Land Surface Temperature Variations Research Articles

Spatiotemporal variation of air pollutants and their relationship with land surface temperature in Bengaluru, India

Rapid development and urbanization in most of the cities of the world have led to increased vehicular traffic, industrial activities, and construction. Air pollution in most cities of developing countries, like India, often exceeds the limits given in relevant national/international standards. Rapid urbanization in Bengaluru is causing increased air pollution and land surface temperature (LST). The present study examines the relationship of LST with air pollutants, meteorological parameters, surface indices, and selected urbanization parameters. The study also aims to find out the air pollutant have the maximum effect on LST during different seasons. Air pollutants and meteorological data were collected from air quality monitoring stations, while LST data of MODIS satellite was downloaded from the internet. Data for the period 2018–2022 was utilized for the current research. The results indicated that PM2.5 concentrations exceeded WHO standards, and Hombegowda Nagar (B9), Peenya (B5), and Bapuji Nagar (B4) were identified as pollution hot-spots. A strong association of LST with air pollutants and meteorological parameters was observed during the winter season (R2 = 0.87 to 0.91), which was stronger than the corresponding association during summer (R2 = 0.72 to 0.79). The association further improved after incorporating spectral indices (NDVI and NDBI), global population density (GPD), digital elevation model (DEM), and road line density (RLD) and a very strong association was obtained (R2 value of 0.91 in summer and 0.96 in winter). The study also revealed a strong relationship between the Urban Heat Island index and the combination of all parameters, both for urban and rural areas as well as during both the summer and winter seasons. PM2.5 and SO2 were identified as the main air pollutants affecting LST variations during the summer season, while NO2 and CO were more influential during the winter season. The study concludes that variation in the LST of an area is governed not only by the surface characteristics but it is also affected by air pollutants and meteorological conditions as well as conditions of urbanization that can be represented by different parameters/indices. The findings of this study hold significant implications for future scientific research, and it may pave the way for the integration of effective mitigation strategies to combat the dual challenges of increasing LST and air pollution in urban areas.

Analysis of Short-Term Drought Episodes Using Sentinel-3 SLSTR Data under a Semi-Arid Climate in Lower Eastern Kenya

This study uses Sentinel-3 SLSTR data to analyze short-term drought events between 2019 and 2021. It investigates the crucial role of vegetation cover, land surface temperature, and water vapor amount associated with drought over Kenya’s lower eastern counties. Therefore, three essential climate variables (ECVs) of interest were derived, namely Land Surface Temperature (LST), Fractional Vegetation Cover (FVC), and Total Column Water Vapor (TCWV). These features were analyzed for four counties between the wettest and driest episodes in 2019 and 2021. The study showed that Makueni and Taita Taveta counties had the highest density of FVC values (60–80%) in April 2019 and 2021. Machakos and Kitui counties had the lowest FVC estimates of 0% to 20% in September for both periods and between 40% and 60% during wet seasons. As FVC is a crucial land parameter for sequestering carbon and detecting soil moisture and vegetation density losses, its variation is strongly related to drought magnitude. The land surface temperature has drastically changed over time, with Kitui and Taita Taveta counties having the highest estimates above 20 °C in 2019. A significant spatial variation of TCWV was observed across different counties, with values less than 26 mm in Machakos county during the dry season of 2019, while Kitui and Taita Taveta counties had the highest estimates, greater than 36 mm during the wet season in 2021. Land surface temperature variation is negatively proportional to vegetation density and soil moisture content, as non-vegetated areas are expected to have lower moisture content. Overall, Sentinel-3 SLSTR products provide an efficient and promising data source for short-term drought monitoring, especially in cases where in situ measurement data are scarce. ECVs-produced maps will assist decision-makers with a better understanding of short-term drought events as well as soil moisture loss episodes that influence agriculture under arid and semi-arid climates. Furthermore, Sentinel-3 data can be used to interpret hydrological, ecological, and environmental changes and their implications under different environmental conditions.

Variations in land surface temperatures in small-scale urban areas in Vietnam during Covid-19 restrictions: Case studies from Da Nang, Hue and Vinh City.

