Land Surface Temperature Distribution Research Articles

Spatial distribution and influencing factors on urban land surface temperature of twelve megacities in China from 2000 to 2017

China witnessed a rapid urbanization progress in the past 20 years. Urbanization can greatly influence urban environment, especially thermal environment. Causes for urban heat island (UHI) formation vary in different climatic regions. Understanding characteristics of spatial distribution of urban land surface temperature (ULST) and influencing factors of UHI formation is important for sustainable urban development. In this study, twelve megalopolises in China, each with a population over 5 million, were selected as research sites. Built-up areas and ULST of these megalopolises were extracted from remote sensing data of earth observation satellites of Landsat program for the three periods of 2000–2017. Spatial variation characteristics of ULST were analyzed. Influences of soil moisture, surface water availability, surface albedo, vegetation, energy consumption, gross domestic product, latitudes, precipitation, topography, and distance to the downtown on ULST were investigated through multivariate regression analysis. Results show that: all the cities expanded continuously from 2000 to 2017, but expansion rates varied significantly among the megalopolises. Urban expansion has significant effects on the spatial distribution of ULST. ULST changes more quickly when the expanded area and expansion rate are higher. Simultaneously, spatial distribution of ULST was found to relate to the shape of an urban boundary. Circularly expanding cities showed the most concentrated distribution of high-temperature regions. Correlation analysis revealed that surface water use, evapotranspiration, electric energy consumption, surface albedo, and vegetation activity were the primary influencing factors on ULST, while GDP, latitude, distance, and precipitation had no significant effects on ULST. Among the primary influencing factors, surface water was the main controlling factor on ULST in cities with more surface water distribution. Building area played a more important role in ULST in cities with less surface water distribution. In addition, vegetation area played a relatively more important role in ULST in semi-humid cities than humid cities.

Spatio-temporal variability in remotely sensed LST and its impacts by FVC in the Greater Khingan Mountains

The present study explored both the temporal variation and spatial distribution of fractional vegetation cover (FVC) and land surface temperature (LST) in the Greater Khingan Mountains, a location distinguished by four types of surface cover formations and strong gradients in meteorological conditions. Furthermore, we assessed the relationships between FVC and LST in different time-space dimensions. We measured the spatio-temporal variability in LST through a harmonic analysis of time series (HANTS) for 8 days of LST time series product data from the Moderate Resolution Imaging Spectroradiometer (MODIS). Furthermore, the FVC was extracted through linear spectral mixture analysis (LSMA). The results show that the time series image data reconstructed via HANTS are effective for quantifying the morphological dynamics of the thermal environment and enabling sound calculations of environmental variables, such as vegetation abundance. We found significant differences in within-year and interannual variation among different eco-geographical regions. The within-year maximum value of vegetation coverage was observed in July, whereas the surface temperature was highest in June. This finding suggests that the decrease in warming can be mostly attributed to the increase in evapotranspiration associated with increased vegetation activity. In addition, a strong negative correlation was found between the FVC and LST throughout the study area only from April to September, whereas a triangular relationship was found in other months. This study investigated the time series variations in different eco-geographical regions for 2000 – 2015 and revealed that the most obvious LST and FVC variations occurred at the region II and junction between regions III and IV. An analysis of the average FVC and LST from April to September indicated that 86.9% of the entire study area showed a negative correlation, such that when the FVC increased by 10%, the LST showed a maximum average decrease of 2.76°C. We conclude that the increase in vegetation activity is the main cause of the reduction in the LST and that surface activities and latitude zonality are the main driving factors of the LST differentiation in the Greater Khingan Mountains. These findings will serve as a foundation for future studies seeking to better understand climate change processes and to estimate ecosystem responses to changing climatic conditions.

