Land Surface Temperature Estimation Research Articles

A Cloud Coverage Image Reconstruction Approach for Remote Sensing of Temperature and Vegetation in Amazon Rainforest

Remote sensing involves actions to obtain information about an area located on Earth. In the Amazon region, the presence of clouds is a common occurrence, and the visualization of important terrestrial information in the image, like vegetation and temperature, can be difficult. In order to estimate land surface temperature (LST) and the normalized difference vegetation index (NDVI) from satellite images with cloud coverage, the inpainting approach will be applied to remove clouds and restore the image of the removed region. This paper proposes the use of the neural network LaMa (large mask inpainting) and the scalable model named Big LaMa for the automatic reconstruction process in satellite images. Experiments are conducted on Landsat-8 satellite images of the Amazon rainforest in the state of Acre, Brazil. To evaluate the architecture’s accuracy, the RMSE (root mean squared error), SSIM (structural similarity index) and PSNR (peak signal-to-noise ratio) metrics were used. The LST and NDVI of the reconstructed image were calculated and compared qualitatively and quantitatively, using scatter plots and the chosen metrics, respectively. The experimental results show that the Big LaMa architecture performs more effectively and robustly in restoring images in terms of visual quality. And the LaMa network shows minimal superiority for the measured metrics when addressing medium marked areas. When comparing the results achieved in NDVI and LST of the reconstructed images with real cloud coverage, great visual results were obtained with Big LaMa.

Monitoring Land Surface Temperature in the Karajae Watershed as an influential ecosystem for Pare-Pare City

Temperature is an indicator that is often used to assess environmental conditions in an area because it affects food and air resources, hydrology, and all ecosystems on earth. Monitoring the temperature conditions of an area is also carried out from time to time to see trends that occur. Land surface temperature monitoring using remote sensing which has spatial resolution, temporal resolution, and spectral resolution. Monitoring is only in urban areas without looking at the surrounding conditions. However, temperature is influenced by several factors such as geographic location, position relative to the sun, altitude, and biophysical conditions such as land. This study looks at changes in land surface temperature in the Karajae watershed area as an ecosystem that affects urban areas, Pare-Pare City or vice versa. Analysis of land surface temperature monitoring using remote sensing methods in the form of multi-temporal Landsat Imagery data 2004-2021. The land image displays changes in land surface temperature with a single channel algorithm. In addition, land images are the basis for analyzing land cover changes which are the basis for estimating land surface temperature changes with normalized fractional vegetation cover relationships. The results show that changes in land cover must occur, which are estimated to be twice as large in the future. Settlements have experienced a very large increase in area, while the closure of forest land and dry agricultural land has experienced a reduction in area which will cause disruption to ecosystems and food. The trend of increasing land surface temperature continues to increase every period. The increase in settlements as built-up land, rice fields, dry land agriculture as a form of community activity causes an increase in land surface temperature. In addition, agricultural activities have an influence on increasing land surface temperatures in vegetated land cover. Indirectly, the city of Pare-Pare has an impact on increasing land surface temperatures in the upstream area of the Karajae watershed. This is a concern in the future, if urban activities, transportation, to industrial activities are much faster, and will cause a greater increase in land surface temperature.

Improving the Accuracy of Landsat 8 Land Surface Temperature in Arid Regions by MODIS Water Vapor Imagery

Land surface temperature (LST) is a significant environmental factor in many studies. LST estimation methods require various parameters, such as emissivity, temperature, atmospheric transmittance and water vapor. Uncertainty in these parameters can cause error in LST estimation. The present study shows how the moderate resolution imaging spectroradiometer (MODIS) water vapor imagery can improve the accuracy of Landsat 8 LST in different land covers of arid regions of Yazd province in Iran. For this purpose, water vapor variation is analyzed for different land covers within different seasons. Validation is performed using T-based and cross-validation methods. The image of atmospheric water vapor is estimated using the MODIS sensor, and its changes are investigated in different land covers. The bare lands and sparse vegetation show the highest and lowest accuracy levels for T-based validation, respectively. The root mean square error (RMSE) is also calculated as 0.57 °C and 1.41 °C for the improved and general split-window (SW) algorithms, respectively. The cross-validation results show that the use of the MODIS water vapor imagery in the SW algorithm leads to a reduction of about 2.2% in the area where the RMSE group is above 5 °C.

