Mapping and managing Earth’s mineral resources demands advanced techniques for characterizing surface composition, a challenge that can be effectively addressed by spaceborne Earth observation. Thermal infrared (TIR) sensors hosted on orbital platforms provide a powerful tool for regional scale (~1000s km2), high-resolution (≤100m) identification of mineral composition and surface thermal properties. In this study, we demonstrate the potential of multispectral TIR image data acquired by the ECOSTRESS and ASTER spaceborne sensors with near-global coverage to produce the first mineral maps of the Earth’s arid and semi-arid regions. TIR data complement Visible to ShortWave InfraRed (VSWIR) data because most important rock forming minerals do not have features in the VSWIR. Thus, integrating TIR-derived mineralogy is essential to comprehensively map the surface composition and interpret the geology. The mapping results were validated at three sites—the Algodones Dunes (quartz), White Sands Dunes (gypsum), and Mehdi Ridge (calcite)—showing strong spatial and abundance agreement with laboratory data from field samples and reference literature. These results confirm the reliability of high spatial resolution multispectral TIR data in capturing major surface mineral distributions.

In this work, comparison results between data collected on the ground and by the Hyperspectral Thermal Emission Spectrometer (HyTES) acquired during the 2023 European airborne campaign are showed. NASA-Jet Propulsion Laboratory (JPL), European Space Agency (ESA), Italian Space Agency (ASI), National Centre for Space Studies (CNES), and various European universities and national research institutes collaboratively organized the campaign. Multiple test areas were designated in Italy, France, and Switzerland, with flights conducted from late May to mid-July. Here, we show the Italian volcanic areas, were specifically targeted for the HyTES flights. By this work, the authors aim to give an overview about the Italian Calibration and Validation (Cal/Val) sites, classified as thermally active sites, and to present preliminary results obtained in such area. The sites can be defined "thermally active" due to the presence of thermal anomalies at the surface related to volcanic activity ranging from 60°C to 1000°C. In addition, the same test sites have been selected in the THERESA (THErmal infRarEd SBG Algorithms) project, aimed to enhance algorithms for processing Thermal InfraRed data from the Surface Biology and Geology (SBG) – Thermal InfraRed (TIR) mission. During the lifetime of the project, THERESA will contribute to enhance the investigation of terrestrial phenomena by using both visible and thermal images.

One of the Designated Observables (DOs) identified in the Decadal Survey for Earth SFcience and Applications from Space was Surface Biology and Geology (SBG). NASA has formulated this and several of the other DOs into the Earth System Observatory, which provides a framework from which to answer many of the questions posed by the Decadal Survey and address the goals of the DOs. The SBG mission concept, now in formulation, has an overarching goal of acquiring global hyperspectral visible to shortwave infrared (VSWIR; 0.38–2.5 μm) and multispectral mid and thermal infrared (MIR: 3–5 μm; TIR: 8–12 μm) image data at high spatial resolution (~30 m in the VSWIR and ~ 60 m in the TIR). The VSWIR and TIR are separate instruments on separate platforms and thus will have different characteristics such as local overpass and temporal revisit times as a function of the individual scientific objectives. The SBG-TIR is a joint-endeavor between NASA and ASI in Italy, with the instrument being built at the NASA Jet Propulsion Laboratory (JPL). It will have a wide swath width (935 km) resulting in a three-day equatorial revisit time. During Phase A development, the TIR spectral resolution was increased from five to six bands (plus the original two planned for the MIR). The addition of a 10.3 μm band vastly improves the capability of surface mineralogy mapping and aerosol detection in sulfur dioxide (SO2) plumes. For the first time, an Earth-orbiting TIR mission is planning an operational surface mineralogy (SM) L3 product. This product uses the L2 TIR surface emissivity data as input together with a spectral library of the most common Earth surface minerals to produce mineral and weight percent silica (WPS) maps of the Earth’s arid lands. Here, we describe the current SM algorithm testing and development, initial results, and plans for ongoing work prior to the planned 2028 launch.

The European ECOSTRESS Hub (EEH) funded by European Space Agency targets at generating land surface temperature (LST), evapotranspiration (ET) and gross primary productivity (GPP) from the high-resolution ECOSTRESS observations. In Phase 1 (2020-2022), EEH LST obtained using the split-window and temperature and emissivity separation algorithms achieved good accuracy with an overall RMSE around 2 K. Evaluation of three ET estimates using different models, namely the Surface Energy Balance System (SEBS), Two Source Energy Balance (TSEB) parametric models, and the non-parametric Surface Temperature Initiated Closure (STIC) model, indicated that STIC ET had the highest accuracy (RMSE of ~70 W m-2). In Phase 2 (2023-2026), the surface energy balance will be coupled with photosynthesis through canopy-stomatal conductance. Overall, EEH is promising to advance the science of terrestrial ecosystem processes and facilitate the preparation for the future high-resolution thermal missions.

