Calibration and Validation of Thermal Infrared Remote Sensing Sensors and Land/Sea Surface Temperature algorithms over the Iberian Peninsula

La Temperatura de la Superficie Terrestre (TST) y la Temperatura de la Superficie del Mar (TSM) son parámetros clave en los procesos físicos de intercambio de energía entre la superficie y la atmósfera. La TST/TSM están directamente relacionadas con el espectro Infrarrojo Térmico (TIR) que constituye la principal fuente de emisión de radiación de la superficie terrestre. El control de los datos térmicos se puede realizar con la Calibración Vicarea (VC) para, de esta forma, garantizar la calidad de los datos una vez el sensor a bordo de satélite está en órbita. Normalmente, la validación directa de los algoritmos de TST y la VC del espectro térmico se realiza con datos in-situ en tierra, mientras que la TSM se puede validar con datos de boyas. En el marco del proyecto CEOS-Spain, la Unidad de Cambio Global (UCG) ha instalado seis estaciones fijas y automáticas en la península Ibérica, en tres sitios de validación (Barrax, Doñana y Cabo de gata) los cuales obtienen datos para la realización de las actividades de calibración y validación (cal/val) de sensores con una baja y media resolución espacial. La validación de la TSM ha sido realizada con datos de boyas disponibles en la página web de Puertos del Estado. Antes de la realización de la cal/val, un estudio completo de los sitios de validación ha sido realizado para obtener la máxima precisión de las medidas realizadas por las estaciones. Las fuentes de error más comunes asociadas a las medidas in-situ de la TST son, entre otras: la homogeneidad del terreno, la emisividad y la radiación descendente. Conociendo cada error y su contribución a la medida de la TST, se ha podido establecer la precisión de nuestras medidas in-situ. Para nuestras estaciones, se ha obtenido un error por debajo de 1 K. Teniendo en cuenta los errores de la medidas in-situ, la VC ha sido realizada la los sensores TIR sensor (TIRS), Enhanced Thematic Mapper Plus (ETM+) y MODIS, mostrando todos ellos valores precisos de las bandas del térmico. La validación de los algoritmos de TST también se ha realizado de forma directa e indirecta (con datos de sensor a bordo de avión). Los resultados de validación muestran valore por debajo de 2 K y, en los mejores casos y en las condiciones más favorables, valores por debajo de 1 K. Los algoritmos de estimación de la TSM (de tipo split-window) también han obtenido una precisión por debajo de 0.8 K y, en los mejores casos (sin radiación solar y con altas velocidades del viento), valores por debajo de 0.5 K. Finalmente, dos algoritmos de la TST (para TIRS y MODIS) y uno de la TSM (para MODIS) han sido propuestos para su inclusión en la cadena de procesado gestionada por la UCG.Land Surface Temperature (LST) and Sea Surface Temperature (SST) are a key parameters in physical processes of surface energy at local and global scales. LST/SST are directly related to Thermal Infrared (TIR) spectra, which constitute the main source of Earth emission. Control of satellite TIR data can be performed through Vicarious Calibration (VC), which is the more common way to guaranty data quality once sensor is on orbit. Usually, direct validation of LST algorithms and VC of TIR data is performed through in-situ measurements of LST while SST is controlled through anchor buoys or ship transect data. In the framework of CEOS-SPAIN project, Global Unit Change (GCU) group has installed six fixed and automatic stations in three test sites over the Iberian Peninsula (Barrax, Doñana and Cabo de Gata), which provides suitable data for calibration and validation (cal/val) activities of middle and low spatial resolution Earth Observation Sensors (EOS). Validation of SST has been performed with buoys web data available in the database of Puertos del Estado webpage. Before sensors cal/val, complete suitability study of land test sites was performed in order to obtain the maximal precision given by our fixed stations (in Kelvin). Uncertainties sources linked to in-situ LST retrievals were analyzed such as area inhomogeneity, emissivity or down-welling radiance among others. Finally, with each uncertainty source contribution it was possible to establish the precision of our in-situ measurements regarding the sensor’s spatial resolution. For our test sites, LST precision was set below 1 K. Keeping in mind the values of in-situ LST precision, VC was performed on Landsat TIR sensor (TIRS) and Enhanced Thematic Mapper Plus (ETM+) as well as Terra/Aqua MODerate-resolution Imaging Spectroradiometer (MODIS), showing no displacement in raw TIR data. Test of LST algorithms was also performed with direct and indirect (through airborne sensor data) validations. Results showed Root Mean Square Errors (RMSE) in LST estimations below 2 K and, in the best cases (with the most favorable external conditions), values of 1 K. SST algorithms (Split-Window type) demonstrated precisions below 0.8 K and, in the best case (no solar radiation and high wind velocity), values of 0.5 K. Finally, two LST algorithms (for TIRS and MODIS) and one SST algorithm (MODIS) have been proposed for its inclusion in the sensor images process chain managed by the GCU group