Estimation of Daily Mean Land Surface Temperature over the Qinghai–Tibet Plateau Based on an RTM-DTC Model

Accurately estimating daily mean land surface temperature (LST) is crucial for studying the urban heat island effect, land–atmosphere energy exchange, and global climate change. However, limited research has been conducted on average surface temperature estimation, particularly in high-altitude regions like the Qinghai–Tibet Plateau with extensive cloud cover. In this study, we propose the Reanalysis Data and Thermal Infrared Remote Sensing Data Merging-Diurnal Temperature Cycle (RTM-DTC) model specifically for the Qinghai–Tibet Plateau, successfully estimating mean LST using the model. We apply the RTM method to reconstruct LST under cloud cover from the MODIS LST product and calculate the average temperature using the DTC model. Validation with in situ measurements from seven meteorological stations on the Tibetan Plateau yielded daily scale RMSEs ranging from 1.81 K to 2.021 K and monthly scale RMSEs ranging from 1.77 K to 2.0 K, with an average RMSE of 1.91 K. These results demonstrate the adaptability of the RTM-DTC model and its ability to depict the annual variation curve of the mean surface temperature, and provide further research on RTM-DTC as a valuable approach.

Estimating land surface temperature (LST) and comparing it with NDMI, NDWI and NDVI indices in order to investigate water stress with an emphasis on land use changes (LULC) in the support vector machine (SVM) system (study area: Anzali wetland)

Estimating land surface temperature (LST) and comparing it with NDMI, NDWI and NDVI indices in order to investigate water stress with an emphasis on land use changes (LULC) in the support vector machine (SVM) system (study area: Anzali wetland)

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

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

An adaptive synchronous extraction (ASE) method for estimating intensity and footprint of surface urban heat islands: A case study of 254 North American cities

The urban heat island (UHI) effect has attracted great attention due to its potential impacts on rapidly growing urban areas. Using remotely sensed estimates of land surface temperature (LST), a large number of studies have focused on the surface UHI (SUHI) effect, which can be characterized by its two fundamental properties: intensity and footprint. The SUHI intensity reflects the LST difference between the urban area and the background reference area (BRA), and the SUHI footprint indicates the spatial extent influenced by the heat island. Currently, numerous methods have been developed to estimate the SUHI intensity and footprint, but are still greatly challenged by three main issues. Namely, the discrepancy in BRA selection criterion brings great uncertainty to the estimated SUHI intensity, the estimation of SUHI footprint is largely constrained by the predefined models, and the quantification of SUHI effect is potentially influenced by several confounding factors. Here, we proposed an adaptive synchronous extraction (ASE) method, which is capable of adaptively selecting the most optimal BRA while removing the influence of confounding factors, and achieving synchronous estimation of SUHI intensity and footprint. We applied the ASE method to 254 North American cities and conducted an in-depth comparative analysis to discuss its applicability and benefits. The main results include: (1) The ASE method avoids the limitations of existing methods in BRA selection and model presetting, and shows resilience to parameter variations. This makes the ASE method highly applicable to quantify the SUHI intensity and footprint in cities with various thermal characteristics. (2) The ASE method can better highlight the spatial, seasonal and day-night contrasts in the estimated SUHI intensity. This superiority is particularly evident when comparing it to methods based on the equal-area buffer or the simplified urban-extent algorithm. (3) Confounding factors pose non-negligible impacts on the quantification of the SUHI effect. Typically, ignoring the influence of topographic relief or missing LST data can lead to an overall overestimation of the SUHI intensity, while not removing surrounding urban areas will cause some underestimation of the SUHI intensity. Overall, the proposed ASE method provides a new generalizable tool for quantifying the SUHI effect, which has great potentials for future studies and urban climate assessments.

