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Urbanization has significantly impacted the environment and urban life, with urban green spaces playing a crucial role in enhancing sustainability and public health. Urban Green Spaces (UGS) offer benefits such as temperature regulation, ecological services, and improved thermal comfort, while providing entertainment, culture, disaster avoidance, and ecological services. This study evaluates the cooling effects of UGS in mitigating the Urban Heat Islands (UHIs) phenomenon in Delhi, India, using satellite imagery to assess the Land Surface Temperature (LST) of 24 parks. We calculated the Park Cooling Intensity (PCI) by applying the mono-window algorithm. We found that parks larger than 1.55 hectares with dense vegetation and water bodies significantly reduced surrounding temperatures by up to 8.28 °C. The PCI effect was significantly influenced by park size, vegetation density, and the presence of water bodies, with larger parks and denser vegetation and water bodies demonstrating enhanced cooling capacity. These findings highlight the importance of integrating green spaces into urban planning as vital infrastructure for enhancing urban resilience, reducing heat-related health risks, and ensuring equitable access to public health benefits. The study also highlights the importance of addressing socio-economic disparities in park accessibility, which have significant implications for equitable urban development. This study emphasized the importance of formulating a strategic urban planning approach that focuses on green spaces' growth, conservation, and equitable allocation, thereby fostering eco-friendly, habitable, and robust urban environments. [ABSTRACT FROM AUTHOR]

Since 2000, Clouds and the Earth's Radiant Energy System (CERES) instruments on Aqua and Terra satellites have recorded top-of-atmosphere (TOA) clear-sky outgoing radiation fluxes. Over northwest India, analysis of these two decades of data revealed significant negative trends in reflected shortwave radiation, indicating positive shortwave radiative forcing. To understand the drivers of this trend, we investigate changes in the surface reflectance, aerosol optical depth (AOD), Normalized Difference Vegetation Index (NDVI), and land surface temperature from MODIS observations during the same period. We find decreasing trends in surface reflectance and AOD, while NDVI shows a pronounced increase. Notably, despite positive shortwave radiative forcing, daytime land surface temperature exhibits a significant decrease, with only a slight nighttime increase. Sensitivity analysis using a radiative transfer model identifies decrease in the surface reflectance as the primary contributor to the decrease in TOA shortwave fluxes. This suggests that the observed greening, potentially linked to expanded irrigation projects, has played a crucial role by reducing both surface reflectance and aerosol production. Our findings highlight the potential for increasing vegetation as a locally effective strategy to mitigate the effects of global warming through enhanced evapotranspiration process, even in the presence of positive shortwave radiative forcing. Investigating regional climate trends is vital for understanding and quantifying climate change; these results highlight the need for future climate change impact studies to consider interplay between vegetation dynamics and regional climate. [ABSTRACT FROM AUTHOR]

The temporal behaviors of land surface temperature (LST) coupled with its associated parameters play a crucial role in determining the microclimate at the city scale. The increasing pattern of LST and consequent changes in biophysical parameters (parameters specify the amalgamation of living system with their physical characteristics including vegetation, water, built-up, bareness and drought parameters) at monthly, seasonal and annual time spans and from regional to global scale need to be comprehensively evaluated. The present study deals with LST estimation along with other spectral indices including Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Built-up Index (NDBI), Normalized Difference Bareness Index (NDBaI), and Normalized Multi-band Drought Index (NMDI) using Landsat series datasets from 1991 to 2022 of Aligarh city, Uttar Pradesh, India. The spatial pattern of LST indicates that the areas having water bodies and dense vegetation are colder such as the Aligarh Muslim University Campus and the catchment of Ganga canal areas, whereas the areas of high urbanization and bare grounds reflect high LST trends. Study finds a positive correlation of LST with NDBI (R2-0.56), NDBaI (R2-0.22) and NDWI (R2-0.22), whereas a negative correlation with NDVI (R2-0.35), MNDWI (R2-0.36) and NMDI (R2-0.41). Land use land cover (LULC)-based change detection in land cover classes was found consistent with the obtained results for spectral indices and LST patterns in the study area. Finally, the cross-validation using Modern-Era Retrospective analysis for Research and Applications (MERRA) reanalysis-based products of earth skin temperature and rainfall showed a good fit between observed and reanalysis products. [ABSTRACT FROM AUTHOR]

Droughts pose significant challenges to food security, affecting millions of people and hectares of land in India. Despite their widespread impact, assessing drought patterns at a high spatial resolution remains a challenge, particularly in regions with poor data availability. Standardised Precipitation Evapotranspiration Index (SPEI) is one of the most widely accepted drought indices. The publicly available SPEI reanalysis dataset has a poor spatial resolution for regional drought studies. Land Surface Temperature (LST) has the potential to act as an effective proxy for SPEI and can be used for high-resolution drought studies. The study considered six homogeneous drought regions (NWH, CPR, NEI, NCP, NWI, and SPR) over India, and the recent trend in LST and SPEI over these regions was estimated using modified Mann-Kendell (MMK) trend and Sen's slope estimator at monthly, seasonal, and annual time scales. The correlation between SPEI and LST was also checked at a 95% confidence level. Results indicate statistically significant decreasing annual LST trends in NWH (slope = −0.10), CPR (slope = −0.09), and NWI (slope = −0.11) regions, while increasing SPEI trends were observed in NWI (slope = 0.06) and NCP (slope = 0.01) regions during 2001–2019. Additionally, NCP (p = 0.025), CPR (p = 0.002), and NWI (p = 0.020) exhibited significantly decreasing LST trends during winter, with a notable post-monsoon decrease observed in NWI. Significant correlations between annual LST and SPEI were observed only in CPR (r =−0.527) and NWI (r = −0.601), with seasonal associations found predominantly in winter across CPR, NEI, NCP, and NWI regions. Notably, a significant correlation was observed in all months except July over NWI, with consistent correlations during most of the winter months in NWH, CPR, NCP, and NWI regions. The observed trends and correlations can provide valuable insights for policymakers and stakeholders in formulating effective drought mitigation strategies. [ABSTRACT FROM AUTHOR]

The phenomenon of urban heat island (UHI) is characterized by industrial, economic development, unplanned and unregulated land use as well as a rapid increase in urban population, resulting a warmer inner core in contrast to the surrounding natural environment, thus requiring immediate attention for a sustainable urban environment. This study examined the land use/land cover (LULC) change, pattern of spectral indices (Normalized Difference Vegetation Index, NDVI; Normalized Difference Water Index, NDWI; Normalized Difference Built-up Index, NDBI and Normalized Difference Bareness Index, NDBaI), retrieval of land surface temperature (LST) and Urban Thermal Field Variance Index (UTFVI) as well as identification of UHI from 2000 to 2022. The relationship among LST and LULC spectral indices was estimated using Pearson's correlation coefficient. The Landsat-5 (TM) and Landsat-8 (OLI/TIRS) satellite data have been used, and all tasks were completed through various geospatial tools like ArcGIS 10.8, Google Earth Engine (GEE), Erdas Imagine 2014 and R-Programming. The result of this study depicts over the period that built-up area and water bodies increased by 119.78 and 35.70%, respectively. On the contrary, fallow and barren decreased by 55.33 and 32.31% respectively over the period. The mean and maximum LST increased by 3.61 °C and 2.62 °C, and the study reveals that a high concentration of UTFVI and UHI in industrial areas, coal mining sites and their surroundings, but the core urban area has observed low LST and intensity of UHI than the peripheral areas due to maintained vegetation cover and water bodies. An inverse relationship has been found among LST, NDVI and NDWI, while adverse relationships were observed among LST, NDBI and NDBaI throughout the period. Sustainable environment planning is needful for the urban area, as well as the periphery region and plantation is one of the controlling measures of LST and UHI increment. This work provides the scientific base for the study of the thermal environment which can be one of the variables for planning of Asansol City and likewise other cities of the country as well as the world. [ABSTRACT FROM AUTHOR]

