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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Assessment of the Performance of the Atmospheric Correction Algorithm MAJA for Sentinel-2 Surface Reflectance Estimates

The correction of atmospheric effects on optical remote sensing products is an essential component of Analysis Ready Data (ARD) production lines. The MAJA processor aims at providing accurate time series of surface reflectances over land for satellite missions, such as Sentinel-2, Venμs, and Landsat 8. The Centre d’Études Spatiales de la Biosphère (CESBIO) and the Centre National d’Études Spatiales (CNES) share a common effort to maintain, validate, and improve the MAJA processor, using state-of-the-art ground measurement sites, and participating in processor inter-comparisons, such as the Atmospheric Correction Intercomparison Exercise (ACIX). While contributing to the second ACIX-II Land validation exercise, it was found that the candidate MAJA dataset could not adequately be compared to the main reference dataset. MAJA reflectances were corrected for adjacency and topography effects while the reference dataset was not, excluding MAJA from a part of the performance metrics of the exercise. The first part of the following study aims at providing complementary performance assessment to ACIX-II by reprocessing MAJA surface reflectances without adjacency nor topographic correction, allowing for an un-biased full resolution comparison with the reference Sentinel-2 dataset. The second part of the study consists of validating MAJA against surface reflectance measurements time series of up to five years acquired at three automated stations. Both approaches provide extensive insights on the quality of MAJA Sentinel-2 Level 2 products.

ROSAS↗
DOE OSTI2021

Validation of SAS4A/SASSYS-1 for predicting steady-state single-phase natural circulation

The validation of system analysis codes for nuclear reactor systems is required for the development and application of these computational tools. Designed as a comprehensive system analysis code for advanced nuclear reactors, SAS4A/SASSYS-1 requires validation of its physics model for capturing single-phase natural circulation behavior. To support the validation of SAS4A/SASSYS-1, high-precision experiments are performed capturing steady-state single-phase natural circulation on a scaled facility with comprehensive instrumentation. In this work, dedicated tests are performed quantifying the critical modeling parameters, and a single-phase natural circulation benchmark dataset is obtained with well-documented uncertainty and comprehensive facility description. The validation is then performed against the dataset examining the capability of SAS4A/SASSYS-1 in simulating steady-state single-phase natural circulation. The experimental facility is modeled in the candidate code. Solution verification is performed using Richardson-extrapolation-based estimators which quantify and restrict numerical errors from discretization. Input uncertainty provided by the benchmark dataset is forward propagated through the candidate code, quantifying the output uncertainty in a Monte Carlo approach. The composition of the output uncertainty is also quantified through a variance-based sensitivity analysis. With the uncertainty quantified for each individual condition, a detailed comparison between the simulation results and experimental data is performed covering the whole dataset. The results show consistent agreement for all primary parameters. The current validation activity provides a valuable benchmark dataset for the validation of system analysis codes in capturing single-phase natural circulation and demonstrates satisfactory prediction capability of SAS4A/SASSYS1 for steady-state single-phase natural circulation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Assessing Spatial Representativeness of Global Flux Tower Eddy-Covariance Measurements Using Data from FLUXNET2015

Large datasets of carbon dioxide, energy, and water fluxes were measured with the eddy-covariance (EC) technique, such as FLUXNET2015. These datasets are widely used to validate remote-sensing products and benchmark models. One of the major challenges in utilizing EC-flux data is determining the spatial extent to which measurements taken at individual EC towers reflect model-grid or remote sensing pixels. To minimize the potential biases caused by the footprint-to-target area mismatch, it is important to use flux datasets with awareness of the footprint. This study analyze the spatial representativeness of global EC measurements based on the open-source FLUXNET2015 data, using the published flux footprint model (SAFE-f). The calculated annual cumulative footprint climatology (ACFC) was overlaid on land cover and vegetation index maps to create a spatial representativeness dataset of global flux towers. The dataset includes the following components: (1) the ACFC contour (ACFCC) data and areas representing 50%, 60%, 70%, and 80% ACFCC of each site, (2) the proportion of each land cover type weighted by the 80% ACFC (ACFCW), (3) the semivariogram calculated using Normalized Difference Vegetation Index (NDVI) considering the 80% ACFCW, and (4) the sensor location bias (SLB) between the 80% ACFCW and designated areas (e.g. 80% ACFCC and window sizes) proxied by NDVI. Finally, we conducted a comprehensive evaluation of the representativeness of each site from three aspects: (1) the underlying surface cover, (2) the semivariogram, and (3) the SLB between 80% ACFCW and 80% ACFCC, and categorized them into 3 levels. The goal of creating this dataset is to provide data quality guidance for international researchers to effectively utilize the FLUXNET2015 dataset in the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Comparative Performance of Three Eye-Tracking Devices in Detection of Mild Traumatic Brain Injury in Acute Versus Chronic Subject Populations