This work aims to characterize the surface of an Italian geothermal field, Parco Naturalistico delle Biancane (PNB), by using hyperspectral data and define the main diagnostic spectral features of lithotypes affected by mineral alteration due to geothermal activity. Hyperspectral data acquired by PRISMA (Hyperspectral Precursor of the Application Mission) and AVIRIS-NG (Airborne Visible / Infrared Imaging Spectrometer – Next Generation), coupled with a spectral library of the main lithotype of the area, represent the dataset used for the analysis. All the spectral data cover the VNIR (Visible and Near InfraRed) and SWIR (Short-Wave InfraRed) spectral range. The Material Identification and Characterization Algorithm (MICA) has been used to perform the comparison between the spectral features from the spectral library and the PRISMA and AVIRIS hyperspectral images in order to obtain an automatic lithotype classification map.

Pursuant to recommendations by the National Academies of Science, Engineering and Medicine's Earth Science Decadal Survey [1], the National Aeronautics and Space Administration (NASA) has announced the development of an Earth System Observatory (ESO), a series of missions designed to observe processes across the Earth's interior, surface and atmosphere. A key component of this system is the Surface Biology and Geology (SBG) investigation. SBG will measure the composition and properties of Earth's land, inland waters, and coastal oceans. The notional architecture consists of multiple spacecraft slated for launch in the 2027–2028 timeframe (Figure 1). Target science questions and geophysical variables span diverse disciplines including terrestrial and aquatic ecology, geology, vulcanology, hydrology and cryospheric sciences (Figure 2). Beyond simply measuring geophysical variables for each discipline, SBG will provide information about the links between the different domains, enabling a more comprehensive understanding of the Earth as a connected system. SBG measurements will also benefit a wide range of societal applications including agriculture, terrestrial and aquatic biodiversity, natural hazards, public health, and management of water and other natural resources [2]. SBG will also coordinate measurements, data products, and analyses with other ESO elements to deliver an integrated Earth System perspective of Earth and its changing climate.

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) was launched to the International Space Station (ISS) on June 29, 2018, and currently provides the highest spatial resolution thermal infrared (TIR) data (38 m$\times \,\, 69$m) available from space. In this study, we validated the ECOSTRESS level-2 Land Surface Temperature (LST) and emissivity product at fourteen global sites to Stage-1 status. Two primary methods are recommended for the validation of LST data: Temperature-based (T-based) and Radiance-based (R-based) methods. The T-based method requires calibrated measurements of the ground leaving radiance concurrent with the satellite overpass. In contrast, the R-based method uses a radiative closure simulation with external atmospheric profiles and an$a$prioriknowledge of surface emissivity. Using these standard methods, we validated 1139 ECOSTRESS clear-sky observations between August 1, 2018, and March 31, 2020. For LST, the results show good agreement with ground-based measurements with an average root mean square error (RMSE) of 1.07 K, mean absolute error (MAE) of 0.40 K, and$r^{2}>0.988$at all sites. However, a cold bias of ~0.75 K was identified for temperatures below 295 K linked to calibration issues that will be addressed in future reprocessing of the data. Retrieved emissivity comparisons with laboratory spectra had an RMSE of 0.023 (2.3%) for all bands on average. With the decommissioning of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on Terra in 2023, the multispectral and high-spatial-resolution characteristics of ECOSTRESS data serve as a pathfinder to the National Aeronautics and Space Administration’s (NASA) Surface Biology and Geology (SBG) designated observable with an expected launch in 2026.

On Jan. 5, 2018, at the request of NASA, the National Oceanic and Atmospheric Administration (NOAA) and the U.S. Geological Survey (USGS), the Committee on the Decadal Survey for Earth Science and Applications from Space (ESAS) of the National Academies of Sciences, Engineering and Medicine (NASEM) Space Studies Board, Division on Engineering and Physical Sciences released the 2017 Decadal Survey, “Thriving on Our Changing Planet: A Decadal Strategy for Earth Observations from Space” [1]. The 700-page document is the second such Earth sciences survey produced by NASEM. The first, “Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond,” was released in 2007. The 2018 study designated a global “Surface Biology and Geology” (SBG) investigation that would include both imaging spectroscopy and thermal infrared observations [1]. This suite of measurements would address a wide range of global science questions. Its themes include: flows of energy, carbon, water, and nutrients sustaining terrestrial and marine ecosystems; the variability of the land surface and the fluxes of water and energy; inventory of the world's volcanoes, and the composition and temperature of volcanic products immediately following eruptions; other natural hazards including wildfires; snow accumulation and melt; water balance from the headwaters to the continent; land and water use effects on evapotranspiration; functional traits and diversity of terrestrial and aquatic ecosystems and vegetation; and more. Figure 1 shows example spectra from these surfaces, illustrating the enormous diversity of scene content that would be observed. Tables 1 and 2 show examples of the core and higher-level products that the SBG mission would produce.

The last five years have seen dramatic growth in the use of Visible Shortwave Infrared (VSWIR) and Thermal Infrared (TIR) imaging spectrometers to detect and characterize greenhouse methane sources. Targets include: dairy and animal husbandry emissions; landfills; fossil fuel extraction, storage, and transport infrastructure; geologic sources; natural emissions associated with sensitive arctic ecosystems; and more. These campaigns have resulted in significant new discoveries and advances in our understanding of the North American CH4 budget. Recent algorithm improvements have been critical for these campaigns, enabling robust statistical CH4measurement, fully-automated image-space source identification, and quantification of flux. Here we survey recent campaigns by NASA's Next Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG) and NASA's Hyperspectral Thermal Emission Spectrometer (HyTES). We describe their algorithmic advances and major findings.