A framework for reconstructing 1km all-weather hourly LST from MODIS data

ABSTRACT Land surface temperature (LST) is an essential parameter in environmental monitoring. However, due to the cloud contamination and the limitations of sensors, existing thermal infrared LST products are challenging to provide all-weather LST with high spatiotemporal resolution. Therefore, this paper presents a framework for reconstructing hourly LST under clouds based on the moderate resolution imaging spectroradiometer (MODIS) LST products. This framework consists of two main steps: (1) Instantaneous LST estimation for MODIS using a modified annual temperature cycle (ATCM) model and (2) Hourly LST reconstruction under all-weather conditions from atmospheric reanalysis data and MODIS instantaneous LST with the linear model (LM). The proposed framework is evaluated with the full year data of 2019 with tile number H26V05. The results show that the spatial distribution of the estimated LST is similar with the MODIS LST. Additionally, it could provide an accurate indication of the spatial and temporal variability of LST. Comparing the MODIS LST of the selected area for tile number H26V05 in 2019 with the estimated LST of the ATCM, the root mean squared errors (RMSEs) of the model are around 1K ~ 3K. The hourly LST is then reconstructed based on the LM method, and the RMSE is around 2K during the daytime and around 1K during the night-time. Finally, to verify the robustness of the hourly LST reconstruction method, the accuracy of the hourly LST was evaluated by the MODIS LST at different situations in the day. The results show that the missing data at one moment will cause the accuracy of the model decreasing by 0.09 ~ 1.67K at the corresponding moment. In general, the proposed framework has the potential to reconstruct the 1 km all-weather hourly LST with high accuracy and a certain level of robustness.

The effect of pixel heterogeneity on surface heat and water vapor flux estimated by the remote sensing-based model coupled with deep learning

Accurate quantification of sensible (H) and latent (LE) heat fluxes are desperately crucial for agricultural irrigation strategy, drought monitoring, water resource management, and climate change. Regional-scale H and LE were derived generally from satellite remote sensing data using relevant modeling methods. However, one of the challenges of modeling methods is to deal with sub-pixel heterogeneity, because the model usually assumes homogeneous conditions within a pixel. In this study, a hybrid model, which coupled deep neural network (DNN) and the remote sensing-based two-source energy balance (TSEB) model, was developed to estimate H and LE at multiple spatial resolutions to further quantify the influence of pixel size on the modeled fluxes. First, the DNN-based land surface temperature (LST) estimation model was developed and validated. LST at multiple spatial resolutions (i.e., 1, 5, 10, 30, 100, and 1000 m) were generated by the DNN method using multi-source satellite data including the GaoFen-2 (GF-2, a Chinese high-resolution optical remote-sensing satellite), Sentinel-2, and Landsat-8 images, respectively. Then, the hybrid DNN-TSEB model was used to estimate H and LE at multi-pixel sizes. Finally, modeled H and LE at 1 ∼ 1000 m were validated by eddy covariance (EC) and optical-microwave scintillometer (OMS) flux observation. Results revealed that the DNN-based LST model was a potential approach to generate LST at multiple pixel sizes. The hybrid DNN-TSEB model can produce more accurate H and LE when different land cover types were discriminated over heterogeneous farmland (e.g., the average bias for H and LE at a spatial resolution of 1 m were −9 and −6 W/m−2, respectively). Besides, the deviation of modeled fluxes from the observations increased as the spatial resolution became coarser, particularly the pixel size was greater than 30 m (average bias for H and LE were −34 and 30 W m−2, respectively). However, the magnitude of bias of fluxes estimates remained unchanged when the pixel sizes of satellite data were between 100 and 1000 m. The more important finding was that the hybrid model was a promising approach, which can obtain accurate estimates of H and LE at a high spatial resolution. In addition, the performance of the model based on GF-2 data was better than that based on Sentinel-2 and Landsat-8 data. The results suggest that pixel heterogeneity should be considered when estimating H and LE over heterogeneous landscapes with various surface types.