ABSTRACT Introduction Presently, traumatic brain injury (TBI) triage in field settings relies on symptom-based screening tools such as the updated Military Acute Concussion Evaluation. Objective eye-tracking may provide an alternative means of neurotrauma screening due to sensitivity to neurotrauma brain-health changes. Previously, the US Army Medical Research and Development Command Non-Invasive NeuroAssessment Devices (NINAD) Integrated Product Team identified 3 commercially available eye-tracking devices (SyncThink EYE-SYNC, Oculogica EyeBOX, NeuroKinetics IPAS) as meeting criteria toward being operationally effective in the detection of TBI in service members. We compared these devices to assess their relative performance in the classification of mild traumatic brain injury (mTBI) subjects versus normal healthy controls. Materials and Methods Participants 18 to 45 years of age were assigned to Acute mTBI, Chronic mTBI, or Control group per study criteria. Each completed a TBI assessment protocol with all 3 devices counterbalanced across participants. Acute mTBI participants were tested within 72 hours following injury whereas time since last injury for the Chronic mTBI group ranged from months to years. Discriminant analysis was undertaken to determine device classification performance in separating TBI subjects from controls. Area Under the Curves (AUCs) were calculated and used to compare the accuracy of device performance. Device-related factors including data quality, the need to repeat tests, and technical issues experienced were aggregated for reporting. Results A total of 63 participants were recruited as Acute mTBI subjects, 34 as Chronic mTBI subjects, and 119 participants without history of TBI as controls. To maximize outcomes, poorer quality data were excluded from analysis using specific criteria where possible. Final analysis utilized 49 (43 male/6 female, mean [x̅] age = 24.3 years, SD [s] = 5.1) Acute mTBI subjects, and 34 (33 male/1 female, x̅ age = 38.8 years, s = 3.9) Chronic mTBI subjects were age- and gender-matched as closely as possible with Control subjects. AUCs obtained with 80% of total dataset ranged from 0.690 to 0.950 for the Acute Group and from 0.753 to 0.811 for the Chronic mTBI group. Validation with the remaining 20% of dataset produced AUCs ranging from 0.600 to 0.750 for Acute mTBI group and 0.490 to 0.571 for the Chronic mTBI group. Conclusions Potential eye-tracking detection of mTBI, per training model outcomes, ranged from acceptable to excellent for the Acute mTBI group; however, it was less consistent for the Chronic mTBI group. The self-imposed targeted performance (AUC of 0.850) appears achievable, but further device improvements and research are necessary. Discriminant analysis models differed for the Acute versus Chronic mTBI groups, suggesting performance differences in eye-tracking. Although eye-tracking demonstrated sensitivity in the Chronic group, a more rigorous and/or longitudinal study design is required to evaluate this observation. mTBI injuries were not controlled for this study, potentially reducing eye-tracking assessment sensitivity. Overall, these findings indicate that while eye-tracking remains a viable means of mTBI screening, device-specific variability in data quality, length of testing, and ease of use must be addressed to achieve NINAD objectives and DoD implementation.

General & Internal Medicine↗
DOE OSTI2026

Good practices for documenting AI-based studies on energy and buildings

Artificial intelligence has transformed building science research over the past decade, with applications spanning energy modeling, energy prediction, HVAC optimization and controls, fault detection, and occupancy modeling. However, many studies lack adequate documentation of datasets, algorithms, training procedures, and validation methods. Building science research faces additional challenges including inconsistent evaluation metrics, limited generalizability across building types, climates, and significant gaps between experimental studies and deployed systems. This communication provides practical guidance for good practices in documenting and publishing AI-based research following established standards from the computer science and machine learning communities. By adopting frameworks such as Datasheets for Datasets, Model Cards, and standardized reproducibility checklists, researchers can ensure their work meets the rigorous documentation standards necessary for reproducible, comparable, and impactful building science research.