The National Academies Decadal Survey for Earth Science recommended that NASA pursue global imaging spectroscopy and thermal infrared measurements in the coming decade [1]. Both measurements would offer repeat coverage on approximately five-day to biweekly cadence, with comprehensive coverage of the globe's coastal and terrestrial area. This would be an unprecedented volume of data with the potential to transform remote sensing practice. To address this recommendation, NASA has sponsored a concept study by NASA research centers and associated university partners (https://sbg.jpl.nasa.gov). This study is determining a family of architecture options - including launch vehicle, spacecraft, instrument, and suborbital components - that could address the Decadal Survey objectives. The architecture study is driven by science needs and builds on input of the research community. As of this writing, the study is entering a phase in which a large field of system possibilities is pared down to a representative handful for an ultimate decision by NASA.

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) was launched on June 29, 2018, to the International Space Station (ISS). Landsats 7 and 8 were launched on April 15, 1999 and February 11, 2013, respectively. The thermal channels of all three instruments have been validated at the Lake Tahoe, CA/NV, USA, and Salton Sea, CA, USA, automated validation sites. These sites have been used to validate a large number of thermal infrared radiometers including ASTER, MODIS, and VIIRS. We have validated 41 cloud-free ECOSTRESS scenes acquired between July 29, 2018 and June 23, 2019; 625 cloud-free Landsat 7 scenes acquired between June 30, 1999 and February 7, 2019; and 375 cloud-free Landsat 8 scenes acquired between March 10, 2013 and February 8, 2019. Validation involved propagating ground measurements to equivalent at-sensor (vicarious) values and comparing them to the measurements obtained from the sensor using its on-board calibration (OBC). The overall correlation between the in situ measurements and at-sensor radiance for the thermal channels from all three instruments was excellent with R2of 0.98-0.99 in all the spectral channels. All three instruments were shown to meet or improve on their preflight absolute radiometric accuracy requirement with absolute radiometric values of better than ±1 K at 300 K. All three instruments were also shown to have in-flight noise equivalent delta temperatures which were similar to their preflight values and between 0.1 and 0.3 K depending on the spectral channel.

Evolution in the Copernicus Space Component (CSC) is foreseen in the mid-2020s to meet priority Copernicus user needs not addressed by the existing infrastructure, and/or to reinforce services by monitoring capability in the thematic domains of CO2, polar, and agriculture/forestry. This evolution will be synergetic with the enhanced continuity of services for the next generation of CSC. The “High Spatio-Temporal Resolution Land Surface Temperature Monitoring (LSTM) Mission”, identified as one of the CSC Expansion High Priority Candidate Missions (HPCM), currently undergoes an ESA preparatory phase (phase A/B1) study to establish mission feasibility. The LSTM mission shall provide enhanced measurements of land surface temperature with a focus responding to user requirements related to agricultural monitoring.

Thermal sensors onboard Landsat satellites have been underutilized due to the lack of consistent and accurate methodologies for retrieving the land surface temperature (LST) at global scales over all land cover types. We present an operational algorithm for generating Landsat LST consistently for all sensors that will be implemented by the United States Geological Survey/The National Aeronautics and Space Administration and made available at the Land Processes Distributed Active Archive Center. The LST algorithm involves three steps. The observed thermal radiance is atmospherically corrected using a radiative transfer model and reanalysis data. The Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Data Set version 3 is spectrally adjusted and then modified to account for vegetation phenology and snow cover using Landsat visible-shortwave infrared data. The LST is retrieved by inverting the atmospherically and emissivity corrected Landsat radiances with a lookup-table approach. Landsat-derived emissivities were validated at two pseudoinvariant sand dune sites within an average absolute error of 0.54% when compared with laboratory measurements. The Landsat LST retrievals were validated within situobservations from four surface radiation budget network (SURFRAD) sites, and two inland water bodies (Salton Sea and Lake Tahoe) in the USA. The LST retrievals for Landsat 5 and 7 had a mean bias (root mean square error) of 0.7 K (2.2 K) and 0.9 K (2.3 K) for the SURFRAD sites, and −0.3 K (0.6 K) and 0.4 K (0.7 K) for the inland water bodies, respectively. The operational algorithm will provide a consistent LST record from four decades of historical Landsat thermal data enabling the long-term monitoring of temperature and trends, land cover and land use changes, and improved utilization in models.

