observed in the LST-NDVI (-0.835< r <0.255, p=0.00) and LST-MNDWI (-0.632< r <0.628, p=0.00) correlation coefficients. According to the results, NDBI can be used as a good intra- and inter-annual predictor of LST in Chengdu, especially in the context of its fast-paced physical expansion and increasing intensity of UHI.

Assessing the influence of elevation on satellite derived normalized difference vegetation index and land surface temperature in Rajasthan

Land surface temperature (LST) and its interaction with normalized difference vegetation index (NDVI) is crucial for better understanding of environmental changes in current scenario. However, very few or scanty research on the interrelationship between LST, NDVI and topographic elements has been done in India. Therefore, the purpose of conducting this study was to examine, how LST and NDVI change as a function of elevation in Rajasthan. In present study, MODIS derived NDVI and LST and digital elevation model (DEM) from shuttle radar topography mission (SRTM) have been used. Results revealed that the LST and NDVI both were significantly influenced by elevation. Elevation, NDVI and LST varied from -6 to 1698 m, -0.09 to 0.65 and 24 to 45°C throughout the study region. In contrast to LST, which has a decreasing gradient from western to eastern portions, the spatial variability of NDVI has decreasing gradients from southern and eastern to western regions. The highest mean LST value (39.76 ± 0.2.9 0C) was obtained at an elevation range of -6 to 168 m, whereas NDVI value (0.38 ± 0.06) at elevation ranges of 589 – 1698 m. The analysis of the correlations between LST, NDVI and elevation indicated that the elevation has strong positive correlation with NDVI (r2 = 0.26) and negative correlation with LST (r2 = 0.28). Findings from this kind of research can be utilized as a platform for environmental and land use planning for sustainable ecosystem management.

Urban Heat Island effect and Urban Thermal Field Variance Index phenomenon at the heart of ‘Chicken's Neck Corridor’ of India

Siliguri corridor, the ‘Chicken Neck Corridor of India’, is one of the largest Municipal areas in West Bengal and is of great geo-strategic importance as it lies at the crossroads between India, Nepal, Bhutan and Bangladesh connecting the north-eastern part of India with mainland India. This study uses the LANDSAT 7 ETM+ and LANDSAT 9 TIR OLI imageries from USGS Earth Explorer to determine Land Surface Temperature (LST) changes, Normalized Differential Vegetation Index (NDVI) changes; Normalized Differential Built-up Index (NDBI) changes; Land use and Land cover (LULC) changes; Urban Heat Island (UHI) effect; Urban Hot Spots (UHS) and Urban Thermal Field Variance Index (UTFVI) phenomenon. With booming trade and increasing urbanisation, Siliguri city (Municipal Corporation) has seen rise in the mean LST by 2.92 °C resulting in increased UHI intensity and UTFVI phenomena between 2000 and 2022. The average LST in UHI and UHS has risen by approx. 3 °C causing more areas to come under bad, worse and worst thermal comfort level categories of the ecological evaluation index. An attempt is made to determine the reason for such changes in this micro climate through multi-dimensional perspectives and to exhibit the land use transformations that took place between the aforesaid time periods.

Land surface temperature responses to land use dynamics in urban areas of Doha, Qatar

Rapid urbanization primarily converts naturally vegetated areas and pervious surfaces into impervious built-up areas, significantly transforming microclimates and ecological dynamics. The impervious surfaces, marked by their higher thermal conductivity, disrupt surface energy balance and accumulate solar heat, subsequently elevating the land surface temperatures (LSTs). This study investigates the impact of land use and land cover changes on summer and winter LSTs in Doha and Al Dayeen municipalities of Qatar, spanning from the years 2000 to 2023, using remote sensing techniques and Geographic Information Systems (GIS). The analysis of land use and land cover changes reveals a remarkable 343.16 % increase in the built-up area from 2000 to 2023, at the expense of previously existing desert lands and water bodies. While Qatar's desert land has high land surface temperature, substituting such areas with built-up exhibits a notable rise in temperatures. Additionally, land reclamation also results in elevated LSTs. The LST data derived from remote sensing sources demonstrates an upward trend for summer and a contrasting trend for winter. Specifically, the mean summer LST increases by 7.64 °C (0.34 °C annually), and the mean winter LST decreases by 4.87 °C (0.22 °C annually). Notably, built-up areas and desert lands consistently recorded the highest mean LST in both seasons in all observed years. A strong correlation was observed between summer and winter LST with land use and land cover patterns using Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Built-up index (NDBI) and Normalized Difference Barrenness Index (NDBal). The results imply the negative influence of climate change and the urgent need for urban planning mitigation measures to counteract the adverse effects of increasing LSTs, particularly in summer months, to ensure the human well-being and resilience of the urban environments.