Hong, Tianzhen [Lawrence Berkeley National Laborat↗
NASA NTRS2005

Re-Examination of the Observed Decadal Variability of Earth Radiation Budget Using Altitude-Corrected ERBE/ERBS Nonscanner WFOV Data

This paper gives an update on the observed decadal variability of Earth Radiation Budget using the latest altitude-corrected Earth Radiation Budget Experiment (ERBE)/Earth Radiation Budget Satellite (ERBS) Nonscanner Wide Field of View (WFOV) instrument Edition3 dataset. The effects of the altitude correction are to modify the original reported decadal changes in tropical mean (20N to 20S) longwave (LW), shortwave (SW), and net radiation between the 1980s and the 1990s from 3.1/-2.4/-0.7 to 1.6/-3.0/1.4 Wm(sup -2) respectively. In addition, a small SW instrument drift over the 15-year period was discovered during the validation of the WFOV Edition3 dataset. A correction was developed and applied to the Edition3 dataset at the data user level to produce the WFOV Edition3_Rev1 dataset. With this final correction, the ERBS Nonscanner observed decadal changes in tropical mean LW, SW, and net radiation between the 1980s and the 1990s now stand at 0.7/-2.1/1.4 Wm(sup -2), respectively, which are similar to the observed decadal changes in the HIRS Pathfinder OLR and the ISCCP FD record; but disagree with the AVHRR Pathfinder ERB record. Furthermore, the observed interannual variability of near-global ERBS WFOV Edition3_Rev1 net radiation is found to be remarkably consistent with the latest ocean heat storage record for the overlapping time period of 1993 to 1999. Both data sets show variations of roughly 1.5 Wm(sup -2) in planetary net heat balance during the 1990s.

Wong, Takmeng↗
DOE OSTI2024

CryoTRANS: predicting high-resolution maps of rare conformations from self-supervised trajectories in cryo-EM

Cryogenic electron microscopy (cryo-EM) has revolutionized structural biology, enabling efficient determination of structures at near-atomic resolutions. However, a common challenge arises from the severe imbalance among various conformations of vitrified particles, leading to low-resolution reconstructions in rare conformations due to a lack of particle images in these quasi-stable states. We introduce CryoTRANS, a method that predicts high-resolution maps of rare conformations by constructing a self-supervised pseudo-trajectory between density maps of varying resolutions. This trajectory is represented by an ordinary differential equation parameterized by a deep neural network, ensuring retention of detailed structures from high-resolution density maps. By leveraging a single high-resolution density map, CryoTRANS significantly improves the reconstruction of rare conformations and has been validated on four real-world datasets: alpha-2-macroglobulin, actin-binding protein complexes, SARS-CoV-2 spike glycoprotein, and the 70S ribosome. CryoTRANS can also predict high-resolution structures in cryogenic electron tomography maps using a high-resolution cryo-EM map.Cryogenic electron microscopy (cryo-EM) has revolutionized structural biology, enabling efficient determination of structures at near-atomic resolutions. However, a common challenge arises from the severe imbalance among various conformations of vitrified particles, leading to low-resolution reconstructions in rare conformations due to a lack of particle images in these quasi-stable states. We introduce CryoTRANS, a method that predicts high-resolution maps of rare conformations by constructing a self-supervised pseudo-trajectory between density maps of varying resolutions. This trajectory is represented by an ordinary differential equation parameterized by a deep neural network, ensuring retention of detailed structures from high-resolution density maps. By leveraging a single high-resolution density map, CryoTRANS significantly improves the reconstruction of rare conformations and has been validated on four real-world datasets: alpha-2-macroglobulin, actin-binding protein complexes, SARS-CoV-2 spike glycoprotein, and the 70S ribosome. CryoTRANS can also predict high-resolution structures in cryogenic electron tomography maps using a high-resolution cryo-EM map.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Application of artificial intelligence methods in the international roughness index prediction of rigid and composite pavements: a systematic review

