A Spatial Model of Land Surface Temperature Based on the Integration of Satellite Data and IoT Sensors for Urban Heat Island Mitigation in the Climate Village Program Banjarmasin

Urban temperatures are rising due to climate change and rapid urbanization, leading to the urban heat island (UHI) effect, significantly affecting local climates. Satellite-derived land surface temperature (LST) is crucial for understanding urban thermal dynamics. Existing satellite thermal infrared sensors have a coarse spatial resolution that fails to accurately capture the complex thermal variations within cities. This limitation affects the assessment of UHI effects and hinders effective mitigation strategies. To address these challenges, we developed a land cover-enhanced nonlinear model named high-resolution urban thermal sharpener per land cover (HUTS-LC), which builds on the high-resolution urban thermal sharpener (HUTS) algorithm. The proposed method uses high spatial resolution visible and near-infrared data from Sentinel-2 to enhance the LST derived from Landsat-8/9 data. Our model was tested in Perth, Australia. Validated by ground measurements, HUTS-LC demonstrated a significant improvement in accuracy, yielding a Pearson’s correlation coefficient of 0.85, a root mean square error (RMSE) below 3 °C, a mean absolute error less than 2.5 °C, and a normalized RMSE under 7 percent. The results were compared with the original HUTS and linear regression methods, exhibiting an outperformance of HUTS-LC and making it a valuable tool for urban thermal studies involving high-resolution LST data.

Urban surface temperatures are increasing because of climate change and rapid urbanisation, contributing to the urban heat island (UHI) effect and significantly influencing local climates. Satellite-derived land surface temperature (LST) plays a vital role in analysing urban thermal patterns. However, current satellite thermal infrared (TIR) sensors have a low spatial resolution, making it difficult to accurately capture the complex thermal variations within urban areas. This limitation affects the assessments of UHI effects and hinders effective mitigation strategies. We proposed a hybrid model named “geospatial machine learning” (GeoML) to address these challenges, combining random forest and kriging downscaling techniques. This method utilises high spatial resolution data from Sentinel-2 to enhance the LST derived from Landsat 8/9 data. Tested in Perth, Australia, GeoML generated an enhanced LST with good agreement with ground-based measurements, with a Pearson’s correlation coefficient of 0.85, a root mean square error (RMSE) of 2.7 °C, and a mean absolute error (MAE) of less than 2.2 °C. Validation with LST derived from another TIR sensor also provided promising outputs. The results were compared with the high-resolution urban thermal sharpener (HUTS) downscaling methods, which GeoML outperformed, demonstrating its effectiveness as a valuable tool for urban thermal studies involving high-resolution LST data.

Over the years, urban heat island (UHI) has emerged as a significant contributor to global warming, thereby necessitating considerable attention. Currently, satellite technology is a basic tool for the future – particularly, for its effective and efficient urban analysis. Thus, this study aims to assess the progress of existing satellite-based UHI studies by reviewing scientific publications that were released between 1972 and early 2024. Moreover, we observed that 1991 was a pivotal year, marking the integration of satellite technologies into the development of UHI monitoring and identification systems based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this review methodology examines the UHI phenomenon by focusing on its characteristics based on sensors, algorithms, and accuracy. The results of the systematic review revealed that Landsat and MODIS were the most-deployed sensors for UHI identification and monitoring, while the land surface temperature (LST) indicator and normalized difference vegetation index (NDVI) were the most-deployed algorithms. Regarding accuracy, the integration of satellite sensors and algorithms into UHI studies provides a promising range of accuracies. The review found that the future of satellite-based UHI monitoring is promising, with technological advancements driving the development of effective techniques such as data fusion, gap filling, machine learning (ML), and deep learning. Additionally, Google Earth Engine (GEE) is a cloud-based platform for performing large-scale geospatial analyses, which facilitates the assessments of local, regional, and global-scale UHIs. Finally, the other review findings for future directions indicated that future satellite-based UHI studies will prioritize six crucial points: enhancing data resolution, integrating satellite data with ground-based sensors, artificial intelligence, and ML, climate change modeling, and a global study of UHIs and their impacts.