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission was selected as a NASA Earth-Ventures Instrument (EV-I) Class-D mission on the International Space Station (ISS) to be launched in April 2018. ECOSTRESS will answer science questions related to water use and availability in several key biomes of the terrestrial biosphere using surface temperature information measured from the thermal infrared (TIR) measurement. The instrument includes a TIR multispectral scanner with five spectral bands in the TIR between 8 and 12.5 μm and a spatial resolution of 60m. Model-derived evapotranspiration information derived from ECOSTRESS will help reveal how ecosystems change with climate and provide a critical link between the water cycle and plant health, both natural and agricultural. The inclined, precessing ISS orbit will also enable ECOSTRESS to sample the diurnal cycle in critical regions across the globe at spatiotemporal scales unexploited by current instruments in Sun-synchronous polar and high-altitude geostationary orbits. In this paper we will discuss the primary science objectives of ECOSTRESS, details of the Level 2, 3, and 4 science products and their derivation, validation strategies, and community outreach efforts.

Land surface temperature (LST) is a key climate variable for studying the energy and water balance of the earth surface and monitoring the effects of climate change. This paper presents a physics-based temperature emissivity separation (TES) algorithm for the simultaneous retrieval of LST and emissivity (LST&E) from the thermal infrared bands of the Suomi National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (VIIRS) payload. The new VIIRS LST&E product (VNP21) was developed to provide continuity with the Moderate-Resolution Imaging Spectroradiometer (MODIS) equivalent LST&E product (MxD21) product, which is available in Collection 6, and to address inconsistencies between the current MODIS and VIIRS split-window LST products. The TES algorithm uses full radiative transfer simulations to isolate the surface emitted radiance, and an emissivity calibration curve based on the variability in the surface radiance data to dynamically retrieve both LST and spectral emissivity. Furthermore, an improved water vapor scaling model was implemented to improve the accuracy and stability of the atmospheric correction for conditions with high atmospheric water vapor content. An independent assessment of the VIIRS LST retrievals was performed against in situ LST measurements over two dedicated validation sites at Lake Tahoe and Salton Sea in the Southwestern USA, while the VIIRS emissivity retrievals were evaluated with the latest ASTER Global Emissivity Dataset Version 3 (GEDv3). The bias and root-mean-square error (RMSE) in retrieved VIIRS LST were 0.50 and 1.40 K, respectively for the two sites combined, while mean emissivity differences between VIIRS and ASTER GEDv3 were 0.2%, 0.1%, and 0.3% for bands M14 ($8.55~\mu \text{m}$), M15 ($10.76~\mu \text{m}$), and M16 ($12.01~\mu \text{m}$), respectively, with an RMSE of 1%. We further demonstrate close agreement between the MODIS and VIIRS TES algorithm LST products to within ~0.3 K difference, as opposed to the current MODIS and VIIRS split window products, which had an average difference of 3 K.

THIRSTY is a joint mission concept study between CNES and NASA that is nearing the end of phase 0 studies. During phase 0 the group has identified the key science objectives the mission will address and begun estimating the feasibility of the mission. As its name implies, this mission concept is devoted to remotely sensing our planet in the thermal infrared (TIR) part of the electromagnetic spectrum, at a high spatial scale (resolution of a few tens of meters) coupled with a high temporal scale (revisit of less than 3 days).

Thermal infrared satellite observations of the Earth's surface are key components in estimating the surface skin temperature over global land areas. This work presents validation methodologies to estimate the quantitative uncertainty in Land Surface Temperature (LST) product derived from the Visible Infrared Imager Radiometer Suite (VIIRS) onboard Suomi National Polar-orbiting Partnership (NPP) using ground-based measurements currently made operationally at many field and weather stations around the world. Over heterogeneous surfaces in terms of surface types or biophysical properties (e.g., vegetation density, emissivity), the validation protocol accounts for land surface spatial variability around the ground station. Over sparse vegetation canopies, the methodology accounts for viewing directional effects and sun configuration when validating VIIRS LST products.

This study uses the existing archive of spaceborne thermal infrared imagery to generate multi-decadal time series of lake surface temperature for 169 of the largest inland water bodies worldwide, and to estimate trends for the water bodies. The results indicate that the nighttime summertime/dry-season surface temperatures of the studied water bodies have been increasing with an average rate of 0.045 ± 0.011 °C yr−1for the period 1985 to 2009. Individual lakes have shown warming rates as high as 0.13 ± 0.01 °C yr−1. On the global scale, the data show the greatest warming in the mid- and high latitudes of the Northern hemisphere, and particularly in Northern Europe with spatially consistent rates of approximately 0.08 ± 0.01 °C yr−1. The results of this study provide a critical new independent data source on studying the effects of climate change.

Since March 2000, the project science team for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) onboard NASA's Terra satellite has conducted vicarious calibration (VC) experiments periodically to verify the onboard calibration (OBC) of ASTER thermal infrared (ASTER/TIR) bands. In the present paper, 287 matchup data obtained from ten experimental sites by three organizations are analyzed. The radiance difference of OBC and VC shows almost no correlation with surface temperature and precipitable water vapor at each experimental site, but shows some dependence on the type of the experimental site. For example, the OBC-VC comparisons at Cold Springs Reservoir (NV) which is a small water body shows some bias due to the strayhght effect of ASTER/TIR. The comparisons at Lake Kussharo covered by snow also show some bias maybe because of an extrapolation effect of ASTER radiometric calibration. The comparisons at Mauna Loa lava flows show a large deviation due to non- uniformity of surface temperature caused by the rough surface. Except for these cases, the results show that the latest version of radiometric calibration coefficients (version 3.x) for ASTER/TIR has been keeping the designed accuracy (1 K for the temperature range of 270 to 320 K).