Four-decade response of land surface temperature to urban expansion in Beijing

Urban expansion with increased impervious surface area (ISA) can affect the thermal regimes by altering the water and energy balance. Previous studies have indicated that ISA is the primary surrogate of rising land surface temperature (LST). However, urban-induced LST changes are not yet well quantified because of the temporal limitations of the LST datasets and the uncertainties from climate variabilities. In this study, we integrated multiple satellite-based products (vegetation coverage, albedo and radiation forcing) into a physically-based land surface model i.e., Variable Infiltration Capacity (VIC), to detect multi-temporal scale response of LST to the increasing ISA in Beijing. Heat transfer-related parameters in the model were updated along with urbanization processes. The integrated modeling presented high performance for simulating LST, as evaluated with ground-based observations and the MODerate-resolution Imaging Spectroradiometer (MODIS) LST product. We found that the average LST over Beijing increased from 10.3 °C in 1980–1990 to 11.2 °C in 2010–2020, with nearly 33% contributed by the urban growth. The urban-induced thermal effect was particularly strong in summer daytime and winter nighttime. The frequency of heat days (the day with the maximum LST over 40 °C) in a year for Beijing appeared to linearly correlate with the impervious surface fraction (ISF) when ISF > 25%. For the urbanizing area, moreover, the four-decade LST estimates indicated that the annual overheating duration was obviously extended with a rate of about 5 days per decade. We therefore conclude that the urban expansion in Beijing not only amplified heat stress but also altered heat phenology, which have implications for urban planning and the construction of heat mitigation facilities.

A new approach to estimate daily evapotranspiration, based on Landsat data and FAO56 principles

ABSTRACT The accurate estimation of evapotranspiration is crucial for enhancing crop water productivity and effectively managing water resources. This research offers a novel method that integrates satellite data and crop coefficients to calculate ET c and ET a on a daily basis and overcome the limitation of low temporal frequency of non-commercial satellite data. The study was carried out in the southern part of Khuzestan Province, Iran, on sugarcane crops in the Amirkabir Agro-industries area. The method involves obtaining Landsat-8 data with an 8-day temporal resolution, which was used to estimate Land Surface Temperature (LST) using a Single-Channel Algorithm. The estimated LST was then validated with in-situ canopy temperature measurements and used to predict the crop stress coefficient (K s) based on its relationship with the crop water stress index (CWSI). The crop coefficient (K c) was obtained using the Surface Energy Balance Algorithm for Land (SEBAL) algorithm, and both Ks and Kc were utilized to calculate daily ETa by multiplying by the daily reference evapotranspiration (ET 0) obtained from local meteorological data. The results indicated that the crop coefficients of sugarcane in the initial and mid-stages were 12% and 18% higher, respectively, compared to the FAO56 guideline. The aggregated decadal and monthly ET a showed good agreement with the WaPOR datasets, with an RMSE of 8.7 and 1.93 mm, respectively. This approach offers a potential solution to the challenge of obtaining remote sensing data with a higher temporal frequency.

Satellite-based land surface temperature and soil moisture observations accurately predict soil respiration in temperate deciduous and coniferous forests

