Study Of Land Surface Temperature Research Articles

Analytical study of land surface temperature for evaluation of UHI and UHS in the city of Chandigarh India

The Land Surface Temperature (LST) plays a crucial role in the ecological well-being of cities worldwide, intricately tied to the presence of vegetation. This investigation aims to assess the relationship between seasonal LST fluctuations and the Normalized Difference Vegetation Index (NDVI), as well as the correlation between LST and the Normalized Difference Built-up Index (NDBI), utilizing Pearson's correlation coefficient technique with extensive Landsat-8 data spanning 2016 to 2020. Additionally, the study evaluates the ecological and thermal qualities of the city using the Urban Thermal Field Variance Index (UTFVI). The ecological evaluation based on UTFVI for 2020 indicates that 52.03% of the area falls under the excellent category and 31.82% under the worst category. Notably, there is a significant increase in summer mean LST from 32.42 °C in 2016 to 38.27 °C in 2020. Identified Urban Heat Islands (UHIs), indicative of the warmest areas within city limits due to human activities, show an increasing trend in the summer UHI temperature from 36.92 °C to 38.06 °C between 2016 and 2020, respectively. The study reveals a robust inverse correlation between LST and NDVI, as well as a direct relationship between LST and NDBI across the entire city. The differentiation between UHI and non-UHI areas is achieved using the LST range. Furthermore, LST maps pinpoint UHI hotspots in Chandigarh, highlighting areas unsuitable for human habitation due to extreme heat. Validation of LST values employs Moderate Resolution Imaging Spectroradiometer (MODIS) data as a baseline, where MOD11A1 data's thermal infrared band offers a spatial resolution of 1 km, contrasting with Landsat 8 (Band-10) 100 m resolution. Remarkably, a highly positive correlation is observed between approximated LST values derived from Landsat 8 and MOD11A1 data, without necessitating upscaling or downscaling.

Application of Thermal Remote Sensing to Study of Land Surface Temperature as Indicator Land Cover Change in Wetland and Vegetation

Application of Thermal Remote Sensing to Study of Land Surface Temperature as Indicator Land Cover Change in Wetland and Vegetation

Longitudinal study of land surface temperature (LST) using mono- and split-window algorithms and its relationship with NDVI and NDBI over selected metro cities of India

This study was designed to compare the pattern of land surface temperature (LST) over four metro cities of India (Mumbai, Chennai, Delhi, and Kolkata) selected on a longitudinal basis in relation to the built-up and vegetation indices. Two different methods were employed for the retrieval of LST, i.e., mono-window algorithm (MWA) and split-window algorithm (SWA) on the Landsat 8 (OLI/TIRS) datasets, to analyze the spatial pattern of LST over selected cities in relation to normalized differential built-up index (NDBI) and normalized differential vegetation index (NDVI). The result shows that the LST was high over the densely built areas while low over the densely vegetated areas. The highest LST, NDBI, and NDVI were found in Mumbai, while Kolkata records the lowest LST and NDVI. Furthermore, the spatial analysis of LST shows that the LST was high in central parts of all cities except in the case of Delhi where some peripheral areas also record high LST. The comparison from in situ LST (field observations) reveals that the SWA has higher accuracy in the retrieval of LST in maritime areas like Mumbai and Chennai because it reduces the atmospheric effects, while the MWA has higher accuracy for inland areas like Delhi. The spatial relationships of LST with NDVI and NDBI show that vegetation cover has more impact on LST in Delhi while low in Chennai and Mumbai, and the built-up surfaces have a higher impact on LST in Chennai and Mumbai than Kolkata and Delhi.

LAND SURFACE TEMPERATURE PROFILING AND ITS RELATIONSHIPS WITH LAND INDICES: A CASE STUDY ON LUCKNOW CITY

Abstract. Globally, 54.5% of the total population was living in urban settings in 2016 and a projection indicates that if the same trend goes, then this population will be 60% in 2030. Natural land has been converted to impervious space rapidly which is altering the climate change. The main focus of the present paper is the study of Land Surface Temperature (LST) dynamics and its relationship with Land Indices, viz., Normal Difference Vegetation Index (NDVI) which is found negative, Normal Difference Built-up Index (NDBI) which is found positive, Enhanced Built-up and Bareness Index (EBBI) which is found positive in Lucknow city on both time points of 1993 and 2019. This study also includes the effects of land indices on LST profiling in nine different parts and eight different directions to explore the spatial dynamics of city landscape. The NDVI is found higher in the southern side than any other parts of the city in 2019 because of high vegetation growth which resulted in reduction of LST by 4.42 °C to 5.76 °C as compared to parts of the city. The results of NDBI and EBBI exhibit high built-up growth in the landscape of the Lucknow city especially from city center to 13 kms (least growth in south-eastern side) from 1993 to 2019. The results indicate intensification of LST in the range of 0.26 °C to 2.24 °C between city centre and city periphery from 1993 to 2019. The findings of the present study will help urban planners and policy makers to adopt suitable measures for sustainable planning for Lucknow city landscape to reduce the adverse effects of LST.