Land surface temperature and emissivity (LST&E) products are generated by the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on the National Aeronautics and Space Administration's Terra satellite. These products are generated at different spatial, spectral, and temporal resolutions, resulting in discrepancies between them that are difficult to quantify, compounded by the fact that different retrieval algorithms are used to produce them. The highest spatial resolution MODIS emissivity product currently produced is from the day/night algorithm, which has a spatial resolution of 5 km. The lack of a high-spatial-resolution emissivity product from MODIS limits the usefulness of the data for a variety of applications and limits utilization with higher resolution products such as those from ASTER. This paper aims to address this problem by using the ASTER Temperature Emissivity Separation (TES) algorithm, combined with an improved atmospheric correction method, to generate the LST&E products for MODIS at 1-km spatial resolution and for ASTER in a consistent manner. The rms differences between the ASTER and MODIS emissivities generated from TES over the southwestern U.S. were 0.013 at 8.6 μm and 0.0096 at 11 μm, with good correlations of up to 0.83. The validation with laboratory-measured sand samples from the Algodones and Kelso Dunes in CA showed a good agreement in spectral shape and magnitude, with mean emissivity differences in all bands of 0.009 and 0.010 for MODIS and ASTER, respectively. These differences are equivalent to approximately 0.6 K in the LST for a material at 300 K and at 11 μm.

Landsat-7 Enhanced Thematic Mapper+ (ETM+), launched in April 1999, and Landsat-5 Thematic Mapper (TM), launched in 1984, both have a single thermal band. Both instruments' thermal band calibrations have been updated previously: ETM+ in 2001 for a pre-launch calibration error and TM in 2007 for data acquired since the current era of vicarious calibration has been in place (1999). Vicarious calibration teams at Rochester Institute of Technology (RIT) and NASA/Jet Propulsion Laboratory (JPL) have been working to validate the instrument calibration since 1999. Recent developments in their techniques and sites have expanded the temperature and temporal range of the validation. The new data indicate that the calibration of both instruments had errors: the ETM+ calibration contained a gain error of 5.8% since launch; the TM calibration contained a gain error of 5% and an additional offset error between 1997 and 1999. Both instruments required adjustments in their thermal calibration coefficients in order to correct for the errors. The new coefficients were calculated and added to the Landsat operational processing system in early 2010. With the corrections, both instruments are calibrated to within ±0.7K.

The land surface temperature (LST) product of the Advanced Along-Track Scanning Radiometer (AATSR) was validated with ground measurements at the following two thermally homogeneous sites: Lake Tahoe, CA/NV, USA, and a large rice field close to Valencia, Spain. The AATSR LST product is based on the split-window technique using the 11- and 12- mum channels. The algorithm coefficients are provided for 13 different land-cover classes plus one lake class (index i). Coefficients are weighted by the vegetation-cover fraction (f). In the operational implementation of the algorithm, i and f are assigned from a global classification and monthly fractional vegetation-cover maps with spatial resolutions of 0.5deg times 0.5deg. Since the validation sites are smaller than this, they are misclassified in the LST product and treated incorrectly despite the fact that the higher resolution AATSR data easily resolve the sites. Due to this problem, the coefficients for the correct cover types were manually applied to the AATSR standard brightness temperature at sensor product to obtain the LST for the sites assuming they had been correctly classified. The comparison between the ground-measured and the AATSR-derived LSTs showed an excellent agreement for both sites, with nearly zero average biases and standard deviations les 0.5degC. In order to produce accurate and precise estimates of LST, it is necessary that the land-cover classification is revised and provided at the same resolution as the AATSR data, i.e., 1 km rather than the 0.5deg resolution auxiliary data currently used in the LST product.

Surface temperature is a key component for understanding energy fluxes between the Earth's surface and atmosphere. Accurate retrieval of surface temperature from satellite observations requires proper correction of the thermal channels for atmospheric emission and attenuation. Although the split-window method has offered relatively accurate measurements, this empirical approach requires in situ data and will only perform well if the in situ data are from the same surface type and similar climatology. Single channel correction reduces uncertainty inherent to the split-window method, but requires an accurate radiative transfer model and description of the atmospheric profile. Unfortunately, this method is impractical for operational correction of satellite retrievals due to the size of data sets and computation time required by radiative transfer modeling. We present a thermal parametric model based upon the MODTRAN radiative transfer code and tuned to Moderate Resolution Imaging Spectrometer (MODIS) channels. Comparison with MODTRAN showed a good performance for the parametric model and computation speeds approximately three orders of magnitude faster. Sea surface temperature (SST) calculated using atmospheric correction parameters generated from our model showed consistent results (rmse = 0.49 K) and small bias (-0.45 K) with the MODIS SST product (MYD28). Validation of surface temperatures derived using our model with in situ land and water temperature measurements exhibited accuracy (mean bias < 0.35 K) and low error (rmse < 1 K) for MODIS bands 31 and 32. Finally, an investigation of profile sources and their effect on atmospheric correction offered insight into the application of the parametric model for operational correction of MODIS thermal bands.

