Land Surface Temperature Dynamics Research Articles

Spatiotemporal Thermal Variations in Moroccan Cities: A Comparative Analysis.

This study examines the Land Surface Temperature (LST) trends in eight key Moroccan cities from 1990 to 2020, emphasizing the influential factors and disparities between coastal and inland areas. Geographically weighted regression (GWR), machine learning (ML) algorithms, namely XGBoost and LightGBM, and SHapley Additive exPlanations (SHAP) methods are utilized. The study observes that urban areas are often cooler due to the presence of urban heat sinks (UHSs), more noticeably in coastal cities. However, LST is seen to increase across all cities due to urbanization and the degradation of vegetation cover. The increase in LST is more pronounced in inland cities surrounded by barren landscapes. Interestingly, XGBoost frequently outperforms LightGBM in the analyses. ML models and SHAP demonstrate efficacy in deciphering urban heat dynamics despite data quality and model tuning challenges. The study's results highlight the crucial role of ongoing urbanization, topography, and the existence of water bodies and vegetation in driving LST dynamics. These findings underscore the importance of sustainable urban planning and vegetation cover in mitigating urban heat, thus having significant policy implications. Despite its contributions, this study acknowledges certain limitations, primarily the use of data from only four discrete years, thereby overlooking inter-annual, seasonal, and diurnal variations in LST dynamics.

Evaluating the Spatiotemporal Dynamics of Land Surface Temperature in Relation to the Land Use/Land Cover changes in Nag-Hammadi District, Egypt, using Remote Sensing and GIS

In this study, three multi-temporal remotely sensed data acquired from Landsat-5 Thematic Mapper (TM) and Landsat -8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) in 1990, 2005, and 2020 were used. The maximum likelihood classifier (MLC) was opted to classify land use and land cover (LULC). Land surface temperature (LST) and LULC spectral indices i.e., Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Latent Heat Index (NDLI) and Bare Soil Index (BSI) have been computed and their relationships were examined. The overall accuracy of LULC was more than 93%. The analyses showed a notable transformation in LULC over the study period. For instance, built-up areas increased 103.7% with a rate of 45.5 ha/year and agriculture land increased by 28.9% with a rate of 186.4 ha/year. Whereas, bare soil was sharply decreased by 36.4% at a rate of 227.7ha/year. The minimum and maximum LST values increased by 2.9°C and 4.9°C, respectively, from 1990 to 2020. Furthermore, LST has a negative relationship with NDVI and NDLI (NDVI: 1990: r2 = 0.62; 2005: r2 = 0.62; 2020: r2 = 0.65. NDLI: 1990: r2 = 0.79; 2005: r2 = 0.78; 2020: r2 = 0.61) and a positive relationship with NDBI and BSI (NDBI: 1990: r2 = 0.68; 2005: r2 = 0.73; 2020: r2 = 0.44. BSI: 1990: r2 = 0.77; 2005: r2 = 0.78; 2020: r2 = 0.53). These results provided useful information about LULC changes and its impact on LST, which are necessary for experts and land-use planners to formulate sustainable LST mitigation policies, create an environmental comfort in Nag-Hammadi district, and other geographical locations with similar conditions.

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

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

Satellite-Derived Land Surface Temperature Dynamics in the Context of Global Change—A Review

Satellite-derived Land Surface Temperature (LST) dynamics have been increasingly used to study various geophysical processes. This review provides an extensive overview of the applications of LST in the context of global change. By filtering a selection of relevant keywords, a total of 164 articles from 14 international journals published during the last two decades were analyzed based on study location, research topic, applied sensor, spatio-temporal resolution and scale and employed analysis methods. It was revealed that China and the USA were the most studied countries and those that had the most first author affiliations. The most prominent research topic was the Surface Urban Heat Island (SUHI), while the research topics related to climate change were underrepresented. MODIS was by far the most used sensor system, followed by Landsat. A relatively small number of studies analyzed LST dynamics on a global or continental scale. The extensive use of MODIS highly determined the study periods: A majority of the studies started around the year 2000 and thus had a study period shorter than 25 years. The following suggestions were made to increase the utilization of LST time series in climate research: The prolongation of the time series by, e.g., using AVHRR LST, the better representation of LST under clouds, the comparison of LST to traditional climate change measures, such as air temperature and reanalysis variables, and the extension of the validation to heterogenous sites.

