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DOE OSTI2025

Estimating soybean yields from high-temporal-resolution multi-source data using deep learning

Accurate and timely crop yield prediction is crucial for ensuring food security and maintaining stable agricultural markets. In recent years, there has been a surge in interest in leveraging high-temporal-resolution, multi-source data for effective crop growth monitoring and yield estimation. A notable challenge arises from the difficulty in capturing the intricate interactions between variables across different time steps within these high-temporal-resolution time series datasets. This complexity hinders the reliable extraction of yield information from voluminous and often noisy datasets, especially during periods of extreme weather events. Here, in this study, we propose an Attention and Graph Isomorphism Network-enhanced Bi-directional Long Short-Term Memory network (AGB-LSTM) for estimating county-level soybean yield in the United States. This model integrates a diverse set of remote sensing data, including Near-Infrared Reflectance of Vegetation (NIRv), Sun-Induced chlorophyll Fluorescence (SIF), and Gross Primary Productivity (GPP), along with environmental covariates. The AGB-LSTM effectively leverages information related to crop yield from high-temporal-resolution time series data (5-days), achieving an accuracy of R²= 0.67 and rRMSE = 14.46%. This approach significantly outperforms traditional machine learning methods such as Random Forest (RF) (R²= 0.52, rRMSE = 17.36%) and Bi-LSTM (R²= 0.58, rRMSE = 16.17%). Sensitivity experiments with different time steps and ranges demonstrated that our model could accurately and stably predict yields 1 to 2 months before harvest. Moreover, data with a finer temporal resolution consistently improved prediction performance, resulting in an approximately 20% increase in and an approximately 20% decrease in rRMSE compared to using monthly composites. We also evaluated the robustness of the model under extreme climate events and observed strong performance (R²= 0.50, rRMSE = 21.32%). Finally, yield mapping for major soybean-producing regions in North America in 2023 revealed spatial patterns that closely matched USDA yield reports. Our findings suggest that the AGB-LSTM model is a promising and effective method for estimating yield and has notable potential for global crop yield forecasting.

Deep learning↗

DOE OSTI2025

Abiotic influences on continuous conifer forest structure across a subalpine watershed

Understanding the abiotic drivers of high-elevation forest physiognomy is essential for forecasting how mountain ecosystems will respond to emerging environmental pressures. Most prior studies of these relationships have relied on small samples of the full landscape, resulting in limited power to detect dominant covariates and their interactions. Here we report the first evaluation of abiotic influences on a complement of accurate, wall-to-wall estimates of conifer forest structure and composition at the watershed scale. In a subalpine conifer domain in the Colorado Rocky Mountains (USA), we developed a novel method for deriving stand structure metrics from waveform LiDAR data, which showed high fidelity with field inventory. We quantified the relationships between structural and compositional metrics and climate, topographic, edaphic, and geologic factors. Our results showed that peak snow water equivalent (SWE), snow disappearance rate, and elevation explained most of the variation in forest structure. The highest stand density, basal area, maximum canopy height, and quadratic mean diameter occurred in sites with SWE around one standard deviation below mean, but with long snow residence times. Stand density decreased linearly with elevation, while other metrics peaked between 3000 m and 3200 m. Substrate properties had weaker influence. Continuous mapping of through-canopy forest structure enabled our novel findings of the dominant role of snowpack in explaining structural and compositional variation, and of elevation thresholds. Our reproducible approach facilitates assessment of forest-topoclimate relationships in other conifer-dominated landscapes and improves understanding of the baseline patterns controlling forest structure, which is needed for predicting long-term ecological change.

forest ecology↗

DOE OSTI2021

Nonlinear proper orthogonal decomposition for convection-dominated flows

Autoencoder techniques find increasingly common use in reduced order modeling as a means to create a latent space. This reduced order representation offers a modular data-driven modeling approach for nonlinear dynamical systems when integrated with a time series predictive model. In this Letter, we put forth a nonlinear proper orthogonal decomposition (POD) framework, which is an end-to-end Galerkin-free model combining autoencoders with long short-term memory networks for dynamics. By eliminating the projection error due to the truncation of Galerkin models, a key enabler of the proposed nonintrusive approach is the kinematic construction of a nonlinear mapping between the full-rank expansion of the POD coefficients and the latent space where the dynamics evolve. We test our framework for model reduction of a convection-dominated system, which is generally challenging for reduced order models. Our approach not only improves the accuracy, but also significantly reduces the computational cost of training and testing.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Hemispherical variance anomaly and reionization optical depth