Land surface temperature (LST) is an important variable in urban microclimate research. At the end of 2019, the emergence of Covid-19 pandemic has changed the world in a manner that forced many countries to impose restrictions in human activities. As a measure to prevent the expansion of Covid-19 infections, most of the major cities have entered a prolonged lockdown period and reduction in human activities between the early 2020 and the late 2021. These restrictions were strict in most of the cities in Southeast Asia, particularly in Vietnam. The present study investigated the variations in LST and NDVI observed in three rapidly growing urban areas, namely Da Nang, Hue and Vinh, in Vietnam using Landsat-8 imagery acquired between 2017 and 2022. There has been a slight reduction in LST in the study sites, particularly in Da Nang City, during the lockdown period but not as high as observed in recently conducted studies from big metropolitan cities, including in Vietnam. It is also observed that LST estimated from built-up areas and other impervious surfaces remained relatively stable during the study period which is similar to the results from recent studies.

Correction for LST directionality impact on the estimation of surface upwelling longwave radiation over vegetated surfaces at the satellite scale

Surface upwelling longwave radiation (SULR) is a major component of the Earth's radiation budget and directly influences the retrieval of evapotranspiration (ET) in the terrestrial ecosystems. Land surface temperature (LST) is an Essential Climate Variable (ECV) for direct estimation of SULR. However, accurate retrieval of SULR from satellite observations may be severely hindered by the anisotropic properties of land surface targets since most of them show marked angular variations in LST. This study aims at investigating the magnitude and impact factors of the directional effects of LST on SULR estimation over vegetated surfaces given that angular variation in emissivity has a limited impact on SULR estimation over most land surface types. It follows an attempt to correct for such effects in SULR estimation. We further explore the possibility to find a viewing direction at which SULR estimated from the directional LST can surrogate the hemispherical integration. To do so, a parametric model mimicking LST anisotropy with the hot spot is incorporated into the physical temperature-emissivity method. Two widely used Moderate Resolution Imaging Spectroradiometer (MODIS) LST products (i.e., MYD11_L2 and MYD21_L2) are analyzed. SULR estimates before and after correcting for LST directionality are compared with in-situ measurements acquired at 15 sites from the FLUXNET and SURFRAD networks in different regions. Our analysis reveals that LST directional effects on SULR estimation exhibit diurnal and seasonal variations, which are substantial in spring and summer for the daytime. The effects are negligible (<5 W m−2) in autumn and winter for the daytime except for in arid and semiarid regions. For the night-time, the effects are insignificant over all the biomes. Using MYD21 LST, after correction, the average root-mean-square error (RMSE) and bias of SULR estimates for all sites decrease by 8 and 8.34 W m−2 in spring, and by 8.9 and 12.13 W m−2 in summer. Using MYD11 LST, after correction, the average RMSE is between 10 and 15 W m−2 and the average bias is close to zero in all seasons. The RMSE and absolute bias of SULR estimates for sites with low to moderate vegetation (LAI <3) is lowered substantially (7–14 W m−2) after correction. Interestingly, SULR estimates from LST viewed at 54° backward and hemispherically integrated are close, with differences <3 W m−2 at most of the sites. These findings support a strategy for SULR estimation in ET retrieval over vegetated surfaces from directional LST.

A Study on the Spatial and Temporal Variation of Summer Surface Temperature in the Bosten Lake Basin and Its Influencing Factors

The land surface temperature (LST) is an important indicator reflecting the ecological environment condition. As a sensitive area to climate change, mastering the spatial and temporal changes of summer LST in the Bosten Lake basin (BLB) helps gain insight into the evolution of the thermal environment in the Bosten Lake basin and for long-term monitoring of the basic ecological changes in the basin. Based on MOD11A1 data from 2005 to 2020, this paper investigates the diurnal LST spatiotemporal series variation and its influencing factors in the Bosten Lake basin by using surface temperature class classification, trending analysis, the Hurst index, and geographic probes. The results show that (1) the wetland grasslands in and around the Bayinbruck steppe in the northwestern part of the study area exhibit a heat island effect during the day, while the opposite is true at night. In terms of temporal changes, LST changes in the BLB fluctuate widely, having a general rising and then decreasing trend. (2) The decreasing trend of LST from 2005 to 2020 is significant during the daytime and vice versa at night, and the change at night is greater than during the day. The areas with significantly higher diurnal LST in the future have all expanded compared to the area occupied by them now, with an overall trend of a steady increase. (3) The dominant factor of LST variation has the strongest explanatory power when altitude and NDVI are combined during the daytime and the strongest explanatory power when NPP and temperature are combined at night.