Soil respiration, defined as the total flux of carbon dioxide (CO2) from the soil to the atmosphere, is a key ecosystem process that affects the regional and global carbon (C) cycles and is highly sensitive to temperature and soil moisture. It is challenging to quantify soil respiration at the ecosystem level from commonly used in-situ soil chamber measurements because of large spatial variability. Methods that provide temporally and spatially continuous estimates of soil respiration at various scales are vital to understand the impact of climate change on soil C stock. In this study, we evaluate three commonly used empirical models and a Random Forest machine learning algorithm applied to satellite derived estimates of land surface temperature (LST) and soil moisture to estimate soil respiration in temperate deciduous and coniferous forests in Canada. The models were calibrated using in-situ soil temperature and moisture and validated against in-situ measurements of soil CO2 fluxes (gCm−2day−1) from automatic soil chambers. We separately evaluate the performance of nighttime and daytime satellite-based LST and soil moisture observations in modeling soil respiration. The soil respiration models were also evaluated at daily and monthly time scales against in-situ measurements. Results indicate that models based on satellite LST, and soil moisture can explain more than 70% of the variability in observed soil respiration. Nighttime LST at a monthly time scale resulted in consistently higher accuracy than daytime LST in estimating soil respiration. Satellite observations resulted in comparable accuracy in estimating soil respiration as in-situ measurements. Satellite LST and soil moisture observations are indispensable data sources to estimate soil respiration at ecosystem level and its upscaling to regional and global scales.

Investigating surface UHI using local climate zones (LCZs), the case study of Cairo’s River Islands

The spatial configuration of the built environment influences the thermal behaviour at the local level and may generate the urban heat island (UHI) phenomenon. This impact has been investigated through several physical and climatological approaches. In this respect, the local climate zones (LCZs) scheme is recently adopted to facilitate the spatial study and understanding of UHIs. This research investigates the formation of surface UHIs in the case of the Nile River Islands, Cairo. It adopts the classification scheme of LCZs for selecting and comparing samples and conducting analyses of the distribution of land surface temperature (LST) for the area of interest. The adopted workflow aims at defining the homogenous zones of LCZs by three main steps: (i) the classification of LCZs based on supervised pixel-based classification of geospatial data, using the web-based tool of LCZ Generator, (ii) the detection of UHIs through the estimation of LST, and (iii) spatial statistical tests of Global Moran’s Index and Hotspot analysis for studying the significance of LST distribution within the study area. The adopted workflow successfully assists in estimating the LST and mapping UHI, and it contributes to the understanding of the physical and thermal characteristics of the selected urban areas. The analyses of outputs show reliable results with the recent studies of spatial relations between LCZs and LST.

Land Surface Temperature Estimation from Landsat-9 Thermal Infrared Data Using Ensemble Learning Method Considering the Physical Radiance Transfer Process

Accurately estimating land surface temperature (LST) is a critical concern in thermal infrared (TIR) remote sensing. According to the thermal radiance transfer equation, the observed data in each channel are coupled with both emissivity and atmospheric parameters in addition to the LST. To solve this ill-posed problem, classical algorithms often require the input of external parameters such as land surface emissivity and atmospheric profiles, which are often difficult to obtain accurately and timely, and this may introduce additional errors and limit the applicability of the LST retrieval algorithms. To reduce the dependence on external parameters, this paper proposes a new algorithm to directly estimate the LST from the top-of-atmosphere brightness temperature in Landsat-9 two-channel TIR data (channels 10 and 11) without external parameters. The proposed algorithm takes full advantage of the adeptness of the ensemble learning method to solve nonlinear problems. It considers the physical radiance transfer process and adds the leaving-ground bright temperature and atmospheric water vapor index to the input feature set. The experimental results show that the new algorithm achieves accurate LST estimation results compared with the ground-measured LST and is consistent with the Landsat-9 LST product. In subsequent work, further studies will be undertaken on developing end-to-end deep learning models, mining more in-depth features between TIR channels, and reducing the effect of spatial heterogeneity on accuracy validation.

Application of GIS using NDVI and LST estimation to measure climate variability-induced drought risk assessment in Ethiopia

Abstract The contribution of this paper is to assess the drought patterns using the Land Surface Temperature (LST) estimation index and examine the correlation between the Normalized Difference Vegetation Index (NDVI). The main objective was to evaluate the spatiotemporal variation in agricultural drought patterns and severity in order to estimate and measure climate variability. According to the study's findings, the region experienced mild to severe meteorological drought periods 15–18 times over the study period. The majority of these periods (62.72%) were classified as mild drought (unusual dry circumstances), which usually only showed a slight departure from the distribution of rainfall that is close to normal. According to the findings, cropping seasons from 2013 to 2022 saw an increase in agricultural dryness and a decrease in grain yield, with varying degrees of severity in different geographic locations. Based on the result, the major difficulties and challenges identified were the shortage of drinking water, and impacts on air quality, and food and nutrition. Accordingly, agricultural drought risk mapping can be utilized to mitigate the risk associated with drought on agricultural productivity and guide decision-making processes in drought monitoring.