The International Roughness Index (IRI) is a widely adopted metric for quantifying pavement roughness, directly influencing vehicle safety, ride comfort, and overall roadway performance. In recent years, the use of Machine Learning (ML) models for IRI prediction has gained momentum, with the goal of improving the allocation of maintenance and rehabilitation resources by enabling accurate assessments of pavement conditions. Most prior reviews, however, have concentrated on flexible pavements, leaving a notable gap regarding rigid and composite pavements. To address this gap, the present study conducts a systematic review of Artificial Intelligence (AI) methods applied to IRI prediction for rigid and composite pavements. Literature published between 2004 and 2025 is synthesized to highlight prevailing trends, methodological contributions, and directions for future research. Particular attention is given to the types of models employed, the datasets used for training and validation, and the role of input variables and data-processing strategies. Across the included studies, ensemble learning methods (especially gradient boosting variants such as XGBoost), artificial neural networks, and hybrid architectures frequently achieved high predictive skill, with several models reporting test-set coefficients of determination approaching 0.9–0.96, indicating strong potential for capturing the influence of traffic, pavement structure, and climatic factors. Since these results are obtained from heterogeneous datasets and evaluation protocols, they are interpreted qualitatively rather than as strict cross-study rankings. Analysis of input variables revealed that pavement age and initial IRI were included in 91% (21 of 23) and 78% (18 of 23) of studies, respectively. Climatic variables such as the freezing index appeared in 57% (13 of 23), while traffic-related factors were considered in 65% (15 of 23). The findings underscore the importance of standardized, high-quality datasets, such as those from the Long-Term Pavement Performance (LTPP) program, along with data consistency, model interpretability, computational efficiency, and replicability in enhancing IRI prediction. Future research should focus on incorporating input variable selection techniques to identify the most influential predictors, thereby improving accuracy and robustness. Integrating these approaches with advanced non-linear data-driven models, coupled with robust hyperparameter optimization, holds considerable promise for strengthening the reliability of IRI prediction and supporting resilient pavement management strategies.

42 ENGINEERING↗
NASA NTRS2013

Comparison Between NPP-VIIRS Aerosol Data Products and the MODIS AQUA Deep Blue Collection 6 Dataset Over Land

Aerosols are small particles suspended in the atmosphere and have a variety of natural and man-made sources. Knowledge of aerosol optical depth (AOD), which is a measure of the amount of aerosol in the atmosphere, and its change over time, is important for multiple reasons. These include climate change, air quality (pollution) monitoring, monitoring hazards such as dust storms and volcanic ash, monitoring smoke from biomass burning, determining potential energy yields from solar plants, determining visibility at sea, estimating fertilization of oceans and rainforests by transported mineral dust, understanding changes in weather brought upon by the interaction of aerosols and clouds, and more. The Suomi-NPP satellite was launched late in 2011. The Visible Infrared Imaging Radiometer Suite (VIIRS) aboard Suomi-NPP is being used, among other things, to determine AOD. This study compares the VIIRS dataset to ground-based measurements of AOD, along with a state-of-the-art satellite AOD dataset (the new version of the Moderate Resolution Imaging Spectrometer Deep Blue algorithm) to assess its reliability. The Suomi-NPP satellite was launched late in 2011, carrying several instruments designed to continue the biogeophysical data records of current and previous satellite sensors. The Visible Infrared Imaging Radiometer Suite (VIIRS) aboard Suomi-NPP is being used, among other things, to determine aerosol optical depth (AOD), and related activities since launch have been focused towards validating and understanding this new dataset through comparisons with other satellite and ground-based products. The operational VIIRS AOD product is compared over land with AOD derived from Moderate Resolution Imaging Spectrometer (MODIS) observations using the Deep Blue (DB) algorithm from the forthcoming Collection 6 of MODIS data

Sayer, Andrew M.↗
NASA NTRS2009

NASA Global Precipitation Mission Ground Validation Implementation

The Global Precipitation Mission (GPM; core-satellite launch 2013) will provide Ka/Ku-band dual-frequency precipitation radar (DPR) and accompanying passive microwave radiometer-diagnosed precipitation estimates over a latitude range of 65 N to 65 S. The extended latitudinal domain of GPM coverage combined with requirements to detect (and in the case of liquid, estimate) liquid and frozen precipitation rates for values ranging from several hundred to just a few tenths of a millimeter per hour present new challenges to the development of physically-based satellite precipitation retrieval algorithms. On regional scales select national and international resources such as existing calibrated radar and rain gauge networks can provide basic datasets that enable direct statistical validation of GPM core-satellite reflectivitys and core/constellation rain rate measurements. Near-term planned field campaign involvements include Finland/Baltic Sea (fall 2010; joint CloudSat,GPM, and European study of precipitation in low-altitude melting layers and snowfall in the vicinity of the Helsinki testbed), central Oklahoma (spring 2011; joint with DOE ARM- precipitation retrievals over a mid-latitude continental land surface), and the Great Lakes region (winter 2011-12, snowfall retrieval).