This paper focuses on the monitoring of the urban heat island (UHI) effect with temporal and spatial variation, combining Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Thematic Mapper (TM) data. Our study area is located in the central urban area of Beijing, which mainly refers to the areas within the fifth ring road. For detecting UHI changes over the years 2002–2006, three ASTER images in the summers of 2003, 2004 and 2006 and two TM datasets in the summers of 2002 and 2005 were collected. For monitoring UHI changes with the seasons, three ASTER images and one TM image in 2004 in winter, spring, summer and autumn, respectively, were employed. To calculate the urban heat island intensity, the land surface temperatures were retrieved iteratively for ASTER data and using a generalized single-channel method for the TM image. Four separated regions located in four directions outside the fifth ring road were selected as representing rural comparative regions. Their averaged land surface temperature was regarded as the rural comparative temperature. The UHI intensity was computed by the difference between the pixel urban land surface temperature in the urban area and the comparative temperature in the rural area. Detection of the UHI effect over 2002 to 2006 indicated that most of the areas with high UHI effect were the industrial land use regions and the areas having a high density of buildings, roads, transportations and residents; and the areas without UHI effect were located around the regions with large areas of grassland, trees and water bodies. Our results also showed that the UHI effect was not proportional to urbanization over time. Statistical UHI data during 20 July to 20 September in 2003–2008 also support this point. The monitoring of the UHI effect over seasons (winter, spring, summer and autumn) showed that the urban area of Beijing city had a high UHI effect except in winter, when the urban area of Beijing was in an urban heat sink; the UHI effect increased in spring, summer and autumn.

Under the trend of rapid urbanization, the urban heat island (UHI) effect has become a hot issue for scholars to study. In order to better alleviate UHI effect, it is important to understand the effect of landuse/landcover (LULC) and landscape patterns on the urban thermal environment from perspective of landscape ecology. This research aims to quantitatively investigate the effect of LULC landscape patterns on UHI effects more accurately based on a landscape metrics analysis. In addition, we also explore the complex relationship between land surface temperature (LST) and vegetation cover. Taking Zhengzhou City of China as a case study, an integrated method which includes the geographic information system (GIS), remote-sensing (RS) technology, and landscape metrics was employed to facilitate the analysis. Landsat data (2000–2014) were applied to investigate the spatiotemporal evolution patterns of LST and LULC. The results indicated that the mean LST value increased by 2.32°C between 2000 and 2014. The rise of LST was consistent with the trend of rapid urbanization in Zhengzhou City, which resulted in sharp increases in impervious surfaces (IS) and substantial losses of vegetation cover. Furthermore, the investigation of LST and vegetation cover demonstrated that fractional vegetation cover (FVC) had a stronger negative effect on LST than normalized differential vegetation index (NDVI). In addition, LST was obviously correlated with LULC landscape patterns, and both landscape composition and spatial configuration affected UHI effects to varying degrees. This study not only illustrates a feasible way to investigate the relationship between LULC and urban thermal environment but also suggests some important measures to improve urban planning to reduce UHI effects for sustainable development.

Urban and suburban areas experience elevated temperatures compared to their surrounding rural areas. This is well known as the urban heat island (UHI) effect, which is a common and an important effect of urbanization. The UHI effect is well known for the large cities around the world, and most of the recent studies were conducted there. UHI effects are less intense or less frequent in smaller urban areas compared to larger urban areas. However, this is not well understood since there is a significant lack of UHI effect studies on smaller cities. The city of Chattanooga, TN, is one of the fastest-growing cities in Tennessee. As urbanization continues in the greater Chattanooga area, it is very important to understand the possible UHI effect on sustainable development efforts in this area. Remote sensing technology has been used successfully to study the UHI effect for many large cities in the United States and the rest of the world. Using thermal images acquired by NASA’s Landsat‐8 satellite during the summers of 2018 and 2019, a recent study conducted in the Geological and Environmental Remote Sensing Laboratory at the University of Tennessee at Chattanooga found that the city of Chattanooga behaves like an UHI, and its effect is significant. Recently, we installed eight temperature-measuring stations across the city of Chattanooga, TN, to acquire air temperature at a 2‐m height from the ground. We have been acquiring temperature data every 10 min since March 2023. This study incorporated ground measured air temperature with the ongoing remote sensing-based study to better understand the UHI effect. The obtained results confirmed that the city of Chattanooga, TN, indeed behaves like an UHI. Both satellite-derived land surface temperature and direct air temperature measurements clearly captured the UHI effect, and their UHI profiles were comparable, with root mean square error values ranging from 5.0 to 7.5°F for different dates and 2.2 to 7.7°F for each station. While thermal images are particularly useful for mapping the spatial variation of UHI effects, air temperature measurements provide the advantage of capturing temporal variations throughout the day.