The Landsat-5 thematic mapper (TM) has been operational since 1984. For much of its life, the calibration of TM has been neglected, but recent efforts are attempting to monitor stability and absolute calibration. This letter focuses on the calibration of the TM thermal band from 1999 to the present. Initial studies in the first two years of the TM mission showed that the thermal band was calibrated within the error in the calibration process (plusmn 0.9 K at 300 K). The calibration was not rigorously monitored again until 1999. While the internal calibrator has behaved as expected, recent vicarious calibration results show a significant offset error of 0.092 W/m2ldr sr ldr mum or about 0.68 K at 300 K. This offset error was corrected on April 2, 2007 within the U.S. processing system through the modification of a calibration coefficient for all data acquired on or after April 1, 1999. Users can correct their own Level-1 data processed prior to April 2, 2007, by adding 0.092 W/m2ldr sr ldr mum to their radiance level products. The state of the calibration between 1985 and 1999 is unknown; no changes for data acquired in those years are being recommended here.

In December 1999, the first Moderate Resolution Imaging Spectroradiometer (MODIS) instrument and an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument were launched into polar orbit on the Terra spacecraft. Both instruments measure surface radiance, which requires that they are calibrated and validated in flight. In-flight validation is essential to independently verify that instrument calibration correctly compensates for any changes in instrument response over time. In order to meet this requirement, an automated validation site was established at Lake Tahoe on the California/Nevada border in 1999 to validate the ASTER and MODIS thermal infrared (TIR, 7-13 mum) and MODIS mid infrared (MIR, 3-5 mum) land-monitoring channels. Daytime and nighttime data were used to validate the TIR channels, and only nighttime data were used to validate the MIR channels to avoid any reflected solar contribution. Sixty-nine ASTER scenes and 155 MODIS-Terra scenes acquired between years 2000 and 2005 with near-nadir views were validated. The percent differences between the predicted and instrument at-sensor radiances for ASTER channels 10-14 were 0.165plusmn0.776, 0.103plusmn0.613, -0.305plusmn0.613, -0.252plusmn0.464, and -0.118plusmn0.489, respectively. The percent differences for MODIS-Terra channels 20, 22, 23, 29, 31, and 32 were -1.375plusmn0.973, -1.743plusmn1.027, -0.898plusmn0.970, 0.082plusmn0.631, 0.044plusmn0.541, and 0.151plusmn0.563, respectively. The results indicate that the TIR at-sensor radiances from ASTER and MODIS-Terra have met the preflight radiometric calibration accuracy specification and provide well-calibrated data sets that are suitable for measuring absolute change. The results also show that the at-sensor radiances from the MODIS-Terra MIR channels have greater bias than expected based on the preflight radiometric calibration accuracy specification

The Multispectral Thermal Imager (MTI) is a 15-band satellite-based imaging system. Two of the bands (J, K) are located in the mid-infrared (3-5 /spl mu/m) wavelength region: J, 3.5-4.1 /spl mu/m and K, 4.9-5.1 /spl mu/m, and three of the bands (L, M, N) are located in the thermal infrared (8-12 /spl mu/m) wavelength region: L, 8.0-8.4 /spl mu/m; M, 8.4-8.8 /spl mu/m; and N, 10.2-10.7 /spl mu/m. The absolute radiometric accuracy of the MTI data acquired in bands J-N was assessed over a period of approximately three years using data from the Lake Tahoe, CA/NV, automated validation site. Assessment involved using a radiative transfer model to propagate surface skin temperature measurements made at the time of the MTI overpass to predict the vicarious at-sensor radiance. The vicarious at-sensor radiance was convolved with the MTI system response functions to obtain the vicarious at-sensor MTI radiance in bands J-N. The vicarious radiances were then compared with the instrument measured radiances. In order to avoid any reflected solar contribution in the mid-infrared bands, only nighttime scenes were used in the analysis of bands J and K. Twelve cloud-free scenes were used in the analysis of the data from the mid-infrared bands (J, K), and 23 cloud-free scenes were used in the analysis of the thermal infrared bands (L, M, N). The scenes had skin temperatures ranging between 4.4 and 18.6/spl deg/C. The skin temperature was found to be, on average, 0.18/spl plusmn/0.36 degC cooler than the bulk temperature during the day and 0.65/spl plusmn/0.31 degC cooler than the bulk temperature at night. The smaller skin effect during the day was attributed to solar heating. The mean and standard deviation of the percent differences between the vicarious (predicted) at-sensor radiance convolved to the MTI bandpasses and the MTI measured radiances were -1.38/spl plusmn/2.32, -2.46/spl plusmn/1.96, -0.04/spl plusmn/0.78, -1.97/spl plusmn/0.62, -1.59/spl plusmn/0.55 for bands J-N, respectively. The results indicate that, with the exception of band L, the instrument measured radiances are warmer than expected.

