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

Validation of Landsat 8 high resolution Sea Surface Temperature using surfers

This is the final version. Available on open access from Elsevier via the DOI in this record.Nearshore coastal waters are highly dynamic in both space and time. They can be difficult to sample using conventional methods due to their shallow depth, tidal variability, and the presence of strong currents and breaking waves. High resolution satellite sensors can be used to provide synoptic views of Surface Temperature (ST), but the performance of such ST products in the nearshore zone is poorly understood. Close to the shoreline, the ST pixels can be influenced by mixed composition of water and land, as a result of the sensor’s spatial resolution. This can cause thermal adjacency effects due to the highly different diurnal temperature cycles of water bodies and land. Previously, temperature data collected during surfing sessions has been proposed for validation of moderate resolution (1 km pixel size) satellite ST products. In this paper we use surfing temperature data to validate three high resolution (100 m resampled to 30 m pixel size) ST products derived from the Thermal InfraRed Sensor (TIRS) on board Landsat 8 (L8). ST was derived from Collection 1 and 2 Level 1 data (C1L1 and C2L1) using the Thermal Atmospheric Correction Tool (TACT), and was obtained from the standard Collection 2 Level 2 product (USGS C2L2). This study represents one of the first evaluations of the new C2 products, both L1 and L2, released by USGS at the end of 2020. Using automated matchup and image quality control, 88 matchups between L8/TIRS and surfers were identified, distributed across the NorthWestern semihemisphere. The unbiased Root Mean Squared Difference (uRMSD) between satellite and in situ measurements was generally < 2 K, with warm biases (Mean Average Difference, MAD) of 1.7 K (USGS C2L2), 1.3 K (TACT C1L1) and 0.8 K (TACT C2L1). Large interquartile ranges of ST in 5 × 5 satellite pixels around the matchup location were found for several images, especially for the summer matchups around the Californian coast. By filtering on target stability the number of matchups reduced to 31, which halved the uRMSD across the three methods (to around 1.1K), MAD were much lower, i.e. 1.1 K (USGS C2L2), 0.6 K (TACT C1L1), and 0.2 K (TACT C2L1). The larger biases of the C2L2 product compared to TACT C2L1 are caused as a result of: (1) a lower emissivity value for water targets used in USGS C2L2, and (2) differences in atmospheric parameter retrieval, mainly from differences in upwelling atmospheric radiance and lower atmospheric transmittance retrieved by USGS C2L2. Additionally, tiling artefacts are present in the C2L2 product, which originate from a coarser atmospheric correction process. Overall, the L8/TIRS derived ST product compares well with in situ measurements made while surfing, and we found the best performing ST product for nearshore coastal waters to be the Collection 2 Level 1 data processed with TACT.UK Research and InnovationFederal Belgian Science Policy Office (BELSPO)Lost Bird Foundatio

LAND COVER MAPPING IN THE BRAZILIAN PAMPA WITH LANDSAT OLI AND TIRS BANDS: Mapeamento da cobertura de terra no Pampa Brasileiro com LANDSAT OLI e BANDA TIRS

