Remote Sensing Monitoring of Land Surface Temperature (LST)

This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research

Landsat Program

Landsat initiated the revolution in moderate resolution Earth remote sensing in the 1970s. With seven successful missions over 40+ years, Landsat has documented - and continues to document - the global Earth land surface and its evolution. The Landsat missions and sensors have evolved along with the technology from a demonstration project in the analog world of visual interpretation to an operational mission in the digital world, with incremental improvements along the way in terms of spectral, spatial, radiometric and geometric performance as well as acquisition strategy, data availability, and products

TIRS-2 and Future Thermal Instrument Band Study and Stray Light Study

Landsat thermal instruments have been a significant source of data for thermal remote sensing applications, and future Landsat missions will continue this tradition. This work was designed to help inform the requirements for several parameters of future Landsat thermal instruments, and assess the impact that these parameters can have on the retrieved Land Surface Temperature (LST). Two main studies were conducted in this research. The first will investigate the impact that uncertainty in the spectral response of the bands will have on the LST product using the Split Window Algorithm. The main parameters that will be tested are the center and width of he bands. The second study will investigate the impact of stray light on LST, including different magnitudes of stray light and different combinations of in-field and out-of-field targets. The results of the band study showed that shifting of the bands seems to be have a larger impact on the LST than widening of the bands. Small shifts of only +/- 50 nm can cause errors of over 1 K in the LST. This study also showed that atmospheres with more water vapor content will have more effected than those with lower water vapor. The stray light study showed that using the stray light coefficients from TIRS-2 will not have a significant impact, when compared to the residual errors associated with the Split Window Algorithm

Spatial and Multi-Temporal Analysis of Land Surface Temperature through Landsat 8 Images: Comparison of Algorithms in a Highly Polluted City (Granada)

Over the past decade, satellite imaging has become a habitual way to determine the land surface temperature (LST). One means entails the use of Landsat 8 images, for which mono window (MW), single channel (SC) and split window (SW) algorithms are needed. Knowing the precision and seasonal variability of the LST can improve urban climate alteration studies, which ultimately help make sustainable decisions in terms of the greater resilience of cities. In this study we determine the LST of a mid-sized city, Granada (Spain), applying six Landsat 8 algorithms that are validated using ambient temperatures. In addition to having a unique geographical location, this city has high pollution and high daily temperature variations, so that it is a very appropriate site for study. Altogether, 11 images with very low cloudiness were taken into account, distributed between November 2019 and October 2020. After data validation by means of R2 statistical analysis, the root mean square error (RMSE), mean bias error (MBE) and standard deviation (SD) were determined to obtain the coefficients of correlation. Panel data analysis is presented as a novel element with respect to the methods usually used. Results reveal that the SC algorithms prove more effective and reliable in determining the LST of the city studied here.ERDF (European Rural Development Fund)Ministry of Science and Innovation (State Research Agency) EQC2018-004702-

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

COMPARATIVE ANALYSIS OF SPLIT-WINDOW AND SINGLE-CHANNEL ALGORITHMS FOR LAND SURFACE TEMPERATURE RETRIEVAL OF A PSEUDO-INVARIANT TARGET

Land surface temperature (LST) acquired from remote sensing observations is essential to monitor surface energy and water exchange processes at the land-atmosphere interface. Most LST retrieval methodologies are developed focusing on Northern hemisphere. Consequently, Southern hemisphere has a great need for investigating the performance of LST retrieval algorithms already consolidated in the literature. In this paper, we compared a Splitwindow (SW) and a Single-channel (SC) method to retrieve LST from Landsat 8 OLI/TIRS images in a dune field, Southern Brazil. To validate the results, the Atmospheric Correction Parameter Calculator (ACPC) tool and Radiative Transfer Equation (RTE) were used. Results demonstrated that both methodologies are in accordance with the RTE, despite they overestimated the LST. Analysis of variance (ANOVA) indicated that the means are not statistically significant (0.05 level). The correlations between LST retrieved and RTE were strong, producing R² of 0.984 and 0.973 for the SW and SC, respectively, and RMSE values of 1.18 and 1.6. SW also exhibited the best values of MSD (±0.983) and Bias (0.773), thus reinforcing its superior performance. SW can be applied with an accuracy of 1.18 K in Southern Brazil, without needing complex modeling or specific radiosonde

Methodology for the Integration of Optomechanical System Software Models with a Radiative Transfer Image Simulation Model

