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.

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.

Land Surface Emissivity (LSE) is a critical variable in the quantification of the surface energy budget and for the estimation of surface parameters from earth observation data, in particular the Land Surface Temperature (LST). A new LSE product is proposed that combines two widely used methods: the Vegetation Cover Method (VCM) and the Temperature Emissivity Separation (TES) algorithm. The so-called V-TES approach maximizes the strengths of each method, considering their different performance over a wide range of surface conditions. As such, over vegetated areas, where thermal spectral contrasts are low and retrievals using TES are less accurate, we use the VCM method, while over bare areas, where the VCM relies entirely on ancillary information, the TES method is preferred. The proposed methodology was applied to observations from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard Meteosat Second Generation (MSG) satellites to derive emissivity channel and broad-band emissivities in the 3-14 μm range. Daily LSE maps are then derived using estimates of fraction of vegetation cover and snow cover. The product shows good agreement with in-situ data, with accuracies of 0.009 and 0.014 in the 8-14 μm and 3-8 μm regions, respectively. The methodology described in this article will be used to improve LST estimates and will be applied by the LSA-SAF for LST production from EUMETSAT’s Meteosat Second and Third Generation (MSG/MTG) and the Polar System-Second Generation (EPS-SG) missions.

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 San Francisco Estuary and Sacramento–San Joaquin River Delta (Bay Delta) is a highly sensitive and critical habitat for the Delta Smelt, an endangered endemic fish, with water temperature being a key determinant of habitat suitability. This study investigates the relationship between open water surface and subsurface conditions from spaceborne thermal measurements (ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and Landsat-8) andin situsensor data from the California Data Exchange Center (CDEC) to produce estimates of spatially continuous bulk temperature in the Bay Delta. We found that ECOSTRESS and Landsat-8 surface temperature measurements are well-correlated with bulk water temperatures ($N =236$and$r = 0.907$and$N = 226$and$r = 0.976$, respectively). For the ECOSTRESS-in situcomparison, accounting for time of day improved the correlation between surface and subsurface conditions ($r = 0.946$, 0.881, and 0.944 for morning, midday, and evening, respectively). We found that ECOSTRESS surface temperatures were warmer than bulk temperatures in the midday period (2 °C peak at 2 P.M.) and cooler in the morning and evening periods (−1°C peak at 6 A.M.). We also found that a simple harmonic regression model can capture the diurnal variability of the skin effect to predict bulk water temperature (root-mean-square error (RMSE) = 0.809°C). With ECOSTRESS, we found that across the Bay Delta, including open waters and pelagic bays, temperature conditions causing stress and mortality for the Delta Smelt were persistent throughout the day during summer months. ECOSTRESS is a unique dataset capable of informing conservation efforts in the Bay Delta.

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.

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 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.

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.

In this paper, we analyze the feasibility of applying the temperature and emissivity separation (TES) algorithm to thermal-infrared data acquired with three bands of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation platform (SEVTES). The performance of the SEVTES algorithm was tested using data simulated over different atmospheric conditions and surface emissivities, with errors around 1.5% for emissivity and 1.5 K for temperature when atmospheric correction is accurate enough. In contrast, errors on land-leaving radiances higher than 2% or uncertainties on total atmospheric water vapor amount higher than 5% lead to errors on emissivity higher than 2% and errors on land surface temperature higher than 3 K, especially when the atmospheric absorption is overestimated. SEVIRI data acquired in August 2011 were also used to validate SEVTES emissivities against in situ measurements collected in five different homogeneous areas over Africa. Values were also intercompared to Moderate Resolution Imaging Spectroradiometer (MODIS)-derived and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)-derived emissivities and to the LSA SAF emissivity product. Results show that SEVTES-derived emissivity values are consistent with MODIS-TES and ASTER-TES retrievals and that SEVTES also improves the retrievals included in LSA SAF and MOD11Cx v5 products. When compared to laboratory measurements, accuracies of around 1%-2% were obtained, although occasional inaccuracies (2%-3%) were also found in some cases at band 8.7 μm. The results presented in this paper show the potential SEVTES has for improving the LSA SAF product over arid and semiarid areas.

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.

Thermal infrared satellite observations of the Earth's surface are key components in estimating the surface skin temperature over global land areas. However, depending on sun illumination and viewing directional configurations, satellites measure different surface radiometric temperatures, particularly over sparsely vegetated regions where the radiometric contributions from soil and vegetation vary with the sun and viewing geometry. Over an oak tree woodland located near the town of Evora, Portugal, we compare different satellite-based land surface temperature (LST) products from the Moderate Resolution Imaging Spectroradiometer on board the Terra and Aqua polar-orbiting satellites and from the Spinning Enhanced Visible and Infrared Imager on board the geostationary Meteosat satellite with ground-based LST. The observed differences between LSTs derived from polar and geostationary satellites are up to 12 K due to directional effects. In this letter, we develop a methodology based on a radiative transfer model and dedicated field radiometric measurements to interpret and validate directional remote sensing measurements. The methodology is used to estimate the quantitative uncertainty in LST products derived from polar-orbiting satellites over a sparse vegetation canopy.

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.