Single-Channel Algorithm

Over the last few decades, Casablanca city became the biggest industrial, commercial center in Morocco with rapid urbanization and explosive population growth, more than 4 million people. Urban expansion has reached to suburban areas due to population growth and socio economic development, not to mention the rapid increase of transportation. Result of these changes causes a change of microclimate in urban areas. The most evident phenomenon is the increase of urban surface temperature as compared with suburban areas, "heat island" is formed in the atmospheric boundary above urban area. It could make serious environmental problems for its inhabitants (e.g., urban waterlogged and thermal pollution). Thermal infrared remote sensing bands, proved its capability in monitoring temperature field. The purpose of this study is to evaluate the use of Landsat TM, ETM + , OLI and TIRS data for indicating temperature differences in urban areas, in order to achieve a spatiotemporal study, using data between 1984 and 2014, and showing the relationship between urban expansion and the heat island effect during time, producing maps that shows the distribution of urban temperature. Results can be combined with land use/ land cover maps or thermal-land cover and operated as reference for urban planning and future solutions to reduce heat island effect.

Microwave remote sensing has high potential for soil moisture retrieval. However, the efficient retrieval of soil moisture depends on optimally choosing the soil moisture retrieval parameters. In this study first the initial evaluation of SMOS L2 product is performed and then four approaches regarding soil moisture retrieval from SMOS brightness temperature are reported. The radiative transfer equation based tau-omega rationale is used in this study for the soil moisture retrievals. The single channel algorithms (SCA) using H polarization is implemented with modifications, which includes the effective Land Surface Temperatures (LSTs) simulated from ECMWF (downscaled using WRF-NOAH Land Surface Model (LSM)) and MODIS satellite. The retrieved soil moisture is then utilized for soil moisture deficit (SMD) estimation using empirical relationships with Probability Distributed Model based SMD as a benchmark. The square of correlation during the calibration indicates a value of R 2 =0.359 ...

Microwave remote sensing has high potential for soil moisture retrieval. However, the efficient retrieval of soil moisture depends on optimally choosing the soil moisture retrieval parameters. In this study first the initial evaluation of SMOS L2 product is performed and then four approaches regarding soil moisture retrieval from SMOS brightness temperature are reported. The radiative transfer equation based tau-omega rationale is used in this study for the soil moisture retrievals. The single channel algorithms (SCA) using H polarization is implemented with modifications, which includes the effective Land Surface Temperatures (LSTs) simulated from ECMWF (downscaled using WRF-NOAH Land Surface Model (LSM)) and MODIS satellite. The retrieved soil moisture is then utilized for soil moisture deficit (SMD) estimation using empirical relationships with Probability Distributed Model based SMD as a benchmark. The square of correlation during the calibration indicates a value of R 2 =0.359 ...

Microwave remote sensing has high potential for soil moisture retrieval. However, the efficient retrieval of soil moisture depends on optimally choosing the soil moisture retrieval parameters. In this study first the initial evaluation of SMOS L2 product is performed and then four approaches regarding soil moisture retrieval from SMOS brightness temperature are reported. The radiative transfer equation based tau-omega rationale is used in this study for the soil moisture retrievals. The single channel algorithms (SCA) using H polarization is implemented with modifications, which includes the effective Land Surface Temperatures (LSTs) simulated from ECMWF (downscaled using WRF-NOAH Land Surface Model (LSM)) and MODIS satellite. The retrieved soil moisture is then utilized for soil moisture deficit (SMD) estimation using empirical relationships with Probability Distributed Model based SMD as a benchmark. The square of correlation during the calibration indicates a value of R 2 =0.359 ...

Over the last few decades, Casablanca city became the biggest industrial, commercial center in Morocco with rapid urbanization and explosive population growth, more than 4 million people. Urban expansion has reached to suburban areas due to population growth and socio economic development, not to mention the rapid increase of transportation. Result of these changes causes a change of microclimate in urban areas. The most evident phenomenon is the increase of urban surface temperature as compared with suburban areas, "heat island" is formed in the atmospheric boundary above urban area. It could make serious environmental problems for its inhabitants (e.g., urban waterlogged and thermal pollution). Thermal infrared remote sensing bands, proved its capability in monitoring temperature field. The purpose of this study is to evaluate the use of Landsat TM, ETM + , OLI and TIRS data for indicating temperature differences in urban areas, in order to achieve a spatiotemporal study, using data between 1984 and 2014, and showing the relationship between urban expansion and the heat island effect during time, producing maps that shows the distribution of urban temperature. Results can be combined with land use/ land cover maps or thermal-land cover and operated as reference for urban planning and future solutions to reduce heat island effect.