ABSTRACT Cosmic microwave background (CMB) full-sky temperature data show a hemispherical asymmetry in power nearly aligned with the Ecliptic, with the Northern hemisphere displaying an anomalously low variance, while the Southern hemisphere appears consistent with expectations from the best-fitting theory, Lambda Cold Dark Matter (ΛCDM). The low signal-to-noise ratio in current polarization data prevents a similar comparison. Polarization realizations constrained by temperature data show that in ΛCDM the lack of variance is not expected to be present in polarization data. Therefore, a natural way of testing whether the temperature result is a fluke is to measure the variance of CMB polarization components. In anticipation of future CMB experiments that will allow for high-precision large-scale polarization measurements, we study how the variance of polarization depends on ΛCDM-parameter uncertainties by forecasting polarization maps with Planck’s Markov chain Monte Carlo chains. We show that polarization variance is sensitive to present uncertainties in cosmological parameters, mainly due to current poor constraints on the reionization optical depth τ, which drives variance at low multipoles. We demonstrate how the improvement in the τ measurement seen between Planck’s two latest data releases results in a tighter constraint on polarization variance expectations. Finally, we consider even smaller uncertainties on τ and how more precise measurements of τ can drive the expectation for polarization variance in a hemisphere close to that of the cosmic-variance-limited distribution.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

BitGNN: Unlocking the Performance Potential of Binary Graph Neural Networks on GPUs

Graph Neural Networks (GNNs) have shown compelling results in many graph-based learning tasks. They are, however, time-consuming. Recent work has shown a promising direction in improving GNN speed and shrinking the size — network binarization, which binarizes network values and operations. Prior work, however, mainly focused on algorithm designs, leaving it open on how to fully materialize the performance potential. This work fills the gap by proposing techniques to best map binary GNNs and their computations to fit the nature of bit manipulations, optimizations and algorithms to maximize BSpMM kernel efficiency, and solutions to other factors influencing the end-to-end time on GPUs. Results on real-world graphs show that the proposed techniques outperform state of-the-art binary GNN implementations by 21-67× with little accuracy loss.

Chen, Jou-An↗

DOE OSTI2025

Investigating Kinetic Mechanisms of Soot Formation in Plasma Pyrolysis of Methane via Active Learning (Final Technical Report)

Plasma pyrolysis of methane is an effective route for zero-carbon hydrogen production. Yet, soot generated from pyrolysis of hydrocarbons is detrimental to the climate and human health. There is ample experimental and theoretical evidence that suggests polycyclic aromatic hydrocarbons (PAHs) are the molecular precursors to soot particles. The reaction pathways of PAH formation are intricately dependent on a multitude of process parameters, whose kinetic mechanisms are not well-understood in plasma pyrolysis. This project aims to leverage advances in the kinetic modeling of soot formation in combustion, as well as in surrogate modeling and active learning, to systematically investigate the effects of process parameter on the kinetics of PAH formation in plasma pyrolysis of methane. To this end, we propose to use the PAH formation kinetics model developed by the PPPL/PU group based on the well-established ABF and HACA mechanisms, coupled with low-temperature plasma models. We will develop an active learning (AL) framework based on Bayesian optimization to systematically and data-efficiently explore the complex and multivariable parameter space of plasma pyrolysis in order to quantify the effects of plasma and feed parameters on the ABF and HACA kinetic pathways. AL is the branch of machine learning concerned with systematically querying samples from a system (experimental or computational) to train a data-driven model that maps design parameters to a performance criterion. We will use the data generated via AL to perform global sensitivity analysis, combined with uncertainty quantification, to elucidate the impact of different reaction pathways on minimizing formation of soot precursors. This study will result in an improved understanding of kinetics of PAH formation in plasma pyrolysis and can pave the way for more advanced mechanistic studies (e.g., soot nucleation mechanisms). Additionally, the findings will be useful for establishing practical strategies for increasing the pyrolysis efficiency and producing high-grade carbon for synthesis of nanomaterials.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS1973

Preliminary evaluation of ERTS-1 for determining numbers and distribution of prairie ponds and lakes

ERTS-1 and aircraft multispectral data collected over a North Dakota test site during July 1972, are compared to evaluate the capability of the satellite sensors to detect numbers and distribution of prairie ponds and lakes. Recognition maps using ERTS-1, MSS 7 data are generated using a level slicing technique. Surface water areas larger than two acres are recognized, but ponds in the one-to two-acre range are detected only at random. The proportion estimation technique will improve the accuracy of area determination and small pond detection.