Small vegetated patches greatly reduce urban surface temperature during a summer heatwave in Adelaide, Australia

As the global climate warms, cities worldwide face more frequent and extreme heatwaves. These events can affect human health and decrease liveability. While the mitigating effects of vegetation on land surface temperature (LST) are well characterised at large spatial scales and during typical weather conditions, the cooling benefits that urban greening can provide at local scales, particularly during summer heatwaves, are poorly quantified.We quantified LST variation across an urban landscape to assess relationships with land use and vegetation cover from land unit (average ~ 460 m2) to suburb scale (~1.66 km2) under extreme summer heat conditions. High resolution (2 m), day- and night-time LST was measured at the peak of a heatwave event, after three consecutive days with air temperature exceeding 40 ˚C, by flying an aircraft fitted with a thermal imager over 90 suburbs in Adelaide, Australia. Daytime, night-time, and diurnal range LST distributions were related to measures of land use, vegetation cover (i.e., tree, grass) and urban morphology at both the suburb and land unit scale.At the suburb scale, vegetation cover did not affect LST. However, at the land unit scale, tree canopy cover, and to a lesser extent grass cover, decreased local LST by up to 6 ˚C during the day, but not at night. Overall night-time LST was poorly predicted by the land use and land cover predictors. Moving inland from the coast, small vegetation patches, mostly contained in yards and gardens, was associated with the greatest localised LST reductions in the hotter inland suburbs. LST within land units was further decreased during the day when vegetation was present within 30 m buffers around each land unit, suggesting a moderate landscape cooling effect on LST.Our results suggest that even small urban vegetation patches can be managed to provide substantial heat mitigation during increasingly frequent summer heatwaves, particularly around the residential environments where people live.

Seasonal Variations of Daytime Land Surface Temperature and Their Underlying Drivers over Wuhan, China

Rapid urbanization greatly alters land surface vegetation cover and heat distribution, leading to the development of the urban heat island (UHI) effect and seriously affecting the healthy development of cities and the comfort of living. As an indicator of urban health and livability, monitoring the distribution of land surface temperature (LST) and discovering its main impacting factors are receiving increasing attention in the effort to develop cities more sustainably. In this study, we analyzed the spatial distribution patterns of LST of the city of Wuhan, China, from 2013 to 2019. We detected hot and cold poles in four seasons through clustering and outlier analysis (based on Anselin local Moran’s I) of LST. Furthermore, we introduced the geographical detector model to quantify the impact of six physical and socio-economic factors, including the digital elevation model (DEM), index-based built-up index (IBI), modified normalized difference water index (MNDWI), normalized difference vegetation index (NDVI), population, and Gross Domestic Product (GDP) on the LST distribution of Wuhan. Finally, to identify the influence of land cover on temperature, the LST of croplands, woodlands, grasslands, and built-up areas was analyzed. The results showed that low temperatures are mainly distributed over water and woodland areas, followed by grasslands; high temperatures are mainly concentrated over built-up areas. The maximum temperature difference between land covers occurs in spring and summer, while this difference can be ignored in winter. MNDWI, IBI, and NDVI are the key driving factors of the thermal values change in Wuhan, especially of their interaction. We found that the temperature of water area and urban green space (woodlands and grasslands) tends to be 5.4 °C and 2.6 °C lower than that of built-up areas. Our research results can contribute to the urban planning and urban greening of Wuhan and promote the healthy and sustainable development of the city.

Identifikasi Pertumbuhan Urban Heat Island secara Spasial-Temporal di Kota Palangka Raya Menggunakan Penginderaan Jauh dan Sistem Informasi Geografis

Developments in the city of Palangka Raya y giving different temperature between urban and sub-urban. Phenomenon that cities have warmer temperatures than sub-urban and rural areas called Urban Heat Island (UHI). This study aims to find out about the development of the UHI phenomenon in the city of Palangka Raya from 2000 to 2018 using remote sensing and geographical system. Based on the analysis of the TIR band landsat 7, in Kota Palangka Raya since 2000 has been UHI phenomenon, where high Land Surface Temperature (LST) was found dominantly in urban areas compared to sub-urban areas . In 2018, as Palangka Raya city had developed, based on the result of TIR band Landsat 8, the distribution of high LST not only found in the urban area, but in the sub-urban zone, especially at Menteng Urban Village and Panarung Urban Village. The development of UHI in Palangka Raya city over eighteen years (2000-2018) show in the sub-urban area experienced changes of UHIindex’s area more dynamic than the urban area. Urban development causing to conversion of vegetated land into impervious land, which greatly affects the energy balance. The increase in impervious areas causes more solar radiation that reaches the surfaces of the earth more absorbing and it is converted into sensible thermal energy which increases the surface temperature.