Land Surface Temperature Product Validation Best Practice Protocol Version 1.0 - October, 2017

The Global Climate Observing System (GCOS) has specified the need to systematically generate andvalidate Land Surface Temperature (LST) products. This document provides recommendations on goodpractices for the validation of LST products. Internationally accepted definitions of LST, emissivity andassociated quantities are provided to ensure the compatibility across products and reference data sets. Asurvey of current validation capabilities indicates that progress is being made in terms of up-scaling and insitu measurement methods, but there is insufficient standardization with respect to performing andreporting statistically robust comparisons.Four LST validation approaches are identified: (1) Ground-based validation, which involvescomparisons with LST obtained from ground-based radiance measurements; (2) Scene-based intercomparisonof current satellite LST products with a heritage LST products; (3) Radiance-based validation,which is based on radiative transfer calculations for known atmospheric profiles and land surface emissivity;(4) Time series comparisons, which are particularly useful for detecting problems that can occur during aninstrument's life, e.g. calibration drift or unrealistic outliers due to undetected clouds. Finally, the need foran open access facility for performing LST product validation as well as accessing reference LST datasets isidentified

Google Earth Engine Open-Source Code for Land Surface Temperature Estimation from the Landsat Series

Land Surface Temperature (LST) is increasingly important for various studies assessing land surface conditions, e.g., studies of urban climate, evapotranspiration, and vegetation stress. The Landsat series of satellites have the potential to provide LST estimates at a high spatial resolution, which is particularly appropriate for local or small-scale studies. Numerous studies have proposed LST retrieval algorithms for the Landsat series, and some datasets are available online. However, those datasets generally require the users to be able to handle large volumes of data. Google Earth Engine (GEE) is an online platform created to allow remote sensing users to easily perform big data analyses without increasing the demand for local computing resources. However, high spatial resolution LST datasets are currently not available in GEE. Here we provide a code repository that allows computing LSTs from Landsat 4, 5, 7, and 8 within GEE. The code may be used freely by users for computing Landsat LST as part of any analysis within GEE

Recuperação de temperatura de superfície terrestre da radiância termal coletada pelo sensor TIRS/Landsat 8 : aplicações de medidas de campo e laboratório