Over the last few decades, Casablanca city became the biggest industrial, commercial center in Morocco with rapid urbanization and explosive population growth, more than 4 million people. Urban expansion has reached to suburban areas due to population growth and socio economic development, not to mention the rapid increase of transportation. Result of these changes causes a change of microclimate in urban areas. The most evident phenomenon is the increase of urban surface temperature as compared with suburban areas, "heat island" is formed in the atmospheric boundary above urban area. It could make serious environmental problems for its inhabitants (e.g., urban waterlogged and thermal pollution). Thermal infrared remote sensing bands, proved its capability in monitoring temperature field. The purpose of this study is to evaluate the use of Landsat TM, ETM + , OLI and TIRS data for indicating temperature differences in urban areas, in order to achieve a spatiotemporal study, using data between 1984 and 2014, and showing the relationship between urban expansion and the heat island effect during time, producing maps that shows the distribution of urban temperature. Results can be combined with land use/ land cover maps or thermal-land cover and operated as reference for urban planning and future solutions to reduce heat island effect.

Over the last few decades, Casablanca city became the biggest industrial, commercial center in Morocco with rapid urbanization and explosive population growth, more than 4 million people. Urban expansion has reached to suburban areas due to population growth and socio economic development, not to mention the rapid increase of transportation. Result of these changes causes a change of microclimate in urban areas. The most evident phenomenon is the increase of urban surface temperature as compared with suburban areas, "heat island" is formed in the atmospheric boundary above urban area. It could make serious environmental problems for its inhabitants (e.g., urban waterlogged and thermal pollution). Thermal infrared remote sensing bands, proved its capability in monitoring temperature field. The purpose of this study is to evaluate the use of Landsat TM, ETM+, OLI and TIRS data for indicating temperature differences in urban areas, in order to achieve a spatiotemporal study, ...

Environmental factors change such as built-up area density, vegetation density, moisture and temperature can affect the health problems. Dengue Fever Disease (DFD) that are still high rates of incidence because of environmental factors change in Indonesia including Surakarta City. The purpose of this study are mapping environmental factors consisting of built-up area density, vegetation density, land surface moisture (LSM) and land surface temperature (LST) in Surakarta City 2002 and 2015 through image transformation and correlation analysis of environmental factors change on the evidence of DFD in Surakarta City. This research uses Landsat-7 ETM+, Landsat-8 OLI/TIRS and the evidence of DFD data on 2002 and 2015. Landsat image is processed by using LST, VGNIR-BI to be built-up area density, NDVI for vegetation density, NDMI for LSM. Based on the result of image transformation, built-up area density and LST in Surakarta City increased from 2002-2015, while vegetation density and LSM tended to decrease. The result of this research shows that the environmental factor change in the form of built-up area density, vegetation cover density, LSM and LST have correlation are 74%, 68%, 75% and 69% of the evidence of DFD in Surakarta, respectively. Overall, the environmental factors correlated 86.1% of the evidence of DFD in Surakarta City.

During the communism, the accent of development was put mainly on industry. This is also the case of The Industrial Platform Săvineşti, from Neamţ county, which assured two major productions: textile fibers and chemical fertilizers. After the fall of the regime, the platform was divised and some of the new owners have sold all the equipments, have demolished the buildings, leaving the place barren. In the present, only a few of the old factories still work, and new stakeholders started their businesses. The study aims to show how the land surface temperature (LST) evolved in association with the events that followed from the flowering period of the factory. Three summer months from different years (1991, 2008 and 2018) where taken into consideration and LST was calculated based on Landsat scenes from missions 5-TM and 8-OLI. In order to make the whole process faster, two models were constructed using ArcMap extesion Model Builder. Besides the land surface temperature, there were calculated another two indices: Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI), which offer additional arguments to the changes that took place on the platform. The purpose of the study is to show how land surface temperature changes depending on the use of the territory, thus the importance of remote sensing being emphasised.