Work, E. A., Jr.↗

NASA NTRS1974

Remote sensing of land use changes in US metropolitan regions: Techniques of analysis and opportunities for application

A graphic description is given of the Census Cities ERTS experiment in urban change detection using remote sensors. The relationship or model between land use data from sensors and socio-demographic data from the census is partly demonstrated. The example suggests how knowledge of land use changes acquired by sensors can be used to make estimates of population, and other attributes. The feasibility of nationwide mapping of land use, and land use changes, by direct computer classification of ERTS-1 multispectral digital data is also demonstrated. Potential applications in state and regional planning are many, and some are named. But the longer-range gains are likely to be improved understanding by legislators, managers and voters as to what it is that makes the country tick. One of the specific tasks could be the allocation of revenues to be shared.

Wray, J. R.↗

NASA NTRS1978

Nonthermal galactic emission below 10 megahertz

The Radio Astronomy Explorer 2 (RAE 2) lunar-orbiting satellite has provided measurements of the nonthermal galactic radio emission at frequencies below 10 MHz. Measurements of the emission spectra are presented for the center, anticenter, north polar, and south polar directions at 22 frequencies between 0.25 and 9.18 MHz. Survey maps of the spatial distribution of the observed low-frequency Galactic emission at 1.31, 2.20, 3.93, 4.70, 6.55, and 9.18 MHz are presented. The observations were obtained with the 229-m traveling-wave V antenna on this lunar-orbiting spacecraft. The improved frequency coverage offers additional insights into the structure of the local Galactic neighborhood.

Milky Way↗

NASA NTRS1982

Inverse scattering for an exterior Dirichlet problem

Scattering caused by a metallic cylinder in the field of a wire carrying a periodic current is studied, with a view to determining the location and shape of the cylinder in light of far field measurements between the cylinder and the wire. The associated direct problem is the exterior Dirichlet problem for the Helmholtz equation in two dimensions, and an improved low frequency estimate for its solution by integral equation methods is shown by inverse scattering calculations to be accurate to this estimate. The far field measurements are related to low frequency boundary integral equations whose solutions may be expressed in terms of a mapping function for the exterior of the unknown curve onto the exterior of a unit disk. The conformal transformation's Laurent expansion coefficients can be related to those of the far field, the first of which leads to the calculation of the distance between the source and the cylinder, while the other coefficients are determined by placing the source in a different location.

Hariharan, S. I.↗

NASA NTRS1995

Fuzzy inference enhanced information recovery from digital PIV using cross-correlation combined with particle tracking

Particle Image Velocimetry provides a means of measuring the instantaneous 2-component velocity field across a planar region of a seeded flowfield. In this work only two camera, single exposure images are considered where both cameras have the same view of the illumination plane. Two competing techniques which yield unambiguous velocity vector direction information have been widely used for reducing the single exposure, multiple image data: cross-correlation and particle tracking. Correlation techniques yield averaged velocity estimates over subregions of the flow, whereas particle tracking techniques give individual particle velocity estimates. The correlation technique requires identification of the correlation peak on the correlation plane corresponding to the average displacement of particles across the subregion. Noise on the images and particle dropout contribute to spurious peaks on the correlation plane, leading to misidentification of the true correlation peak. The subsequent velocity vector maps contain spurious vectors where the displacement peaks have been improperly identified. Typically these spurious vectors are replaced by a weighted average of the neighboring vectors, thereby decreasing the independence of the measurements. In this work fuzzy logic techniques are used to determine the true correlation displacement peak even when it is not the maximum peak on the correlation plane, hence maximizing the information recovery from the correlation operation, maintaining the number of independent measurements and minimizing the number of spurious velocity vectors. Correlation peaks are correctly identified in both high and low seed density cases. The correlation velocity vector map can then be used as a guide for the particle tracking operation. Again fuzzy logic techniques are used, this time to identify the correct particle image pairings between exposures to determine particle displacements, and thus velocity. The advantage of this technique is the improved spatial resolution which is available from the particle tracking operation. Particle tracking alone may not be possible in the high seed density images typically required for achieving good results from the correlation technique. This two staged approach offers a velocimetric technique capable of measuring particle velocities with high spatial resolution over a broad range of seeding densities.