This study investigates the incidence of heat wave and cold wave condition during 2000 – 2022 in the Bathinda district of South-Western region of Punjab. Notable spikes in heat wave (HW) activity were observed in 2002 and 2022 with 29 and 27 days respectively. Similarly, for severe heat waves (SHW), 2010 and 2022 witnessed the highest frequencies recording 16 and 18 days respectively. Conversely, cold wave (CW) events peaked in 2005 and 2008 with 10 and 11 days respectively. Notably, 2008 also observed the highest frequency of severe cold wave (SCW) days with 15 days. However, results revealed decline in cold wave days towards the latter years, while severe cold wave days also exhibited decreasing frequencies like 2015 and 2016 recorded zero CW and SCW days. One key finding highlights a substantial correlation between land surface temperature (LST) and maximum air temperature during heat wave periods (R² = 0.83), indicating LST’s efficacy as an indicator for monitoring temperature trends during heat wave events. [ABSTRACT FROM AUTHOR]

Rapid urbanization creates complexity, results in dynamic changes in land and environment, and influences the land surface temperature (LST) in fast-developing cities. In this study, we examined the impact of land use/land cover (LULC) changes on LST and determined the intensity of urban heat island (UHI) in New Town Kolkata (a smart city), eastern India, from 1991 to 2021 at 10-a intervals using various series of Landsat multi-spectral and thermal bands. This study used the maximum likelihood algorithm for image classification and other methods like the correlation analysis and hotspot analysis (Getis–Ord Gi* method) to examine the impact of LULC changes on urban thermal environment. This study noticed that the area percentage of built-up land increased rapidly from 21.91% to 45.63% during 1991–2021, with a maximum positive change in built-up land and a maximum negative change in sparse vegetation. The mean temperature significantly increased during the study period (1991–2021), from 16.31°C to 22.48°C in winter, 29.18°C to 34.61°C in summer, and 19.18°C to 27.11°C in autumn. The result showed that impervious surfaces contribute to higher LST, whereas vegetation helps decrease it. Poor ecological status has been found in built-up land, and excellent ecological status has been found in vegetation and water body. The hot spot and cold spot areas shifted their locations every decade due to random LULC changes. Even after New Town Kolkata became a smart city, high LST has been observed. Overall, this study indicated that urbanization and changes in LULC patterns can influence the urban thermal environment, and appropriate planning is needed to reduce LST. This study can help policy-makers create sustainable smart cities. [ABSTRACT FROM AUTHOR]

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The aim of the present study was to develop a multi‐criteria decision support system to find out possible aquaculture sites available in Nagapattinam district of Tamil Nadu, India. This study focuses on identifying possible aquaculture sites available in Nagapattinam district using remote sensing and GIS approaches. A geographical model has been developed to choose the probable aquaculture sites. The remote sensing and geographic information system techniques were applied to study the decisive parameters that play a vital role in the site suitability for aquaculture purposes. The chosen decisive parameters viz., normalized difference vegetation index, digital elevation model, soil type, normalized difference water index, land use land cover area and land surface temperature were subjected to the development of decision support system. Moreover, four different multi‐criteria decision support techniques viz., weighted overlay, analytical hierarchy process, fuzzy classified analytical hierarchy process and fuzzy classified weighted overlay were applied in the present study. The results obtained from the study rendered that the weighted overlay and the analytical hierarchy process outperforms the fuzzy classified models. The suitability ratio of analytical hierarchy process and weighted overlay methods were found to be 12.7 and 12.4 respectively. A total of 25 locations were selected and mapped in the Nagapattinam region and their accuracy was checked. The analytical hierarchy process results were obtained with an accuracy of 84%. Hence, the analytical hierarchy process provided better results than the weighted overlay technique. The study rendered that the area of 7.22% is highly suitable for aquaculture. These findings are essential for the development of sustainable aquaculture, assisting stakeholders in selecting suitable aquaculture sites that maximize productivity while minimizing environmental degradation. [ABSTRACT FROM AUTHOR]

The significant natural energy sources for reducing the global usage of fossil fuels are renewable energy (RE) sources. Solar energy is a crucial and reliable RE source. Site selection for solar photovoltaic (PV) farms is a crucial issue in terms of spatial planning and RE policies. This study adopts a Geographic Information System (GIS)-based Multi-Influencing Factor (MIF) technique to enhance the precision of identifying and delineating optimal locations for solar PV farms. The choice of GIS and MIF is motivated by their ability to integrate diverse influencing factors, facilitating a holistic analysis of spatial data. The selected influencing factors include solar radiation, wind speed, Land Surface Temperature (LST), relative humidity, vegetation, elevation, land use, Euclidean distance from roads, and aspect. The optimal sites of solar PV power plant delineated revealed that 'very low' suitability of site covering 4.866% of the study area, 'low' suitability of site 13.190%, 'moderate' suitability of site 31.640%, 'good' suitability of site 32.347%, and 'very good' suitability of site for solar PV power plant encompassing 17.957% of the study area. The sensitivity analysis results show that the solar radiation, relative humidity, and elevation are the most effective on the accuracy of the prediction. The validation of the results shows the accuracy of solar PV power plant prediction using MIF technique in the study area was 81.80%. The integration of GIS and MIF not only enhances the accuracy of site suitability assessment but also provides a practical implementation strategy. This research offers valuable insights for renewable energy policymakers, urban planners, and other stakeholders seeking to identify and develop optimal locations for solar energy power farms in their respective regions. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm to detect coal fires using time-series Land Surface Temperature (LST) maps generated from data acquired by a wide-swath Visible Infrared Imaging Radiometer Suite (VIIRS) satellite sensor. VIIRS provides remotely sensed thermal images daily during daytime and nighttime. The proposed algorithm primarily uses the trend component extracted at pixel level from time-series LST maps by using the Seasonal Trend decomposition based on Loess (STL) model. The proposed algorithm is suitable for detecting coal fires at the regional scale. The obtained results were validated with coal fire observations collected during a field survey. [ABSTRACT FROM AUTHOR]

The alteration of the Earth's surface due to urbanization and the formation of urban heat islands is one of the most evident and widely discussed anthropogenic impacts on Earth's microclimate. The elevated land surface temperature in the urban perimeter compared with the surrounding non-urban area is known as the surface urban heat island (SUHI) effect. India has experienced swift urban growth over the past few decades, and this trend is expected to persist in years to come. The literature published on SUHI in India focuses only on a few specific cities, and there is limited understanding of its geospatial variation across a broader region and its long-term trend. Here, we present one of the first studies exploring the long-term diurnal (daytime, and nighttime), seasonal, and annual characteristics of SUHI in the 20 largest urban centers of India and its neighboring countries. The study highlights a statistically significant (95% confidence interval) rise in nighttime surface temperatures across major cities based on a linear fit over 23 years (2000–2023) of MODIS land surface temperature satellite observations. The nighttime SUHI was found to be more conspicuous, positive, and consistent when compared with daytime satellite observations. The nighttime SUHI for April–May–June representing the pre-monsoon and onset of monsoon months for the top 10 cities, ranged from 0.92 to 2.33 °C; for December–January–February, representing the winter season, it ranged from 1.38 to 2.63 °C. In general, the total change in the nighttime SUHI based on linear fit (2000–2023) for the top ten cities showed warming over the urban region ranging from 2.04 to 3.7 °C. The highest warming trend was observed during the months of May–June–July (3.7 and 3.01 °C) in Ahmedabad and Delhi, cities that have undergone rapid urbanization in the last two to three decades. The study identified strongly positive annual SUHI intensity during nighttime, and weakly negative to moderately positive annual SUHI intensity during daytime, for major cities. Jaipur (India), Lahore (Pakistan), Dhaka (Bangladesh), and Colombo (Sri Lanka) showed a nighttime SUHI intensity of 2.17, 2.33, 0.32, and 0.21 °C, respectively, during the months of April–May–June, and a nighttime SUHI intensity of 2.63, 1.68, 0.94, 0.33 °C, respectively, for the months of December–January–February (2000–2023). It is apparent that the geographical location (inland/coastal) of the city has a high influence on the daytime and nighttime SUHI patterns. The current research is intended to help city planners and policymakers better understand SUHI intensity (day and night/seasonal basis) for developing strategies to mitigate urban heat island effects. [ABSTRACT FROM AUTHOR]