Over the last few decades, Casablanca city became the biggest industrial, commercial center in Morocco with rapid urbanization and explosive population growth, more than 4 million people. Urban expansion has reached to suburban areas due to population growth and socio economic development, not to mention the rapid increase of transportation. Result of these changes causes a change of microclimate in urban areas. The most evident phenomenon is the increase of urban surface temperature as compared with suburban areas, "heat island" is formed in the atmospheric boundary above urban area. It could make serious environmental problems for its inhabitants (e.g., urban waterlogged and thermal pollution). Thermal infrared remote sensing bands, proved its capability in monitoring temperature field. The purpose of this study is to evaluate the use of Landsat TM, ETM + , OLI and TIRS data for indicating temperature differences in urban areas, in order to achieve a spatiotemporal study, using data between 1984 and 2014, and showing the relationship between urban expansion and the heat island effect during time, producing maps that shows the distribution of urban temperature. Results can be combined with land use/ land cover maps or thermal-land cover and operated as reference for urban planning and future solutions to reduce heat island effect.

West Sumatra is one of has big geothermal energy resources potential. Remote sensing technology can have a role in geothermal exploration activity to measure the distribution of land surface temperatures (LST) and predict the geothermal potential area. Main study to obtain the assessment of Landsat 8 TIRS (Landsat`s Thermal Infrared Sensor) data capability for geothermal energy resources estimation. Mono-window algorithms were used to generate the LST maps. Data set was combined with a digital elevation model (DEM) to identify the potential geothermal energy based on the variation in surface temperature. The result that were derived from LST map of West Sumatra shows that ranged from -8.6 C0 to 32.59 C0 and the different temperatures are represented by a graduated pink to brown shading. A calculated result clearly identifies the hot areas in the dataset, which are brown in colour images. Lima Puluh Kota, Tanah Datar, Solok, and South Solok areas showed the high-temperature value (Brown) in the range of 28.1 C0 to 32.59 C0 color in images which means that they possess high potential for generating thermal energy. In contrast, the temperatures were lower (Pink) in the north-eastern areas and the range distribution was from-8.5 C0 to 5 C0.

The growth of urban areas and similar landscape transformation has been a major driver of local, regional and global environmental change. The turning of urban greenery to impervious landscapes has been identified as a key factor influencing the distinctive urban heat and related consequences. Due to the high demand for space in urban areas, creation and preservation of urban greenery as heat sinks is commonly perceived as unrequired space. Consequently, there is an expanding requirement for creation and preservation of such spaces, because of these physical changes, i.e. decrease in green cover and increase in built-up area in cities, the land surface temperature LST is bound to increase. Urban heat islands can cause deterioration of living environment, increase in energy consumption, elevation in ground level ozone, and even an increase in mortality rates. To determine the contribution of urban greenery as possible remedy to Urban Heat Island UHI we have to analyse the association between built-up, green cover NDVI and land surface temperature for which we are studying the 3 major cities of India i.e. Delhi, Ahmedabad and Bangalore. This study sought to quantify multi-seasonal heat contribution of major Land Use Land-Cover LULC within these cities using the recently launched Landsat 8 and MODIS Land Surface and Temperature LST data sets. , this study demonstrates the value of remotely sensed data sets in understanding the implication of LULC types on the urban micro climate. The study is particularly valuable for designing sustainable urban socioeconomic and environmental strategies at local, regional and global climate change.

L and surface temperature (LST) is very important environmental parameter to be studied. The meaning of land surface temperature is the temperature of earth surface when we directly contact with it. It is also called skin temperature of the earth surface. LST is one of the important factors in global ABSTRACT : To estimate land surface temperature (LST) has an important role for agriculture as well as global change of climate, growth of vegetation and glacier melting. It combines the results of all surface atmosphere interactions and energy fluxes between the surface and the atmosphere. Now-a-days, estimation of temperature of land surface is being calculated with the help of satellite images containing thermal infrared band. Though land surface temperature derived from satellite, could be a beneficial complement to conventional land surface temperature data sources. This research, proposed a methodology for determining land surface temperature through using a structured mathematical algorithm viz., split window (SW) algorithm. Split window algorithm has been used on LANDSAT 8 with operational land imager i.e. OLI sensor and thermal infrared sensor i.e. TIRS dataset of Udham Singh Nagar district. TIRS shows two thermal bands i.e. band 10 and band 11. SW approach requires brightness temperature value of both band 10 and band 11 as well as land surface emissivity which is calculated from OLI bands i.e. NIR and Red, for the estimation of land surface temperature. The spectral radiance was determined using thermal infrared bands i.e. band 10 and band 11. Emissivity was calculated by using normalized difference vegetation index i.e. NDVI threshold technique for which OLI bands 2, 3, 4 and 5 were utilized. SW approach uses brightness temperature of two bands of thermal infrared, mean and difference in land surface emissivity for estimating land surface temperature. In this paper, 6 Dec. 2015 date was selected as an example to show the approach of using SW technique to estimate the LST of Udham Singh Nagar district of Uttarakhand state in India.