Distinguishing Dominant Drivers on LST Dynamics in the Qinling-Daba Mountains in Central China from 2000 to 2020

Land surface temperature (LST) is an important driving factor in the land-atmosphere energy cycle. To examine the spatiotemporal patterns of LST changes and the internal mechanisms driven by multiple factors, we used a trend analysis method on TRIMS LST data from 2000 to 2020 in the Qingling-Daba Mountains. The optimal parameter geographic detector (OPGD) model was used to detect the influence of twelve factors, including elevation, precipitation, albedo, relative humidity (RH) and normalized difference vegetation index (NDVI), on the spatial distribution of LST, as well as to explore the dominant factors affecting LST differentiation in the study area. The results showed that: (1) From 2000 to 2020, the average annual LST of the Qinling-Daba Mountains was 18.17 °C. The warming trend was obvious (0.034 °C/a), and the warming effect at nighttime (0.066 °C/a) was stronger than that during daytime (0.0004 °C/a). The difference between day and night temperature (DIF) was decreasing. (2) The seasonal changes in LST and DIF in the Qinling-Daba Mountains were significant, and the spatial distribution of their average values in the summer was slightly larger and fluctuated more than in the other seasons. (3) Elevation was the main driving factor affecting the spatial distribution of LST, with the contribution scores of 62.9% in the daytime and 92.7% in the nighttime. The controlling effects of these factors were generally stronger in the nighttime than in the daytime. (4) Nighttime elevation had the strongest interaction with precipitation (contribution score of 95%), while daytime elevation had the strongest interaction with albedo (contribution rate of 83%). We revealed the temporal and spatial variation in LST in the Qinling-Daba Mountains since 2000 and explored the main driving factors involved, thereby improving our understanding of LST changes in the Qinling-Daba Mountains. This study can provide a scientific basis for distinguishing dominant drivers of LST dynamics in the Qinling-Daba Mountains.

Impacts of rapid urbanization on spatial dynamics of land use–based carbon emission and surface temperature changes in the Semarang Metropolitan Region, Indonesia

This study explores the relationship between carbon emission patterns and the land surface temperature (LST) changes due to the rapid urbanization in the Semarang Metropolitan Region (SMR), an Indonesian area that has experienced rapid urban growth compared to other urban areas. This research used the stock-difference and gain-loss methods to calculate carbon stocks and emissions. Then, band 6 on Landsat 5 TM (2008) and band 10 on Landsat 8 OLI (2013 and 2018) were used to calculate the LST changes. These results showed that the peri-urban area had a more significant change. The correlation between carbon emissions and an increased SMR temperature correlates to 0.646. This shows that the carbon emissions pattern promotes temperature dynamics in the SMR. Furthermore, this study proved the release of carbon emissions in line with LST dynamics spatially. In this case, this study proved that rapid urbanization in the SMR promotes both carbon emission and LST. Those changes are also affected by vegetation canopy availability and other activities. As a result, the government must prioritize spatial planning in the SMR to mitigate environmental change risk. In addition, the government must develop novel strategies to deal with a wide range of fast and unpredictable potential changes in the urban area and its surroundings.

Assessment of Urban Heat Islands (UHIs) Using SatelliteDerived Normalized Difference Vegetation Index (NDVI), and Land Surface Temperature (LST) in Three Metropolitan Cities of Nepal

Urban Heat Islands (UHIs) are urban areas that are relatively warmer than nearby rural areas due to the presence of infrastructures, such as buildings, roads, and associated development. This study explored the UHIs in Nepal's three largest metropolitan cities, i.e., Pokhara, Bharatpur, and Nepalgunj. Using freely available data, we explored LST dynamics between 2000 and 2019 and how changes in NDVI affect LST and their relationship with UHI. We used the Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day product (MOD11A2) to evaluate LST and the MODIS-derived NDVI 16-day product (MOD13Q1) to quantify land surface characteristics. Using a simple linear regression technique, we explored the relationship between LST and NDVI. The results indicated that LSTs for the urban areas are consistently greater than LSTs for the nearby rural areas, and an inverse relation between LST and NDVI was obtained. The results from Pokhara and Bharatpur showed that increasing LST resulting from declining NDVI is responsible for UHIs. However, the results from Nepalgunj suggested that factors other than NDVI are responsible for variation in LST. These results indicate a need for systematic mapping, planning, and managing open and green areas in large cities. This research also highlights the scope of applying UHI conceptual models to rapidly developing urban areas in different locations of Nepal for better planning and management of open spaces.