When different time periods are considered, detection of past and present changes in land cover are enabled, also for quantifying and qualifying those changes. Land cover/use maps are the primary tools for the management and conservation of natural and man-made areas. For this, remote sensing bands of the reflected spectrum are usually used, leaving aside the thermal data. The objective of this work was to evaluate the inclusion of the thermal band (b10) of the TIRS (Thermal Infrared Sensor) sensor of landsat 8 satellite to increase the land cover maps accuracy in the Pampa biome from object-oriented classification. For the development of the research, 11 scenes of the Landsat 8, OLI sensor and TIRS were used. Thus, 14 cells were selected in the Brazilian Pampa, totaling 5% of its area. The following steps were performed: obtaining land surface temperature (LST) data and vegetation indices; data preparation; object-oriented classification; validation with 1354 reference points and analysis of the results. The results showed that the insertion of thermal bands, especially from different dates, increased the discrimination among classes. The classification presented 86% of global accuracy. Therefore, it is recommended to insert thermal data for mapping and environmental monitoring of the Pampa biomeQuando diferentes períodos de tempo são considerados, a detecção de mudanças passadas e presentes na cobertura do solo é utilizada, também para quantificar e qualificar essas mudanças. Os mapas de uso / cobertura do solo são as principais ferramentas para a gestão e conservação de áreas naturais e artificiais. Para isso, normalmente são utilizadas bandas de sensoriamento remoto do espectro refletido, deixando de lado os dados térmicos. O objetivo deste trabalho foi avaliar a inclusão da banda térmica (b10) do sensor TIRS (Thermal Infrared Sensor) do satélite Terrestre 8 para aumentar a precisão dos mapas de cobertura do solo no bioma Pampa a partir da classificação orientada a objetos. Para o desenvolvimento da pesquisa, foram utilizadas 11 cenas do Landsat 8, sensor OLI e TIRS. Assim, foram selecionadas 14 células no Pampa brasileiro, totalizando 5% de sua área. As seguintes etapas foram realizadas: obtenção de dados de temperatura da superfície da terra (LST) e índices de vegetação; preparação de dados; classificação orientada a objetos; validação com 1354 pontos de referência e análise dos resultados. Os resultados mostraram que a inserção de faixas termais, principalmente a partir de datas diferentes, aumentou a discriminação entre as classes. A classificação apresentou 86% de acurácia global. Portanto, recomenda-se inserir dados térmicos para mapeamento e monitoramento ambiental do bioma Pampa

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

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

Remote Sensing of Environment: Current status of Landsat program, science, and applications

Formal planning and development of what became the first Landsat satellite commenced over 50 years ago in 1967. Now, having collected earth observation data for well over four decades since the 1972 launch of Landsat- 1, the Landsat program is increasingly complex and vibrant. Critical programmatic elements are ensuring the continuity of high quality measurements for scientific and operational investigations, including ground systems, acquisition planning, data archiving and management, and provision of analysis ready data products. Free and open access to archival and new imagery has resulted in a myriad of innovative applications and novel scientific insights. The planning of future compatible satellites in the Landsat series, which maintain continuity while incorporating technological advancements, has resulted in an increased operational use of Landsat data. Governments and international agencies, among others, can now build an expectation of Landsat data into a given operational data stream. International programs and conventions (e.g., deforestation monitoring, climate change mitigation) are empowered by access to systematically collected and calibrated data with expected future continuity further contributing to the existing multi-decadal record. The increased breadth and depth of Landsat science and applications have accelerated following the launch of Landsat-8, with significant improvements in data quality. Herein, we describe the programmatic developments and institutional context for the Landsat program and the unique ability of Landsat to meet the needs of national and international programs. We then present the key trends in Landsat science that underpin many of the recent scientific and application developments and followup with more detailed thematically organized summaries. The historical context offered by archival imagery combined with new imagery allows for the development of time series algorithms that can produce information on trends and dynamics. Landsat-8 has figured prominently in these recent developments, as has the improved understanding and calibration of historical data. Following the communication of the state of Landsat science, an outlook for future launches and envisioned programmatic developments are presented. Increased linkages between satellite programs are also made possible through an expectation of future mission continuity, such as developing a virtual constellation with Sentinel-2. Successful science and applications developments create a positive feedback loop—justifying and encouraging current and future programmatic support for Landsat

Remote sensing and night time urban heat island

The urban climate literature has highlighted the remarkable prominence of nighttime UHI phenomenon. During nighttime the UHI effects become more evident due to the greater thermal inertia of the materials used in urban fabric. It is during the night when the heat accumulated in urban materials, especially in contexts of heat waves, can generate significant health risks. The low cooling capacity of urban construction materials negatively affects the comfort and the health of urban dwellers. However, and despite the great importance of night stress due to heat, the study of night UHIs is still underdeveloped. In this context, this paper aims to determine nighttime LST contrasting Landsat's very limited nighttime images with daytime ones. The example developed refers to heat wave situations during the summer 2015. The case study is the Metropolitan Area of Barcelona (35 municipalities, 636¿km2, 3.3 million inhabitants).Peer ReviewedPostprint (published version