Development of a geographic human heat balance equation to support public health analyses: An Arizona urban sun corridor application

Land surface temperature (LST) estimates often serve as urban heat islands maps and to infer human thermal comfort. Parallel to this, physiological heat balance calculations have been well documented to measure changes in body core temperature and measure risk of heat-related illness. However, there is a need for an improved spatially explicit method to assess human thermal comfort. Using spatial climate data measuring temperature, airflow, and humidity, we developed a geographic body heat storage (BHS) model based on heat exchange and evaporative heat loss from the human body. As proof of concept, we used heat-related illness emergency department visits in two Arizona metropolitan areas to demonstrate that BHS can improve LST's shortcomings, with its increased explanatory power of and linear fit to emergency records. The BHS model can support decision making for public health outcomes as heat risk increases with climate change and urban overheating to more closely approximate the human heat experience. BHS allows can be implemented in different climate regions and with investigations of additional physiological and community variables to better describe risk of heat-related illness.

Estimation of land surface temperature from AMSR2 microwave brightness temperature using machine learning methods

ABSTRACT Passive microwave has been used in land surface temperature (LST) inversion because of its all-weather availability. This paper proposed a LST retrieval method based on machine learning by integrating multiple datasets, including Advanced Microwave Scanning Radiometer 2 (AMSR2), Global Forecast System (GFS) reanalysis datasets, ERA5-land reanalysis products, Moderate Resolution Imaging Spectroradiometer (MODIS) products, Shuttle Radar Topography Mission (SRTM), and in situ measurements. Four algorithms, including linear regression (LR), random forests (RF), extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM), were explored and compared by ten-fold cross-validation. The best-performing LightGBM algorithm was applied to train the model, and day and night models were developed separately. The models were validated using in situ LST of training stations from 2017 to 2019. The day and night models root mean square error (RMSE) are 3.23 and 2.43 K, and that of bias are −0.24 and −0.36 K, respectively. The in situ LST from 2015 to 2019 that not used for model training were selected to further validate both day and night models, with RMSE = 5.42 and 2.91 K, and bias = −0.60 and 0.06 K, respectively. Validation results indicated that the temporal performance of the models is better than those of spatial performance and the night model performed better than the day model. Additionally, model performance in different seasons and land cover types demonstrated the robustness of the models over complicated surfaces. These results suggest that the LightGBM algorithm has good accuracy in LST estimation, making it possible to apply for the generation of LST at a global scale.