‫إﻋداد‬ ‫ﻣن‬ ‫اﻟطﺎﻟب‬ : ‫اﻷﺳﺗﺎذ‬ ‫اف‬ ‫إﺷر‬ : ‫ﻣﺣﻣود‬ ‫ﺟرﯾﺑﯾﻊ‬ ‫ﯾﺎﺳﯾن‬ ‫ﻗرﻓﻲ‬ ‫اﻟﺟﺎﻣﻌﻲ‬ ‫اﻟﻣوﺳم‬ 2014 / 2015 ‫و‬ ‫ﻟﻬﺎ‬ ‫ﺟﺎﻧﺑﯾن‬ : ‫ﺳﯾﺎﺳﻲ‬ ‫ﺟﺎﻧب‬ ‫ﻓﻲ‬ ‫ﯾﺗﻣﺛل‬ ‫ﺗ‬ ‫اﻷﺟﻬز‬ ‫ﻣﻛﯾن‬ ‫ﻣﺑدأ‬ ‫ﯾﺣﻘق‬ ‫ﻣﻣﺎ‬ ‫ﺑﯾدﻫﺎ‬ ‫ﺷؤوﻧﻬﺎ‬ ‫ﺗﺳﯾﯾر‬ ‫اﻟﺷﻌب‬ ‫ﻗﺑل‬ ‫ﻣن‬ ‫اﻟﻣﻧﺗﺧﺑﺔ‬ ‫اﻟﻣﺣﻠﯾﺔ‬ ‫ة‬ ، ‫ﯾﺔ‬ ‫اﻹدار‬ ‫اطﯾﺔ‬ ‫اﻟدﯾﻣﻘر‬ ‫ﺑﯾن‬ ‫اﻟدوﻟﺔ‬ ‫ﻓﻲ‬ ‫ﯾﺔ‬ ‫اﻹدار‬ ‫اﻟوظﯾﻔﺔ‬ ‫ﯾﻊ‬ ‫ﺗوز‬ ‫ﻓﻲ‬ ‫ﻓﯾﺗﺟﺳد‬ ‫اﻟﻘﺎﻧوﻧﻲ‬ ‫اﻟﺟﺎﻧب‬ ‫أﻣﺎ‬ ‫اﻟﻣﺻﻠﺣﻲ‬ ‫أو‬ ‫اﻟﻣرﻓﻘﻲ‬ ‫اﻟطﺎﺑﻊ‬ ‫ذات‬ ‫اﻟﻣﺳﺗﻘﻠﺔ‬ ‫اﻟﻬﯾﺋﺎت‬ ‫و‬ ‫ﯾﺔ‬ ‫اﻟﻣرﻛز‬ ‫ة‬ ‫اﻷﺟﻬز‬ . 1 ‫ﻣﯾﻠوي‬ ‫زﯾن‬ ، ‫اﻹداري،‬ ‫اﻟﻘﺎﻧون‬ ‫واﻟﺗوزﯾﻊ‬ ‫واﻟﻧﺷر‬ ‫اﻟرﺷﺎد‬ ‫ﻣﻛﺗﺑﺔ‬ ، 2013 ، ‫ص‬ 94 . 2 ‫ﺑﻌﻠﻲ،‬ ‫اﻟﺻﻐﯾر‬ ‫ﻣﺣﻣد‬ ‫اﻹداري‬ ‫اﻟﻘﺎﻧون‬ ) ‫اﻹداري‬ ‫اﻹداري،اﻟﻧﺷﺎط‬ ‫اﻟﺗﻧظﯾم‬ ( ‫،د‬ ‫اﺋر،‬ ‫ﯾﻊ،اﻟﺟز‬ ‫اﻟﺗوز‬ ‫و‬ ‫ﻟﻠﻧﺷر‬ ‫اﻟﻌﻠوم‬ ‫ار‬ 2004 ‫،ص‬ 61 .