Cobertura vegetal y la distribución de islas de calor/oasis urbanos en Hermosillo, Sonora

The case of an arid Northwest city of Mexico is studied with the general objective of assessing the influence of the percentage of vegetation cover (VC) in Land Surface Temperature (LST) and mapping its spatial distribution, through a geographic information system using remote sensing data. Results showed: 1) on average, 12% (min. 0 to max. 59%) of a city block is covered with vegetation, 38% of the blocks had % VC ≤ 10; 2) the LST regression model estimated temperatures range from 37 to 45°C, the main explanatory variable was % VC, increasing % VC in 10 is associated with cooling effect of 1.1 °C. The spatial heterogeneity in the distribution of LST can be interpreted as the human effect modifying the climate on a small scale; this creates internal diurnal oasis.

Exploring the Relationship between LST, LULC and NDVI in Saïss Plain using Geospatial Techniques

Land use/land cover (LULC) change has been confirmed that have a significant impact on climate through various pathways that modulate land surface temperature (LST) and precipitation. However, there are no studies illustrated this link in the Saïss plain using remote sensing data. Thus, the aim of this study is to monitor the LST relationship between LULC and vegetation index change in the Saïss plain using GIS and Remote Sensing Data. We used 18 Landsat images to study the annual and interannual variation of LST with LULC (1988, 1999, 2009 and 2019). To highlight the effect of biomass on LST distribution, the Normalized Difference Vegetation Index (NDVI) was calculated, which is a very good indicator of biomass. The mapping results showed an increase in the arboriculture and urbanized areas to detriment of arable lands and rangelands. Based on statistical analyzes, the LST varies during the phases of plant growth in all seasons and that it is diversified due to the positional influence of LULC type. The variation of land surface temperature with NDVI shows a negative correlation. This explains the increase in the surface temperature in rangelands and arable land while it decreases in irrigated crops and arboriculture.

Spatiotemporal Patterns of Land Surface Temperature Change in the Tibetan Plateau Based on MODIS/Terra Daily Product From 2000 to 2018

Under the background of climate change, the Tibetan Plateau presents high spatial and temporal variability in land surface temperature (LST). To understand the spatiotemporal patterns of LST change, this article conducted a spatiotemporal analysis using the Mann-Kendall trend analysis method with time series of mean annual surface temperature extracted from the moderate resolution imaging spectroradiometer/Terra daily LST product from 2000 to 2018. The analysis indicated that both daytime and nighttime MASTs show an obvious warming trend with the average rates of 0.028 K/year and 0.069 K/year, and the nighttime variation has larger spatial coverage. Areas ranging from 4500 to 5000 m exhibited the strongest warming effect. The geodetector method was applied to detect the impacts from seven factors, including elevation, land cover type, latitude, normalized difference vegetation index (NDVI), precipitation, air temperature, and solar radiation on the spatial distribution of LST. The controlling effects of these factors were generally stronger in the nighttime than those in the daytime, and elevation was the most important factor with the contribution scores of 27.12% and 62.98% in the daytime and nighttime, respectively. In addition, the analysis revealed that the temporal changes of LST were mainly caused by surface properties (vegetation, snow cover, and water surface area) changes, radiant flux changes induced by cloud amount changes, and climate warming. In general, this article provides important insights into the spatiotemporal dynamics of LST in the TP since 2000 and helps to reveal the impact of climate change on ecoenvironmental conservation.