Wernet, Mark P.↗

NASA NTRS1998

MOLA Topography of Impact Basins in the Northern Hemisphere of Mars

Coverage of the northern hemisphere of Mars by the Mars Orbiter Laser Altimeter (MOLA) during the aerobraking hiatus and the two Science Phasing Operation periods provides improved definition and characterization of large impact basins. Gridded MOLA data show the Utopia Basin has a pronounced bowl-like structure, as opposed to the interior rises suggested by the earlier USGS DEM. The elevation structure is concentric about the basin center as mapped by McGill. In particular, the proposed inner ring closely follows the -4 km contour over much of the southern, western and northwestern sides. Higher topography along portions of the dichotomy boundary aligns with the basin's outer ring. High topography in the polar region also occurs where the outer ring should lie, raising the possibility that perhaps some of the polar topography is due to basin structure as well as ice. Two MOLA passes near Phison Rupes provide evidence for a large "stealth" hole where Viking imagery show little evidence of any major structure. The 2 km deep, 600 km wide depression at 31OW, 3ON is as large as the Cassini impact basin 1000 km to the SW. While Cassini is easily recognized in image data, the "MOLA Hole" is not. If this depression is a deeply eroded and buried impact basin (as perhaps suggested by a decrease in the crater density and somewhat smoother terrain than in adjacent areas), it is not clear why it has managed to maintain its great depth. In Tempe at the dichotomy boundary a 300 km wide impact basin is revealed by pronounced bowl-like topography centered at 87W, 47N, even though only about 1/3 of the basin rim structure is obvious. The basin lies on a sloping boundary zone, with the more buried N rim up to 2 km below the rugged S rim. A similar N-S asymmetry in basin ring structure occurs for the much larger Isidis Basin, where the S rim rises 6 km but the subdued N rim rises barely 2 km above the floor. There is essentially no topographic expression of the main ring in the NE quadrant of Isidis where, if it exists, it lies below Hesperian-age plains.

Frey, Herbert↗

NASA NTRS2012

Recovering Swift-XRT Energy Resolution through CCD Charge Trap Mapping

The X-ray telescope on board the Swift satellite for gamma-ray burst astronomy has been exposed to the radiation of the space environment since launch in November 2004. Radiation causes damage to the detector, with the generation of dark current and charge trapping sites that result in the degradation of the spectral resolution and an increase of the instrumental background. The Swift team has a dedicated calibration program with the goal of recovering a significant proportion of the lost spectroscopic performance. Calibration observations of supernova remnants with strong emission lines are analysed to map the detector charge traps and to derive position-dependent corrections to the measured photon energies. We have achieved a substantial recovery in the XRT resolution by implementing these corrections in an updated version of the Swift XRT gain file and in corresponding improvements to the Swift XRT HEAsoft software. We provide illustrations of the impact of the enhanced energy resolution, and show that we have recovered most of the spectral resolution lost since launch

Pagani, C.↗

NASA NTRS2014

Anisotropic Solar Wind Sputtering of the Lunar Surface Induced by Crustal Magnetic Anomalies

The lunar exosphere is generated by several processes each of which generates neutral distributions with different spatial and temporal variability. Solar wind sputtering of the lunar surface is a major process for many regolith-derived species and typically generates neutral distributions with a cosine dependence on solar zenith angle. Complicating this picture are remanent crustal magnetic anomalies on the lunar surface, which decelerate and partially reflect the solar wind before it strikes the surface. We use Kaguya maps of solar wind reflection efficiencies, Lunar Prospector maps of crustal field strengths, and published neutral sputtering yields to calculate anisotropic solar wind sputtering maps. We feed these maps to a Monte Carlo neutral exospheric model to explore three-dimensional exospheric anisotropies and find that significant anisotropies should be present in the neutral exosphere depending on selenographic location and solar wind conditions. Better understanding of solar wind/crustal anomaly interactions could potentially improve our results.