Search and assessment of urban heat islands on digital satellite images

Formulation of the problem. The development of urbanization processes resulted in Urban Heat Islands (UHI) - ur-ban areas of high temperatures due to the dominance of artificial surfaces that absorb and retain heat. UHIs adversely affect the environmen-tal conditions of cities and people's health. Satellite images in the thermal infrared range of the electromagnetic spectrum allow identifying areas of the high thermal back-ground of urbanized territories for management decisions support aimed at improving urban landscapes. The aim of the work is to develop a methodology for temperature anomalies analy-sis and urban heat islands detection on digital space images from Landsat 8 satellite in the thermal infrared range of the electromagnetic spectrum. Methodology of UHI detection. The methodology proposed in the paper includes a sequence of stages of collecting, and preprocessing satellite data, generating land sur-face temperature maps, identifying temperature anomalies, comprehensive analysis of high-temperature zones based on different-time data, and identifying areas of high temperatures stable over time. Land Surface Temperature estimation. Land Surface Temperature (LST) is esti-mated from the band 10 data of space images from the Landsat 8 satellite. The initial data are subjected to atmospheric correction, after which the spectral radiation in the band is converted to brightness temperature, and then to LST (Celsius degrees). UHI mapping. On each survey date, UHIs are identified as temperature anomalies - LST map pixels with values higher than the LST mean plus standard deviation within the study area. Time series analysis of satellite images. UHI mapping is based on a time series of Landsat 8 images acquired during the warm season (when ambient temperatures are above zero) to eliminate the effect of snow and ice on the observation results. According to the time series of LST maps of different years, stable temperature anomalies are dis-tinguished - UHIs presented on all images of the time series. Conclusions. Time-stable UHIs of the urbanized territory are mapped and ana-lyzed using Landsat 8 LST data time series. The areas of the high thermal background are compared with the classes of the earth's surface. It is concluded that increasing ur-ban density contributes to the accumulation of heat and increases the ambient tem-perature. The maps of LST distribution, vegetation indices, and UHI generated in the work can be used in urban planning tasks together with traditional sources of spatial information to obtain new information on improving the city's resistance to heat waves. UHI observation data are necessary for zoning areas with different land use types, for example, increasing the amount of open space and greenery, and improving the thermal comfort of citizens.

Spatiotemporal Characterization Of Land Surface Temperature In Relation Landuse/Cover: A Spatial Autocorrelation Approach

Abstract The land use and land cover (LULC) characteristics of Ghaziabad have experienced dynamic changes because of the city’s ongoing industrialization and urbanisation processes. These shifts can be directly attributed to human actions. These shifts can be directly attributed to human actions. Thermal variation in the study area necessitates LULC analysis. Landsat and Sentinel satellite data for 2011 and 2021 were used to map LULC, estimate land surface temperature (LST) and analysis spatial autocorrelation among the variables using ArcGIS software and the Google Earth Engine (GEE) cloud platform. A sharp descent is observed in the cropland while built-up area has increased during the study period. With the increase in the built-up surface in the area, the ambient temperatures have also increased from 18.70 °C in 2011 to 21.81 °C in 2021 leading to urban heat island effect. At all spatial scales, spatial autocorrelation is a characteristic property of most ecological parameters. The spatial clustering of LST in an ecosystem can play a crucial role in determining the dynamics of LULC.The Moran’s, I show that there is a considerable level of spatial autocorrelation in the values of LST and highly clustered pattern for both the years. Monitoring and understanding the surface thermal environment is crucial to discerning the causes of climate change.

Modeling the intensity of surface urban heat island based on the impervious surface area

The urban heat island (UHI) effect is anticipated to be intensified and cause severe damage to human society under future urbanization and climate change. Due to the complicated human-nature interactions in the urban domain, it is challenging to characterize the intensity of UHI quantitatively, especially at the global scale. Here, we proposed a framework to estimate the surface urban heat island intensity (SUHII) based on satellite-derived land surface temperature (LST) and the derived impervious surface area (ISA) data across 355 cities worldwide from 2003 to 2018. First, we employed linear regression models to estimate LST using ISA as the explanatory variable, given that the LST generally elevated with increasing urban intensity along the urban-rural gradient. Then, using the modeled LST, we estimated the spatiotemporal patterns of SUHII worldwide. The established linear regression models with ISA as the explanatory variable perform reasonably well in estimating LST for both daytime and nighttime, with average RMSEs of 1.40 and 0.80 °C, respectively. The resulting SUHII reasonably captures the spatial heterogeneity across cities in the world, with the highest value in the tropical region at daytime and in the arid region at nighttime. The estimated and observed SUHII show good agreement with average R2 values of 0.81 and 0.72 for daytime and nighttime, respectively. Using ISA as an indicator for LST estimation, this study first implemented a comprehensive estimation of SUHII at the global scale. The proposed methodology is of great potential to project future SUHII by expanding urban impervious surfaces under diverse scenarios jointly determined by shared socioeconomic pathways and climate change scenarios.