Petersen, Walter A.↗
DOE OSTI2023

A large expert-curated cryo-EM image dataset for machine learning protein particle picking

Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of biological macromolecular complexes. Picking single-protein particles from cryo-EM micrographs is a crucial step in reconstructing protein structures. However, the widely used template-based particle picking process is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) based particle picking can potentially automate the process, its development is hindered by lack of large, high-quality labelled training data. To address this bottleneck, we present CryoPPP, a large, diverse, expert-curated cryo-EM image dataset for protein particle picking and analysis. It consists of labelled cryo-EM micrographs (images) of 34 representative protein datasets selected from the Electron Microscopy Public Image Archive (EMPIAR). The dataset is 2.6 terabytes and includes 9,893 high-resolution micrographs with labelled protein particle coordinates. The labelling process was rigorously validated through 2D particle class validation and 3D density map validation with the gold standard. The dataset is expected to greatly facilitate the development of both AI and classical methods for automated cryo-EM protein particle picking.

42 ENGINEERING↗
DOE OSTI2021

Science Validation for Dark Energy Research with Optical Imaging Surveys

The Universe has been expanding at an accelerating rate over the past several billion years, as though an unknown form of dark energy permeates all of space. The ultimate scientific goal of the proposed research is to distinguish between different physical mechanisms that could account for this observed accelerated expansion, for example, the zero-point energy of the vacuum, a dynamical form of energy that varies in time and/or space, or a modification to our theory of gravity. Wide- field optical imaging surveys of the night sky can test these competing models by measuring both the cosmic expansion history and the growth of large-scale structure. To this end, the Dark Energy Survey (DES) has cataloged several hundred million galaxies and thousands of supernovae. The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), will enlarge the census to billions of galaxies and hundreds of thousands of supernovae. A critical question for these dark energy experiments is whether systematic uncertainties can continue to be controlled at a level to keep pace with the statistical precision offered by such enormous datasets. The immediate research objectives of this project were (1) to prepare and validate input datasets that are the foundation of cosmological analyses with DES, and (2) to prepare for value-added characterization of Rubin Observatory commissioning data to inform early operations and accelerate the realization of dark energy science from LSST data products. For DES, we assembled and curated cosmology-ready data releases that include value-added components such as enhanced photometric and astrometric calibrations, alternative source extraction algorithms, maps of the survey coverage and survey conditions, object classifications, object quality selections, galaxy shapes, and photometric redshifts. We used the galaxy clustering technique to validate the photometric redshift distributions of various galaxy samples to be used as lenses in combined studies of galaxy clustering and weak gravitational lensing. The galaxy clustering redshift analysis was enhanced by use of a larger sample of reference galaxies from the eBOSS spectroscopic survey that extends to higher redshifts. For LSST, we prepared for science validation studies of commissioning data aimed at dark energy science capability that extend beyond the normative system-level tests to be done by the Rubin Observatory Construction Project. We identified a set of proposed survey strategies and candidate target fields that could be observed during the commissioning period to enhance science validation activities related to studies of dark energy.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRSDate not supplied

Overview of SatCORPS Satellite-Derived Cloud and Radiation Retrieval Coverage of ARM Domains

The ARM Climate Research Facility program maintains a number of climatically representative sites, which provide long-term cloud- and climate-monitoring records via ground-based instrumentation. These measurements provide a valuable data record over a localized region, but can be greatly enhanced by use of satellite monitoring. Satellite analyses over larger domains can provide parameters helpful for monitoring climate and evaluating models. The NASA/Langley Cloud group routinely derives such cloud and radiative parameters, from various geostationary and polar-orbiting satellite coverage over ARM sites; the group provides near-realtime analyses covering the 3 ARM fixed sites, as well as the GO-Amazon AMF site. This is accomplished by employing a suite of algorithms including VISST (Visible Infrared Solar Split-Window Technique), SIST (Solar Infrared Split-Window Technique), and SINT (Solar-infrared Infrared Near-Infrared Technique), now collectively called SatCORPS (Satellite Cloud Observations and Radiative Property retrieval System). An overview and catalog of SatCORPS-derived datasets processed for ASR, and available from both the ARM archive and the NASA/Langley Cloud group website, is provided. Specific improvements included in recently added datasets such as GO-Amazon and Azores are highlighted, including an improved cloud-detection mask, as well as improvements in derived Top-of-Atmosphere (TOA) SW albedoes and LW fluxes. New narrowband-to-broadband (NB-BB) fits and corrections for improved TOA fluxes are illustrated, including MTSAT-1/CERES Aqua NB-BB fits for the TWPICE field campaign, as well as new fits covering the Azores region which incorporate GERB TOA fluxes (Geostationery Earth Radiation Budget). Finally, validation of the reprocessed SatCORPS datasets is shown.