The urban planner´s role should be adapted to the current globalised and overspecialised economic and environmental context, envisioning a balance at the regional scale, apprehending not only new technologies, but also new mapping principles, that allow obtaining multidisciplinary integral overviews since the preliminary stages of the design process. The urban heat Island (UHI) is one of the main phenomena affecting the urban climate. In the Netherlands, during the heat wave of 2006, more than 1,000 extra deaths were registered. UHI-related parameters are an example of new elements that should be taken into consideration since the early phases of the design process.\n
\nProblem statement
\n\nThus, the development of urban design guidelines to reduce the heat islands in Dutch cities and regions requires first an overall reflection on the heat island phenomenom (relevance of the large scale assessment, existing tools, instruments) and proposal of integrative and catalysing mapping strategies and then a specific assessment of the phenomenom at the selected locations in The Netherlands (testing those principles).\n
\nMain research question
\n\nCould the use of satellite imagery help analyse the UHI in the Netherlands and contribute to suggest catalysing mitigation acions actions implementable in the existing urban context of the cities, regions and provinces assessed?\n
\nMethod
\n\nThe development of urban design principles that aim at reaching a physical balance at the regional scale is critical to ensure a reduction of the UHI effect. Landsat and Modis satellite imagery can be analysed and processed using ATCOR 2/3, ENVI 4.7 and GIS, allowing not only a neighbourhood, city and regional scale assessment, but also generating holistic catalysing mapping typologies: game-board, rhizome, layering and drift, which are critical to ensure the integration of all parameters. The scientific inputs need to be combined not only with other disciplines but often also with existing urban plans. The connection between scientific research and existing agreed visions is critical to ensure the integration of new aspects into the plans.\n
\nResults
\n\nAt the neighbourhood level the areas that have a greater heat concentration in the cities of Delft, Leiden, Gouda, Utrecht and Den Bosch are the city centres characterised by their red ceramic roof tiles, brick street paving, and canals. Several mitigation strategies could be implemented to improve the UHI effect in those areas; however, since the city centres are consolidated and listed urban areas, the mitigation measures that would be easier to implement would consist in improving the roof albedo. A consistent implementation of albedo improvement measures (improving the thermal behaviour not only of flat roofs, but also of tiled pitched roofs) of all roofs included in the identified hotspots (with an average storage heat flux greater than 90 W/m2) would help reduce the temperatures between 1.4°C and 3°C. Pre-war and post-war compact and ground-based neighbourhoods present similar thermal behaviour of the surface cover, and green neighbourhoods and small urban centres also present similar thermal behaviour.\n
\n\nAt the city scale the analysis of 21 medium-size cities in the province of North Brabant, which belongs to the South region of the county -in relative terms the most affected by the UHI phenomenon during the heat wave of 2006-, reveals that albedo and normalised difference vegetation index (NDVI) are the most relevant parameters influencing the average nightime land surface temperature (LST). Thus, imperviousness, distance to the nearest town and the area of the cities do not seem to play a significant role in the LST night values for the medium-size cities analysed in the region of North Brabant, which do not exceed 7,700 ha in any case. The future growth of most medium-size cities of the regions will not per se aggravate the UHI phenomenon; in turn it will be the design of the new neighbourhoods that will impact the formation of urban heat in the province.\n
\n\nThe average day LST of provincial parks in South Holland varies depending on the land use. The analysis of the average night LST varies depending of the land use of the patches. The following surfaces are arranged from the lowest to the highest temperatures: water surfaces, forests, cropland, and greenhouse areas. For each of these land uses, NDVI, imperviousness and landscape shape index (LSI) shape index influence the thermal behaviour of the patches differently. NDVI is inversely correlated to day LST for all categories, imperviousness is correlated to day LST for all areas which do not comprise a significant presence of greenhouses (grassland and built patches) and inversely correlated to LST for areas with a high presence of greenhouses (cropland and warehouses). Greenhouse surfaces have highly reflective roofs, which contribute to the reduction of day LST. Finally, landscape shape index varies depending on the nature of the surrounding patches, especially for small patches (built areas, forests and greenhouse areas). When the patches analysed are surrounded by warmer land uses, slender and scattered patches are warmer, more compact and large ones are cooler. In turn, when they are surrounded by cooler patches it is the opposite: slenderer and scattered patches are cooler and more compact and larger ones are warmer. In Midden-Delfland (1 of the 6 South Holland provincial parks), most of the hotspots surrounding the park are adjacent to grassland patches. The measure to increase the cooling capacity of those patches would consist in a change of land use and/or an increase of NDVI of the existing grassland patches.\n
\nConclusions
\n\nSatellite imagery can be used not only to analyse the heat island phenomenom in Dutch neighbourhoods, cities and regions (identify neighbourhoods with highest surface temperature, identify impact of city size and morphology in surface temperature, calcuate average surface temperature for different land uses…), but also to suggest mitigation actions for the areas assessed. Moreover, satellite imagery is here used to generate catalysing mapping typologies: game-board, rhizome, layering and drift, ensuring that the measures proposed remain accurate enough to actualy be efficient and open enough to be compatible with the rest of urban planning priorities.