A COMPARATIVE STUDY OF LAND SURFACE TEMPERATURE WITH DIFFERENT INDICES ON HETEROGENEOUS LAND COVER USING LANDSAT 8 DATA

Abstract. The temperature rise in urban areas has become a major environmental concern. Hence, the study of Land surface temperature (LST) in urban areas is important to understand the behaviour of different land covers on temperature. Relation of LST with different indices is required to study LST in urban areas using satellite data. The present study focuses on the relation of LST with the selected indices based on different land cover using Landsat 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) data in Varanasi, India. A regression analysis was done between LST and Normalized Difference Vegetation index (NDVI), Normalized Difference Soil Index (NDSI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Water Index (NDWI). The non-linear relations of LST with NDVI and NDWI were observed, whereas NDBI and NDSI were found to show positive linear relation with LST. The correlation of LST with NDSI was found better than NDBI. Further analysis was done by choosing 25 pure pixels from each land cover of water, vegetation, bare soil and urban areas to determine the behaviour of indices on LST for each land cover. The investigation shows that NDSI and NDBI can be effectively used for study of LST in urban areas. However, NDBI can explain urban LST in the better way for the regions without water body.

Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy

ABSTRACTThe present study focuses on determining the relationship of estimated land surface temperature (LST) with normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) for Florence and Naples cities in Italy using Landsat 8 data. The study also classifies different land use/land cover LU–LC) types using NDVI and NDBI threshold values, iterative self-organizing data analysis technique and maximum likelihood classifier, and analyses the relationship built by LST with the built-up area and bare land. Urban thermal field variance index was applied to determine the thermal and ecological comfort level of the city. Several urban heat islands (UHIs) were extracted as the most heated zones within the city boundaries due to increasing anthropogenic activities. The difference between the mean LST of UHI and non-UHI is 3.15°C and 3.31°C, respectively, for Florence and Naples. LST build a strong correlation with NDVI (negative) and NDBI (positive) for both the cities as a whole, especially for the non-UHIs. But, the strength of correlation becomes much weaker within the UHIs. Moreover, most of the UHIs (85.21% in Naples and 76.62% in Florence) are developed within the built-up area or bare land and are demarcated as an ecologically stressed zone.

Study of land surface temperature and spectral emissivity using multi-sensor satellite data

In this study, an attempt has been made to estimate land surface temperatures (LST) and spectral emissivities over a hard rock terrain using multi-sensor satellite data. The study area, of about 6000 km2, is a part of Singhbhum-Orissa craton situated in the eastern part of India. TIR data from ASTER, MODIS and Landsat ETM+ have been used in the present study. Telatemp Model AG-42D Portable Infrared Thermometer was used for ground measurements to validate the results derived from satellite (MODIS/ASTER) data. LSTs derived using Landsat ETM+ data of two different dates have been compared with the satellite data (ASTER and MODIS) of those two dates. Various techniques, viz., temperature and emissivity separation (TES) algorithm, gray body adjustment approach in TES algorithm, Split-Window algorithms and Single Channel algorithm along with NDVI based emissivity approach have been used. LSTs derived from bands 31 and 32 of MODIS data using Split-Window algorithms with higher viewing angle (50°) (LST1 and LST2) are found to have closer agreement with ground temperature measurements (ground LST) over waterbody, Dalma forest and Simlipal forest, than that derived from ASTER data (TES with AST 13). However, over agriculture land, there is some uncertainty and difference between the measured and the estimated LSTs for both validation dates for all the derived LSTs. LST obtained using Single Channel algorithm with NDVI based emissivity method in channel 13 of ASTER data has yielded closer agreement with ground measurements recorded over vegetation and mixed lands of low spectral contrast. LST results obtained with TIR band 6 of Landsat ETM+ using Single Channel algorithm show close agreement over Dalma forest, Simlipal forest and waterbody with LSTs obtained using MODIS and ASTER data for a different date. Comparison of LSTs shows good agreement with ground measurements in thermally homogeneous area. However, results in agriculture area with less homogeneity show difference of LST up to 2°C. The results of the present study indicate that continuous monitoring of LST and emissivity can be undertaken with the aid of multi-sensor satellite data over a thermally homogeneous region.

Land surface temperature and emissivity estimation based on the two-temperature method: sensitivity analysis using simulated MSG/SEVIRI data

A feasibility study of land surface temperature (LST) and land surface emissivity (LSE) estimation is presented using METEOSAT Second Generation (MSG)/Spinning Enhanced Visible and Infrared Imager (SEVIRI) data, based on the two-temperature method (TTM). The performance of TTM was assessed by taking into consideration the noise in SEVIRI channels IR 10.8 and IR 12.0 as well as the errors in the atmospheric profiles. Imposed errors due to uncertainties on atmospheric information were generated based on the background error covariance matrix used in the assimilation scheme of the European Centre for Medium-Range Weather Forecasts (ECMWF) Global Circulation Model. TTM has provided LST values with averaged error bias [root-mean square error (RMSE)] ranging from 0.0 to 0.5 K (from 0.8 to 2.5 K) for geographical–seasonal model atmospheres stored in MODTRAN4 with water vapor content varying between 0.85 and 4.11 g cm −2. Obtained results suggest that TTM may be used as a complementary method to split-window (SW) algorithms over areas where LSE is not well known a priori.

The extension of the split window technique to passive microwave surface temperature assessment

The theoretical study of both land and sea surface temperature remote sensing is treated through investigating the extension to the microwave region (1-100GHz) of the split window technique, usually used in the thermal infrared region for sea surface temperature measurements. The study of land surface temperature shows that, in both regions (infrared and microwave), the influence (of atmospheric water vapor content and surface emissivity) is critical. The theory is based on the Radiative Transfer Equation, which assumed solutions can be given in both spectral regions, with respect to Wien's and Rayleigh-Jean's laws, respectively. The surface temperature determination is studied in connection with the surface emissivity in both infrared and microwave regions determined with an iterative process. Infrared data is provided by the sensor Advanced Very High Resolution Radiometer (AVHRR) and microwave data by Special Sensor Microwave/Imager (SSM/I), through the WETNET program, directed by NASA/HQS.