In modern days, a sustainable urban planning system requires a balance of vegetation, water, and settlement. The proportions of these surface features directly influence the land surface temperature (LST) in an urban area. LST primarily depends on the emittance of land use/land cover (LULC) categories. In an urban area, changes in LULC categories as well as local warming are the prime regulators of LST change. The study analyses the LULC change and its impact on LST in Imphal City, India. Landsat satellite data for the summer season and winter seasons for 1991, 2001, 2011, and 2021 have been used in this study. Results show that the mean LST of the study area increased at >1% rate/decade. The green area and water area decreased the LST values whereas the built-up area and fallow lands increased the LST values. The study indicates the consequences of proper land conversion to regulate the LST change. Moreover, the influence of population on LST is also determined. The continuous rising trend of population is a positive factor of increasing LST. The study may help the town and country planners to generate sustainable urban land. [ABSTRACT FROM AUTHOR]

This study investigates the diurnal migration of the low‐level jet (LLJ) over the Bay of Bengal in the summer monsoon, which remains understudied despite the well‐documented diurnal offshore propagation of rainfall worldwide. The southwesterly LLJ exhibits maximum strength during the night and morning. The LLJ undergoes a substantial eastward migration of approximately 600 km from the eastern offshore of India at midnight to the center of the Bay of Bengal by afternoon. A linear land‐sea breeze model is effective in capturing the diurnal migration with similar speed and phase. Including background wind offshore extends the migration, rather than confining it to the coast. This migration is driven by inertia‐gravity waves arising from the diurnal land‐sea thermal contrast and is influenced by the westerly upstream wind from the Indian subcontinent. Our results have important implications for understanding diurnal monsoon circulations and their associated precipitation. Plain Language Summary: The low‐level jet (LLJ) in the summer monsoon plays a crucial role in affecting nocturnal downstream precipitation in South Asia. Previous studies have primarily concentrated on changes in the jet's strength during the day, neglecting its varying location. The present study addresses this gap by providing a comprehensive examination of the previously neglected aspect of the diurnal offshore migration of the LLJ over the Bay of Bengal. The diurnal migration is driven by the difference in temperature between the land and sea and is also aided by upstream wind, in a manner similar to the diurnal offshore propagation of rainfall. Overall, this study offers a more complete understanding of the LLJ' behaviors in the summer monsoon, which can help explain how it affects rainfall in the region. Key Points: The monsoon low‐level jet migrates eastward from the eastern offshore of India at midnight to the center of the Bay of Bengal by afternoonThe offshore diurnal migration is driven by inertia‐gravity waves resulting from the diurnal thermal contrast between land and seaThe westerly upstream wind from the Indian subcontinent extends the offshore migration, rather than confining it to the coast [ABSTRACT FROM AUTHOR]

Land-use and land-cover (LULC) is an important component for sustainable natural resource management, and there are considerable impacts of the rapid anthropogenic LULC changes on environment, ecosystem services, and land surface processes. One of the significant adverse implications of the rapidly changing urban LULC is the increase in the Land Surface Temperature (LST) resulting in the urban heat island effect. In this study, we used a time series of Landsat satellite images from 1992 to 2020 in the Srinagar city of the Kashmir valley, North-western Himalaya, India to understand the linkages between LULC dynamics and LST, derived from the archived images using the Google Earth Engine (GEE). Furthermore, the relationship between LST, urban heat island (UHI), and biophysical indices, i.e., Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI), was also analysed. LULC change detection analysis from 1992 to 2020 revealed that the built-up area has increased significantly from 12% in 1992 to 40% in 2020, while the extent of water bodies has decreased from 6% in 1992 to 4% in 2020. The area under plantations has decreased from 26% in 1992 to 17% in 2020, and forests have decreased from 4 to 2% during the same period. Urban sprawl of Srinagar city has resulted in the depletion of natural land covers, modification of natural drainage, and loss of green and blue spaces over the past four decades. The study revealed that the maximum LST in the city has increased by 11°C between 1992 and 2020. During the same period of time, the minimum LST in the city has increased by 5°C, indicating the impact of urbanization on the city environment, which is reflected by the observed changes in various environmental indices. UHI impact in the city is quite evident with the maximum LST at the city centre having increased from 13.03°C in 1992 to 22.01°C in 2020. The findings shall serve as a vital source of knowledge for urban planners and decision-makers in developing sustainable urban environmental management strategies for Srinagar city. [ABSTRACT FROM AUTHOR]

This study shows how remote sensing and Geographic Information System (GIS) can extract land surface temperature (LST) from the Landsat 5, 7, and 8 datasets. In this research, LST over Kharun's lower catchment, located in Chhattisgarh, India, has been estimated. LST data from 2000, 2006, 2011, 2016, and 2021 were analyzed to see how the LULC pattern changed and how that changed LST. In 2000, the average temperature of the study region was 27.73 °C, whereas in 2021, it reached 33.47 °C. When the average temperature values for each class were determined, it was discovered that forest and adjacent waterbodies had the lowest values, with about 24.15 °C in 2000 and 27.65 °C in 2021, whereas urban regions had more variation in values, ranging from 30.15 °C in 2000 to 38.95 °C in 2021. There could be an increase in LST over time because cities are replacing the green cover. For example, there was a notable increase of 5.74 °C in the mean LST over the research area. The findings revealed that places with extensive urban sprawl had LST between 26 and 45°, which was greater than other natural land cover types, such as vegetation and waterbodies, which was between 24 and 35°. These findings support the suggested method's effectiveness for retrieving LST from the Landsat 5, 7, and 8 thermal bands when combined with integrated GIS approaches. So, the goal of this study is to look at Land Use Change (LUC) and changes in LST using Landsat data and figure out how they are related to LST, the Normalized Difference Vegetation Index (NDVI), and the Normalized Built-up Index (NDBI), which are used as major components. [ABSTRACT FROM AUTHOR]

For several decades, researchers throughout the world have been motivated and contributed to the research on land use/land cover (LU/LC) change prediction analysis. This research work builds the ensemble learning model using unsupervised metaheuristic optimization and supervised machine learning algorithms for obtaining an LU/LC classification and prediction map in the area of Javadi Hills, India. The unsupervised fuzzy Harris hawks optimization (HHO) algorithm was used for finding the unknown features from the pre-processed satellite image. The feature-extracted map was used as an input for finding the LU/LC classes by using the supervised machine learning classifiers (support vector machine, random forest, and maximum likelihood). The principal component analysis (PCA) has been used for fusing the results of the supervised machine learning classifiers. The fused results were combined with the Adjusted Vegetation and Bareness Index (AVBaI) map and the final ensemble LU/LC (E-LU/LC) map was achieved for the years 2012 and 2015 with good classification accuracy, with an average of 95.275%. The impact of the LU/LC changes through the Land Surface Temperature (LST) map has been calculated and used as the input along with the E-LU/LC map during the process of LU/LC prediction. The ensemble-based prediction (EP)-LU/LC map for the years 2018, 2021, 2024, and 2027 has been forecasted by using the Markovian-Cellular Automata with the Multilayer Perceptron Neural Network model. The result of the EP-LU/LC map provides an average prediction accuracy of 95.763%. Our research on the LU/LC prediction will assist the concerned government officials in taking essential measures for protecting the LU/LC environment. [ABSTRACT FROM AUTHOR]