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on the Terra satellite has five bands in the thermal infrared (TIR) spectral region between 8-12 /spl mu/m. The TIR bands have been regularly validated in-flight using ground validation targets. Validation results are presented from 79 experiments conducted under clear sky conditions. Validation involved predicting the at-sensor radiance for each band using a radiative transfer model, driven by surface and atmospheric measurements from each experiment, and then comparing the predicted radiance with the ASTER measured radiance. The results indicate the average difference between the predicted and the ASTER measured radiances was no more than 0.5% or 0.4 K in any TIR band, demonstrating that the TIR bands have exceeded the preflight design accuracy of <1 K for an at-sensor brightness temperature range of 270-340 K. The predicted and the ASTER measured radiances were then used to assess how well the onboard calibration accounted for any changes in both the instrument gain and offset over time. The results indicate that the gain and offset were correctly determined using the onboard blackbody, and indicate a responsivity decline over the first 1400 days of the Terra mission.

The absolute radiometric accuracy of the thermal infrared band (B6) of the Thematic Mapper (TM) instrument on the Landsat-5 (L5) satellite was assessed over a period of approximately four years using data from the Lake Tahoe automated validation site (California-Nevada). The Lake Tahoe site was established in July 1999, and measurements of the skin and bulk temperature have been made approximately every 2 min from four permanently moored buoys since mid-1999. Assessment involved using a radiative transfer model to propagate surface skin temperature measurements made at the time of the L5 overpass to predict the at-sensor radiance. The predicted radiance was then convolved with the L5B6 system response function to obtain the predicted L5B6 radiance, which was then compared with the radiance measured by L5B6. Twenty-four cloud-free scenes acquired between 1999 and 2003 were used in the analysis with scene temperatures ranging between 4/spl deg/C and 22/spl deg/C. The results indicate L5B6 had a radiance bias of 2.5% (1.6/spl deg/C) in late 1999, which gradually decreased to 0.8% (0.5/spl deg/C) in mid-2002. Since that time, the bias has remained positive (predicted minus measured) and between 0.3% (0.2/spl deg/C) and 1.4% (0.9/spl deg/C). The cause for the cold bias (L5 radiances are lower than expected) is unresolved, but likely related to changes in instrument temperature associated with changes in instrument usage. The in situ data were then used to develop algorithms to recover the skin and bulk temperature of the water by regressing the L5B6 radiance and the National Center for Environmental Prediction (NCEP) total column water data to either the skin or bulk temperature. Use of the NCEP data provides an alternative approach to the split-window approach used with instruments that have two thermal infrared bands. The results indicate the surface skin and bulk temperature can be recovered with a standard error of 0.6/spl deg/C. This error is larger than errors obtained with other instruments due, in part, to the calibration bias. L5 provides the only long-duration high spatial resolution thermal infrared measurements of the land surface. If these data are to be used effectively in studies designed to monitor change, it is essential to continue to monitor instrument performance in-flight and develop quantitative algorithms for recovering surface temperature.

The Multispectral Thermal Imager (MTI) is a satellite based push-broom imager that images in fifteen spectral bands from the visible into the long wavelength infrared. Five of its bands operate in the thermal infrared: J, 3.5-4.1 /spl mu/m; K, 4.9-5.1 /spl mu/m; L, 8.0-8.4 /spl mu/m; M, 8.4-8.8 /spl mu/m; and N, 10.2-10.7 /spl mu/m. These bands allow the retrieval of water temperatures with a nominal ground sampling distance of 20 m. Several different algorithms have been developed to do retrievals with these bands. Comparisons with lake and ocean buoy data have been used to validate these algorithms.

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scanner on NASA's Earth Observing System (EOS)-AM1 satellite (launch scheduled for 1998) will collect five bands of thermal infrared (TIR) data with a noise equivalent temperature difference (NE/spl Delta/T) of /spl les/0.3 K to estimate surface temperatures and emissivity spectra, especially over land, where emissivities are not known in advance. Temperature/emissivity separation (TES) is difficult because there are five measurements but six unknowns. Various approaches have been used to constrain the extra degree of freedom. ASTER's TES algorithm hybridizes three established algorithms, first estimating the normalized emissivities and then calculating emissivity band ratios. An empirical relationship predicts the minimum emissivity from the spectral contrast of the ratioed values, permitting recovery of the emissivity spectrum. TES uses an iterative approach to remove reflected sky irradiance. Based on numerical simulation, TES should be able to recover temperatures within about /spl plusmn/1.5 K and emissivities within about /spl plusmn/0.015. Validation using airborne simulator images taken over playas and ponds in central Nevada demonstrates that, with proper atmospheric compensation, it is possible to meet the theoretical expectations. The main sources of uncertainty in the output temperature and emissivity images are the empirical relationship between emissivity values and spectral contrast, compensation for reflected sky irradiance, and ASTER's precision, calibration, and atmospheric compensation.