Assessing spatiotemporal variations in land surface temperature and SUHI intensity with a cloud based computational system over five major cities of India

• The study showed that all cities have undergone a change in LULC pattern. • Concurrent data from space-based sensors for effective disaster response. • The impact of LST and SUHI over the vegetation cover with a cloud-based computing. • Biophysical parameters like NDVI, NDBI, and MNDWI are used for analysis and results derivations. The surface urban heat island (SUHI) is one of the most evident climate phenomena in low- and mid-latitude cities. Extensive growth and intense urbanization has brought a phenomenal change to the urban landscape. The rapid expansion of cities have the potential to generate distinctive local urban climates by increasing land surface temperature (LST) and the intensities of surface urban heat islands (SUHI). The study uses Landsat data in a cloud based computational system to investigate the spatiotemporal variation of land use land cover (LULC) for five major cities (Mumbai, New Delhi, Bangalore, Hyderabad, and Ahmadabad) from 2007 to 2020. Selected biophysical parameters like Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Modified Normalized Difference Water Index (MNDWI) are used. The interrelationship with the dynamics of LST to analyze the SUHI profiles reveals that the mean LST has a positive correlation for impermeable surfaces and a negative correlation for green surfaces, which are common for all Indian cities and results in degradation of environmental quality. The results obtained from this study showed that all cities have undergone a change in LULC pattern, which induced a rise in overall temperature and SUHI intensity. The expansion of the urban landscape along with the changing vegetation and cropping patterns due to the rise in anthropogenic activities has resulted in susceptible climatic conditions and necessitates further environmental investigations. The findings of the study can be recommended to monitor the impacts of SUHI intensity at different locations for future studies to adapt sustainable urban planning of cities.

The Land Surface Temperature Dynamics and Its Impact on Land Cover in District Peshawar, Khyber Pakhtunkhwa

The landcover has most essential impact on the climatic condition of any region. The rapid urbanization induces key changes into the climatic condition of any region. This changing pattern of urbanization has induced micro level climatic changes with demographic variation as the key factor. In this study we analyzed the effect of landcover classes on the LST in the Peshawar for 1990. 2000, 2010 and 2019. The results revealed that the builtup area has been increased from 14.20% to 20.22% during 1990 to 2019. The results also show that the LST values are between the ranges of 25°C to 36°C. The air temperature and LST have very compromising correlation. The hype and increase in the UHI zones are resultant to rapid urbanization and expansion of Old Municipal Corporation with initiation of developmental projects in the area. This study will help urban planners and decision makers in landuse planning and management to mitigate the increase in LST with the replacement of possible impervious surfaces with green built and road side plantation for comfortable urban environment.

Analysis of Land Surface Temperature Dynamics in Islamabad by Using MODIS Remote Sensing Data

The rapid pace of unattended urbanization has caused the urban heat island phenomenon, due to which the United Nations SDGs agenda 2030 calls for immediate actions for “sustainable cities and communities”. In this context, the case of the emerging metropolitan city Islamabad has been studied based on its developmental discourse vis-à-vis associated environmental problems. A time-series trend for the land surface temperature was generated by investigating the change in minimum and maximum variability against a dataset of 1960–2012 which was obtained from the Pakistan Meteorological Department, along with MODIS LST images from January 2000 to December 2015. The statistical comparison of an eight-day composite of the maximum (Tmax) and minimum (Tmin) temperature reveals an increasing trend with R2 values of 0.2507 (Tmin) and 0.1868 (Tmax). The box plots for both the Tmin and Tmax depict changes in seasonal patterns for Islamabad, with summers becoming longer and winters becoming harsher. Moreover, the application of the Mann–Kendall test affirmed the slope of the R2 linear trend map and showed the temperature regression in the Margalla Hills National Park and in such urban zones which had an expanded vegetative cover. These findings will act as a guide for urban planners and future researchers to maintain a standardized urban heat island and promote the concept of sustainable cities in the future course of action.