Landsat Surface Temperature Product: Global Validation and Uncertainty Estimation

Surface temperature is an important Earth system data record that is useful to fields such as change detection, climate research, environmental monitoring, and many smaller scale applications like agriculture. Earth-observing satellites can be used to derive this metric, with the goal that a global product can be established. There are a series of Landsat satellites designed for this purpose, whose data archives provides the longest running source of continuously acquired multispectral imagery. The moderate spatial and temporal resolution, in addition to its well calibrated sensors and data archive make Landsat an unparalleled and attractive choice for many research applications. Through the support of the National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS), a Landsat Surface Temperature product (LST) has been developed. Currently, it has been validated for Landsat 5 scenes in North America, and Landsat 7 on a global scale. Transmission and cloud proximity were used to characterize LST error for various conditions, which showed that 30% of the validation data had root mean squared errors (RMSEs) less than 1 K, and 62% had RMSEs less than 2 K. Transmission and cloud proximity were also used to develop a LST uncertainty estimation method, which will allow the user to choose data points that meet their accuracy requirements. For the same dataset, about 20% reported LST uncertainties less than 1 K, and 63% had uncertainties less than 2 K. Enabling global validation and establishing an uncertainty estimation method were crucially important achievements for the LST product, which is now ready to be implemented and scaled so that it is available to the public. This document will describe the LST algorithm in full, and it will also discuss the validation results and uncertainty estimation process

Earth Observations for Addressing Global Challenges

"Earth Observations for Addressing Global Challenges" presents the results of cutting-edge research related to innovative techniques and approaches based on satellite remote sensing data, the acquisition of earth observations, and their applications in the contemporary practice of sustainable development. Addressing the urgent tasks of adaptation to climate change is one of the biggest global challenges for humanity. As His Excellency António Guterres, Secretary-General of the United Nations, said, "Climate change is the defining issue of our time—and we are at a defining moment. We face a direct existential threat." For many years, scientists from around the world have been conducting research on earth observations collecting vital data about the state of the earth environment. Evidence of the rapidly changing climate is alarming: according to the World Meteorological Organization, the past two decades included 18 of the warmest years since 1850, when records began. Thus, Group on Earth Observations (GEO) has launched initiatives across multiple societal benefit areas (agriculture, biodiversity, climate, disasters, ecosystems, energy, health, water, and weather), such as the Global Forest Observations Initiative, the GEO Carbon and GHG Initiative, the GEO Biodiversity Observation Network, and the GEO Blue Planet, among others. The results of research that addressed strategic priorities of these important initiatives are presented in the monograph

Teledetección. Nuevas plataformas y sensores aplicados a la gestión del agua, la agricultura y el medio ambiente

Este libro recoge las comunicaciones presentadas al XVII Congreso de la Asociación Española de Teledetección (AET), celebrado del 3 al 7 de octubre de 2017 en el auditorio y palacio de congresos de Murcia y organizado por el Grupo de Sistemas de Información Geográfica y Teledetección del Instituto Murciano de Investigación y Desarrollo Agrario y Alimentario (IMIDA),con el soporte de la AET,el Instituto Geográfico Nacional (IGN), las universidades politécnicas de Cartagena y Valencia, la Confederación Hidrográfica del Segura, el ayuntamiento de Murcia,las empresas Gade Eventos y Geodim y la Universidad Católica de San Antonio El lema elegido para el Congreso ha sido "Nuevas plataformas y sensores de teledetección" aplicados a la gestión del agua,la agricultura y el medio ambiente, con la intención de promover el encuentro entre las comunidades académicas, científicas e industriales en el área de la teledetección, destacando las nuevas plataformas de bajo coste y los logros conseguidos en la generación y difusión de productos útiles para la sociedadRuiz Fernández, LÁ.; Estornell Cremades, J.; Erena Arrabal, M. (2017). Teledetección. Nuevas plataformas y sensores aplicados a la gestión del agua, la agricultura y el medio ambiente. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/90688EDITORIA