Prediction Of Land Surface Temperature Research Articles

Analysis and prediction of land surface temperature with increasing urbanisation using satellite imagery

The unplanned growth of urbanization in towns and cities has led to variations in Land Surface Temperature (LST) as the green lands are converted into impervious structures without land cover management. In this study, an effort is made to study the transformational change in natural land cover area over the years and its impact on LST in Ernakulum District of Kerala, India. As per the current statistics from one of the reports on “Observed Rainfall Variability and Changes Over Kerala State”, by Indian Metrological Department in January 2020 shows that there is a significant decreasing trend in rainy days in many districts of Kerala which has resulted in highest temperature records in recent years. To understand the change in temperature trends in the district, Landsat data is collected for four different years 2005, 2010, 2015 and 2020 that maps Land Use and Land Cover (LULC) pattern for vegetation cover and built-up areas. It is important to interpret LULC maps as converting the vegetative land in built-up areas leads to multiple reflecting surfaces and temperature trapping which serves as the most direct way to understand the influence of global warming. The major merit of this research is (i) to interpret the temporal changes in LULC for categorizing the images in different land covers, (ii) to analyze Urban Thermal Field Variance Index (UTFVI) to quantify the urban heat island (UHI) effect in the region, (iii) to assess change in temperature by analyzing the spatio-temporal variations in mean temperatures for different land cover classes, and (iv) to analyze the trend of LST wrt to vegetation spectral index i.e., Normalized Differential Vegetation Index (NDVI) and spectral building index i.e., Normalized Difference Building Index (NDBI) to predict the percentage change in temperature in successive years using the widely used regression model. The computational simulation findings show that the percentage area of vegetated land decreases from 47.91 % to 29.04 % and the LST change analysis observed for years from 2005 to 2020 is 2.55 °C. For built up land, the percentage area is increased from 6.50 % to 15.27 % and the change in LST observed from year 2005–2020 is 4.82 °C which is a significant rise in temperature. The overall accuracy achieved for four respective years comes out to be 93, 89, 90, and 92 %. It has been observed from the analysis, that there is a change in temperature over the period of time from 2005 to 2020 b 3.45 °C. This is due to unplanned use of land cover classes that show considerable change in LULC maps that can be seen in the simulated results for a given study area. If the rate of change in area appeared to constantly change in this manner, then there is a possibility of further increase in land surface temperature and that will contribute in increase of temperature. The rise in mean LST from year 2020–2025 would be 0.5 °C and 1.0 °C for year 2030.

Consecutive one-week model predictions of land surface temperature stay on track for a decade with chaotic behavior tracking

Temperature prediction over decades provides crucial information for quantifying the expected effects of future climate changes. However, such predictions are extremely challenging due to the chaotic nature of temperature variations. Here we devise a prediction method involving an information tracking mechanism that aims to track and adapt to changes in temperature dynamics during the prediction phase by providing probabilistic feedback on the prediction error of the next step based on the current prediction. We integrate this information tracking mechanism, which can be considered as a model calibrator, into the objective function of the proposed method to obtain the corrections needed to avoid error accumulation. Experimental results on the task of global weekly land surface temperature prediction over a decade validate the effectiveness of the proposed method.

Prediction of Potential Urban Hot Spots in Urban Environments Using Convolutional Neural Networks and Remote Sensing Techniques

Abstract. With the increase in built-up areas and rising urban populations, Land Surface Temperature (LST) has significantly increased, leading to the proliferation of Urban Hot Spots (UHS) in urban environments. To mitigate UHS proactively, researchers have conducted studies using various models to predict LST. However, current predictions are primarily based on data samples from isolated stations, making them unfeasible for continuous LST prediction on larger scales, such as regional levels. Therefore, this research aims to use Singapore as a case study to predict UHS on a regional scale using machine learning based on essential variables. Specifically, this research proposes training a Convolutional Neural Network (CNN) model using identified independent variables, including elevation, Normalized Difference Built-up Index (NDBI), Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), population, and Land Use and Land Cover (LULC), along with the target variable, UHS. After training, the model achieves high test accuracy and is fed projected data, subsequently producing projected UHS locations. The findings indicate that new UHS primarily appear in southwestern areas, Marina Bay, and the northwest regions of the country. This research predicts that 2.24 percent of the site could be classified as UHS by 2025, compared to the current percentage of 0.95 percent. Based on these projections, the research proposes preventative measures to proactively mitigate UHS. This research fills the gap by constructing a prediction model that can predict UHS locations on a regional scale.