Forest fires have become a growing concern worldwide, with climate change exacerbating their frequency and intensity. In the Simlipal region of India, forest fires are relatively rare; however, in 2021, significant damage occurred in the buffer area's forests. Understanding the driving factors behind these events is essential for developing effective management strategies. This study investigates the impact of hydro-meteorological conditions on forest fire causes in the Simlipal region by analyzing Terra climatic data and geo-statistics for the period of 1984 to 2021. Long-term trends were determined using non-parametric tests on the Google Earth Engine (GEE) cloud computing platform. Our findings reveal that the maximum burned area location has a decreasing trend in Land Surface Temperature (LST), with a small portion (<10%) showing an increasing trend (0.02 °C/year) near burned locations. Wind speed is decreasing at a rate of −0.006 m/s/year. The sudden forest fires are caused by the combined effect of increasing LST and decreasing wind speed in some areas (<10% of the region). However, the major factor contributing to forest fires in the entire area is the rising trend of annual potential water deficit and actual evapotranspiration, as well as an increasing trend of minimum temperature. The soil moisture deficit during the summer season, especially between 2012 and 2021, contributed to forest fires in the burned area. The soil moisture deficit during the summer season, particularly from 2012 to 2021, played a significant role in the occurrence of forest fires in the affected area. The study emphasized the need for increased attention to this region in order to preserve biodiversity, which was assessed through an analysis of burned severity mapping in GEE (Google Earth Engine). These findings have important implications for future forest management strategies in the Simlipal region. Climate variability is likely to exacerbate the frequency and intensity of forest fires in the region, necessitating effective management strategies to mitigate their impact. Such strategies could involve improving fire prevention and control measures, such as creating fire breaks and increasing the availability of fire-fighting equipment, as well as enhancing forest monitoring systems to detect potential fires early. Additionally, efforts to address climate change, proper management of land use practices, and reduce greenhouse gas emissions could help to mitigate the future impacts of forest fires in the Simlipal region and elsewhere. [ABSTRACT FROM AUTHOR]

The cities of arid and semi-arid regions have distinctive landscape patterns and large-scale variations in soil moisture and vegetation cover which causes significant variations in land surface temperature (LST) and surface urban heat island intensity (SUHII) pattern. Therefore, the study aims to analyse the seasonal and spatial variation in LST and SUHII in the eight semi-arid cities of India in response to soil moisture and vegetation conditions. LST was retrieved from the thermal bands of Landsat data and then SUHII was calculated. The global Moran's I was used to analysis the spatial pattern of SUHII. The result shows that the mean SUHII was higher during spring and summer seasons to a tune of 0.2 to 1.0 °C in comparison to the winter and autumn season. SUHII zones exhibit seasonal variation in coverage, with high and very high zones increasing during spring and summer, while low and very low zones increase during autumn and winter. Furthermore, the highest LST was noticed in outskirt areas of the selected cities. The regression coefficient shows that soil moisture is closely associated with SUHII, while there is a weak association between vegetation condition and SUHII. This indicates that soil moisture has a higher impact on SUHII than vegetation condition in semi-arid environment. Global Moran's I showed that the SUHII had a clustered distribution pattern across all cities. The outcome of this study may provide useful insight for the urban planners in SUHII mitigation in the selected cities as well as in other semi-arid cities of the world with similar geographical conditions. [ABSTRACT FROM AUTHOR]

Our study aimed to enhance the understanding of Land Surface Temperature (LST) dynamics and its impact on environment using remote sensing techniques. By developing LST prediction models based on MODIS data, we analysed both day and night LST patterns in Bharatpur, Rajasthan, India. Multiple evaluation metrics were employed to assess the accuracy of models and performance comprehensive assessment. Our findings revealed valuable insights into the correlation between diurnal LST at different times. Notably, night-time LST predictions exhibited a higher Pearson's correlation coefficient compared to day-time predictions, indicating a stronger linear relationship. The determination coefficient (R2) indicated a good fit between the predicted and test LST. Model selection was guided by the Akaike's Information Criteria (AIC) and Bayesian Information Criteria (BIC). The lower AIC and BIC values associated with night-time LST predictions suggested a better balance between model fit and complexity. Our study contributes to the advancement of remote sensing and climate research through a robust LST model-based prediction framework. The insights gained from our results have implications for various fields including environmental monitoring, urban planning and climate change studies. By improved understanding of LST dynamics, we can make informed decisions and develop effective strategies for sustainable development and resource management. [ABSTRACT FROM AUTHOR]

Land Use/Land Cover (LULC) changes have a significant impact on Land Surface Temperature (LST). The LST is an important parameter in various environmental and climatological studies, as it plays a crucial role in understanding the Earth's surface–atmosphere interactions. The LULC changes can modify the surface energy balance and alter the radiation budget, leading to changes in LST. Urbanization, deforestation, and agricultural land use changes are some of the primary drivers of LULC change that have a significant impact on LST. Deforestation and agricultural land use changes result in a reduction in evapotranspiration, leading to an increase in LST. The main objective of the study is to analyze the spatio-temporal change in Land Use/Land Cover (LULC) and its effect on Land Surface Temperature (LST), as well as to establish a correlation of LST with the Normalized Difference Vegetation Index (NDVI) and Normalize Difference Snow Index (NDSI). Understanding the impact of LULC on LST is essential for developing effective land use policies that can mitigate the adverse effects of LULC change on the environment and human health. [ABSTRACT FROM AUTHOR]

The spatial monitoring of variation in land cover and its mapping support the local authorities and decision-makers in strengthening comprehensive and sustainable plans for future urbanization and sustainability of the city. This research work was carried out to evaluate the change in the land cover pattern and its impact on the land surface temperature of Prayagraj city from 2000 to 2020. This study also examined the correlation between land surface temperature (LST) and other improved urban biophysical indices, such as soil-adjusted vegetation index (SAVI) and enhanced built-up and bareness index (EBBI), to determine the significant factors affecting the LST of the urban area. The result indicates that the built-up area and cropland have been escalated by 10 and 6.5%, whereas tree cover, water bodies and fallow land were reduced by 12, 1.38 and 1.27%, respectively. LST was increased by 1.47°C between 2000 and 2020 due to an increase in the built-up area. LST showed a positive correlation with EBBI but a negative one with vegetation indices (normalized difference vegetation index, enhanced vegetation index and SAVI) and water indices (MNDWI). The results indicate that the land use pattern governs LST and any change in land use will ultimately change the LST. [ABSTRACT FROM AUTHOR]

The largest reservoir of drinkable water on Earth is surface water. It is crucial for maintaining ecosystems and enabling people to adapt to diverse climate changes. Despite surface freshwater is essential for life, the current research shows a striking lack of understanding in its spatial and temporal dynamics of variations in outflow and storage across a sizable country: India. Numerous restrictions apply to current research, including the use of insufficient machine learning techniques and limited data series. This work uses cutting-edge and SOTA-method to use the available data and machine learning to accurately understand spatial and temporal dynamics of variations in surface freshwater outflow and storage using extended data series. The authors did the examination of thematic maps produced using ArcMap 10.8 from June'2005 to June'2020 using JRC dataset to track changes in the intensity of surface water. Google Earth Engine in Python API has been devised to detect changes in surface water levels and quantifying shifting map trends. Raster image viewing, editing, and calculation are done with ArcMap. For determining the relationship between declines in Surface water levels, changes in rainfall intensity and land surface temperature, variables were averaged over 13 rivers for 15 years. The change in surface water is reliant on independent variables of change in land surface temperature and rainfall intensity. The authors use the correlation between these parameters to achieve an average R-squared adjusted value of 0.402. The study's findings contribute to a better understanding of the matter and can be used across the world. [ABSTRACT FROM AUTHOR]