A temperatura da superfície terrestre (Land surface temperature - LST) é um importante parâmetro na investigação de mudanças ambientais e climáticas em várias escalas. Entretanto, estimar esse parâmetro da radiação emitida na região do infravermelho termal (TIR) é uma tarefa difícil, pois as radiações medidas pelos sensores dos satélites são fortemente afetadas por efeitos atmosféricos. Todos métodos de recuperação de LST requerem validação com medidas de campo. Porém, a validação deste tipo de dado é um desafio, visto que a LST muda rapidamente no tempo e as medidas devem ser realizadas em conjunto com a passagem do sensor. Além disso, a maioria das metodologias são desenvolvidas e testadas com foco no hemisfério norte. Tendo em vista que maneiras operacionais de se obter LST devem ser constantemente investigadas, o objetivo desta pesquisa foi estudar o efeito do uso de medidas de emissividade de laboratório tomadas com base em temperaturas na determinação da LST a partir de dados de sensoriamento remoto orbital. Ademais, pretendeu-se realizar uma análise comparativa entre os algoritmos single-channel mais recentes existentes na literatura, aplicados à banda 10 (10,6-11,19 μm) do Landsat 8 TIRS. Os algoritmos considerados foram: Single-Channel Generalizado (SCG), Improved Single-Channel (ISC) e Improved Mono-Window (IMW). Um campo de dunas costeiras foi escolhido como área de estudo. Dois conjuntos de medidas de emissividade de laboratório foram construídos com amostras de campo em diferentes temperaturas com uso de um Fourier Transform Infrared (FT-IR). Dados de emissividade e temperatura foram obtidos na área de estudo concomitamente com a passagem do sensor. A equação de transferência radiativa (Radiative Transfer Equation - RTE) com parâmetros de perfis atmosféricos globais foi testada como forma de validação de dados. Uma variação de aproximadamente 2% na emissividade em relação à temperatura foi observada, podendo ser negligenciada. O FT-IR apresenta limitações quanto ao período para adquirir estabilidade, porém respeitando esta limitação e realizando abordagem correta de calibração, medidas laboratoriais podem atingir ótima acurácia e substituir a validação de campo. Bibliotecas espectrais disponíveis de emissividade demonstraram ser também uma alternativa válida. Todos métodos single-channel avaliados são adequados para obter LST; no entanto, o ISC forneceu resultados superiores em todas as análises, produzindo maior R² (0,99978) e menor RMSE (0.019) em relação aos demais.Land surface temperature (LST) is an important parameter in the investigation of environmental and climatic changes at various scales. However, estimating this parameter from the radiation emitted in the thermal infrared (TIR) region is a difficult task because the radiation measured by the satellite sensors is strongly affected by atmospheric effects. All LST retrieval methods require validation with field measurements. Nonetheless, the validation of this type of data is a challenge because the LST changes rapidly in time and the measurements must be performed togheter with the sensor overpass. In addition, most methodologies are developed and tested focusing on the Northern Hemisphere. Considering that operational ways of obtaining LST should be constantly investigated, the aim of this paper was to study the effect of the use of temperature-based laboratory measurements in the determination of the emissivity and LST retrieval from orbital remote sensing data. Moreover, it was intended to perform a comparative analysis among the most recent single-channel algorithms available on the literature, applied to band 10 (10.6-11.19 μm) of the Landsat 8 TIRS. The algorithms considered were: Single-channel generalized (SC), Improved Single-channel (ISC) and Improved Mono-window (IMW). A field of coastal dunes was chosen as study area. Two sets of laboratory emissivity measurements were performed with field samples at different temperatures using a Fourier Transform Infrared (FT-IR). Emissivity and temperature data were obtained in the study area concomitantly with the satellite overpass. The Radiative Transfer Equation (RTE) with parameters of global atmospheric profiles was tested as a method of validation. A variation of approximately 2% in the emissivity in relation to the temperature was observed, which could be neglected. The FT-IR presents limitations on the period to acquire stability, however as long as this limitation is respected and the calibration approach correctly carried out, laboratory measurements can achieve optimum accuracy and replace field validation. Available spectral libraries of emissivity have also proved to be a good alternative. All evaluated single-channel methods are suitable for obtaining LST; however, ISC provided superior results in all analyzes, producing higher R² (0.99978) and lower RMSE (0.019) relative to the other algorithms tested

Historic thermal calibration of landsat 5 TM through an improved physics based approach

This investigation is motivated by the current need for a detailed post launch calibration of the Thematic Mapper (TM) thermal band (Band 6), aboard NASA’s Landsat 5 spacecraft. The historical calibration spans the period from 1984 to 2007. It is through fusion of environmental data sources (i.e. buoy observations, surface observations, and radiosonde observations) that a vicarious calibration approach will be implemented to construct the complete calibration record of the Landsat 5 TM thermal band. The vicarious calibration process takes advantage of the long standing National Data Buoy Center (NDBC) moored buoy fleet to acquire historic ground truth measurements needed over the lifetime of Landsat 5. These measurements are propagated to the sensor through the use of physics based models to establish a predicted at sensor radiance. Through comparison of the predicted at sensor radiance and the actual sensor observed radiance, a calibration metric is established. Results indicate the Landsat 5 TM thermal band, originally planned for a 3 year mission, has fluctuated only slightly ( 1 K) over the 24+ years in orbit. The calibration curve developed in this study is consistent with previous results from campaigns preformed in 1985 and post 1999. The data indicated that the sensor exhibited a clear gain issue (i.e. over estimates low radiance targets and under estimates high radiance targets) found to be approximately consistent over time. Additionally, an event occurring either prior to or during 1999, caused a discernible fluctuation in sensor performance (i.e. dominant cold bias) for all data post 1999. It is the recommendation of this vicarious calibration I II campaign that a linear (Dual: slope & intercept) correction be applied to the Landsat 5 data archive. As a result of the correction, the Landsat 5 TM Band 6 is radiometrically calibrated to within ±0.488 K, in reference to a 300 K blackbody. This result was verified through an extensive error propagation analysis, which found the proposed methodology to have an expected error of 0.454 K. The proposed methodology was also verified by a comparison study to the traditional approach (i.e. non buoy derived ground truth) using the closely monitored and trusted Landsat 7 data calibrated using the traditional approach. The comparison found the two methods were not statistically different, which offered the confidence that this methodology could be applied successfully over the domain of this study. This comparison not only validates the calibration record of Landsat 5, but also demonstrates the utility of the method in future efforts. This work has demonstrated that a successful historical vicarious calibration campaign can be conducted using exclusively free and easily accessible data. It has been established that the proposed methodology can be implemented to achieve a high level of radiometric integrity, which includes both historic and future efforts, in the calibration of remote thermal infrared systems