Study’s Excerpt:• Uses Landsat OLI (2024), NDVI-LST-UHI analysis, and OLS regression for urban heat study.• Air temperature data improves reliability of study findings through ground truthing.• Confirms NDVI, LST, and UHI links, consistent with earlier urban heat effect studies.• Vegetation's cooling effect confirmed, but no new local mitigation strategies identified.• Findings stress urgent need for green infrastructure to tackle rising UHI from human activity.Full Abstract:Studies on Urban Heat Island (UHI) have become an important way to create sustainable and lively cities; it improves public health and enhances urban quality of life. An increase in the level of Land Surface Temperature (LST) and a corresponding rise in the urban temperature as UHI among many cities of Nigeria brings about more outbreaks of heat related diseases. The current research focused on the assessment of spatial patterns of LST and UHI in the Kaduna metropolis. Land sat 8, operational land imager (OLI) imagery of April 2024 was used for the research. Analysis was conducted through the determination of the Normalized Difference Vegetation Index (NDVI), vegetation density map, satellite brightness temperature, LST, and finally, UHI profile on ArcGIS 10.8 software. Results on vegetation density revealed that areas with very low vegetation cover recorded the highest (44.86%) while those with moderate vegetation cover had the lowest (12.06%). The findings also indicated that the highest (47.55%) percent of the study area experienced higher LST while only 8.93% experienced lower LST. Results also show that most (37.43%) percent of the research area recorded a high (6.55oC) rate of UHI, 16.38% recorded about 8.5oC, while only 1.87% experienced a low UHI of 3.4oC. Results of the regression analysis between NDVI and UHI indicated an inverse relationship between vegetation density and urban heat islands in such a way that, for every 1-unit increase in vegetation density, UHI decreases by ~19.63 units while the negative sign means more vegetation resulted in lower urban heat (cooling effect). In conclusion, the study indicated that the western and heart of the metropolis had high UHI profiles while the eastern part of the area and other areas along the water bodies experienced lower UHI. Based on these, the study recommends the provision of strategies such as plating trees which will help in reducing higher rate of heat, this is more especially during the hot and dry season.

Based on the land surface temperature (LST), the land cover classification map, vegetation coverage, and surface evapotranspiration derived from EOS-MODIS satellite data, and by the use of GIS spatial analytic technique and multivariate statistical analysis method, the urban heat island (UHI) spatial distribution of the diurnal and seasonal variabilities and its driving forces are studied in Beijing city and surrounding areas in 2001. The relationships among UHI distribution and landcover categories, topographic factor, vegetation greenness, and surface evapotranspiration are analyzed. The results indicate that: (i) The significant UHI occur in Beijing city areas in the four seasons due to high heat capacity and multi-reflection of compression building, as well as with special topographic features of its three sides surrounded by mountains, especially in the summer. The UHI spatial distribution is corresponding with the urban geometry structure profile. The LST difference is approximately 4-6℃between Beijing city and suburb areas, comparatively is 8- 10℃between Beijing city area and outer suburb area in northwestern regions. (ii) The UHI distribution and intensity in daytime are different from nighttime in Beijing city area, the nighttime UHI is obvious. However, in the daytime, the significant UHI mainly appears in the summer, the autumn takes second place, and the UHI in the winter and the spring seem not obvious. The surface evapotranspiration in suburb areas is larger than that in urban areas in the summer, and high latent heat exchange is evident, which leads to LST difference between city area and suburb area. (iii) The reflection of surface landcover categories is sensitive to the UHI, the correlation between vegetation greenness and UHI shows obviously negative. The scatterplot shows that there is the negative correlation between NDVI and LST (R2 = 0.6481). The results demonstrate that the vegetation greenness is an important factor for reducing the UHI, and large-scale construction of greenbelts can considerably reduce the UHI effect.