Stray light, any unwanted radiation that reaches the focal plane of an optical system, reduces image contrast, creates false signals or obscures faint ones, and ultimately degrades radiometric accuracy. These detrimental effects can have a profound impact on the usability of collected Earth-observing remote sensing data, which must be radiometrically calibrated to be useful for scientific applications. Understanding the full impact of stray light on data scientific utility is of particular concern for lower cost, more compact imaging systems, which inherently provide fewer opportunities for stray light control. To address these concerns, this research presents a general methodology for integrating point spread function (PSF) and stray light performance data from optomechanical system models in optical engineering software with a radiative transfer image simulation model. This integration method effectively emulates the PSF and stray light performance of a detailed system model within a high-fidelity scene, thus producing realistic simulated imagery. This novel capability enables system trade studies and sensitivity analyses to be conducted on parameters of interest, particularly those that influence stray light, by analyzing their quantitative impact on user applications when imaging realistic operational scenes. For Earth science applications, this method is useful in assessing the impact of stray light performance on retrieving surface temperature, ocean color products such as chlorophyll concentration or dissolved organic matter, etc. The knowledge gained from this model integration also provides insight into how specific stray light requirements translate to user application impact, which can be leveraged in writing more informed stray light requirements. In addition to detailing the methodology\u27s radiometric framework, we describe the collection of necessary raytrace data from an optomechanical system model (in this case, using FRED Optical Engineering Software), and present PSF and stray light component validation tests through imaging Digital Imaging and Remote Sensing Image Generation (DIRSIG) model test scenes. We then demonstrate the integration method\u27s ability to produce quantitative metrics to assess the impact of stray light-focused system trade studies on user applications using a Cassegrain telescope model and a stray light-stressing coastal scene under various system and scene conditions. This case study showcases the stray light images and other detailed performance data produced by the integration method that take into account both a system\u27s stray light susceptibility and a scene\u27s at-aperture radiance profile to determine the stray light contribution of specific system components or stray light paths. The innovative contributions provided by this work represent substantial improvements over current stray light modeling and simulation techniques, where the scene image formation is decoupled from the physical system stray light modeling, and can aid in the design of future Earth-observing imaging systems. This work ultimately establishes an integrated-systems approach that combines the effects of scene content and the optomechanical components, resulting in a more realistic and higher fidelity system performance prediction

Variability of urban surface temperatures and implications for aerodynamic energy exchange in unstable conditions

Sensible heat flux (QH) is a critical driver of surface and boundary layer meteorological processes, especially in urban areas. Aerodynamic resistance methods (ARM) to model QH are promising because, in principle, all that is needed is surface temperature (T0), air temperature (TA) and an aerodynamic resistance term (rH). There are significant challenges in urban areas however, due to uncertainties in satellite-derived land surface temperatures (LST), logistical challenges to obtain high-resolution air temperatures, and limited understanding of spatial and temporal variability of rH and associated variables (e.g. thermal roughness length). This work uses an extensive LST dataset covering six years (2011-2016) in central London and a long-term in situ observation network to analyse variability of LST and rH variables. Results show that LST is spatially correlated with building and vegetation land cover with coherent thermal structures at length scales less than 500-1000 m. Additionally, satellite-observed LST varies with average building height (up to 10% cooler in areas with tall buildings). The rH term and associated variables are observed to vary on daily and seasonal cycles and findings are used to model QH using five variations of an ARM-based approach on a 100 m pixel basis. Modelled QH is compared to observations from three scintillometer paths and an eddy covariance flux tower. We find generally good agreement between observations and models, though there is uncertainty in all methods (mean absolute error ranges from 58.1-129.3 W m-2) due to challenges in determining high-resolution meteorological and surface inputs, particularly LST and friction velocity (u*). Additional complexity in evaluating modelled QH arises from anthropogenic heat sources: long-term tower-based observations show that TA and radiometer-derived T0 are warmer during working weekdays than non-working days (up to 0.7C) and that there is an observed lag (2-3 hours) between energy consumption and observed warming and QH

Urban Heat Island Monitoring and Impacts on Citizen’s General Health Status in Isfahan Metropolis: A Remote Sensing and Field Survey Approach

Urban heat islands (UHIs) are one of the urban management challenges, especially in metropolises, which can affect citizens' health and well-being. This study used a combination of remote sensing techniques with field survey to investigate systematically the effects of UHI on citizens' health in Isfahan metropolis, Iran. For this purpose, the land surface temperature (LST) over a three-year period was monitored by Landsat-8 satellite imagery based on the split window algorithm. Then, the areas where UHI and urban cold island (UCI) phenomena occurred were identified and a general health questionnaire-28 (GHQ-28) was applied to evaluate the health status of 800 citizens in terms of physical health, anxiety and sleep, social function, and depression in UHI and UCI treatments. The average LST during the study period was 45.5 +/- 2.3 degrees C and results showed that the Zayandeh-Rood river and the surrounding greenery had an important role in regulating the ambient temperature and promoting the citizens' health. Citizens living in the suburban areas were more exposed to the UHIs phenomena, and statistical analysis of the GHQ-28 results indicated that they showed severe significant (P < 0.05) responses in terms of non-physical health sub-scales (i.e., anxiety and sleep, social functioning, and depression). Therefore, it can be concluded that not all citizens in the Isfahan metropolis are in the same environmental conditions and city managers and planners should pay more attention to the citizens living in the UHIs. The most important proceedings in this area would be the creation and development of parks and green belts, as well as the allocation of health-medical facilities and citizen education. Keywords:urban heat island; land surface temperature; split window algorithm; general health questionnaire-28; Isfahan metropoli

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