Correção atmosférica de imagens termais utilizando perfis verticais de alta resolução simulados por um modelo de mesoescala

A estimativa da temperatura da superfície terrestre ( LST ) por sensoriamento remoto no infravermelho termal (TIR) é dependente d a realização de uma correção atmosférica apropriada que , em geral, necessita de perfis atmosféricos como dados de entrada. Dados globais de reanálise são uma alternativa prática para a obtenção desses perfis, mas podem apresentar limitações. Nesse contexto, o presente estudo teve como objetivo analisar a utilização do modelo numérico Weather Research and Forecasting (WRF) para gerar perfis verticais de alta resolução , refinando dados de reanálise , visando a correção atmosférica no TIR para o cálculo de valores de LST. Para tal, foram realizadas simulações com o modelo WRF com dados de reanálise do NCEP Climate Forecast System Version 2 (CFSv2) como condições iniciais e utilizando duas grades aninhadas com resoluções horizontais de 12 km (G12) e 3 km (G03). Para estimar a LST, foram empregados: o método da inversão direta da Equação de Transferência Radiativa (RTE) , o modelo MODTRAN e valores de radiância da banda 10 do Landsat 8 TIRS. A pesquisa avaliou o desempenho do modelo através dos perfis verticais, dos parâmetros atmosféricos de correção (transmitância atmosférica e radiâncias upwelling e downwelling ) e dos valores de LST, utilizando como referência dados de radiossondagens in situ , no sul do Brasil . Adicionalmente, foi executada uma análise de sensibilidade a dois esquemas de parametrização de camada limite planetária . Os resultados indicam que o modelo WRF simula de maneira satisfatória os perfis atmosféricos que, por consequência, geram parâmetros de correção e LST com baixos erros. Contudo, não existe melhora significativa nas métricas estatísticas entre os perfis extraídos diretamente da reanálise CFSv2 e os simulados pelo WRF . Em alguns casos, a utilização de um perfil de grade mais refinada resultou, até mesmo, em maiores erros. Os valores gerais de RMSE para a LST foram: 0,55 K ( CFSv2), 0,79 K ( WRF G12 ) e 0,82 K ( WRF G03 ). A escolha do esquema de camada limite mostrou - se caso - dependente. Conclui - se que não há necessidade especial de refinar a resolução dos perfis de reanálise visando estimativa de LST, por meio do método da RTE .The Land Surface Temperature (LST) retrieval from thermal infrared (TIR) remote sensing depends on performing an appropriate atmospheric correction. In general, this approach requires atmospheric profiles as input data. Global reanalysis data are a practical alternative to obtain these profiles, but they may have limitations. In this con text, this study aimed to assess the use of the Weather Research and Forecasting (WRF) numerical model to generate high - resolution vertical profiles, downscaling reanalysis data , to be applied in TIR atmospheric correction for LST retrieval . WRF simulations were carried out using NCEP Climate Forecast System Version 2 (CFSv2) reanalysis as initial conditions and two nested grids with horizontal resolutions of 12 km (G12) and 3 km (G03) . To retrieve the LST, we used: the Radiative Transfer Equation (RTE) based method , the MODTRAN model, and radiance values from Landsat 8 TIRS10 band . Th is research evaluated the model performance through vertical profiles, atmospheric correction parameters (atmospheric transmittance and upwelling and downwelling radiances) , and LST values, using in situ radiosonde data ( in Southern Brazil ) as reference. Moreover, a sensitivity analysis to two planetary boundary layer parameterization schemes was performed . The results indicate that the WRF model satisfactor il y simulates the atmospheric profiles that, consequently, generate correction param eters and LST with low errors. However, there is no significant improvement in statistical metrics between profiles extracted directly from the CFSv2 reanalysis and those simulated by WRF . In some cases, the use of a finer grid profile resulted even in larger errors. The LST overall RMSE values were: 0.55 K (CFSv2), 0.79 K (WRF G12) , and 0.82 K (WRF G03) . The boundary layer scheme choice proved to be case - dependent. We concluded that there is no special need to increase the resolution of reanalysis profiles in order to retrieve LST using the RTE - based method