A GIS and remote sensing approach for measuring summer-winter variation of land use and land cover indices and surface temperature in Dhaka district, Bangladesh

Rapid urbanization has induced land use and land cover change (LULC) that increases land surface temperature (LST). Analyzing seasonal variations of LULC and LST is a precondition for mitigating heat island effects and promoting a sustainable living environment. The objective of this study is to explore the association between the seasonal LST dynamics and LULC indices for the Dhaka district of Bangladesh. The LULC indices are comprised of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Bareness Index (NDBAI), and Modified Normalized Difference Water Index (MNDWI). The results show that the LULC effect on LST in Dhaka is significant, with an increase in summer season LST from 34.58 °C to 37.66 °C and in winter season LST from 24.710C to 26.24 °C. Predictably, the highest and lowest LST values were observed in the built-up and vegetation-covered areas, respectively. Secondly, the correlation values indicate a significant inverse correlation (R2 > 0.50) between NDVI and LST, as well as MNDWI and LST. On the contrary, positive correlations were observed between NDBI and LST, and between NDBAI and LST for both the summer and winter seasons. Finally, subsequent vegetation decline (-69.34%) and increasing built-up area (+11.30%) between 2000 and 2020 in Dhaka district were found to be the most significant factors for the increasing trend and spatial heterogeneity of LST in Dhaka. The methodological approach of this study offers a low-cost efficient technique for monitoring LST hotspots, which can guide land use planners and urban managers for spatial intervention to ensure a livable environment.

Assessing and modeling diurnal temperature buffering and evapotranspiration dynamics in forest restoration using ECOSTRESS thermal imaging

Restoration of temperate forests alters the diurnal dynamics of land surface temperature (LST) and evapotranspiration (ET) through increases in biomass, diversity, and complexity. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is the first space-based thermal imaging instrument that allows for a study of diurnal LST and ET dynamics at a moderate spatial resolution (70 × 70 m).We quantified, compared, and modeled the LST and ET dynamics of two groups of forest restoration sites (43 sites in total, between 2 and 75 ha) in Southern Ontario, Canada that were initially restored from mainly agriculture between 2007 and 2019. We included all useable LST (n = 29) and ET (n = 9) ECOSTRESS image products from a full growing season in 2020 (June 1st to September 30th). Forest restoration sites were compared by age and against agriculture (pre-restoration state), mature forest (post-restoration state), and suburban residential sites (competing land use).The ability of forest restoration sites to buffer temperatures was highest in the afternoon, around 14:00 local time (EDT). As predicted, restoration sites were significantly cooler (x̄ 4.4–7.4 °C) than residential and agricultural areas and significantly warmer (x̄1.6–2.9 °C) compared to mature reference forest sites at both groups of sites. Relative diurnal (24-h) LST variability of forest and restoration sites was also significantly lower (0.9–2.9 °C) compared to agriculture and residential sites (3.3–5.2 °C). Daytime LST decreased significantly by 0.1 °C (3.1%), per year since restoration for one of the groups of sites relative to nearby mature reference forest sites as per a linear mixed effects regression model. It would take these sites ~30 years to reach the same buffering as mature forests. In characterizing ET dynamics of a subset of sites, we found that more recently restored sites had a statistically significant higher overall ET than older ones and that daytime relative instantaneous ET decreased with years since restoration. The variation explained by the ET model was however low.Our study provides insights into how diurnal forest ecosystem energy conversion and storage dynamics changes over time after restoration. These diurnal thermal dynamics impact wildlife habitat as well as human wellbeing. The change in thermal buffering over time could be used to assess the pace and trajectory of restoration by managers. The thermal buffering provided by restored forest can also be quantified as an ecosystem service. As such, the study demonstrates the utility and also limitations of novel thermal remote sensing methods, using free and publicly available imagery from ECOSTRESS.

Spatial and Temporal Assessment of Remotely Sensed Land Surface Temperature Variability in Afghanistan during 2000–2021

The dynamics of land surface temperature (LST) in Afghanistan in the period 2000–2021 were investigated, and the impact of the factors such as soil moisture, precipitation, and vegetation coverage on LST was assessed. The remotely sensed soil moisture data from Land Data Assimilation System (FLDAS), precipitation data from Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS), and NDVI and LST from Moderate-Resolution Imaging Spectroradiometer (MODIS) were used. The correlations between these data were analyzed using the regression method. The result shows that the LST in Afghanistan has a slightly decreasing but insignificant trend during the study period (R = 0.2, p-value = 0.25), while vegetation coverage, precipitation, and soil moisture had an increasing trend. It was revealed that soil moisture has the highest impact on LST (R = −0.71, p-value = 0.0007), and the soil moisture, precipitation, and vegetation coverage explain almost 80% of spring (R2 = 0.73) and summer (R2 = 0.76) LST variability in Afghanistan. The LST variability analysis performed separately for Afghanistan’s river subbasins shows that the LST of the Amu Darya subbasin had an upward trend in the study period, while for the Kabul subbasin, the trend was downward.