Data-driven approach for land surface temperature retrieval with machine learning and sentinel-2 data

This research endeavors to advance land surface temperature (LST) prediction accuracy through the development of a sophisticated machine learning model. Leveraging the potential of Sentinel 2 data and atmospheric parameters, we augment Landsat-based LST with MODIS-based LST, enriching the temporal dimensions of our dataset. A distinctive feature of our study is the pioneering use of Sentinel 2 data as inputs for LST prediction, a facet scarcely explored in the existing literature. Our investigation delves into the correlation dynamics between LST and atmospheric parameters. Notably, the study employs a diverse set of machine learning models, including Extra Trees, Random Forests, LightGBM, XGBoost, and Support Vector Regressor. These models collectively exhibit superior performance, with Extra Trees emerging as a standout performer, with a minimal mean absolute error (MAE) of 0.423, a root mean square error (RMSE) of 1.340 °C, and an impressive coefficient of determination (R2) of 0.984. The exploration of Sentinel 2 data as an input source for LST prediction not only refines predictive accuracy but also opens novel research avenues in the realm of LST dynamics. This study contributes to the existing body of knowledge by introducing innovative methodologies and providing a comprehensive understanding of the intricate correlations influencing LST.

All weather land surface temperature estimation by combining thermal infrared and passive microwave radiometry: a study over India

ABSTRACT Land Surface Temperature (LST) plays a crucial role in water and energy cycle studies. However, clouds pose a significant challenge in obtaining continuous LST time series from Thermal Infrared (TIR) sensors. To overcome this challenge, this study leverages the potential of Passive Microwave Radiometry (PMR), which offers all-weather observation capabilities, albeit at a coarser spatial resolution to estimate all-weather LST over India. In this study, we trained a Random Forest (RF) model using clear sky LST from either Moderate Resolution Spectro Radiometer (MODIS) or Visible Infrared Imaging Radiometer Suite (VIIRS) and passive microwave observations from Advanced Microwave Scanning Radiometer-2 (AMSR2), enabling LST estimation at a 1 km spatial resolution under clear and cloudy conditions. The performance of the models was evaluated by comparing the RF simulated LST with observed LST data from MODIS and VIIRS satellites. The Root Mean Square Error (RMSE) for the RF models was found to be 2.17 K and 2.29 K respectively, with coefficient of determination (R2) values of 0.97 for both the models. Furthermore, comparisons with in-situ observations resulted in RMSE values of 2.24–3.70 K and 2.40–4.07 K for clear sky and cloudy sky LST, respectively, for MODIS. Similarly, for the VIIRS LST prediction model, the RMSE values were 2.61–3.51 K and 2.60–3.97 K for clear and cloudy sky conditions. These findings demonstrate the potential of using passive microwave radiometers to estimate LST and highlight the applicability of the RF model for LST estimation under overcast conditions.

Machine learning assisted prediction of land surface temperature (LST) based on major air pollutants over the Annamayya District of India

Remote sensing (RS), Geographic information systems (GIS), and Machine learning can be integrated to predict land surface temperatures (LST) based on the data related to carbon monoxide (CO), Formaldehyde (HCHO), Nitrogen dioxide (NO2), Sulphur dioxide (SO2), absorbing aerosol index (AAI), and Aerosol optical depth (AOD). In this study, LST was predicted using machine learning classifiers, i.e., Extra trees classifier (ET), Logistic regressors (LR), and Random Forests (RF). The accuracy of the LR classifier (0.89 or 89%) is higher than ET (82%) and RF (82%) classifiers. Evaluation metrics for each classifier are presented in the form of accuracy, Area under the curve (AUC), Recall, Precision, F1 score, Kappa, and MCC (Matthew’s correlation coefficient). Based on the relative performance of the ML classifiers, it was concluded that the LR classifier performed better. Geographic information systems and RS tools were used to extract the data across spatial and temporal scales (2019 to 2022). In order to evaluate the model graphically, ROC (Receiver operating characteristic) curve, Confusion matrix, Validation curve, Classification report, Feature importance plot, and t- SNE (t-distributed stochastic neighbour embedding) plot were used. On validation of each ML classifier, it was observed that the RF classifier returned model complexity due to limited data availability and other factors yet to be studied post data availability. Sentinel-5-P and MODIS data are used in this study.