Extreme heat is one of the deadliest health hazards that is projected to increase in intensity and persistence in the near future. Temperatures are further exacerbated in the urban areas due to the Urban Heat Island (UHI) effect resulting in increased heat-related mortality and morbidity. However, the spatial distribution of urban temperatures is highly heterogeneous. As a result, metrics such as UHI Intensity that quantify the difference between the average urban and non-urban air temperatures, often fail to characterize this spatial and temporal heterogeneity. My objective in this thesis is to understand and characterize the spatio-temporal dynamics of UHI for cities across the world. This has several applications, such as targeted heat mitigation, energy load estimation, and neighborhood-level vulnerability estimation.Towards this end, I have developed a novel multi-scale framework of identifying emerging heat clusters at various percentile-based thermal thresholds Tthr and refer to them collectively as intra-Urban Heat Islets. Using the Land Surface Temperatures from Landsat for 78 cities representative of the global diversity, I have showed that the heat islets have a fractal spatial structure. They display properties analogous to that of a percolating system as Tthr varies. At the percolation threshold, the size distribution of these islets in all cities follows a power-law, with a scaling exponent = 1.88 and an aggregated Area-Perimeter Fractal Dimension =1.33. This commonality indicates that despite the diversity in urban form and function across the world, the urban temperature patterns are different realizations with the same aggregated statistical properties. In addition, analogous to the UHI Intensity, the mean islet intensity, i.e., the difference between mean islet temperature and thermal threshold, is estimated for each islet, and their distribution follows an exponential curve. This allows for a single metric (exponential rate parameter) to serve as a comprehensive measure of thermal heterogeneity and improve upon the traditional UHI Intensity as a bulk metric.To study the impact of urban form on the heat islet characteristics, I have introduced a novel lacunarity-based metric, which quantifies the degree of compactness of the heat islets. I have shown that while the UHIs have similar fractal structure at their respective percolation threshold, differences across cities emerge when we shift the focus to the hottest islets (Tthr = 90th percentile). Analysis of heat islets' size distribution demonstrates the emergence of two classes where the dense cities maintain a power law, whereas the sprawling cities show an exponential deviation at higher thresholds. This indicates a significantly reduced probability of encountering large heat islets for sprawling cities. In contrast, analysis of heat islet intensity distributions indicates that while a sprawling configuration is favorable for reducing the mean Surface UHI Intensity of a city, for the same mean, it also results in higher local thermal extremes. Lastly, I have examined the impact of external forcings such as heatwaves (HW) on the heat islet characteristics. As a case study, the European heatwave of 2018 is simulated using the Weather Research Forecast model with a focus on Paris. My results indicate that the UHI Intensity under this HW reduces during night time by 1oC on average. A surface energy budget analysis reveals that this is due to drier and hotter rural background temperatures during the HW period.To analyze the response of heat islets at every spatial scale, power spectral density analysis is done. The results show that large contiguous heat islets (city-scale) persist throughout the day during a HW, whereas the smaller islets (neighborhood-scale) display a diurnal variability that is the same as non-HW conditions. In conclusion, I have presented a new viewpoint of the UHI as an archipelago of intra-urban heat islets. Along the way, I have introduced several properties that enable a seamless comparison of thermal heterogeneity across diverse cities as well as under diverse climatic conditions. This thesis is a step towards a comprehensive characterization of heat from the spatial scales of an urban block to a megalopolis.