Simulation of future land surface temperature distribution and evaluating surface urban heat island based on impervious surface area

This study aims to simulate the future form of Land Surface Temperature (LST) distribution and Surface Urban Heat Island (SUHI) evaluation based on the impervious surface area. For this purpose, Landsat-5 and Landsat-8 satellite data acquired in 2006, 2011 and 2016, respectively, were utilized, and Zonguldak, a province of Turkey, was selected as a test site. In the study, LST image for 2021 was projected using spectral indices, namely Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI). Actually, the correlation analysis between four spectral indices (NDVI, SAVI, MNDWI and NDBI) and LST was conducted. However, NDBI and NDVI were determined as highly correlated indices with LST having correlation coefficient 0.89 and −0.90, respectively, thus MNDWI and SAVI were not considered for the further steps. A Multiple Linear Regression (MLR) analysis was carried out between these two indices (NDBI and NDVI) and LST to extract a mathematical model for LST retrieval. In order to obtain the future form of LST image (2021), these indices were simulated for 2021 using Markov Chain method and then regression model was applied. Before generating LST image for 2021, LST for 2016 image was simulated and compared with the existing 2016 LST image using Kappa index of agreement technique to evaluate the performance. The Kappa value was obtained as 0.73 which is reasonable for future simulation. SUHI evaluation was conducted based on the observed (2016) and simulated (2021) impervious area and normalized LST images. The obtained results showed that the distribution of SUHI effect was highly related to impervious area and if the urbanization rate in the study area continues as it is, this expansion will cause to a dramatic increase in SUHI distribution unless any proper urban planning model is considered by decision makers.

Parametric Design of a Microwave Radiometer for Land Surface Temperature Retrieval from Moon-Based Earth Observation Platform

Increasing attention is being paid to the monitoring of global change, and remote sensing is an important means for acquiring global observation data. Due to the limitations of the orbital altitude, technological level, observation platform stability and design life of artificial satellites, spaceborne Earth observation platforms cannot quickly obtain global data. The Moon-based Earth observation (MEO) platform has unique advantages, including a wide observation range, short revisit period, large viewing angle and spatial resolution; thus, it provides a new observation method for quickly obtaining global Earth observation data. At present, the MEO platform has not yet entered the actual development stage, and the relevant parameters of the microwave sensors have not been determined. In this work, to explore whether a microwave radiometer is suitable for the MEO platform, the land surface temperature (LST) distribution at different times is estimated and the design parameters of the Moon-based microwave radiometer (MBMR) are analyzed based on the LST retrieval. Results show that the antenna aperture size of a Moon-based microwave radiometer is suitable for 120 m, and the bands include 18.7, 23.8, 36.5 and 89.0 GHz, each with horizontal and vertical polarization. Moreover, the optimal value of other parameters, such as the half-power beam width, spatial resolution, integration time of the radiometer system, temperature sensitivity, scan angle and antenna pattern simulations are also determined.

Quantifying the influences of land surface parameters on LST variations based on GeoDetector model in Syr Darya Basin, Central Asia

Spatiotemporal variability in the land surface energy flux is largely and comprehensively affected by many factors, including land surface temperature (LST), land coverage, soil characteristics, terrain conditions, etc. In contrast with other climatic zones, arid and semiarid lands have fragile ecological environments that are more sensitive to land surface energy flux changes. In this study, we used MODerate Resolution Imaging Spectroradiometer (MODIS) LST products (2001–2015) for air temperature comparisons. Then, we investigated the spatiotemporal variation in LSTs in the Syr Darya Basin (SDB) during 2001–2015. More specifically, a new statistical model known as GeoDetector was adopted to analyze the driving factors controlling the spatiotemporal variation in LSTs. The result shows that the MODIS LST can provide a good estimation of air temperature, especially at night. The LST change rate can be considered as an important indicator of climate change (rapid warming at high altitudes) and human activities (increased water consumption of crop). Based on the GeoDetector model, we determined that the elevation explained more of the LST distribution (84–90%) and spatiotemporal variation (22–26%) than any other land surface parameters. The combination of albedo and the other explanatory variables can significantly increase the explanatory power of each single factor, especially with elevation.