Monitoring Land Surface Temperature (LST) and Land Cover of Basra Province using Remote Sensing Technique and GIS

This study investigates the changes occurring in the province of Basra using geospatial methods and analyzes the variations in land surface temperature among the various types of land cover. For the months of July and December in the years 2013 and 2021, Landsat images were used in Landsat 8 OLI/TIRS, and satellite images were processed using ArcGIS 10.8 software. The study's categories for land use and land cover were generated through the application of supervised classification techniques, and the land surface temperature was calculated using data from a satellite sensor's brightness temperature. According to the study's findings, there has been an increase in urban areas (including barren land). From 2013 to 2021, a greater correlation between urban land and LST was found, indicating an increasing surface urban heat island effect as evidenced by its statistically significant correlation coefficients.It has a significant impact on the variations in land surface temperature.This study also highlighted the key variations in how land use and cover affect LST. Across all time periods of investigation. Therefore, techniques for remote sensing and geographic information systems are useful for tracking and analyzing urban expansion patterns and assessing their effects on land surface temperature.

Spatio and Temporal Analysis of Indonesia Land Surface Temperature Variation During 2001–2020

Spatio and Temporal Analysis of Indonesia Land Surface Temperature Variation During 2001–2020

Seasonal variations in land surface temperature based on local climatic zone in the major metropolitan cities of India

Seasonal variations in land surface temperature based on local climatic zone in the major metropolitan cities of India

Evaluation of UTFVI index effect on climate change in terms of urbanization.

Urban heat island density and occurrence are closely related to land use/land cover and land surface temperature variation. The effect of UHI can be described quantitatively with the urban thermal area variance index. This study aims to evaluate the UHI effect of the city of Samsun with the UTFVI index. LST data from 2000 ETM + and 2020 OLI/TIRS Landsat images were used to analyze UHI. The results showed that the UHI effect increased in Samsun's coastline band in 20years. As a result of the field analysis made from the UTFVI maps created, in 20years, 84% decrease in the none slice, 104% increase in the weak slice, 10% decrease in the middle slice, 15% decrease in the strong slice, 8% increase in the stronger slice, and 179% increase in the strongest slice are observed. The slice with the most intense increase is in the strongest slice and reveals the UHI effect.

Response of soil moisture and vegetation conditions in seasonal variation of land surface temperature and surface urban heat island intensity in sub-tropical semi-arid cities

Response of soil moisture and vegetation conditions in seasonal variation of land surface temperature and surface urban heat island intensity in sub-tropical semi-arid cities

Combined influence of soil moisture and atmospheric humidity on land surface temperature under different climatic background

Soil moisture (SM) and atmospheric humidity (AH) are crucial climatic variables that significantly affect the climate system. However, the combined influencing mechanisms of SM and AH on the land surface temperature (LST) under global warming are still unclear. Here, we systematically analyzed the interrelationships among annual mean values of SM, AH, and LST using ERA5-Land reanalysis data and revealed the role of SM and AH on the spatiotemporal variations of LST through mechanism analysis and regression methods. The results showed that net radiation, SM, and AH could well model the long-term variability of LST well and explain 92% of the variability. Moreover, SM played an essential and different role under the different LST backgrounds. The AH always displayed a greenhouse effect on the LST. This study provides essential insights into the global climate change mechanism from the surface hydrothermal processes perspective.

Contribution of Landscape Transformation in the Development of Heat Islands and Sinks in Urban and Peri-Urban Regions in the Chota–Nagpur Plateau, India

Cities in the contemporary world are experiencing shifts in local climatic conditions that are primarily linked to rapid urban growth, carbon emissions, and environmental degradation. In the present study, land surface temperature (LST) in urban and peri-urban regions of the city of Ranchi was estimated and correlated with land use/land cover (LULC) using temporal LANDSAT satellite images from 2000 to 2014 on a bi-annual basis. The study exhibited a gradual increase in built-up land with total growth of 47.65 sq. km (135%) at the cost of vegetation (−6.8%), rock outcrops (−44.16%), and other LULCs (0.81%). Spatiotemporal LST variability exhibited an increase in area under high-temperature (&gt;38 °C) zones as compared to moderate (30 °C to 38 °C) and low-temperature (&lt;30 °C) zones. Although the LST distinctively escalated in all LULC in the month of May from 2000 to 2014, urban areas exhibited cooler conditions than their environs at a regional scale, except for 2006. A higher surface temperature was observed over bare rock (min./max.: 28–43 °C), fallow land (25–42 °C), and industrial built-up areas (31–43 °C), followed by vegetation (24–34 °C) and waterbodies (22–30 °C). The rapid land transformation to an impervious surface, degradation of vegetation coverage and its health, deforestation due to urbanization, the dumping of non-treated wastewater into reservoirs, and pollution altogether led to an increase in urban heat islands (UHI). The study demonstrated that the LST increased in urban regions but appeared cooler than those of its peri-urban counterparts because of the presence of barren land and rock outcrops. The existing tenancy act had an indirect impact on maintaining natural landscapes and controlling the LST within the Ranchi region. The study recommends nature-based solutions in terms of plantation and green roofing within the rising LST zones located in the urban core and in peripheral rock outcrops collectively to minimize the rising impact of LST in urban and peri-urban regions.