Prediction of land surface temperature using spectral indices, air pollutants, and urbanization parameters for Hyderabad city of India using six machine learning approaches

Urban areas face rising land surface temperatures (LST) and increased air pollution due to urbanization and industrialization. The consistent rise in LST impacts lives of the residents. Thus, LST forecasts can help manage activities more comfortably. The present study aims to predict LST for Hyderabad city, India using five-year (2018–2022) data on air pollution and meteorological parameters (from ambient air quality monitoring stations) and MODIS LST data. Six machine learning models i.e., Multiple Linear Regression (MLR), Support Vector Regression (SVR), Random Forest (RF), Artificial Neural Networks (ANN), XGBoost, and Long Short-Term Memory (LSTM), were used for LST prediction. Considerable influence of PM2.5 and CO (during summer) and SO2 (during winter) on LST was observed which demonstrated high sensitivity of these parameters on LST. LST exhibited a weak correlation with individual air pollutants while strong relationship of LST with all the study variables, when considered simultaneously, was observed. ANN method demonstrated better accuracy with lower error metrics, comprising of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE), compared to the other approaches with ranking in the order ANN > RF > SVR > XGBoost > LSTM > MLR. ANN model for Hyderabad city was validated by using it for prediction of LST of another geographical area i.e. Bengaluru city, India. The result of this study can provide insights for policymakers, urban planners, and environmental agencies for targeted interventions for temperature regulations and to mitigate urbanization's impact.

Revealing the urban heat Island: Investigating spatiotemporal surface temperature dynamics, modeling, and interactions with controllable and non-controllable factors

The unchecked growth of urban populations has spurred rapid and unsustainable urban expansion, exacerbating environmental degradation both locally and globally. This research offers a comprehensive analysis of the Urban Heat Island (UHI) phenomenon in Bangalore and Hyderabad, India, focusing on its spatial and temporal distribution and its correlation with air pollutants. Conducted over the course of summer and winter seasons from 2001 to 2021, this study advances our understanding of UHI dynamics in rapidly urbanizing regions, shedding light on the complex interplay between urbanization, climate, and air quality. The primary objective of this study is to assess and compare the predictive performance of baseline Random Forest (RF) and Extreme Gradient Boosting (XGBoost) models for forecasting Land Surface Temperature (LST) in Hyderabad, India, while examining the impact of integrating spatial information into these models. Incorporating an extensive array of input variables, ranging from spectral indices to terrain features, land use/land cover (LULC) data, atmospheric parameters, and spatial data, the analysis reveals significant relationships between LST and various factors. Notably, Aerosol Optical Depth (AOD) and Normalized Difference Built-up Index (NDBI) exhibit strong positive correlations with LST, while Enhanced Vegetation Index (EVI) demonstrates a pronounced negative correlation. Similarly, Modified Normalized Difference Water Index (MNDWI) displays the strongest positive correlation with LST (r = 0.49), whereas EVI exhibits the most notable negative correlation (r = −0.67). Additionally, Modified Bare Soil Index (MBI) demonstrates a noteworthy negative correlation (r = −0.48) with LST for Bangalore. Atmospheric components such as CO, NO2, and O3 also show significant positive correlations with LST, with NO2 displaying the highest correlation during winter in urban areas. XGBoost emerges as the superior model, achieving high R2 values of 0.84 for Bangalore and 0.88 for Hyderabad. Integration of spatial information consistently enhances the performance of both RF and XGBoost models, resulting in reduced Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) values. However, predictions over water bodies tend to be underestimated. The study underscores the importance of incorporating spatial data for accurate LST prediction, with implications for environmental monitoring and management. Despite the advancements made, further research is needed to elucidate the specific mechanisms through which spatial information improves model performance, suggesting avenues for future investigation and refinement of modeling methodologies. At the intersection of rapid urban growth and environmental conservation, these findings are poised to inform policymakers, urban planners, and researchers, guiding them towards innovative and sustainable urban development strategies aimed at mitigating the adverse impacts of rapid urban expansion while promoting ecosystem well-being. The study contributes to spatial modeling strategies and refines LST prediction methodologies, offering valuable insights for addressing the challenges posed by urbanization and climate change in rapidly urbanizing regions.