Urban Landscape Heterogeneity Influences the Relationship between Tree Canopy and Land Surface Temperature

Urban trees play a key role in alleviating elevated summertime land surface temperatures in cities. However, urban landscape influences the capacity of urban trees to mitigate higher temperatures. We propose that both developed land characteristics and tree cover should be considered to accurately estimate the mitigation effects of canopy cover. We subclassified original land cover based on the canopy cover ratio to capture the within-land cover heterogeneity. We selected two coastal cities with different summertime climatic conditions: Seattle, Washington, USA, and Baltimore, Maryland, USA. We used Landsat-based grid cells (30 m × 30 m) as our spatial analytical unit, with corresponding land surface temperature, canopy area, canopy compactness, population size, and National Land Cover Database (NLCD)-based land cover group. We first used grouped boxplots, Kruskal–Wallis H tests, and post-hoc multiple comparison tests to detect the distribution of land surface temperatures by the land cover group. We then introduced statistical models to test the group effects on the relationship between land surface temperatures and canopy cover variables. We found: (1) land surface temperature increases with level of development, (2) land surface temperature decreases with canopy cover level, (3) the magnitude of the mitigation effects from canopy area differs based on development level and current canopy cover, (4) the differing efficacies of canopy area in decreasing land surface temperature follows a nonlinear threshold relationship, and (5) compactness of canopy cover was not significant in reducing the land surface temperature. These findings suggest the importance of considering heterogeneous canopy cover within developed land cover classes in urban heat island research. Tree planting strategies need to consider the nonlinear relationships between tree canopy cover and land surface temperature alongside environmental equity concerns.

Microclimatic variations of land surface temperatureon Galindez Island (western part of the Antarctic Peninsula)

The study presents analysis of microclimatic conditions on Galindez Island (western part of the Antarctic Peninsula), in particular: seasonal variability and spatial heterogeneity. Based on land surface temperature (LST) data derived from loggers and MicroClimate Monitoring Station, we analyzed areas with active growth of local plants. Seasonal variations formed mainly under annual and semi-annual cycles, with no dependencies of amplitudes and phases form area location. LST highly correlates with air temperature and total incoming irradiance. It is emphasized that spatial orientation of relief microforms plays the most significant role for LST formation on micro-level. Using cluster analysis, it was found that temperature loggers which are located along shoreline and oriented to the north–north-east could be grouped by similar LST distribution.

URBAN ENERGY MODELING USING REMOTE SENSING APPROACHES

This research aims to discover the urban energy spatial pattern based on the analysis of urban heat island (UHI) and the land surface temperature (LST) in Yogyakarta city. The vegetation cover was analyzed using the vegetation index with the normalized difference vegetation index (NDVI) formula. The satellite imagery used Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), recorded on April 22, 2019, for the dry season images, and June 25, 2019, for the wet season images. The processing results showed that the maximum LST in the dry season was 34.09°C, and the minimum was 27.22°C; meanwhile, in the wet season, the maximum LST was 31.27°C, and the minimum was 23.67°C. The presence of vegetation indicated by NDVI values had a strong correlation with LST, i.e. R = 0.74 for the dry season and R = 0.60 for the wet season. The high NDVI had a low temperature, whereas the low NDVI had a high temperature. Based on the analysis results, UHI increases in temperature (7°C) from the temperature threshold. The difference in the spatial distribution of LST and UHI in the dry and wet seasons is strongly influenced by the image recording time and the atmospheric conditions. The percentage of non-UHI area is deficient at around 0.35% in the dry season and 0.59% in the wet season, indicating that the Yogyakarta city's energy needs are very high. Therefore, it requires urban planning that considers urban green space area.