Multiscale Imaging of Evapotranspiration

Evapotranspiration (ET; evaporation + transpiration) is central to a wide range of biological, chemical, and physical processes in the Earth system. Accurate remote sensing of ET is challenging due to the interrelated and generally scale dependent nature of the physical factors which contribute to the process. The evaporation of water from porous media like sands and soils is an important subset of the complete ET problem. Chapter 1 presents a laboratory investigation into this question, examining the effects of grain size and composition on the evolution of drying sands. The effects of composition are found to be 2-5x greater than the effects of grain size, indicating that differences in heating caused by differences in reflectance may dominate hydrologic differences caused by grain size variation. In order to relate the results of Chapter 1 to the satellite image archive, however, the question of information loss between hyperspectral (measurements at 100s of wavelength intervals) laboratory measurements and multispectral (≤ 12 wavelength intervals) satellite images must be addressed. Chapter 2 focuses on this question as applied to substrate materials such as sediment, soil, rock, and non-photosynthetic vegetation. The results indicate that the continuum that is resolved by multispectral sensors is sufficient to resolve the gradient between sand-rich and clay-rich soils, and that this gradient is also a dominant feature in hyperspectral mixing spaces where the actual absorptions can be resolved. Multispectral measurements can be converted to biogeophysically relevant quantities using spectral mixture analysis (SMA). However, retrospective multitemporal analysis first requires cross-sensor calibration of the mixture model. Chapter 3 presents this calibration, allowing multispectral image data to be used interchangeably throughout the Landsat 4-8 archive. In addition, a theoretical explanation is advanced for the observed superior scaling properties of SMA-derived fraction images over spectral indices. The physical quantities estimated by the spectral mixture model are then compared to simultaneously imaged surface temperature, as well as to the derived parameters of ET Fraction and Moisture Availability. SMA-derived vegetation abundance is found to produce substantially more informative ET maps, and SMA-derived substrate fraction is found to yield a surprisingly strong linear relationship with surface temperature. These results provide context for agricultural applications. Chapter 5 investigates the question of mapping and monitoring rice agricultural using optical and thermal satellite image time series. Thermal image time series are found to produce more accurate maps of rice presence/absence, but optical image time series are found to produce more accurate maps of rice crop timing. Chapter 6 takes a more global approach, investigating the spatial structure of agricultural networks for a diverse set of landscapes. Surprisingly consistent scaling relations are found. These relations are assessed in the context of a network-based approach to land cover analysis, with potential implications for the scale dependence of ET estimates. In sum, this thesis present a novel approach to improving ET estimation based on a synthesis of complementary laboratory measurements, satellite image analysis, and field observations. Alone, each of these independent sources of information provides novel insights. Viewed together, these insights form the basis of a more accurate and complete geophysical understanding of the ET phenomenon

Avaliação de métodos single channel na estimativa da temperatura da superfície terrestre no hemisfério sul a partir de dados orbitais no infravermelho termal