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 (>38 °C) zones as compared to moderate (30 °C to 38 °C) and low-temperature (<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. [ABSTRACT FROM AUTHOR]

Changes in land use and land cover (LULC) and urbanization affect the regional energy balance. Accurate estimation of the earth's surface skin temperature and surface thermodynamic variables (temperature, T and relative humidity, RH) is essential to understand regional climate change better. The present study examined the agreeability between the Weather Research and Forecasting (WRF) model simulated surface skin temperature (TSK) and the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite-derived land surface temperature (LST) over heterogeneous LULC (built-up, crop land, forest cover, and barren land) in south-central India. Model performance metrics were computed for daytime and nighttime surface temperatures in January, May, and July 2021, representing winter, summer, and monsoon seasons. Further, the study compared the WRF-simulated near-surface (2 m from surface) T and RH with the observed hourly data from 20 locations in south-central India representing Tier-I, Tier-II, and Tier-III cities (high to low urbanization) to understand the effect of urbanization. Results indicate an overall high correlation (r > 0.9) between WRF-TSK and MODIS-LST. Nighttime correlations (r > 0.55) are relatively good enough for crop and forest land-use group than daytime simulations. In general, the correlation between TSK and LST is relatively poor for barren and built-up land-use groups during all three seasons (for both daytime and nighttime). Similarly, the WRF-simulated T and RH also differ considerably from the observed data in high and moderately dense urban locations, and the seasonal biases are predominant, especially during summer and winter. The WRF-simulated surface variables are a reasonable alternative in the absence of satellite-observed or surface-measured data in better understanding the environmental processes. However, the trends in the surface meteorological variables needs to be interpreted by duly considering the impact of land use and urbanization. [ABSTRACT FROM AUTHOR]

Urban expansion and land surface temperature (LST) fluctuations have been negatively influenced by rapid urbanisation, which degrades the urban thermal environment. This study has endeavoured to incorporate future urban growth modelling and assess the impact of urbanization on LST over Indore City, India, using temporal Landsat satellite data for the years 2010, 2015 and 2020. The present study has utilized the Cellular Automata (CA) model to project future urban growth for the year 2025. The result shows that the built-up land has increased by 28.7% during 2010–2015 and 24.9% during 2015–2020, while a future projection of 4.7% is evident in 2025. The mean LST over the city signifies a decreasing trend during the winter and summer seasons from 2010 to 2020. Moreover, an occurrence of higher LST in the surrounding rural area is observed, witnessing cool heat island phenomenon. The urban thermal field variation index (UTFVI) map shows non-UTFVI in the urban area, which shows an increasing trend and strongest UTFVI in rural regions with decreasing trend in summer season during 2010–2020. Whereas in the winter season, the observed result suggests the opposite behaviour of UTFVI. [ABSTRACT FROM AUTHOR]

Vegetation change can alter surface energy balance and subsequently affect the local climate. This biophysical impact has been well studied for forestation cases, but the sign and magnitude for persistent earth greening remain controversial. Based on long-term remote sensing observations, we quantify the unidirectional impact of vegetation greening on radiometric surface temperature over 2001–2018. Here, we show a global negative temperature response with large spatial and seasonal variability. Snow cover, vegetation greenness, and shortwave radiation are the major driving factors of the temperature sensitivity by regulating the relative dominance of radiative and non-radiative processes. Combined with the observed greening trend, we find a global cooling of −0.018 K/decade, which slows down 4.6 ± 3.2% of the global warming. Regionally, this cooling effect can offset 39.4 ± 13.9% and 19.0 ± 8.2% of the corresponding warming in India and China. These results highlight the necessity of considering this vegetation-related biophysical climate effect when informing local climate adaptation strategies. Using satellite observations over the recent two decades, the authors quantify the biophysical impact of earth greening on land surface temperature and show a considerable cooling effect in India and China, important for climate mitigation. [ABSTRACT FROM AUTHOR]

India's climate is semi-arid, and most of the people in this country depend on agriculture. In this article, we present the long-term relationships of the satellite-derived Normalized Difference Vegetation Index (NDVI) with the rainfall, land surface temperature (LST), surface soil moisture (SSM), and the groundwater storage (GWS) during the Kharif (June–October) season of the monsoon core region (MCR) of India from the year 2000 to 2018. Results show that the NDVI could capture the stages of crop phenology and that it undergoes interannual variability characterized by the El Niño Southern Oscillation. A strong dependence of NDVI on SSM and GWS is evidenced by the statistically significant correlation coefficients of +0.89 and +0.88, respectively. A substantial negative correlation coefficient of −0.91 is observed between NDVI and LST. Wavelet coherence spectrum suggests the in-phase relation of NDVI with rainfall, SSM, GWS, and anti-phase connection with LST, with weaker correlations from 2004 to 2008. The results of this study attain significance since the studies on NDVI and hydrologic variables are much less than those on NDVI and climate variables over the MCR of India. [ABSTRACT FROM AUTHOR]

The present study explores a holistic approach toward better assimilation of the contextual relationship between coal fire-induced land subsidence in the Jharia Coalfield (JCF), India. For the process, 31 consecutive Sentinel-1A and Landsat 8 scenes of 2018 were processed to estimate mean Line-of-Sight displacement and Land Surface Temperature (LST) in JCF, respectively. The results indicated that the displacement rate in JCF significantly varies at active mine benches and overburden dump, and high degree of displacement owing to the additive compression inducted along with the volume reduction at the subsurface. The estimated displacement accounts were then spatially correlated with the thermally anomalous pixels to determine the categories of subsidence. Further, the contextual relationship between the displacements estimates (dependent variable) with a set of explanatory variables, i.e. pixel integrated LST was tested using Binary Logistic Regression. The performance of the model was cross-validated using statistical parameters derived from the confusion matrix. [ABSTRACT FROM AUTHOR]

Wind erosion is a major natural disaster worldwide, and it is a key problem in western Rajasthan in India. The Analytical Hierarchy Process (AHP), the Geographic Information System (GIS), and remote sensing satellite images are effective tools for modeling and risk assessment of land degradation. The present study aimed to assess and model the land degradation vulnerable (LDV) zones based on the AHP and geospatial techniques in the Luni River basin in Rajasthan, India. This study was carried out by examining important thematic layers, such as vegetation parameters (normalized difference vegetation index and land use/land cover), a terrain parameter (slope), climatic parameters (mean annual rainfall and land surface temperature), and soil parameters (soil organic carbon, soil erosion, soil texture, and soil depth), using the Analytical Hierarchical Process (AHP) and geospatial techniques in the Luni River basin in Rajasthan, India. The weights derived for the thematic layers using AHP were as follows: NDVI (0.27) > MAR (0.22) > LST (0.15) > soil erosion (0.12) > slope (0.08) > LULC (0.06) > SOC (0.04) > soil texture (0.03) > soil depth (0.02). The result indicates that nearly 21.4 % of the total area is prone to very high degradation risks; 12.3% is prone to high risks; and 16%, 24.3%, and 26% are prone to moderate, low, and very low risks, respectively. The validation of LDV was carried out using high-resolution Google Earth images and field photographs. Additionally, the Receiver Operating Characteristic (ROC) curve found an area under the curve (AUC) value of 82%, approving the prediction accuracy of the AHP technique in the study area. This study contributes by providing a better understanding of land degradation neutrality and sustainable soil and water management practices in the river basin. [ABSTRACT FROM AUTHOR]