Annual and Seasonal Trends of Vegetation Responses and Feedback to Temperature on the Tibetan Plateau since the 1980s

The vegetation–temperature relationship is crucial in understanding land–atmosphere interactions on the Tibetan Plateau. Although many studies have investigated the connections between vegetation and climate variables in this region using remote sensing technology, there remain notable gaps in our understanding of vegetation–temperature interactions over different timescales. Here, we combined site-level air temperature observations, information from the global inventory modeling and mapping studies (GIMMS) dataset, and moderate-resolution imaging spectroradiometer (MODIS) products to analyze the spatial and temporal patterns of air temperature, vegetation, and land surface temperature (LST) on the Tibetan Plateau at annual and seasonal scales. We achieved these spatiotemporal patterns by using Sen’s slope, sequential Mann–Kendall tests, and partial correlation analysis. The timescale differences of vegetation-induced LST were subsequently discussed. Our results demonstrate that a breakpoint of air temperature change occurred on the Tibetan Plateau during 1994–1998, dividing the study period (1982–2013) into two phases. A more significant greening response of NDVI occurred in the spring and autumn with earlier breakpoints and a more sensitive NDVI response occurred in recent warming phase. Both MODIS and GIMMS data showed a common increase in the normalized difference vegetation index (NDVI) on the Tibetan Plateau for all timescales, while the former had a larger greening area since 2000. The most prominent trends in NDVI and LST were identified in spring and autumn, respectively, and the largest areas of significant variation in NDVI and LST mostly occurred in winter and autumn, respectively. The partial correlation analysis revealed a significant negative impact of NDVI on LST during the annual scale and autumn, and it had a significant positive impact during spring. Our findings improve the general understanding of vegetation–climate relationships at annual and seasonal scales.

Large Uncertainties in Precipitation Exert Considerable Impact on Land Surface Temperature Modeling Over the Tibetan Plateau

AbstractThe performance of land surface temperature (LST) simulations in land surface models is subject to uncertainties in atmospheric forcing datasets. However, the contributions of forcing variables in different datasets to the simulated LST over the Tibetan Plateau (TP) remain unclear. To investigate the sensitivity of LST simulation to atmospheric forcing, we conducted a series of experiments using the Community Land Model (CLM5.0) with different forcing datasets. Results showed that the simulated LST displayed large spatiotemporal variations, and the simulation driven by the China Meteorological Forcing Data set produced more reasonable estimates than the other simulations. Sensitivity analysis suggests that the 1% increase in air temperature causes the largest variation in LST (0.54%), while the same percentage change in precipitation leads to the smallest (only 0.002%) LST variation. However, the actual uncertainty in precipitation is much larger than that in air temperature, and thus uncertainty in precipitation contributes 24% of the LST variability in the cold season, compared to 18% for air temperature. Large precipitation uncertainty leads to strong uncertainties in snow processes (snowfall and snow cover fraction), and hence strong uncertainties in surface albedo and ground absorbed shortwave radiation, further exerting the absolute impact on LST in cold season. Moreover, the snowfall is also influenced by different schemes of snowfall/rainfall partitioning. This may result in uncertainties in snowfall and consequently LST for the southeastern TP. Our study highlights the importance of high‐quality precipitation data, a forcing variable usually ignored in previous studies of LST simulation on the TP.