Describes the preflight and inflight calibration approaches used for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). The system is a multispectral, high-spatial resolution sensor on the Earth Observing System's EOS-AM1 platform. Preflight calibration of ASTER uses well-characterized sources to provide calibration and preflight round-robin exercises to understand biases between the calibration sources of ASTER and other EOS sensors. These round-robins rely on well-characterized, ultra-stable radiometers. An experiment field in Yokohama, Japan, showed that the output from the source used for the visible and near-infrared (VNIR) subsystem of ASTER may be underestimated by 1.5%, but this is still within the 4% specification for the absolute, radiometric calibration of these bands. Inflight calibration will rely on vicarious techniques and onboard blackbodies and lamps. Vicarious techniques include ground-reference methods using desert and water sites. A recent joint field campaign gives confidence that these methods currently provide absolute calibration to better than 5%, and indications are that uncertainties less than the required 4% should be achievable at launch. The EOS-AM1 platform will also provide a spacecraft maneuver that will allow ASTER to see the Moon, allowing further characterization of the sensor. A method for combining the results of these independent calibration results is presented. The paper also describes the plans for validating the Level 2 data products from ASTER. These plans rely heavily upon field campaigns using methods similar to those used for the ground-reference, vicarious calibration methods.

In 1991 one flightline of Thermal Infrared Multispectral Scanner (TIMS) data was acquired over Castaic Lake, CA, and in 1992 four flightlines of TIMS data were acquired over Death Valley, CA, and one flightime of TIMS data over Lake Tahoe, CA. All datasets were obtained from an altitude of 20 km. To produce surface radiance the data were first calibrated, and then corrected for atmospheric effects using MODTRAN. The surface temperature was then extracted from the radiance data assuming a constant emissivity of 0.985 in all the TIMS channels. The surface temperatures for a spectrally flat area in the Castaic Lake data were then examined. Since the emissivity of the area was constant, the surface temperatures in the six TIMS channels should also have been similar. However, this was not the case, the values in channel 4 were much higher (/spl sim/3/spl deg/C) and the values in channel 3 much lower (/spl sim/2/spl deg/C) than those in the other channels. This difference can be explained by an inflight shift in the system response functions of the six TIMS channels. The amount of shift can be determined by incremental shifting of the preflight system response functions to longer wavelengths, then recalculating the surface temperature using the above method until the temperatures for channels 3 and 4 agree. A shift of 85 nm (the narrowest TIMS channel is /spl sim/500 nm wide) was required to make the brightness temperatures of channels 3 and 4 agree to within +/-1/spl deg/C within the Castaic Lake data. The same procedure was undertaken with the TIMS data from the four flightlines acquired at Death Valley that overlapped a spectrally flat target and over Lake Tahoe. The surface temperatures for all the channels were found to be in good agreement after shifts of 79, 88, 91, and 100 nm, respectively, for the four Death Valley flightlines and 98 nm for the Lake Tahoe flightline. The amount of shift increased as a function of the time that TIMS was airborne. The Castaic Lake data were acquired after TEMS had been airborne for a similar amount of time to the Death Valley flightline which showed a shift of 91 nm. The results from this study clearly demonstrate that the system response functions of the six TIMS channels shift to longer wavelengths compared to their preflight values with data acquired from the ER-2. A method is provided for determining the amount of shift permitting its correction. The method could also be used to verify the inflight wavelength calibration of multispectral thermal infrared data acquired from other airborne or spaceborne scanners with a similar optical configuration to TIMS.

The Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) is a high spatial resolution imaging instrument, scheduled to be launched on NASA's Earth Observing System AM-1 satellite platform in 1988, ASTER acquires data in 14 bands, spanning the visible, near-infrared, short-wavelength infrared, and thermal infrared spectral regions, with spatial resolution varying from 15-90 m, depending on wavelength. In order to evaluate the authors ability to use ASTER data for geological mapping, they created a simulated 14-band ASTER data set for Cuprite, Nevada. The study site has sparse vegetation and exposes a wide range of unaltered and hydrothermally altered volcanic rocks. The wide range of wavelengths covered by ASTER allowed them to distinguish iron oxide minerals, clay-bearing minerals, sulfate minerals, ammonia minerals, siliceous rocks, and carbonates. Based on interpretation of the ASTER data, and in conjunction with laboratory and field spectral measurements, they produced an alteration map showing the distribution of argillized rocks, opalized rocks with alunite, silicified rocks, and areas dominated by kaolinite and buddingtonite. The map was as accurate as published maps made by traditional field methods. ASTER should be an improvement over existing satellite systems for geologic mapping.>

The accuracy of three techniques for recovering surface kinetic temperature from multispectral thermal infrared data acquired over land is evaluated. The three techniques are the reference channel method, the emissivity normalization method, and the alpha emissivity method. The methods used to recover the temperature of artificial radiance derived from a wide variety of materials. The results indicate that the emissivity normalization and alpha emissivity techniques are the most accurate, and recover the temperature of the majority of the artificial radiance spectra to within 1.5 K; the reference channel method produces less accurate results. The primary advantage of the alpha emissivity method over the emissivity normalization method is that it works well in terrains of widely varying emissivities, e.g.,those dominated by vegetation and igneous rocks. By contrast, the emissivity normalization method works well only if the emissivity used for normalization is close to the maximum emissivity of the spectra in the scene.>