Identifikasi suhu permukaan terhadap penutupan lahan dari Landsat 8: studi kasus Kota Denpasar

The variation of land surface temperature using Landsat 8, case study City of Denpasar. Land cover is an essential signature that is often used to understand interactions between local temperatures and land surfaces. The integration of remote sensing and geographical information system helps to effectively and efficiently extract data for a vast study area. The purpose of this study was to determine the range and distribution of Land Surface Temperature (LST) and the variation among land covers in City of Denpasar using Landsat 8. The City of Denpasar was selected as the representative study area where human population considerably increased high during past decade and thus, has posed a need to understand urban climates mainly for a city which located in a relatively small tropical island. This study shows that trees in mangrove, urban water areas, and paddy fields had lower LST and can be used as an effective means of offsetting the energy-intensive urban heat island effect.

CA-Markov Chain Analysis of Seasonal Land Surface Temperature and Land Use Land Cover Change Using Optical Multi-Temporal Satellite Data of Faisalabad, Pakistan

Cellular Automata models are used for simulating spatial distributions and Markov Chain models are used for simulating temporal changes. The main aim of this study is to investigate the effect of urban growth on Faisalabad. This research is aimed at predicting seasonal Land-Surface-Temperature (LST) as well as Land-Use and Land-cover (LULC) with a Cellular-Automata-Markov-Chain (CA-Markov-Chain). Landsat 5, 7 and 8 data were used for mapping seasonal LULC and LST distributions during the months of May and November for the years 1990, 1998, 2004, 2008, 2013 and 2018. A CA-Markov-Chain was developed for simulating long-term landscape changes at 10-year time steps from 2018 to 2048. Furthermore, surface temperature during summers and winters were predicted well by Urban Index (UI), a non-vegetation index, demonstrating the highest correlation of R2 = 0.8962 and R2 = 0.9212 with respect to retrieved summer and winter surface temperature. Through the CA-Markov Chain analysis, we can expect that high density and low-density residential areas will grow from 22.23 to 24.52 km2 and from 108.53 to 122.61 km2 in 2018 and 2048, as inferred from the changes occurred from 1990 to 2018. Considering UI as the predictor of seasonal LST, we predicted that the summer and winter temperature 24–28 °C and 14–16 °C and regions would decrease in coverage from 10.75 to 3.14% and from 8.81 to 3.47% between 2018 and 2048, while the summer and winter temperature 35–42 °C and winter 26–32 °C regions will increase in the proportion covered from 12.69 to 24.17% and 6.75–15.15% of city.

Assessment of Landsat 8 TIRS data capability for the preliminary study of geothermal energy resources in West Sumatra

West Sumatra is one of has big geothermal energy resources potential. Remote sensing technology can have a role in geothermal exploration activity to measure the distribution of land surface temperatures (LST) and predict the geothermal potential area. Main study to obtain the assessment of Landsat 8 TIRS (Landsat`s Thermal Infrared Sensor) data capability for geothermal energy resources estimation. Mono-window algorithms were used to generate the LST maps. Data set was combined with a digital elevation model (DEM) to identify the potential geothermal energy based on the variation in surface temperature. The result that were derived from LST map of West Sumatra shows that ranged from -8.6 C 0 to 32.59 C 0 and the different temperatures are represented by a graduated pink to brown shading. A calculated result clearly identifies the hot areas in the dataset, which are brown in colour images. Lima Puluh Kota, Tanah Datar, Solok, and South Solok areas showed the high-temperature value (Brown) in the range of 28.1 C 0 to 32.59 C 0 color in images which means that they possess high potential for generating thermal energy. In contrast, the temperatures were lower (Pink) in the north-eastern areas and the range distribution was from-8.5 C 0 to 5 C 0 .

Exploring the relationship between LST and land cover of Bengaluru by concentric ring approach.