Urbanization is led by haphazard development, resulting into harsh climate change impacts. Climate change mitigation is bolstered by effective urban green space planning. This indirect relationship between Climate Change and urban green coverage, related with the heat island formation, has necessitated this research. The paper sets a goal to identify and measure the city level parameters of climate change. The objective is to analyze, if planned development of urban areas relates to the mitigation factors, or if the currently evolved urban pattern relates to the Urban heat island and the ecological function in direct relation with climate change adaptation. The assessment of this relationship between urbanization and climate change impacts, which is governed by Urban Green coverage is quantified using multi criteria evaluation. A study of Pune city is carried out for land cover change detection for the past 15 years and heat island generation due to urbanization, using Landsat 7 (ETM+) and Landsat 8 (OLI/TIRS) imagery and climatic data. The outcome is quantified for climate change for Pune City. This paper also reviews the policies of World health organization against the requisites for urbanization in India and concludes on the necessary inclusions in policy implementation.

In this paper the urban heat islands (UHI) effect in Madurai region has been identified by retrieving the land surface temperature (LST) distribution.The aim of this paper is to implement an algorithm to measure land surface temperature in Madurai region and it's surrounding from Landsat thermal imagery. The Land surface temperature has been estimated by using Single Channel (SC) algorithm and Split-Window (SW) algorithm. These two algorithms can be implemented using Landsat 7 and Landsat 8 satellite data. The various methods adopted for retrieving the algorithm has been addressed in the present study. The results show that for Single Channel (SC) algorithm the error is approximately 1K-2K and in Split-Window (SW) algorithm the error is reduced becauseas SW algorithm uses two Thermal Infrared (TIR) bands for land surface temperature retrieval. In SW algorithm error is less than 1K. The results show that the LST generated using the SW algorithm was more reliable and accurate. From the final output it is revealed that barren lands, uncultivable land and urban areas experienced high LST and the areas with high vegetation cover and water body experiencing low LST. The results from both the algorithms show a variance of 5-6 o C between urban areas, barren lands and vegetation covers thus indicating the presence of UHI in Madurai city.

In this paper the urban heat islands (UHI) effect in Madurai region has been identified by retrieving the land surface temperature (LST) distribution.The aim of this paper is to implement an algorithm to measure land surface temperature in Madurai region and it's surrounding from Landsat thermal imagery. The Land surface temperature has been estimated by using Single Channel (SC) algorithm and Split-Window (SW) algorithm. These two algorithms can be implemented using Landsat 7 and Landsat 8 satellite data. The various methods adopted for retrieving the algorithm has been addressed in the present study. The results show that for Single Channel (SC) algorithm the error is approximately 1K-2K and in Split-Window (SW) algorithm the error is reduced becauseas SW algorithm uses two Thermal Infrared (TIR) bands for land surface temperature retrieval. In SW algorithm error is less than 1K. The results show that the LST generated using the SW algorithm was more reliable and accurate. From the final output it is revealed that barren lands, uncultivable land and urban areas experienced high LST and the areas with high vegetation cover and water body experiencing low LST. The results from both the algorithms show a variance of 5-6 o C between urban areas, barren lands and vegetation covers thus indicating the presence of UHI in Madurai city.

In this paper the urban heat islands (UHI) effect in Madurai region has been identified by retrieving the land surface temperature (LST) distribution.The aim of this paper is to implement an algorithm to measure land surface temperature in Madurai region and it's surrounding from Landsat thermal imagery. The Land surface temperature has been estimated by using Single Channel (SC) algorithm and Split-Window (SW) algorithm. These two algorithms can be implemented using Landsat 7 and Landsat 8 satellite data. The various methods adopted for retrieving the algorithm has been addressed in the present study. The results show that for Single Channel (SC) algorithm the error is approximately 1K-2K and in Split-Window (SW) algorithm the error is reduced becauseas SW algorithm uses two Thermal Infrared (TIR) bands for land surface temperature retrieval. In SW algorithm error is less than 1K. The results show that the LST generated using the SW algorithm was more reliable and accurate. From the final output it is revealed that barren lands, uncultivable land and urban areas experienced high LST and the areas with high vegetation cover and water body experiencing low LST. The results from both the algorithms show a variance of 5-6 o C between urban areas, barren lands and vegetation covers thus indicating the presence of UHI in Madurai city.