Exploring spatial machine learning techniques for improving land surface temperature prediction

Land Surface Temperature (LST) is a crucial parameter in Earth observation and environmental studies due to its significance in various fields. The purpose of this study is to investigate the effects of including spatial information into the Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) models for forecasting LST. The significance and impact of each input parameter on the models' predictive capabilities are assessed using the SHAP (SHapley Additive exPlanations) approach and the model intercomparisons were done using the error evaluation metrices. The predictions were further validated using the Pearson correlation, independent samples t-test and potential geographic anomalies in the predictions are examined by spatial comparison of predicted errors using classification maps and error envelopes. The projected errors are within the acceptable range and range from −2.267 °C to 1.292 °C for the spatially enhanced RF model and from −1.675 °C to 1.439 °C for the spatially enhanced XGBoost model. These error ranges closely align with the training data's quality flag of ±2 °C, demonstrating the models' capability to predict LST accurately and within a reasonable error range. The findings show the significance of adding spatial information for precise LST prediction and draw attention to possible uses for such models in environmental monitoring and management. The work advances our understanding of spatial modelling strategies and offers practical guidelines for enhancing LST forecasts.

Land surface temperature prediction with dual satellites and BPNN: a comparative transfer function analysis

ABSTRACT Driven by global sustainability goals, understanding land surface temperature (LST) in urban thermal environments (UTE) is crucial. This study analyses remotely sensed LST data from Aqua and Terra satellites in a rapidly growing non-tier Indian city. It uniquely integrates remote sensing and soft computational analysis to predict daytime LST using artificial neural networks (ANN). The study is categorized based on transfer functions and hyper-parameters to enhance the comprehension of LST prediction and simulation scenarios by employing Back Propagation Neural Network (BPNN) model. Results indicate that the tansig transfer function yields the best performance overall in Aqua and Terra LST prediction, while purelin performs the least effectively. This study provides evidence that soft computing techniques effectively estimate LST data, offering a valuable solution for data scarcity issues. However, the findings are specific to the study area and may vary elsewhere. Exploring alternative methods and incorporating data from other areas could potentially improve accuracy and overall performance. Future research could focus on investigating the mechanisms behind different methods and their interactions with various factors. Additionally, integrating other variables like vegetation index, soil moisture, and others could contribute to a more holistic understanding of LST dynamics and enhance predictive capabilities.

Urban land surface temperature forecasting: a data-driven approach using regression and neural network models

The insinuations of the ailments associated with the unrestrained and disorganized proliferation of artificial impervious materials over natural surfaces are prevalent among city dwellers. These impacts can be comprehended by estimating land surface temperature (LST), as it is vital for evaluating urban climate, particularly to explain the intensity of urban heat islands and to define the health and welfare of the planet as well as the living beings. Urbanization-driven landscape changes severely disrupt comfortable living in almost every city, necessitating monitoring and modelling historical, current, and likely future LSTs. This research article proposes two forecasting techniques: Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models. These models have been widely accepted for the efficient prediction of climatic parameters, including LST, over an urban area. The landscape, elevation, and LST trend served as input to the models for an accurate prediction of LST. The analysis was performed over the Kolkata Metropolitan Area (KMA) with an additional 10 km buffer to understand urban growth and its effect on the LST of the entire region. The two developed models (MLR and ANN) effectively anticipated the LST over the KMA region. A continual increment in the surface temperatures ranging from 1 °C to 4 °C, over existing and likely-predicted urban areas was comprehended. It was anticipated that the regions near the urban areas will also experience severe discomfort and heat waves without proper mitigation measures. This scientific literature provides essential insights for decision-makers, stakeholders, and government officials to articulate new policies and modify the existing ones to create a sustainable and livable urban environment for the inhabitants.

