35 research outputs found

    Derivation and Validation of the Stray Light Correction Algorithm for the Thermal Infrared Sensor Onboard Landsat 8

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    It has been known and documented that the Thermal Infrared Sensor (TIRS) on-board Landsat 8 suffers from a significant stray light problem (Reuter et al., 2015; Montanaro et al., 2014a). The issue appears both as a non-uniform banding artifact across Earth scenes and as a varying absolute radiometric calibration error. A correction algorithm proposed by Montanaro et al. (2015) demonstrated great potential towards removing most of the stray light effects from TIRS image data. It has since been refined and will be implemented operationally into the Landsat Product Generation System in early 2017. The algorithm is trained using near-coincident thermal data (i.e., Terra/MODIS) to develop per-detector functional relationships between incident out-of-field radiance and additional (stray light) signal on the TIRS detectors. Once trained, the functional relationships are used to estimate and remove the stray light signal on a per-detector basis from a scene of interest. The details of the operational stray light correction algorithm are presented here along with validation studies that demonstrate the effectiveness of the algorithm in removing the stray light artifacts over a stressing range of Landsat/TIRS scene conditions. Results show that the magnitude of the banding artifact is reduced by half on average over the current (uncorrected) product and that the absolute radiometric error is reduced to approximately 0.5% in both spectral bands on average (well below the 2% requirement). All studies presented here indicate that the implementation of the stray light algorithm will lead to greatly improved performance of the TIRS instrument, for both spectral bands

    Stray Light Artifacts in Imagery from the Landsat 8 Thermal Infrared Sensor

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    The Thermal Infrared Sensor (TIRS) has been collecting imagery of the Earth since its launch aboard Landsat 8 in early 2013. In many respects, TIRS has been exceeding its performance requirements on orbit, particularly in terms of noise and stability. However, several artifacts have been observed in the TIRS data which include banding and absolute calibration discrepancies that violate requirements in some scenes. Banding is undesired structure that appears within and between the focal plane array assemblies. In addition, in situ measurements have shown an error in the TIRS absolute radiometric calibration that appears to vary with season and location within the image. The source of these artifacts has been determined to be out-of-field radiance that scatters onto the detectors thereby adding a non-uniform signal across the field-of-view. The magnitude of this extra signal can be approximately 8% or higher (band 11) and is generally twice as large in band 11 as it is in band 10. A series of lunar scans were obtained to gather information on the source of this out-of-field radiance. Analyses of these scans have produced a preliminary map of stray light, or ghost, source locations in the TIRS out-of-field area. This dataset has been used to produce a synthetic TIRS scene that closely reproduces the banding effects seen in actual TIRS imagery. Now that the cause of the banding has been determined, a stray light optics model is in development that will pin-point the cause of the stray light source. Several methods are also being explored to correct for the banding and the absolute calibration error in TIRS imager

    Landsat 9 Thermal Infrared Sensor 2 Architecture and Design

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    The Thermal Infrared Sensor 2 (TIRS-2) will fly aboard the Landsat 9 spacecraft and leverages the Thermal Infrared Sensor (TIRS) design currently flying on Landsat 8. TIRS-2 will provide similar science data as TIRS, but is not a buildto-print rebuild due to changes in requirements and improvements in absolute accuracy. The heritage TIRS design has been modified to reduce the influence of stray light and to add redundancy for higher reliability over a longer mission life. The TIRS-2 development context differs from the TIRS scenario, adding to the changes. The TIRS-2 team has also learned some lessons along the way

    Remote Sensing Monitoring of Land Surface Temperature (LST)

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    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

    Evaluation of Stray Light Correction for the Thermal Infrared Sensor (TIRS) from Landsat 8

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    The Thermal Infrared Sensor (TIRS) of Landsat 8 suffers from a stray light issue, where out of field-of-view (FOV) radiance reflects into the optical system and is recorded by the sensors. This is confirmed to be resulting from a defect in hardware of the third order lens. The TIRS-on-TIRS algorithm has been proposed to be an operational correction algorithm. This algorithm has an advantage of simple and easy processing steps. However, no comprehensive evaluation of the TIRS-on-TIRS algorithm has been performed to this point. This thesis addresses a full evaluation of the performance of the algorithm with two datasets; especially associated with two artifacts related to the stray light issue: absolute radiometric error and banding effect. The dataset with truth, MODIS, demonstrates a good performance of the TIRS-on-TIRS algorithm in terms of both absolute radiometric error and the banding effect on all situations except for a higher absolute error for desert scenes after correction. The dataset without truth shows good consistency in terms of absolute radiometric error and no worse performance on the worst situations. Residual pattern error was found with band 11, but almost none in band 10. This should be taken into consideration for further calibration work

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

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    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

    Evapotranspiration Retrieval Using S-SEBI Model with Landsat-8 Split-Window Land Surface Temperature Products over Two European Agricultural Crops

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    Crop evapotranspiration (ET) is a key variable within the global hydrological cycle to account for the irrigation scheduling, water budgeting, and planning of the water resources associated with irrigation in croplands. Remote sensing techniques provide geophysical information at a large spatial scale and over a relatively long time series, and even make possible the retrieval of ET at high spatiotemporal resolutions. The present short study analyzed the daily ET maps generated with the S-SEBI model, adapted to Landsat-8 retrieved land surface temperatures and broadband albedos, at two different crop sites for two consecutive years (2017-2018). Maps of land surface temperatures were determined using Landsat-8 Collection 2 data, after applying the split-window (SW) algorithm proposed for the operational SW product, which will be implemented in the future Collection 3. Preliminary results showed a good agreement with ground reference data for the main surface energy balance fluxes Rn and LE, and for daily ET values, with RMSEs around 50 W/m2 and 0.9 mm/d, respectively, and high correlation coefficient (R2 = 0.72-0.91). The acceptable uncertainties observed when comparing with local ground data were reaffirmed after the regional (spatial resolution of 9 km) comparison with reanalysis data obtained from ERA5-Land model, showing a StDev of 0.9 mm/d, RMSE = 1.1 mm/d, MAE = 0.9 mm/d, and MBE = −0.3 mm/d. This short communication tries to show some preliminary findings in the framework of the ongoing Tool4Extreme research project, in which one of the main objectives is the understanding and characterization of the hydrological cycle in the Mediterranean region, since it is key to improve the management of water resources in the context of climate change effects

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

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    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

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

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    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

    Validation of Landsat 8 high resolution Sea Surface Temperature using surfers

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    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
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