Quantifying the Influences of Driving Factors on Land Surface Temperature during 2003–2018 in China Using Convergent Cross Mapping Method

The relationship between land surface temperature (LST) and environmental factors is complex and nonlinear. To determine these relationships for China, this study analyzed the driving effects of air temperature, vegetation index, soil moisture, net surface radiation, precipitation, aerosols, evapotranspiration, and water vapor on LST based on remote-sensing and reanalysis data from 2003–2018, using a convergent cross-mapping method. During the study period, air temperature and net surface radiation were the dominant drivers of LST with a cross-mapping skill above 0.9. Vegetation index and evapotranspiration were the secondary drivers of LST with a cross-mapping skill that was higher than 0.5. Except for air temperature and net surface radiation, the direction and strength of the effects of the driving factors on LST were related to the climate type. The effects of air temperature and net radiation on LST diminished from north to south, indicating that LST was more sensitive to air temperature and net radiation in energy-limited regions. However, the effects of vegetation index and evapotranspiration on LST varied significantly across climate zones; that is, positive effects were mostly in non-monsoonal zones and negative effects were primarily in monsoonal zones. Our results quantified the driving role of environmental factors on LST and provided a comprehensive understanding of LST dynamics.

Assessing spatiotemporal dynamics of land surface temperature and satellite-derived indices for new town development and suburbanization planning

Pervasive development across the country has resulted in unprecedented urbanization with a phenomenal change to the urban landscape. This rapid expansion of cities has generated characteristic local urban climates with an increased land surface temperature (LST) and the intensities of urban heat islands (UHI). The study on LST and UHI can be performed to investigate the spatiotemporal variation in dynamics of land surface temperature and satellite-derived indices for enhanced urban planning and development. The study undertakes a comprehensive assessment for the city of Bangalore to understand its impact on environmental quality. The corresponding satellite-derived indices like Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Modified Normalized Difference Water Index (MNDWI) are used to express the Built-up area (BUA) to analyse the dynamics of LST on UHI profiles. The results of the study reveal that the mean LST has a positive correlation for impervious surfaces and a negative correlation with the green surfaces, which is common for all Indian cities and it results in degradation of environmental eminence. The results shows that the area has undergone a drastic transformation and has resulted in a rise in overall temperature and UHI intensity due to an increase of urbanized areas by 3% between 2007 and 2020. The urban policy and planning for new town development is being seen from various aspects such as design and architecture, geography, and sociology, with much of the literature concentrating on the substantive challenges of the development process. However the account of its technical challenges arising from the influential actors and factors are lacking in the planning process of smart cities. This works address focuses on the critical assessment of the spatiotemporal dynamics of land surface temperature and satellite-derived indices as key factors and actors involved in development decisions of New town development and suburbanization planning. The discoveries of the study can be endorsed to monitor the impacts of UHI intensity for future studies and can be used for sustainable urban planning of cities.

The Land Surface Temperature Dynamics of the Novosibirsk Region from Remote Sensing Data in XXI Century

The results of the study of the land surface temperature regime of the Novosibirsk region in the 21st century based on remote sensing data are presented. The analysis of space-time series of surface temperatures obtained after processing satellite images for various time intervals was carried out.

Dynamics of land surface temperature (LST) and their relation with urban biophysical components in Gorakhpur (India) urban area: a GIS and statistical based analysis for sustainable planning

Dynamics of land surface temperature (LST) and their relation with urban biophysical components in Gorakhpur (India) urban area: a GIS and statistical based analysis for sustainable planning

Identifying the effect of monsoon floods on vegetation and land surface temperature by using Google Earth Engine