The paper utilized Landsat 5 TM and Landsat 8 OLI for analyzing land use/land cover change and its impact on land surface temperature in Sundarban Bio-sphere Reserve, India. Split window algorithm and spectral radiance model were used for determining land surface temperature from Landsat 8 OLI and Landsat 5 TM, respectively. The land use land cover change analysis revealed phenomenal increase in the waterlogged areas followed by settlement and paddy and a decrease in open forest followed by deposition and water body. The distribution of average change in land surface temperature shows that water recorded highest increase in temperature followed by deposition, open forest and settlement. Overlay of the transect profiles drawn on land use/land cover change map over land surface temperature map revealed that the land surface temperature has increased in those areas which were transformed from open forest to paddy, open forest to settlement, paddy to settlement and deposi-tion to settlement. The study demonstrated that increase in non-evaporating surfaces and decrease in vegetation have increased the surface temperature and modified the temperature of the study area.

Determination of surface urban heat island (SUHI) is possible by estimating land surface temperature (LST) with the use of thermal infrared satellite remote sensing. In this study 26 NOAA/AVHRR images of the study area were selected and processed. The images were recorded at cloudless nights (between 1 and 2 UTC) from April to September 2003-2005. The study area was the city of Krakow (Poland) and its vicinity. In order to estimate LST values the Ulivieri et al. (1994) split-window algorithm was applied. Land surface emissivity (LSE) included in the algorithm was estimated by Normalised Difference Vegetation Index Thresholds Method (NDVI THM). NDVI values were calculated on the basis of day-time NOAA/AVHRR images recorded at 11-12 UTC in April-September 2003-2005. The average NDVI maps for three vegetation seasons (spring, summer and autumn) were prepared for further estimation of seasonal LSE. After estimating LST for 26 selected satellite images the average night-time LST map was ...

Land Surface temperature(LST),the skin temperature of the ground,is identified as a significant variable of
microclimate and radiation transfer within the atmosphere.Remote sensing(RS) has been widely recommended
for its potential to detect surface temperature variability and,GIS (Geographic Information System)
supplemented with ground truthing allows spatio-temporal analysis of surface temperature in the urban
regions.In this study,land cover classification is used to define LSE(Land Surface Emissivity),which is required
for the calculation of LST over Thiruvananthapuram, Capital city of the state of Kerala,India.Landsat8 TIRS
(Thermal Infrared Sensor) and OLI (Operational Land Imager) bands were primarily utilized to classify land
cover types and to estimate the surface thermal characteristics.The results showed that surface temperature is a
function of diverse surface soil-water content and vegetation cover.

Over the last few decades, Casablanca city became the biggest industrial, commercial center in Morocco with
rapid urbanization and explosive population growth, more than 4 million people. Urban expansion has reached to suburban
areas due to population growth and socio economic development, not to mention the rapid increase of transportation.
Result of these changes causes a change of microclimate in urban areas. The most evident phenomenon is the increase of
urban surface temperature as compared with suburban areas, “heat island” is formed in the atmospheric boundary above
urban area. It could make serious environmental problems for its inhabitants (e.g., urban waterlogged and thermal
pollution). Thermal infrared remote sensing bands, proved its capability in monitoring temperature field. The purpose of this
study is to evaluate the use of Landsat TM, ETM+, OLI and TIRS data for indicating temperature differences in urban areas, in
order to achieve a spatiotemporal study, using data between 1984 and 2014, and showing the relationship between urban
expansion and the heat island effect during time, producing maps that shows the distribution of urban temperature. Results
can be combined with land use/ land cover maps or thermal-land cover and operated as reference for urban planning and
future solutions to reduce heat island effect.

The importance of land surface temperature (LST) retrieved from high to medium spatial resolution remote sensing data for many environmental studies, particularly the applications related to water resources management over agricultural sites, was a key factor for the final decision of including a thermal infrared (TIR) instrument on board the Landsat Data Continuity Mission or Landsat-8. This new TIR sensor (TIRS) includes two TIR bands in the atmospheric window between 10 and 12 μm, thus allowing the application of split-window (SW) algorithms in addition to single-channel (SC) algorithms or direct inversions of the radiative transfer equation used in previous sensors on board the Landsat platforms, with only one TIR band. In this letter, we propose SC and SW algorithms to be applied to Landsat-8 TIRS data for LST retrieval. Algorithms were tested with simulated data obtained from forward simulations using atmospheric profile databases and emissivity spectra extracted from spectral libraries. Results show mean errors typically below 1.5 K for both SC and SW algorithms, with slightly better results for the SW algorithm than for the SC algorithm with increasing atmospheric water vapor contents.