Comparative study and effects of urban green scape on the land surface temperature of a large metropolis and green city

Previous studies have provided valuable insights into the impact of green space (GS) on land surface temperature (LST). However, there is a need for in-depth comparative research on changing landscape patterns in cities and their effects on the urban thermal environment. This study investigates the spatial arrangement of GS and the influence of impervious surfaces on LST in urban areas, examining their cooling and warming effects in the urban landscapes of Beijing and Islamabad. The study aims to assess the impact of the spatial arrangement of GS on LST using a moving window of 1 km2 to analyze the overall effect of landscape patterns on the urban environment. Using Gaofen (GF–2) and Landsat–8 satellite data, we examined the biophysical surface properties of core urban areas. The results indicate a significant difference in the mean LST of 5.44 °C and 3.31 °C between impervious surfaces and GS in Beijing and Islamabad, respectively. The barren land and GS in Islamabad experience a higher LST of 3.39 °C compared to Beijing, which accounts for 1.39 °C. In Beijing, configuration metrics show no significant effect on urban LST, while edge density (ED) exhibits a slightly negative trend. In contrast, in the city of Islamabad, the landscape shape index (LSI), patch density (PD), and number of patches (NP) metrics have a significant influence on LST. The cooling effect of GS patches (0.1–0.5 ha) is more pronounced, while that of GS patches of 15–20 ha shows no significant effect on LST. The temperature difference (TD) of 5.01 °C was observed from the edge of GS in Beijing and 3.3 °C in Islamabad. Considering Islamabad's lush green scape compared to Beijing, this study suggests that Islamabad may experience an increase in LST in the future due to urbanization. This study's findings may assist urban policy-makers in designing sustainable green city layouts that effectively address future planning considerations.

Modeling the relationship between urban tree canopy, landscape heterogeneity, and land surface temperature: A machine learning approach

Cities across the United States and around the globe are embracing urban greening as a strategy for mitigating the effects of rising temperatures on human health and quality-of-life. Better understanding how the spatial configuration of tree canopy influences land surface temperature should help to increase the positive impacts of urban greening. This study applies a machine learning approach for modeling the relationship between urban tree canopy, landscape heterogeneity, and land surface temperature (LST) using data from nine cities located in nine different climate zones of the United States. We collected summer LST data from the U.S. Geological Survey (USGS) Analysis Ready Data series and processed them to derive mean, minimum, and maximum LST in degrees Fahrenheit for each Census block group within the cities considered. We also calculated the percentage of each block group comprised by the land cover designations in the 2016 or 2019 National Land Cover Database (NLCD) maintained by the USGS, depending on the vintage of the available LST data. High resolution tree canopy data were purchased for all the study cities and the spatial configuration of tree canopy was measured at the block group level using established landscape metrics. Landscape metrics of the waterbodies were also calculated to incorporate the cooling effects of waterbodies. We used a Generalized Boosted Regression Model (GBM) algorithm to predict LST from the collected data. Our results show that tree canopy exerts a consistent and significant influence on predicted land surface temperatures across all study cities, but that the configuration of tree canopy and water patches matters more in some locations than in others. The findings underscore the importance of considering the local climate and existing landscape features when planning for urban greening.

Improving monthly mean land surface temperature estimation by merging four products using the generalized three-cornered hat method and maximum likelihood estimation

Land surface temperature (LST) is a critical parameter associated land surface energy and water balance at the land–atmosphere interface. Monthly mean LST (MMLST) is used not only as an input parameter for land surface models, but also as an indicator for general climate trend studies. Four MMLST products (i.e., NESDC, TPDC, ZENODO, and ERA5) were generated by different research teams and institutes using different algorithms. In this study, a new method was presented to improve MMLST estimation by merging these four products using the generalized three-cornered hat (TCH) method and maximum likelihood estimation (MLE). The generalized TCH method was used to simultaneously calculate the relative uncertainties of these four MMLST products during the period 2003–2019. In general, the smallest uncertainties are obtained for the TPDC MMLST product in most areas of the globe, whereas the largest uncertainties are achieved for the ERA5 MMLST product, especially in high-latitude regions. The MLE method was used to merge these four MMLST products and to generate a new MMLST product in consideration of the uncertainty of each individual MMLST product. In situ measurements at 25 stations from the AmeriFlux and SURFRAD networks during the period 2003–2019 were used to evaluate the accuracies of the individual and merged MMLST products. The largest root mean squared error (RMSE) of approximately 2.4 K is obtained for the TPDC MMLST product, whereas the smallest RMSE of around 1.6 K is reached for the merged MMLST product. The NESDC, ZENODO, and ERA5 MMLST products have similar RMSE of approximately 2 K. The results indicate that the merged MMLST product outperforms each individual MMLST product. The merged MMLST product shows promising prospects for climate- and global-scale studies and applications.

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

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