Flood is one of the most devastating climatic disasters around the world. The physical and infrastructural damage of floods is uncontrollable and challenging to recover. Though the mismanagement of the water system is one cause of flood, some countries face a seasonal natural flood, which is impossible to avoid. The history of flood affection in these countries are long, and people don't have any other choice but to adapt to this circumstance every year. The continuous flood event has several climatic impacts that are not broadly documented and remain in the shadows of severe infrastructural damage. This research aims to identify the effect of monsoon floods on vegetation and land surface temperature (LST). The study area is the northeast part of Bangladesh, a highly flood-prone area. The research incorporates Google Earth Engine (GEE) to manage the satellite image data related to this research which are Sentinel-1 SAR imagery and Landsat-8 imagery. Six years of data from 2015 to 2020 have been taken to continuously monitor the flooded area, vegetation, and LST dynamics. Primary results indicate a yearly increase and decrease of the flooded area with 57.3% highest increase rate in 2019. A continuous increase of Enhanced Vegetation Index (EVI) value and decrease of LST has a changing pattern similarity with flooded area fluctuation over the year. Also, the flooded areas have around 50% less mean EVI value than the non-flooded areas, eventually rising average LST in flooded areas. 10,024 grids of 1 km × 1 km have been used to extensively analyze the relationship of flood and EVI through correlation and linear regression. The final result reveals a clear negative correlation value (less than 0.56 for all the years) of EVI with flooded areas, having the highest R-squared value of 0.4325 in 2017.

Biophysical effects of paddy rice expansion on land surface temperature in Northeastern Asia

The pending extensive rice expansion in northeastern Asia, especially in northeast China, affects regional climate by altering both biogeochemical and biophysical processes. While the biogeochemical effects (e.g., CO2, CH4) of rice expansion have attracted plenty of attention, its biophysical effects have not been well documented, especially its influences on diurnal and seasonal land surface temperature (LST). In this study, we used a pair-wise comparison approach to examine biophysical effects of paddy rice expansion at different temporal scales (diurnal and seasonal) in northeast China, based on satellite-derived biophysical proxies and a high-resolution crop map in 2017. We found that the daily mean LST of rice paddies was 0.5 °C lower than that of corn and soybean fields during the growing season (from May to September), as a result of daytime cooling (-1.8 and -2.0 °C) and nighttime warming (0.8 and 1.1 °C), which subsequently led to a narrower diurnal LST range (-2.6 and -3.0 °C) than in upland crops (i.e. corn and soybean). The cooling effects were stronger in the early period of the growing season (May and June) than in the late season (July to September). Using a temperature response model, we found that the nonradiative processes (i.e., evapotranspiration and sensible heat) dominated the LST response in paddy rice, while the radiative process (i.e., albedo) played a secondary role. The daytime cooling and nighttime warming implies that we need to consider the unsymmetrical diurnal LST dynamics when evaluating the short-term effects of paddy rice expansion. Stronger cooling effects in the early growing season has to be accounted for when modeling its biophysical impacts at seasonal scale. This study explained the local climate effects of rice expansion through the biophysical mechanism with both radiative and nonradiative controls on the surface energy balance, which can contribute to improved modeling of biophysical effects of land use change.

Dynamics of Land Surface Temperature in Response to Land Use Land Cover Change in Phewa watershed, Kaski, Nepal

Geospatial tools play an important role in monitoring Land Use Land Cover Change (LULCC) and Land Surface Temperature (LST). This study assessed the extent of LULCC during 1990, 2007 and 2020 using temporal satellite imageries, and estimates LST during the study periods in relation to LULCC and explores various adaptation practices adopted in response to LST change in Phewa watershed. Landsat imageries were used for LULC classification and LST estimation. Purposive household survey (N=150), key informant survey (N=4), focus group discussion (N=4) and direct field observations were carried out to explore various adaptation practices. LULC maps was generated by using supervised classification followed by post classification change detection technique for LULCC analysis. Agricultural land and urban areas were found to have increased by 6.6% and 0.32% respectively while forest area, barren land and water bodies were found to have decreased by 1.88%, 4.41% and 0.63%, respectively, between 1990 and 2007. Forest area, urban areas and barren land had increased by 5.54%, 1.32% and 1.48%, respectively, while agricultural land and water bodies were observed to have decreased by 8.2% and 0.13%, respectively, between 2007 and 2020. The mean LST were 22.59° C, 22.81° C and 24.56° C in March-April and 14.65° C, 14.97° C and 15.27° C in January-December, respectively. Urban areas and water bodies exhibit highest and lowest mean values of LST, respectively, during study periods. LULCC due to urban growth and infrastructural developments had contributed to increase in LST. The highest rank to adaptation practices were for changes in cropping patterns and species, followed by agroforestry, use of improved seeds, shift to other income generation activities, use of more fertilizers, irrigation practices and growing vegetables in poly house which were statistically significant. This study provides scientific insights for policy makers and urban planners in improving urban planning and management.