Diversos métodos tem sido propostos para estimar a Temperatura da Superfície Terrestre (LST) a partir de dados do infravermelho termal obtidos por satélites. Esses esforços são uma tentativa de minimizar os erros impostos na solução não-linear da transferência radiativa no sistema superfície-atmosfera. Além disso, a correção atmosférica em dados termais é um dos fatores fundamentais para obter LST acurada. Os métodos Single Channel (SC) permitem derivar a LST a partir da radiância medida em uma banda. Assim, consistem em uma oportunidade para estimar LST de longo prazo com os dados termais da série Landsat, com quase 40 anos de registro. Contudo, a maioria dos SC é desenvolvida e projetada para as condições atmosféricas e de superfície do Hemisfério Norte. Nesse contexto, essa dissertação objetivou avaliar o desempenho dos algoritmos Generalized Single Channel (GSC), Improved Single Channel (ISC) e Surface Temperature product (produto ST) na estimativa da LST em uma região litorânea do Hemisfério Sul. Para tanto, um campo de dunas composto por 99,53% de quartzo foi selecionado e dados do Landsat 8 TIRS foram utilizados. Inicialmente, para fundamentar a avaliação, investigou-se a aplicabilidade de perfis verticais de diferentes resoluções espaciais e horizontais, derivados de produtos de reanálise NCEP, na correção atmosférica, bem como, os impactos gerados na estimativa da LST. Os resultados mostraram que os perfis NCEP CFSv2 de resoluções originais e os perfis NCEP FNL utilizados pela Calculadora de Parâmetros de Correção Atmosférica (ACPC) da NASA foram os mais adequados para a correção atmosférica. Considerando as vantagens da ACPC, ela também foi utilizada para estimar os dados atmosféricos empregados nos algoritmos GSC e ISC na avaliação. O algoritmo ISC (RMSE de 0,69 K) apresentou o melhor desempenho na estimativa da LST, seguido do GSC (RMSE de 2,5 K) e do produto ST (RMSE de 4,24 K). De modo geral, concluiu-se que o ISC se mostrou o mais adequado para calcular a LST, podendo ser aplicado em estudos de balanço de energia, onde um erro de até 2 K é aceitável. Confirmou-se, portanto, a importância da análise dos métodos SC no presente trabalho, justificada pela sua aplicação em dados de radiância de sensores termais com uma banda, principalmente para estudos com séries temporais de LST da série Landsat, além de situações de mau funcionamento de canais espectrais.Several methods have been proposed to estimate the Land Surface Temperature (LST) from thermal infrared data obtained by satellites. These efforts are an attempt to minimize errors imposed in the nonlinear solution of radiative transfer in the surface- atmosphere system. Furthermore, atmospheric correction in thermal data is one of the key factors to obtain accurate LST. Single Channel (SC) methods allow to retrieve the LST from the measured radiance in one band. Thus, they provide an opportunity to estimate long-term LST with the Landsat thermal data series, with almost 40 years of record. However, most SCs are developed and designed for the atmospheric and surface conditions of the Northern Hemisphere. In this context, this dissertation aimed to evaluate the performance of the Generalized Single Channel (GSC), Improved Single Channel (ISC) and Surface Temperature product (ST product) algorithms in estimating LST in a coastal region of the Southern Hemisphere. For that, a dune field composed of 99.53% quartz was selected and Landsat 8 TIRS data were used. Initially, to support the evaluation, the applicability of vertical profiles of different spatial and horizontal resolutions, derived from NCEP reanalysis products, in atmospheric correction, as well as the impacts generated in the LST estimation, was investigated. The results showed that the original resolution NCEP CFSv2 profiles and the NCEP FNL profiles used by NASA's Atmospheric Correction Parameters Calculator (ACPC) were the most suitable for atmospheric correction. Considering the advantages of ACPC, it was also used to estimate the atmospheric data used in the GSC and ISC algorithms in the evaluation. The ISC algorithm (RMSE of 0.69 K) presented the best performance in retrieving the LST, followed by the GSC (RMSE of 2.5 K) and the ST product (RMSE of 4.24 K). In general, it was concluded that the ISC proved to be the most suitable to calculate the LST, and can be applied in energy balance studies, where an error of up to 2 K is acceptable. Therefore, the importance of the analysis of SC methods in the present work was confirmed, justified by their application in radiance data from thermal sensors with one band, mainly for studies with time series of LST from Landsat series, in addition to situations of channel malfunction

Detecting Thermal Anomalies In Lahendong Geothermal Prospect Using Aster TIR and Landsat 8

This study intends to determine geothermal anomalies area using remote sensing data in the form of Landsat 8 and ASTER satellite imagery data which have Thermal Infrared Sensor (TIRS). Through pre-processing like georeferencing, radiometric calibration, and atmospheric correction, the Landsat 8 TIRS and ASTER data were wont to invert the land surface temperature of the study area during the daytime and night time using the inversion of planck function and emissivity separation algorithm. Result shows the land surface temperatures during daytime and night time of four natural land cover —water, vegetation, built up area, and bare soil—were classified and analyzed. According to the results, vegetation and bare soil show relatively thermal anomalies during the day and comparatively cold anomalies during the night. Otherwise water shows relatively cold anomalies during the day and relatively thermal anomalies during the night. Meanwhile built up area shows relatively thermal anomalies during the day and cold anomalies during the night. Superimposed and calculating mean of the night and day surface temperature can adequately eliminate the relatively cold/thermal anomalies of land cover caused by solar radiation, thus effectively highlighting geothermal anomalies. Thus, Nine geothermal anomalies areas were successfully extracted