In many regions across the world, including river basins, population growth and land development have enhanced the demand for land and other natural resources. The anthropogenic activities can be detrimental to the vital ecosystems that sustain the river basin region. This work assessed the impact of human modification on land surface temperature (LST) for the Ramganga basin in India. It has been hypothesised that the footprints of anthropogenic activities in the region have been connected to the LST fluctuation for the region, which could indicate environmental degradation. The LST variation between 2000 and 2016 has been estimated to test this hypothesis. The spatio-temporal correlation between human modification and LST has been computed. LST has been calculated with MODIS satellite data in the Google earth engine (GEE) platform, and anthropogenic activities can be visualised using an LU/LC map of the basin created by the Classification and Regression (CART) technique. The statistical parameters (average, maximum and standard deviation) of annual temperature for each pixel in 17 years (2000–2016) have been assessed to establish the links with human modification. The result of this work portrays a positive correlation of 0.705 between maximum LST and human modification. The forest class in the basin region has the lowest average human modification value (0.37), and it also possesses the lowest mean LST of 26.72 °C. Similarly, the settlement class has the highest average human modification value (0.85), and the mean LST temperature of this class has been on the higher side, having a value of 31.07 °C. [ABSTRACT FROM AUTHOR]

In the current scenario of climate change, there has been a substantial increase in the frequency and severity of drought events. Therefore, it is necessary to investigate spatio-temporal characteristics of different drought events to plan for water resource utilization. The present study aims to assess and quantify the impact of meteorological, hydrological, and agricultural drought events from 2001 to 2017 over two large states of India (i.e., Maharashtra and Madhya Pradesh) using multi-temporal earth observation data at a finer resolution of 1 km. Drought indices including Standardized Precipitation Index (SPI), Standardized Water level Index (SWI), and Vegetation Health Index (VHI) were derived from precipitation, groundwater level, vegetation indices, and land surface temperature data respectively to map the spatial extent and severity of meteorological, hydrological, and agricultural drought. Assessment of individual drought indices was carried out to understand the effect of these drought events separately on the study area. Area vulnerable with multiple droughts in the region was identified by integrating multiple drought indices to derive a composite drought map. This included the locations that are hotspots in terms of the occurrence of drought events of different types. The spatial pattern captured in the composite drought map indicates that most of the study areas are prone to drought events varying from mild to extreme severity. Madhya Pradesh is more prone to meteorological and agricultural drought events compared to hydrological drought. Maharashtra state is prone to three types of drought with agricultural drought being the dominant one. This study provides an opportunity to investigate and understand the drought phenomenon in a comprehensive manner at comparatively finer spatial resolution. [ABSTRACT FROM AUTHOR]

This study highlights the land use/land cover change, and its implications on the long-term changes in various hydrometeorological parameters in India during 2001–2018. The study exhibited a decrease in grassland (15.1%), barren land (10.2%), and shrubland (9.5%) in contrast to an increase in deciduous broadleaf forest cover (27.4%) and croplands (25.2%) in India. The study exhibited 31.7% of the geographical area observed a low warming (<0.8°C), in contrast to 52.1% of the area observed a low cooling (> 0.8 °C) in India over the observation period. The transformation of (a) barren land to shrubland and cropland (Western India) and (b) cropland, and grassland to forest cover (Central India) led to the cooling effects. While Indo-Gangetic Plain and North-Eastern parts observed warming effects due to greenness and wetness change. The findings highlight the relevance of bio-geophysical and land-climate feedback, which may help in developing integrated and effective climate mitigation and adaptation strategies. [ABSTRACT FROM AUTHOR]

Anthropogenic impervious surface area (AISA) expansion with its impact on temperature is current research trend. Majority of earlier research focused on land surface temperature (LST) versus AISA relationship in specific year independently. Therefore, present research is a novel attempt to study ΔLST-ΔAISA correlation during 2012-2017 in core urban, semi urban, and rural region of Faridabad. Results demonstrate applicability of object-based image classification (OBIC) over pixel-based image classification (PBIC) to detect AISA with higher accuracy (OBIC: 2012-94%, 2017-93%, PBIC: 2012-79%, 2017-84%). Degree of non-linearity of LST-AISA relationship increases from urban to rural region, evident from the coefficient of AISA% (0.03-core urban, 0.07-semiurban, 0.08-rural). Absolute temperature is low in land with shallow ground water depth up to 10 m. Higher percentage of land with shallow water depth causes significant increase in (ΔLST/ΔAISA) in rural region. Consequently, authors suggest avoiding acquisition of land with water table depth 3-10m for future impervious surface development. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Land Surface Temperature refers back to the calculation of ground temperature that is made with extraordinary factors. Due to changes in the land surface temperature of the area, the climate and vegetation are also affected. The Normalized Difference Vegetation Index (NDVI) serves as an indicator of vegetation abundance to estimate the land surface temperature (LST) vegetation relationship. Landsat 7 ETM+ and Landsat 8 OLI TIRS images for four years (2005, 2010, 2015, and 2020) are used to study the LST changes in the research area (Jammu & Kashmir). Correlations between NDVI and LST are calculated. Through the study, it is clear that the Normalized Difference Vegetation Index plays a major role in the calculation of land surface temperature. In this study, the change in the higher temperature noted in the year 2005 was 20oC, and in the year 2020, the highest temperature was 25oC. If we talk about the lower temperature, the change is 1oC. Both Jammu and Kashmir have semideciduous vegetation that includes subtropical pine forests. [ABSTRACT FROM AUTHOR]

The interrelationship between the land use and land cover (LULC) pattern, land surface temperature (LST), carbon stock, and sequestration has been assessed using satellite data and Geographical Information System (GIS) tools in Imphal-West district, Manipur, India (North Eastern region of India). LST for the past 10 years (2011–2021) was calculated using three retrieval methods such as Mono Window Algorithm (MWA), Radiative Transfer Equation (RTE) and Single Channel Algorithm (SCA). The calculated LST (temperature varies in the range of 10°–40°C) was compared with atmospheric temperature values obtained from MODIS (Moderate Resolution Imaging Spectroradiometer) data as well as field (ground station) data collected. From the comparison, it was observed that the SCA method obtained a minimum mean temperature difference (minimum error) as compared to MODIS data, while the MWA method obtained a minimum mean temperature difference as compared to field data. The calculated (generated) results were again compared and validated using measured soil temperature data and observed similar trend showing MWA and SCA slightly efficient than RTE method. In order to understand the LULC change pattern in the study area as well as to estimate the aboveground biomass, LULC for the years 2011, 2016, 2021, and 2030 were also generated. Then, carbon stock for the years 2011, 2021, and 2030 and sequestration for the year 2030 were calculated using the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) model. It is concluded that the study area has witnessed a gradual increase in urbanisation (built-up area increases by up to 2.9% in 10 years, i.e., between 2011 and 2021, but significantly decreases in barren land, 9.9%), but not an abrupt change in LST and carbon storage. However, the significant spatial change distribution of LST and carbon stock with the change in LULC pattern was observed. [ABSTRACT FROM AUTHOR]

The study focused on investigating the seasonal and spatiotemporal relationship between the relationships of LST with four spectral indices (MNDWI, NDBaI, NDBI, and NDVI) in and around Manipur City of India using eight cloud-free Landsat data from the summer and winter seasons for 1991, 2001, 2011, and 2021. These spectral indices respond differently to the change of LST in an urban landscape. Pearson's linear correlation coefficient was the basis of the correlation analysis. The study finds that LST builds a moderate negative relationship with NDVI (R = -0.42) and MNDWI (R = -0.42), a moderate positive relationship with NDBaI (R=0.48), and NDBI (R = 0.61). The relationship is more stable in the winter season (CV = 7.31, 7.04, 10.45, and 28.71 for MNDWI, NDBaI, NDBI, and NDVI, respectively) than in summer (CV = 44.46, 36.09, 23.67, and 29.71 for MNDWI, NDBaI, NDBI, and NDVI, respectively). The strength of the relationship is gradually increasing in the winter season while there is no such effect noticed on the trend in the summer season. The LST-NDBI relationship is the most consistent (CV = 18.19), while the LST-NDVI relationship is the most variable (CV = 30.37). [ABSTRACT FROM AUTHOR]