Investigating the effect of surface urban heat island on the trend of temperature changes

Urbanization has led to the emergence of surface urban heat islands (SUHI) and air pollution, necessitating comprehensive investigations into the urbanization process. This study explores the impact of urbanization on temperature trends in northern Iran using remote sensing and neural networks. MODIS images from 2001, 2010, and 2019 were employed to determine climatic features and vegetation levels. SUHI indices and the Urban Thermal Field Variance Index (UTFVI) were utilized to assess ecological comfort and urban heat islands. Markov chain methods were used for forecasting future thermal pollution. The relationship between temperature, air pollutants, spectral indices, and thermal indices was investigated through regression analysis. Furthermore, multilayer perceptron (MLP), radial basis function (RBF), and long short-term memory (LSTM) neural networks were employed for predicting indicator values. The findings reveal an increase in thermal pollution indicated by elevated Land Surface Temperature (LST), SUHI, and UTFVI values, along with a decrease in Normalized Difference Vegetation Index (NDVI) values between 2001 and 2019. The Urbanization Index (UI) values demonstrate a 5% increase in urban areas during the same period. The UTFVI values suggest a decline in ecological comfort due to urbanization and reduced vegetation, with the majority of the region experiencing the poorest thermal comfort in 2019. The SUHI index peaked at 11.72 in 2019, with the highest values observed in spring and the lowest in winter. Correlation analysis highlights a significant relationship between LST and various factors, including SUHI, UTFVI, CH4, SO2, and CO2, underscoring the influence of temperature increase on pollution escalation in the study area. Air pollution assessment indicates the highest concentration of CO gas in the northern and southern parts of the region. Tehran exhibits the highest CO gas levels at 0.051 ppm, while Golestan records the lowest at 0.012 ppm. SO2 levels reach their peak in northern regions (0.0088 ppm) and are lowest in southern regions (0 ppm). The Markov and CA-Markov chain methods predict that by 2050, approximately 50% of the region will experience high temperatures. Neural network results demonstrate that the LSTM method outperforms MLP and RBF methods, with R2 values of 0.99 and 0.98, respectively, indicating higher accuracy in LST prediction during testing and validation stages. Overall, this study highlights the potential of neural networks in predicting thermal pollution and facilitating effective land management strategies in urban areas.

Applying geographically weighted regression to quantify the impact of impervious surface density on land surface temperature in Ho Chi Minh City, Vietnam

Surface characteristics are believed to be the primary factor causing spatial variation in land surface temperature (LST), especially in a large and rapidly growing city like Ho Chi Minh City. The study aims to estimate the spatial relationship between land surface temperature (LST) and impervious surface density (ISA) by using three statistical techniques: 1) Optimized hot spot analysis; 2) Global linear regression (OLS); and 3) Geographically weighted regression (GWR). LST and ISA were extracted from Landsat 8 OLI of 2020. According to the optimized hot spot analysis’s findings, over 80% of the areas of high ISA values (mean ISA: 0.86, SD: 0.13) overlap with areas of high LST values (mean LST: 36.2°C, SD: 2.04°C), with a correlation coefficient of r=0.775. Likewise, Moran’s I index (I=0.82, Z=72, P<0.001) indicates that LST in Ho Chi Minh City has a significantly spatial dependence, so geographically weighted regression (GWR) and global linear (OLS) models need to be taken into account simultaneously. Coefficient of determination R2 and AICc (Akaike information criterion corrected) were used to compare the models. R2 increases from 0.78 to 0.87, whereas AICc decreases from 8047 to 7033 in OLS and GWR, respectively. GWR has a more substantial explanatory power for the association between LST and ISA and offers superior LST prediction accuracy compared to OLS. Potential urban heat island areas can be accurately detected by combining GWR and optimized hot spot analysis.

Integrating planar and vertical environmental features for modelling land surface temperature based on street view images and land cover data

With ongoing urbanization, high temperatures occur more frequently in urban environments. The urban heat island (UHI) effect has serious impacts on people's living environment and health, and has become an important concern for climate changes. Existing studies on environmental effects on UHI mainly focus on aspects related to urban horizontal expansion, but investigation into the effects of vertical urban growth (e.g., tall buildings) is limited. This study proposes a comprehensive set of features to characterize the planar and vertical aspects of urban environments by using panoramic Street View images (SVIs) and land cover data, and employs multiple linear regression and machine learning models to model land surface temperature (LST). Furthermore, an explainable AI method is applied to quantify the warming/cooling effects of each individual feature on LST.The results show that the proposed vertical/planar features contribute to the estimation of LST (R2 = 0.693). Adding the vertical features (extracted from SVIs) can improve LST prediction by 9.2% compared to using only planar features. Of all the features, built-up features contribute the most to LST variation, and large trees show strong cooling effects. These results serve as a basis for sustainable urban planning and help mitigate the UHI effect in cities.

