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.

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 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.

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.

Earth and planetary sciences often rely upon the detailed examination of spectroscopic data for rock and mineral identification. This typically requires the collection of high resolution spectroscopic measurements. However, they tend to be scarce, as compared to low resolution remote spectra. This work addresses the problem of inferring high-resolution mineral spectroscopic measurements from low resolution observations using probability models. We present the Deep Gaussian Conditional Model, a neural network that performs probabilistic super resolution via maximum likelihood estimation. It also provides insight into learned correlations between measurements and spectroscopic features, allowing for the tractability and interpretability that scientists often require for mineral identification. Experiments using remote spectroscopic data demonstrate that our method compares favorably to other analogous probabilistic methods. Finally, we show and discuss how our method provides human-interpretable results, making it a compelling analysis tool for scientists.

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.

Terrestrial laser scanning has demonstrated increasing potential for rapid comprehensive measurement of forest structure, especially when multiple scans are spatially registered in order to reduce the limitations of occlusion. Although marker-based registration techniques (based on retro-reflective spherical targets) are commonly used in practice, a blind marker-free approach is preferable, insofar as it supports rapid operational data acquisition. To support these efforts, we extend the pairwise registration approach of our earlier work, and develop a graph-theoretical framework to perform blind marker-free global registration of multiple point cloud data sets. Pairwise pose estimates are weighted based on their estimated error, in order to overcome pose conflict while exploiting redundant information and improving precision. The proposed approach was tested for eight diverse New England forest sites, with 25 scans collected at each site. Quantitative assessment was provided via a novel embedded confidence metric, with a mean estimated root-mean-square error of 7.2 cm and 89% of scans connected to the reference node. This paper assesses the validity of the embedded multiview registration confidence metric and evaluates the performance of the proposed registration algorithm.

Terrestrial laser scanning (TLS) has emerged as an effective tool for rapid comprehensive measurement of object structure. Registration of TLS data is an important prerequisite to overcome the limitations of occlusion. However, due to the high dissimilarity of point cloud data collected from disparate viewpoints in the forest environment, adequate marker-free registration approaches have not been developed. The majority of studies instead rely on the utilization of artificial tie points (e.g., reflective tooling balls) placed within a scene to aid in coordinate transformation. We present a technique for generating view-invariant feature descriptors that are intrinsic to the point cloud data and, thus, enable blind marker-free registration in forest environments. To overcome the limitation of initial pose estimation, we employ a voting method to blindly determine the optimal pairwise transformation parameters, without an a priori estimate of the initial sensor pose. To provide embedded error metrics, we developed a set theory framework in which a circular transformation is traversed between disjoint tie point subsets. This provides an upper estimate of the Root Mean Square Error (RMSE) confidence associated with each pairwise transformation. Output RMSE errors are commensurate with the RMSE of input tie points locations. Thus, while the mean output RMSE=16.3cm, improved results could be achieved with a more precise laser scanning system. This study 1) quantifies the RMSE of the proposed marker-free registration approach, 2) assesses the validity of embedded confidence metrics using receiver operator characteristic (ROC) curves, and 3) informs optimal sample spacing considerations for TLS data collection in New England forests. While the implications for rapid, accurate, and precise forest inventory are obvious, the conceptual framework outlined here could potentially be extended to built environments.

Description and quantification of a landscape or scene can be achieved by assessing its spectral and structural properties. Fusion of spectral information from aerial imagery and 3-D structural information from LiDAR point clouds allows us to integrate these two complementary characteristics. However, in any fusion method, alignment of data sets is crucial. We registered aerial color (RGB) imagery with LiDAR data by computing a homography matrix(H), using the Levenberg-Marquardt nonlinear optimization method. The root mean square error (RMSE) of registration was less than 0.5 m. The overall classification accuracy of our fusion based object extraction algorithm was also increased from 85% to 90%, when applied to a pre and post registered data set, respectively. In this paper, two different regions were selected to demonstrate the registration method and improved classification results.

Determining the intrinsic dimension of a hyperspectral image is an important step in the spectral unmixing process and under- or overestimation of this number may lead to incorrect unmixing in unsupervised methods. In this paper, we discuss a new method for determining the intrinsic dimension using recent advances in random matrix theory. This method is entirely unsupervised, free from any user-determined parameters and allows spectrally correlated noise in the data. Robustness tests are run on synthetic data, to determine how the results were affected by noise levels, noise variability, noise approximation, and spectral characteristics of the endmembers. Success rates are determined for many different synthetic images, and the method is tested on two pairs of real images, namely a Cuprite scene taken from Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) and SpecTIR sensors, and a Lunar Lakes scene taken from AVIRIS and Hyperion, with good results.