A Twenty-Year Assessment of Spatiotemporal Variation of Surface Temperature in the Yangtze River Delta, China

A good understanding of the processes of land surface temperature (LST) change is important for assessing regional climate change. In the present study, we obtained the MODIS MOD11A2 LST products over the Yangtze River Delta (YRD) from 2001 to 2020. In order to comprehensively assess the spatial and temporal variability of LST in the YRD region over the past two decades, the Theil–Sen Median trend analysis and Mann–Kendall test, BFAST01 trend decomposition, and landscape pattern analysis were used in this study. We show that the rate of linear change in LST in the YRD ranges from −0.019 °C/month to 0.046 °C/month. The BFAST01 trend decomposition identifies more details of LST change and monotonic increases, reversal increase, and interruption increase are the main warming trends. The distribution of the different trend types shows strong aggregation with high spatial heterogeneity. The LST breakpoints are mainly located in the northern and southern YRD, which frequently occurred during 2010–2013. Of the various land types, breakpoints occur most frequently in cropland and high NDVI (0.5–0.7) areas, and the intensity of most of them is within 2 °C. In addition, much stronger warming occurs in urban areas than in other land types. Our study provides a better understanding of the dynamics of LST in the YRD region over the past 20 years and highlights that breakpoints cannot be circumvented in regional temperature assessment.

Capturing Small-Scale Surface Temperature Variation across Diverse Urban Land Uses with a Small Unmanned Aerial Vehicle

Urbanization increases the urban land surface temperature (LST), challenging society and the environment. This study measured the LST of diverse land uses (LU) in Dallas–Fort Worth (DFW) using a high-resolution (8 cm) thermal infrared sensor onboard a small, unmanned aerial vehicle (UAV). LUs included park (PA), industrial (IA), residential low-cost (RLC), and residential high-cost (RHC) areas. LST was collected by the UAV at different times on eight nonconsecutive days. UAV-collected LST was compared with that from Landsat 8-9 and in situ measurements. RHC reported the highest mean LST, and PA showed the lowest mean LST. Dark-colored asphalt shingle roofs in RHC had the highest mean LST range at 35.67 °C. Lower LST was measured in shaded areas and under thick green cover, whereas areas with thin green cover occasionally reported higher LST than pavements. The micro-urban heat island (MUHI) was calculated between LUs and within land cover types (roof, pavement, green, and water). The MUHI varied from 4.83 °C to 15.85 °C between LUs and 0.2 °C to 23.5 °C within LUs for the less than 1 km2 study area. While the UAV thermal sensor and Landsat demonstrated a similar trend of LST variation, the UAV sensor reported more intense MUHI. An average percent bias (PBIAS) of 5.1% was calculated between the UAV sensor and in situ measurements. This study helps inform the urban design process by demonstrating how land use decisions impact LST locally and provides valuable insight for studies concerned with fine-scale urban LST variability.

Detection of vegetation drying signals using diurnal variation of land surface temperature: Application to the 2018 East Asia heatwave

Satellite-based vegetation monitoring provides important insights regarding spatiotemporal variations in vegetation growth from a regional to continental scale. Most current vegetation monitoring methodologies rely on spectral vegetation indices (VIs) observed by polar-orbiting satellites, which provide one or a few observations per day. This study proposes a new methodology based on diurnal changes in land surface temperatures (LSTs) using Japan's geostationary satellite, Himawari-8/Advanced Himawari Imager (AHI). AHI thermal infrared observation provides LSTs at 10-min frequencies and ∼ 2 km spatial resolution. The DTC parameters that summarize the diurnal cycle waveform were obtained by fitting a diurnal temperature cycle (DTC) model to the time-series LST information for each day. To clarify the applicability of DTC parameters in detecting vegetation drying under humid climates, DTC parameters from in situ LSTs observed at vegetation sites, as well as those from Himawari-8 LSTs, were evaluated for East Asia. Utilizing the record-breaking heat wave that occurred in East Asia in 2018 as a case study, the anomalies of DTC parameters from the Himawari-8 LSTs were compared with the drying signals indicated by VIs, latent heat fluxes (LE), and surface soil moisture (SM). The results of site-based and satellite-based analyses revealed that DTR (diurnal temperature range) correlates with the evaporative fraction (EF) and SM, whereas Tmax (daily maximum LST) correlates with LE and VIs. Regarding other temperature-related parameters, T0 (LST around sunrise), Ta (temperature rise during daytime), and δT (temperature fall during nighttime) are unstable in quantification by DTC model. Moreover, time-related parameters, such as tm (time reaching Tmax), are more sensitive to topographic slope and geometric conditions than surface thermal properties at humid sites in East Asia, although they correlate with EF and SM at a semi-arid site in Australia. Additionally, the spatial distribution of the DTR anomaly during the 2018 heat wave corresponds with the drying signals indicated as negative SM anomalies. Regions with large positive anomalies in Tmax and DTR correspond to area with visible damage to vegetation, as indicated by negative VI anomalies. Hence, combined Tmax and DTR potentially detects vegetation drying indetectable by VIs, thereby providing earlier and more detailed vegetation monitoring in both humid and semi-arid climates.