LST determination of different urban growth patterns: A modeling procedure to identify the dominant spatial metrics

The growing threat of urban heating has attracted considerable scholarly attention to their modeling and prediction. To improve the urban land surface temperature (LST) determination, this study assumed that the spatial pattern of built-up patches matters in LST prediction. To test this idea in a rapidly-urbanizing landscape, different urban growth types including infilling, edge expansion and outlying patterns and their LST layers were extracted from Landsat images in 1992, 2002, 2012, and 2022. The mean LST of each growth pattern was then modeled using the multiple linear regression (MLR) analysis and an array of independent variables related to the spatial formation and LST of the newly-grown and previously-existed urban patches. Results of the best infilling MLR model (R2 = 0.579) showed that the highest mean LST of the infilling growth is expected to be around the center of large focal patches. The mean LST of the edge expansion growth patches were found to be influenced by the mean LST of the edge of their focal patches and their shape structure (R2 = 0.362). The area of the outlying growth patches was also selected as the sole predictor of their mean LST (R2 = 0.334). According to the findings, the mean LST of the outlying, infilling and edge expansion growth patterns are influenced by the landscape composition, configuration and structure, respectively. Our results corroborate the idea that the performance of the urban LST prediction and their practical implications can be improved when the built-up landscape is divided into more spatially similar growth classes.

A GENERIC MACHINE LEARNING-BASED FRAMEWORK FOR PREDICTIVE MODELING OF LAND SURFACE TEMPERATURE

Abstract. In the realm of data analytics, machine learning (ML) is one of the most successful techniques for making predictions. The ability of ML algorithms has also been studied in various aspects of land surface temperature (LST) besides its retrieval. The few investigations on LST retrieval using ML algorithms suggested that it may potentially obtain the LST values incorporating relevant variables of land surface parameters; however, the variables and ML models used differ, and so do their accuracies. The accuracy of the model is affected by the variable's contribution, its quality and quantity, and the fulfilment of each technique's assumptions. Hence this study provides a wide range of LST indicators to be employed for LST retrieval using a widely used ML algorithm, random forest. The ML algorithm framework for LST prediction is illustrated with significant spectral indices and terrain parameters across the highly industrialised district of Jharkhand, India. With the exception of one (aspect) variable, the analysis shows that all 20 variables that were included as independent factors were significant and equally contributed to the model. The model built with all the variables including the aspect of the terrain obtained an RMSE of 1.13 degree Celsius and R2 of 0.48. However, after the removal of aspect, the model obtained an R2 of 0.89 and RMSE of 0.74 °C. The performance of the model on consecutive removal of lesser significant variables are evaluated and the study made clear how crucial it is to consider several environmental or land-use factors that could be pertinent to LST.

Multiinformation Fusion Network for Mapping Gapless All-Sky Land Surface Temperature Using Thermal Infrared and Reanalysis Data

Towards fully exploring multidisciplinary research topics under global warming, the spatiotemporal discontinuities of land surface temperature (LST) products due to cloud contamination still challenge such research topics. Data fusion that seeks the best compromise between multiple data sources plays a key role in providing gapless LST data. However, most models only use information about the variation of LST at different times or the difference between different LST products without effectively combining the two pieces of information. With the rapid development of deep learning methods, powerful modeling capabilities can solve this problem. This article proposes a novel multi-information fusion network (MIFN) based on convolutional neural networks (CNNs) and attention mechanisms to map gapless all-sky LST, taking temporal-changing (TC) and data-differentiated (DD) information into consideration. Temporal normalization is used as a pre-processing step to match the time of Moderate Resolution Imaging Spectroradiometer (MODIS) and European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis fifth Generation (ERA5) LSTs. The MIFN extracts multiscale multi-temporal TC and DD features by network constraints. Then, the weights of the target LST features reconstructed by TC and DD features are assigned through an attention mechanism to fuse them reasonably. Finally, we design a loss function that combines TC, DD, and LST reconstruction to improve the accuracy of LST prediction further. We performed comprehensive data experiments to validate the performance of the new method. Our technique is more advantageous in generating MODIS-like LSTs than four state-of-the-art methods and two CNN models using a single piece of information.