Prayagraj city (India) has been selected as a smart city by the Ministry of Housing and Urban Affairs, Government of India in 2015. However, long-term spatiotemporal seasonal Land Use/Land Cover (LULC) dynamics and Land Surface Temperature (LST) interactions with ecological vulnerability for different seasons are lacking. Accordingly, this research has been carried out to study the seasonal (summer and winter) LULC and its change pattern, thermal dynamics, and their role in exploring the ecological state over Prayagraj city and its surroundings using multi-temporal Landsat (1987–2018) and MODIS Terra data (2007–2018) at both diurnal and nocturnal scenarios. The LULC classification was carried out using Maximum Likelihood Classifier (MLC) by adopting the Anderson classification scheme with more than 85% of overall accuracy. The Landsat data–based LST has been estimated using Mono-Window Algorithm (MWA) for diurnal scenario whereas MODIS-based LST was calculated for nocturnal scenario. Ecological vulnerability state has been evaluated both in day-time and night-time using Urban Thermal Field Variance Index (UTFVI) in summer and winter during 1987–2018 and 2007–2018, respectively. Overall, built-up land increased the most by 18.25% which was responsible for massive urbanization during 1987–2018. In contrast, forest land decreased by 2.22% during 1987–2018. The most vulnerable class was agriculture land followed by forest land irrespective of seasons. Thermal state was intensified by mean LST by 1.25 ℃ in summer and 0.58 ℃ in winter in day-time. However, in night-time, the mean LST intensified by 6.64 ℃ in summer and 1.86 ℃ in winter. The excellent ecological class having no SUHI effects declined in summer during 1988–2018 by 1.59% but surged in winter by 12.33% during 1987–2018 in north-west regions at day-time, whereas in night-time the excellent ecological class having no SUHI effects severely declined in summer as well as in winter during 2007–2018 by 11.1% and 1.32%, respectively. However, the worst ecological class having strongest SUHI effects severely spread in night-times compared to day-time which mainly concentrated in central core part of the city during 2007–2018 by 5.33%. The present study has generated a comprehensive long-term geospatial database which can be used for urban planning to achieve sustainable development to make Prayagraj city a truly smart city in future. [ABSTRACT FROM AUTHOR]

Soil moisture and vegetation water content play an important role in studies involving vegetation, drought, and climate change. The present paper gives details of how to calculate the Soil Moisture Index (SMI), Normalized Difference Moisture Index (NDMI), and Normalized Difference Water Index (NDWI) using Landsat 7 ETM + and Landsat 8 OLI (Operational Land Imager) and Thermal Infrared Sensor (TIRS) data and how spatially and temporally it has been changed over Palakkad District, Kerala. The present study develops an image processing method using band 3 (Green), band 4(Red), band 5 (NIR), band 6 (SWIR 1), and band 10 (TIR) of Landsat 8 and band 2 (Green), band 3 (Red), band 4 (NIR), band 5 (SWIR) and band 6 (TIR) of Landsat 7 data for determining the different spectral indices. The relationship between Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) are based on experimental parameterization for soil moisture index. Both LST and NDVI are considered indispensable data to obtain SMI calculation. The statistical analysis of NDVI and LST was exposed in the standardized regression coefficient. The study found a weak negative correlation between NDVI and LST in both 2001 (R2 = 0.37) and 2021 (R2 = 0.16). As the LST increases, the vegetative cover reduces due to the record of high temperatures in the study area. The SMI result for 2021 shows that the majority of the study area experienced extreme anomalies when compared to the 2001 result. Second, a significant negative correlation existed between SMI and LST (R2 = 0.664 in 2001 and R2 = 1 in 2021), which indicates that the surface soil moisture reduces with LST. The normalized difference moisture index (NDMI) value ranges from – 0.71 to 0.75 in 2001 and – 0.33 to 0.40 in 2021. In 2001 and 2021, the normalized difference water index (NDWI) ranges from – 0.47 to 0.64 and – 0.531 to 0.154, respectively. Therefore, the current study gives a clear understanding of considerable variations in soil moisture and vegetation water content in the Palakkad District during the study period. [ABSTRACT FROM AUTHOR]

For the purpose of deriving spatiotemporal estimates of soil moisture, the triangle method is one of the most widely used approaches today utilizing remote sensing data. Generally, those techniques are based on the physical relationships that exist when a satellite-derived land surface temperature (Ts) is plotted against a spectral vegetation index (VI). The present study proposes an improvement in the triangle method in retrieving soil moisture over heterogeneous areas. In particular, it proposes a new approach in robustly identifying the extreme points required for the technique's implementation. Those extreme points are then used in calculating fractional vegetation cover (Fr) and scaled Ts. Furthermore, the study proposes a new approach for calculating the coefficients required to develop the relationships between surface soil moisture (SSM) and Fr/Ts, which is implemented using a model and field data. As a case study, an agricultural field in the Varanasi district in India has been used, on which the triangle method is implemented using ECOSTRESS and Sentinel-2 data. The much-improved spatial resolution satellite data of ~70 m from ECOSTRESS allowed deriving more vivid results of SSM spatial variability for the study area. Comparisons between field soil moisture calculated using the proposed method returned an RMSE of 0.03 and R2 value of 0.84, which are considered very satisfactory. The methodology proposed herein and the results obtained are of significant value with regards to the triangle method, contributing to ongoing efforts at present examining its use for operational product development at a global scale. [ABSTRACT FROM AUTHOR]

It is axiomatically true that urbanization in India's metropolises and large cities has been exacerbated since the beginning of the millennium, consuming the natural and semi-natural ecosystem on the outskirts of the city, resulting in a zone with a distinct climate known as urban climate. Such a climate—the result of a built-up environment is distinctly different from the natural climate as the paved surface and concrete skyscrapers not only destroy the natural ecosystem, it peculiarly induce a different kind of insolation, cooling and air drainage were lacking in green space, water bodies and open space cannot accommodate with environmental rhythm properly, resulting into the accumulation of heat, ecological derangement of subsurface soil which can easily be predicted by GIS analysis. This paper is an attempt to measure urban growth and its impact on the environment in the metropolitan city Kolkata. The use of satellite data and GIS techniques to detect urban expansion is a highly scientific strategy. Using geospatial techniques, the current study attempts to examine major urban changes in Kolkata and its surroundings from 1988 to 2021. Landsat 5 TM and Landsat 8 OLI temporal data are used to identify land-use change through unsupervised classification; Spectral Radiance Model and Split Window Algorithm method are used for identifying land surface temperature change. SRTM DEM (30 m) has been used to identify flood risk zones and several spectral indices like Normalized Difference Vegetation Index and Modified Normalized Difference Water Index are a further extension for environmental assessment. By all such suitable methods, a clearer change in an urban environment is detected within the period of 33 years (1988–2021). The result shows that the population changes, vegetation cover and built-up area, and accessibility are at a rapid rate. These changes are causing major environmental degradation in the city. The classification result indicates that appropriate land use planning and environmental monitoring are required for the long-term exploitation of these resources. [ABSTRACT FROM AUTHOR]