The present study aims at investigating the impact of land cover features in enhancing or mitigating Land Surface Temperature (LST) in a semi-arid tropical metropolitan city of Bengaluru, India. Spatial distribution of LST and land cover types of the area were examined in the circumferential direction, and the contribution of land cover classes on LST was studied over 28 years. Urban growth and LST were modelled using Landsat and MODIS data for the years 1989, 2001, 2005 and 2017 based on the concentric ring approach. The study provides an efficient methodology for modelling and parameterisation of LST and urban growth by fitting an inverse S-curve into urban density (UD) and mean LST data. In addition, multiple linear regression models which could effectively predict the LST distribution based on land cover types were developed for both day and night time. Based on the analysis of remotely sensed data for LST, it is observed that over the years, urban core area has increased circumferentially from 5 to 10 km, and the urban growth has spread towards outskirts beyond 15 km from the city centre. As urban expansion occurs, the area under the study experiences an expansive cooling effect during day time; at night, an expansive heating effect is experienced in accordance with the growth in UD in the suburban area and outskirts. The regression models that were developed have relatively high accuracy with R2 value of more than 0.94 and could explain the relationship between LST and land cover types. The study also revealed that there exists a negative correlation between urban, vegetation, water body and LST during day time while a positive correlation is observed during night. Thus, this study could assist urban planners and policymakers in understanding the scientific basis for urban heating effect and predict LST for the future development for implementing green infrastructure. The proposed methodology could be applied to other urban areas for quantifying the distribution of LST and different land cover types and their interrelationships.

Land surface temperature and normalized difference vegetation index relationship: a seasonal study on a tropical city

Land surface temperature (LST) and its relationship with normalized difference vegetation index (NDVI) are significantly considered in environmental study. The aim of this study was to retrieve the LST of Raipur City of tropical India and to explore its seasonal relationships with NDVI. Landsat images of four specific seasons for three particular years with fourteen years time interval were analyzed. The result showed a gradual rising (3.63 °C during 1991–2004 and 1.54 °C during 2004–2018) of LST during the whole period of study. The mean LST value of three particular years was the lowest (27.21 °C) on green vegetation and the highest (29.81 °C) on bare land and built-up areas. The spatial distribution of NDVI and LST reflects an inverse relationship. The best (− 0.63) and the least (− 0.17) correlation were noticed in the post-monsoon and winter seasons, respectively, whereas a moderate (− 0.45) correlation were found both in the monsoon and pre-monsoon seasons. This LST-NDVI correlation was strong negative (− 0.51) on vegetation surface, moderate positive on water bodies (0.45), and weak positive on the built-up area and bare land (0.14). In summary, the LST is greatly controlled by surface characteristics. This study can be used as a reference for land use and environmental planning in a tropical city.

Impact of urban morphology and landscape characteristics on spatiotemporal heterogeneity of land surface temperature

Urban microclimates have undergone extreme changes against the current backdrop of global warming and rapid urbanization. Although several studies on the spatiotemporal characteristics and influencing factors of land surface temperatures (LSTs) have been reported, only a few of them describe the key factors and specific spatial relationships that affect LSTs from time series. Therefore, it is of great significance to explore the spatial heterogeneity of LST from multiple time series and analyze its relationship with various influencing factors. A case study of the Ganjingzi District (GJZ) of Dalian City, China, was performed, with multi-source data and spatial analysis methods being jointly used to explore the spatiotemporal characteristics and driving factors of multi-temporal LSTs. The results indicate the following: (i) the GJZ LSTs gradually increased, with the average increase being 0.55 °C; (ii) there was a strong and positive correlation among the spatial distribution of LSTs, and the Moran's I value for global spatial autocorrelation exceeded 0.85; and (iii) the fitting degree between the LST and various factors (Adjust-R2) for the geographically weighted regression (GWR) model was better than that for the ordinary least squares approach. NDBI and LST are highly correlated and have good spatial stability. From the spatial distribution of Local-R2, it can be seen that with the expansion of urban space, the influence of urban areas on LSTs increases. Therefore, reasonable planning of urban land is of great significance for mitigating heat islands.