Global warming has obtained more and more attention because the global mean land surface temperature (LST) has increased since the late 19th century. Urban heat island (UHl) effect and its thermal environment are the most important themes in urban climatology and environment researches. One of the possible causes to UHI effect is the drastic reduction in the green space in cities.The coastal cities of southeast Fujian province is the most economically developed and densely populated areas of Fujian province,even in China,and also it is experiencing rapid urbanlization that has resulted in remarkable UHI effect,which will be sure to inference the regional and urban climate,environment,and socio-ecomomic development. In this study, MODIS data of July to August acquired from 2001 to 2007 in coastal cities of southeast Fujian province were elaborately selected out to retrieve the metrics of,land surface tempertuare,normalized difference vegetation index (NDVI) and albedo so as to investigate the intensity, the spatial-temporal pattern, the tendency of UHI effect,monitor annual changes and evaluate the UHI effect though urban radio index (URI) over the study period. Additionally, a new methodology and analysis techniques what was called Urban Thermal Environment Information TuPu (UTEITP) was introduced to detect and measure within-class changes of LST from the spatial, temporal and processes view,try to find the conversion mechanism and analyze the relationship between LST,albedo and NDVI qualitatively. Morever,a correlation model was built to quantitatively analyze and better understanding the relationships between LST and NDVI,LST and albedo.The results showed that UHI effect was keeping on strengthening during the whole study period with a increase tendency overall.UTEITP provided a systematic and effective way to derive comparable changes and processes.Our analysis based on UTEITP and correlation model indicates that there was a linear relationship among surface temperature,NDVI and albedo for all study years, whereas the relationship between LST and NDVI was negative,but positive correlation was shown between NDVI and albedo.These result evidence reminds us to take some instructive measures to weaken the effect of urban heat island,improve the city's thermal and habitat environment and urban sustainable development.

Land surface temperature is one of the most important factors in urban climatology studies and human - environmentinteractions. Warmer air in urban area - urban heat island is a pressing issue for all big cities. Besides, land surfacetemperature is also an important factor when monitoring soil moisture. Ground-based observations reflect only thermalcondition of local area around the station and in fact cannot establish the number of meteorological stations withexpected density due to the high cost. Remote sensing technology with advantages such as wide area coverage andshort revisit interval has been used effectively in the study of land surface temperature distribution. The study indicateshow to estimate surface temperature using LANDSAT satellite data. With 120m (TM), 60m (ETM+) and 100m(LANDSAT 8) spatial resolution, thermal infrared image LANDSAT performance applications in the region study. Thisarticle also considers the method determining surface emissivity and building program LST for calculating land surfacetemperature. The land surface temperature distribution map and the analyses of thermal - land cover relationships canbe used as the reference for urban planning and the solution to the reduction of heat island effect.ReferencesAlipour T., Sarajian M.R., Esmaseily A., 2004. Land surface temperature estimation from thermal band of LANDSAT sensor, case study: Alashtar city. The international archives of the Photogrammetry, Remote sensing and Spatial information sciences, Vol. XXXVIII-4/C7. Balling R.C., Brazel S.W., 1988. High - resolution surface temperature patterns in a complex urban Terrain, Photogrametric engineering and Remote sensing, Vol. 54, No.9, pp. 1289 - 1293. Carnahan W.H., Larson R.C., 1990. An analysis of an urban heat sink. Remote sensing of Environment, 33:65 - 71. Fei Yuan, Marvin E. Bauer, 2007. Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in LANDSAT imagery. Remote sensing of Environment 106:375 - 386. Gallo K.P., Owen T.W., 1998. Satellite - Based adjustments for the urban heat island temperature bias, Journal of applied meteorology, Vol. 38, pp. 806 - 813. Garcia Cueto O.R., Jauregui Ostos E., Toudert D., Tejeda Martinez A., 2007. Detection of the urban heat island in Mexicali and its relationship with land use. Atmosfera 20(2), pp. 111 - 131. Holben B.N., 1986. Characteristics of maximum value composite image from temporal AVHRR data. International Journal of Remote sensing, 7:1417 - 1434. Hyung Moo Kim, Beob Kyun Kim, Kang Soo You, 2005. A statistic correlation analysis algorithm between land surface temperature and vegetation index, International journal of information processing systems, Vol. 1, No. 1, pp. 102 - 106. Javed Maltick, Yogesh Kant, D.B. Bharath, 2008. Estimation of land surface temperature -over Delhi using LANDSAT-7 ETM+”, Journal Ind. Geophys. Union, Vol. 12, No. 3, pp. 131 - 140. Lê Đình Quang, 2005. Sự hình thành đảo nhiệt ở nội thành thành phố Hà Nội. Tạp chí Khí tượng Thủy văn, 530, trang 44 - 46. Lo C.P., D.A. Quattochi, J.C. Luvall, 1997. Application of high resolution thermal infrared remote sensing and GIS to assess the urban heat island effect. International journal of Remote sensing, 18:287 - 304. Luke Howard, 1833. The climate of London. International association for urban climate (IAUC), 285p. Oke T.R., 1979. Technical note No. 169: Review of urban climatology. World meteorological organization, Geneva, Switzerland, 43p. Sundara Kumar K., Udaya Bhaskar P., Padmakumari K., 2012. Estimation of land surface temperature to study urban heat island effect using LANDSAT ETM+ image”, International journal of Engineering Science and technology, Vol. 4, No. 2, pp. 771 - 778. Tran H., Yasuoka Y., 2004. Surface Climatic impacts of Urbanization in the Hochiminh city, Vietnam: An intergrated study with remote sensing and modeling. Proceedings of the 3rd ICUS international symposium “New technologies for urban safety of megacities in Asia”, Agra, India, October 18 - 19. Tran Hung, Daisuke Uchihama, Shiro Ochi, Yoshifumi Yasuoka, 2006. Assessment with satellite data of the urban heat island effects in Asian mega cities. International journal of applied Earth observation and Geoinformation, Vol. 8, pp. 34 - 48. Trần Thị Vân, Hoàng Thái Lan, Lê Văn Trung, 2009. Phương pháp viễn thám nhiệt trong nghiên cứu phân bố nhiệt độ đô thị. Tạp chí Các Khoa học về Trái Đất, T.31, (2), trang 168 - 177. Valor E., Caselles V., 1996. Mapping land surface emissivity from NDVI. Application to European African and South American areas. Remote sensing of Environment, 57, pp. 167 - 184. Van de Griend A.A., Owen M., 1993. On the relationship between thermal emissivity and the normalized difference vegetation index for natural surface. International journal of remote sensing 14, pp. 1119 - 1131. LANDSAT Conversion to Radiance, Reflectance and At-Satellite Brightness Temperature (NASA).