The Simulation and Prediction of Land Surface Temperature Based on SCP and CA-ANN Models Using Remote Sensing Data: A Case Study of Lahore

Over the last two decades, urban growth has become a major issue in Lahore, accelerating land surface temperature (LST) rise. The present study focused on estimating the current situation and simulating the future LST patterns in Lahore using remote sensing data and machine learning models. The semi-automated classification model was applied for the estimation of LST from 2000 to 2020. Then, the cellular automata-artificial neural networks (CA-ANN) module was implemented to predict future LST patterns for 2030 and 2040, respectively. Our research findings revealed that an average of 2.8 °C of land surface temperature has increased, with a mean LST value from 37.25 °C to 40.10 °C in Lahore during the last two decades from 2000 to 2020. Moreover, keeping CA-ANN simulations for land surface temperature, an increase of 2.2 °C is projected through 2040, and mean LST values will be increased from 40.1 °C to 42.31 °C by 2040. The CA-ANN model was validated for future LST simulation with an overall Kappa value of 0.82 and 86.2% of correctness for the years 2030 and 2040 using modules for land-use change evaluation. The study also indicates that land surface temperature is an important factor in environmental changes. Therefore, it is suggested that future urban planning should focus on urban rooftop plantations and vegetation conservation to minimize land surface temperature increases in Lahore.

Examining the Relationship between Land Use/Land Cover (LULC) and Land Surface Temperature (LST) Using Explainable Artificial Intelligence (XAI) Models: A Case Study of Seoul, South Korea

Understanding the relationship between land use/land cover (LULC) and land surface temperature (LST) has long been an area of interest in urban and environmental study fields. To examine this, existing studies have utilized both white-box and black-box approaches, including regression, decision tree, and artificial intelligence models. To overcome the limitations of previous models, this study adopted the explainable artificial intelligence (XAI) approach in examining the relationships between LULC and LST. By integrating the XGBoost and SHAP model, we developed the LST prediction model in Seoul and estimated the LST reduction effects after specific LULC changes. Results showed that the prediction accuracy of LST was maximized when landscape, topographic, and LULC features within a 150 m buffer radius were adopted as independent variables. Specifically, the existence of surrounding built-up and vegetation areas were found to be the most influencing factors in explaining LST. In this study, after the LULC changes from expressway to green areas, approximately 1.5 °C of decreasing LST was predicted. The findings of our study can be utilized for assessing and monitoring the thermal environmental impact of urban planning and projects. Also, this study can contribute to determining the priorities of different policy measures for improving the thermal environment.

Developing a Spatiotemporal Model to Forecast Land Surface Temperature: A Way Forward for Better Town Planning

The change in the local climate is attributed primarily to rapid urbanization, and this change has a strong influence on the adjacent areas. Lahore is one of the fast-growing metropolises in Pakistan, representing a swiftly urbanizing cluster. Anthropogenic materials sweep the usual land surfaces owing to the rapid urbanization, which adversely influences the environment causing the Surface Urban Heat Island (SUHI) effect. For the analysis of the SUHI effect, the parameter of utmost importance is the Land Surface Temperature (LST). The current research aimed to develop a model to forecast the LST to evaluate the SUHI effect on the surface of the Lahore district. For LST prediction, remote sensing data from Advanced Spaceborne Thermal Emission and the Reflection Radiometer Global Digital Elevation Model and Moderate-Resolution Imaging Spectroradiometer sensor are exploited. Different parameters are used to develop the Long Short-Term Memory (LSTM) model. In the present investigation, for the prediction of LST, the input parameters to the model included 10 years of LST data (2009 to 2019) and the Enhanced Vegetation Index (EVI), road density, and elevation. Data for the year 2020 are used to validate the outcomes of the LSTM model. An assessment of the measured and model-forecasted LST specified that the extent of mean absolute error is 0.27 K for both periods. In contrast, the mean absolute percentage error fluctuated from 0.12 to 0.14%. The functioning of the model is also assessed through the number of pixels of the research area, classified based on the error in the forecasting of LST. The LSTM model is contrasted with the Artificial Neural Network (ANN) model to evaluate the skill score factor of the LSTM model in relation to the ANN model. The skill scores computed for both periods expressed absolute values, which distinctly illustrated the efficiency of the LSTM model for better LST prediction compared to the ANN model. Thus, the LST prediction for evaluating the SUHI effect by the LSTM model is practically acceptable.