How does 2D and 3D of urban morphology affect the seasonal land surface temperature in Island City? A block-scale perspective

The global climate warming caused by urbanization has significantly affected the urban environment. Whilst land surface temperature (LST) is an important factor reflecting urban temperature, previous research on LST mostly focused on two-dimensional (2D) factors and rarely mentioned about the role of three-dimensional (3D) factors, particularly the LST variation characteristics of island cities. Therefore, this study examined the seasonal variation characteristics of urban LST by analyzing the impact of 2D and 3D urban morphology factors of different urban block types on LST in Xiamen Island. The main results are as follows. First, compact low layer (CL), a block type with a higher density of low-rise buildings, has a higher LST in any season. Under the same block density (BD), the higher the block average height (BH), the lower the LST. Second, among the 2D urban morphology factors, normalized difference vegetation index (NDVI) was the main factor for cities to reduce urban LST, especially in summer, while normalized difference built-up index (NDBI) was the opposite. Different from land cities, we found a positive correlation between modified normalized difference water body index (MNDWI) and LST in autumn and winter. Third, in the 3D urban morphology factors, sky view factor (SVF) was significantly positively correlated with LST, while building fluctuation (BF) was negatively correlated. The higher the SVF, the worse the radiation shielding effect between buildings. On the contrary, the higher the BF, the higher the building undulation, and the better the building radiation shielding. These findings should provide some quantitative insights for the future construction and planning of island cities, which can be used to improve the thermal environment of island cities and support the sustainable development of cities.

Exploring the relationship between seasonal variations of land surface temperature and urban morphological factors in complex urban areas.

The urban heat island effect is an increasingly serious problem in urban areas. Previous studies suggest that spatial variation in the urban land surface temperature (LST) is determined by interactions among urban morphological factors, but few studies have explored the main factors that affect the LST in different seasons in complex urban areas, especially at a fine scale. By considering the central Chinese city of Jinan as an example, we selected 19 parameters related to the architectural morphological factors, ecological basis factors, and humanistic factors and explored their effects on the LST in different seasons. A correlation model was used to identify the key factors and to analyze the main impact thresholds in different seasons. In the four seasons, the 19 factors all had significant correlations with LST. In particular, architectural morphological factors comprising the average building height and high building ratio had significant negative correlations with the LST in the four seasons. The architectural morphological factors comprising the floor area ratio, spatial concentration degree, building volume density, and urban surface pattern index comprising the mean nearest neighbor distance to green land, as well as humanistic factors comprising the point of interest density, nighttime light intensity, and human activity intensity of land surface had significant positive correlations with LST in the summer and autumn. Ecological basis factors made the main contributions to the LST in the spring, summer, and winter, whereas humanistic factors contributed the most in the autumn. The contributions of architectural morphological factors were relatively low in the four seasons. The dominant factors differed in each season but their thresholds had similar characteristics. The results obtained in this study deepen our understanding of the relationships between urban morphology and the urban heat island effect, and provide practical suggestions for improving the urban thermal environment through reasonable building planning and management.

Analyzing the Urban Heat Island in Ho Chi Minh City in Relation to Urban Planning Processes from 2010 to 2020

This study aims to analyze the variation of land surface temperature (LST) and its effect on the urban heat island (UHI) in relation to the urban planning processes of Ho Chi Minh City (HCMC) from the years 2010 to 2020. These changes have impacted the urban environmental conditions and the city development. The study used the time series of Landsat 5 TM and Landsat 8 OLI acquired for the years 2010, 2015, and 2020, and used the algorithms for calculating the land surface temperature (LST), the normalized difference vegetation index (NDVI) and the normalized difference built-up index (NDBI) to investigate the relationships between these phenomena in the city planning processes. The results highlight the increase in land surface temperature was related to the dense of impervious surface area and the decrease of vegetation area. The UHI zones occurred in the areas with high LST and NDBI values, and low NDVI values (concentrated in the central districts and the planned areas) compared to the surrounding areas with lower LST and NDBI and higher NDVI. The recommend planning solutions for the new urban development areas should focus on increasing the amount green areas to combat the UHI phenomenon and environmental problems against the development.