Urbanization produces substantial land use changes by causing the construction of different urban infrastructures in the city region for habitation, transportation, industry, and other reasons. As a result, it has a significant impact on Land Surface Temperature (LST) by disrupting the surface energy balance. The objective of this paper is to assess the impact of land-use/land-cover (LU/LC) dynamics on urban land surface temperature (LST) of Bhubaneswar City in Eastern India during 30 years (1991–2021) using Landsat data (TM, ETM + , and OLI/TIRS) and machine learning algorithms (MLA). The finding reveals that the mean LST over the entire study domain grows significantly between 1991 and, 2021due to urbanization (β coefficient 0.400, 0.195, 0.07, and 0.06 in 1991, 2001, 2011, and 2021 respectively) and loss of green space (β coefficient − 0.295, − 0.025, − 0.125 and − 0.065 in 1991, 2001, 2011 and 2021 respectively). The highest class recorded for agricultural land (49.60 km2, accounting for 33.94% of the total land area) was in 1991 followed by vegetation (41.27 km2, 28.19% of the total land area), and built-up land (27.59 km2, 18.84% of the total land area). The sharp decline of vegetation cover will continue until 2021 due to increasing built-up areas (r = − 0.531, − 0.329, − 0.538, and − 0.063 in the 1991, 2001, 2011 and 2021 respectively). Built-up land (62.60 km2, accounting for 42.76% of the total land area, an increase of 35.01 km2 from 1991) as the highest class followed by water bodies (21.57%, 32.60 km2 of the land area), and agricultural land (31.57 km2, 21.57% of the land area) in 2021. Remote sensing techniques proved to be an important tool to urban planners and policymakers to take adequate steps to promote sustainable development and minimize urbanization influence on LST. Urban green space (UGS) can help improve the overall liveability and environmental sustainability of Bhubaneswar city. [ABSTRACT FROM AUTHOR]

Increasing land surface temperature (LST) is one of the major anthropogenic issues and is significantly threatening the urban areas of the world. Therefore, this study was designed to examine the spatial variations and patterns of LST during the different seasons in relation to influencing factors in Kolkata Municipality Corporation (KMC), a city of India. The spatial distribution of LST was analyzed regarding the different surface types and used 25 influencing factors from 6 categories of variables to explain the variability of LST during the different seasons. All-subset regression and hierarchical partitioning analyses were used to estimate the explanatory potential and independent effects of influencing factors. The results show that high and low LST corresponded to the artificial lands and bodies of water for all seasons. In the individual category regression model, surface properties gave the highest explanatory rate for all seasons. The explanatory rates and the combination of influencing factors with their independent effects on the LST were changed for the different seasons. The explanatory rates of integration of all influencing factors were 89.4%, 81.4%, and 88.7% in the summer, transition, and winter season, respectively. With the decreasing of LST (summer to transition, then to winter) more influencing factors were required to explain the LST. In the integrated regression model, surface properties were the most important factor in summer and winter, and landscape configuration was the most important factor in the transition season. LST is not the result of single categories of influencing factors. Along with the effects of surface properties, socio-economic parameters, landscape compositions and configurations, topographic parameters and pollutant parameters mostly explained the variability of LST in the transition (11.22%) and summer season (15.22%), respectively. These findings can help to take management strategies to reduce urban LST based on local planning. [ABSTRACT FROM AUTHOR]

The study focused on deriving the LST of the Raipur City of India and generating the relationships of LST with six selected remote sensing indices, like MNDWI, NDBaI, NDBI, NDVI, NDWI, and NMDI. The entire study was performed by using 210 cloud-free Landsat data of different months from 1988 to 2019. The LST retrieval mono-window algorithm was applied in the study. Based on Pearson's linear correlation coefficient (r), the study finds that LST builds a strong positive correlation (r = 0.65) with NDBI, a moderate positive correlation (r = 0.30) with NDBaI, a weak positive correlation with NDWI (r = 0.19), a strong negative relation with NMDI (r = −0.54), and a moderate negative correlation (r = −0.38) with MNDWI and NDVI. These relationships were consistent and stronger in earlier years. The LST-NDBI correlation is the most consistent (CV = 9.09), while the LST-NDBaI correlation is the most variable (CV = 60.21). [ABSTRACT FROM AUTHOR]

Hot extremes are anticipated to be more frequent and more intense under climate change, making the Indo-Gangetic Plain of India, with a 400 million population, vulnerable to heat stress. Recent studies suggest that irrigation has significant cooling and moistening effects over this region. While large-scale irrigation is prevalent in the Indo-Gangetic Plain during the two major cropping seasons, Kharif (Jun-Sep) and Rabi (Nov-Feb), hot extremes are reported in the pre-monsoon months (Apr-May) when irrigation activities are minimal. Here, using observed irrigation data and regional climate model simulations, we show that irrigation effects on heat stress during pre-monsoon are 4.9 times overestimated with model-simulated irrigation as prescribed in previous studies. We find that irrigation increases relative humidity by only 2.5%, indicating that irrigation is a non-crucial factor enhancing the moist heat stress. On the other hand, we detect causal effects of aerosol abundance on the daytime land surface temperature. Our study highlights the need to consider actual irrigation data in testing model-driven hypotheses related to the land-atmosphere feedback driven by human water management. Pre-monsoon irrigation over the Indo-Gangetic Plain is often misrepresented in model-driven hypothesis. Using actual census-based data and realistic model simulations, the authors show that irrigation has limited role in enhancing heat stress in the region. [ABSTRACT FROM AUTHOR]

Drought poses a tremendous challenge to India's socioeconomic development, livelihood, agriculture, and water management. While existing drought monitoring systems have characterized drought impact at different scales, policymaking and management require drought assessment at sub-district or taluka (sub-district) levels. Here, we develop high resolution (250 m) agriculture drought indices for the Indian region to overcome the shortcomings of the coarse resolution datasets. We used the co-kriging to downscale the Land Surface Temperature (LST) from 1000m to 250m. The LST and Enhanced Vegetation Index (EVI) are obtained at 8-day intervals at 250m spatial resolution. The high-resolution datasets show significant improvement in identifying the severity and coverage of drought. Soil Moisture Agriculture Drought Index (SMADI), which accounts for water stress and vegetation lag response, shows high reliability in drought detection. We evaluated drought extent and severity using the newly developed dataset and found that the high-resolution dataset can be used to separate the irrigation impact on drought alleviation. The high resolution drought indices from SMADI and the Normalized Vegetation Supply Water Index (NVSWI) effectively represent the drought conditions at district and taluka levels that can be used in drought impacts assessments in India. [ABSTRACT FROM AUTHOR]

The present work intended to model the vegetation health index (VHI) of rabi crop and explored its time series change (1990–2019) at pixel scale using Landsat satellite imageries. Identification of consistency level of rabi crop was another objective. VHI was estimated by integrating crop condition index (CCI) and temperature condition index (TCI). The CCI and TCI were derived from normalized difference vegetation index (NDVI) and land surface temperature (LST). 80% of the study area covered by rabi crop with 8.73% of variability. Remarkable change in crop health (VHI) was detected in the last 30 years. 5% of rabi cropped area was under good health category in the present decade (2010–2019), while it was around 10% during 1990–2009. During last decade, the poor health category of rabi crop was only 7.40% area, while it reached to 66.74% in the present decade. Good VHI area was getting fragmented and vice versa. [ABSTRACT FROM AUTHOR]

Continuous monitoring and observing of the earth's environment has become interactive research in the field of remote sensing. Many researchers have provided the Land Use/Land Cover information for the past, present, and future for their study areas around the world. This research work builds the Novel Vision Transformer–based Bidirectional long-short term memory model for predicting the Land Use/Land Cover Changes by using the LISS-III and Landsat bands for the forest- and non-forest-covered regions of Javadi Hills, India. The proposed Vision Transformer model achieves a good classification accuracy, with an average of 98.76%. The impact of the Land Surface Temperature map and the Land Use/Land Cover classification map provides good validation results, with an average accuracy of 98.38%, during the process of bidirectional long short-term memory–based prediction analysis. The authors also introduced an application-based explanation of the predicted results through the Google Earth Engine platform of Google Cloud so that the predicted results will be more informative and trustworthy to the urban planners and forest department to take proper actions in the protection of the environment. [ABSTRACT FROM AUTHOR]