M M Khader↗
DOE OSTI2020

Highlighting the Impact of Yaw Control by Parsing Atmospheric Conditions Based on Total Variation: Preprint

Identification of atmospheric conditions within a multivariate atmospheric dataset is a necessary step in the validation of wind plant control strategies. Most often, operating conditions are characterized in terms of aggregated observations and assume that the atmosphere is 'quasi-steady'. Aggregation of observations without regard to covariance between time series discounts the dynamical nature of the atmosphere and is not sufficiently representative of wind plant operating conditions. Identification and characterization of continuous time periods with atmospheric conditions that have a high value for analysis or simulation sets the stage for more advanced model validation and the development of real-time control and operation strategies. Controlling observational data for statistical stationarity highlights significant enhancements to the power production of waked turbines under wake steering wind plant control. Considering the combined energy ratio of the turbine to which yaw offsets are prescribed as well as the waked turbine show only moderate improvements when filtering for total validation. The same quality control that highlights wake steering also serves to underpin the off-nominal operation of the controlled turbine.

17 WIND ENERGY↗
DOE OSTI2023

Granger Causality for prediction in Dynamic Mode Decomposition: Application to power systems

Here, the dynamic mode decomposition (DMD) technique extracts the dominant modes characterizing the innate dynamical behavior of the system within the measurement data. For appropriate identification of dominant modes from the measurement data, the DMD algorithm necessitates ensuring the quality of the input measurement data sequences. On that account, for validating the usability of the dataset for the DMD algorithm, the paper proposed two conditions: Persistence of excitation (PE) and the Granger Causality Test (GCT). The virtual data sequences are designed with the hankel matrix representation such that the dimensions of the subspace spanning the essential system modes are increased with the addition of new state variables. The PE condition provides the lower bound for the trajectory length, and the GCT provides the order of the model. Satisfying the PE condition enables estimating an approximate linear model, but the predictability with the identified model is only assured with the temporal causation among data searched with GCT. The proposed methodology is validated with the application for coherency identification (CI) in a multi-machine power system (MMPS), an essential phenomenon in transient stability analysis. The significance of PE condition and GCT is demonstrated through various case studies implemented on 22 bus six generator system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRSDate not supplied

Automated classification of scientific publications linked to GES DISC datasets

The data collections archived and distributedby the GES DISC NASA data center arewidely utilized for various Earth Science studies.As these collections are created, many researchworks are published regarding the collections, algorithms,validations and applications. SinceGES DISC collects these publications and providestheir citations for the users, it is helpful tocategorize them based on how they relate to the datasetsthey are associated with. Specifically,whether the publication that is linked to GES DISCdataset is using it for applicational research,or if it describes the algorithm for dataset creation,or the validation of the dataset, or providesthe general overview of the data collection. Currently,this process requires simple manuallabelling, and as such, may be possible to solve viaautomation. To approach this problem, wedeveloped machine learning classifiers to predictthe category a publication belongs to. We usedmanually labeled publications as training data forsupervised machine learning algorithms:Random Forest and Naive Bayes. We achieved classificationaccuracy that is substantially betterthan the baseline accuracy, thus greatly improvingthe efficiency of the publication internalanalysis.

Rohan Dayal↗
DOE OSTI2025

Reference Shapefiles and Pre-trained Random Forest Classification Models for Detecting Aufeis on the North Slope of Alaska in Landsat Imagery

This dataset provides shapefiles and trained machine learning models used for aufeis detection at four sites on the North Slope of Alaska. It includes reference data for evaluating Landsat-based detection methods, supporting research on remote sensing approaches for identifying aufeis. The ReferenceData folder contains ArcGIS shapefiles of semi-automated land cover classifications for 217 Landsat Collection 2 images, categorizing pixels into six classes: aufeis, snow, ground, none, water, and cloud. The SiteBuffers.zip file includes 10-kilometer buffer shapefiles defining regions of interest around four aufeis fields (Canning21, FH1, Firth, and Kuparuk), used to test three detection techniques. Additionally, the TrainedRFModels folder contains six pre-trained Scikit-Learn Random Forest classifiers (100 trees, max depth = 30) designed to predict aufeis presence in Landsat Collection 2 Surface Reflectance images using Red, Blue, SWIR2, NDVI, and NDWI bands. This dataset supports the development and validation of remote sensing methods for mapping aufeis in Arctic environments.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