Urban temperatures are typically, but not necessarily, elevated compared to their rural surroundings. This phenomenon of urban heat islands (UHI) exists both above and below the ground. These zones are coupled through conductive heat transport. However, the precise process is not sufficiently understood. Using satellite-derived land surface temperature and interpolated groundwater temperature measurements, we compare the spatial properties of both kinds of heat islands in four German cities and find correlations of up to 80%. The best correlation is found in older, mature cities such as Cologne and Berlin. However, in 95% of the analyzed areas, groundwater temperatures are higher than land surface temperatures due to additional subsurface heat sources such as buildings and their basements. Local groundwater hot spots under city centers and under industrial areas are not revealed by satellite-derived land surface temperatures. Hence, we propose an estimation method that relates groundwater temperatures to mean annual land-surface temperatures, building density, and elevated basement temperatures. Using this method, we are able to accurately estimate regional groundwater temperatures with a mean absolute error of 0.9 K.

A physical model-based method for retrieving urban land surface temperatures under cloudy conditions

Urban heat redistribution is mainly result of surface energy process. Surface energy process is also contributed in urban environment. Urban heat island (UHI) is mainly defined when urban temperature is elevated compared to surrounding rural area. Both in above and below the ground UHI is observed. This is happened due to conductive heat transport. How the Land surface temperature and ground water temperature are affected by the urban land use in Bangalore is the primary plan of investigation. Landsat data of 1999 and 2009 are used to understand the LU/LC changes. In this present study we used satellite derived Land surface temperature and field collected Ground water temperature which was analyzed using interpolation method and Supervised Classification Change Detection technique applied for change analysis. Here investigation was done for a period of one decade (1999/2009) on LST changes over different land use. Moreover, relationship between NDVI and LST was also considered. To understand urban surface, we estimated Normalized Difference Built-Up Index (NDBI) and Built up Area Index (BUAI); with this we are trying to find relationship with ground water temperature. Changing land use pattern, mostly expansion of built-up area has effect over land surface temperature in Bangalore urban district. The correlation between LST and the Ground water temperature (GWT); has been studied on 2009 data and found 80% correlation between them. In result, it is showing that GWT is less than LST but wherever LST is high there GWT is also high. In city core area like residential, outside industrial and road, GWT and LST both show high but near to lake or park area both show low temperature. Results show that during years 1999 to 2009 that LST and GWT directly affected due to rapid urban growth which reflects over built-up area enlarged from 39 to 57%. We can understand that urbanization has an impact on both on LST and GWT. The study showed that land use land cover change has important role of increasing GWT which is a marker of the strength of urban heat island effect and can be utilized to evaluate the extent of the urban heat island effect.KeywordsGround water temperatureLand surface temperatureBuilt up area indexSub surface urban heat islandRemote sensing