exosphere↗

NASA NTRS2017

From LIMS to OMPS-LP: Limb Ozone Observations for Future Reanalyses

High vertical resolution and accuracy of ozone data from satellite-borne limb sounders has made them an invaluable tool in scientific studies of the middle and upper atmosphere. However, it was not until recently that these measurements were successfully incorporated in atmospheric reanalyses: of the major multidecadal reanalyses only ECMWF's (European Centre for Medium-Range Weather Forecasts') ERA (ECMWF Re-Analysis)-Interim/ERA5 and NASA's MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications-2) use limb ozone data. Validation and comparison studies have demonstrated that the addition of observations from the Microwave Limb Sounder (MLS) on EOS (Earth Observing System) Aura greatly improved the quality of ozone fields in MERRA-2 making these assimilated data sets useful for scientific research. In this presentation, we will show the results of test experiments assimilating retrieved ozone from the Limb Infrared Monitor of the Stratosphere (LIMS, 1978/1979) and Ozone Mapping Profiler Suite Limb Profiler (OMPS-LP, 2012 to present). Our approach builds on the established assimilation methodology used for MLS in MERRA-2 and, in the case of OMPS-LP, extends the excellent record of MLS ozone assimilation into the post-EOS era in Earth observations. We will show case studies, discuss comparisons of the new experiments with MERRA-2, strategies for bias correction and the potential for combined assimilation of multiple limb ozone data types in future reanalyses for studies of multidecadal stratospheric ozone changes including trends.

Wargan, K.↗

NASA NTRS2017

Aerial Vehicles to Detect Maximum Volume of Plume Material Associated with Habitable Areas in Extreme Environments

Current technologies of exploring habitable areas of icy moons are limited to flybys of space probes. This research project addresses long-term navigation of icy moons by developing a MATLAB adjustable trajectory based on the volume of plume material observed. Plumes expose materials from the sub-surface without accessing the subsurface. Aerial vehicles capable of scouting vapor plumes and detecting maximum plume material volumes, which are considered potentially habitable in inhospitable environments, would enable future deep-space missions to search for extraterrestrial organisms on the surface of icy moons. Although this platform is still a prototype, it demonstrates the potential aerial vehicles can have in improving the capabilities of long-term space navigation and enabling technology for detecting life in extreme environments. Additionally, this work is developing the capabilities that could be utilized as a platform for space biology research. For example, aerial vehicles that are sent to map extreme environments of icy moons or the planet Mars, could also carry small payloads with automated cell-biology experiments, designed to probe the biological response of low-gravity and high-radiation planetary environments, serving as a pathfinder for future human missions.

Vehicles Extreme Environment↗

DOE OSTI2020

Shallow Geothermal Potential of the Snake River Plain

The Snake River Plain (SRP) terrestrial heat flow and subsurface thermal regime are not well understood but are important for assessing the local geothermal resource potential, both for conventional and for Enhanced Geothermal Systems (EGS) development in the region. Resource evaluation for the SRP is complicated by the disparate data density, along with the known lateral advection of heat in the Eastern Snake River Plain Aquifer and vertical heat transport by fluids in the bounding faults, primarily in the southwestern section. Fortunately, recent studies, e.g., the Snake River Plain Play Fairway Analysis, the Idaho FORGE site, and site-specific investigations, which included drilling within the Camas Prairie and on the Mountain Home Air Force Base, near Twin Falls, and in the Eastern Snake River Plain as part of the HOTSPOT Project, add both additional drilling and geophysical data. The SMU Geothermal Laboratory has conducted detailed studies of SRP tectonics and heat flow since the 1970's and used this knowledge as part of the EGS geothermal potential estimation for the conterminous United States in 2006 and again in 2011, calculating geothermal potential from 3.5 km to 10 km depth. Recent temperature modeling refined the calculation methodology to estimate shallow (1 km to 4 km) resource potential using an improved thermal conductivity model and incorporation of shallow groundwater flow. By incorporating the new SRP geology, geophysics, and 206 thermal data sites into the SMU thermal modeling methodology, this project updates the resource estimate for the SRP, and generates new temperature-at-depth maps for the shallow subsurface (1 km to 4 km). The project results highlight the EGS potential resource areas (=150°C) and areas with more exploration risks based on minimal and/or low-quality data. The newest temperature modeling results suggest EGS potential is near five times greater in the SRP than previously estimated.

EGS↗

NASA NTRS2011

Retrospective Analog Year Analyses Using NASA Satellite Data to Improve USDA's World Agricultural Supply and Demand Estimates

The USDA World Agricultural Outlook Board (WAOB) is responsible for monitoring weather and climate impacts on domestic and foreign crop development. One of WAOB's primary goals is to determine the net cumulative effect of weather and climate anomalies on final crop yields. To this end, a broad array of information is consulted, including maps, charts, and time series of recent weather, climate, and crop observations; numerical output from weather and crop models; and reports from the press, USDA attach s, and foreign governments. The resulting agricultural weather assessments are published in the Weekly Weather and Crop Bulletin, to keep farmers, policy makers, and commercial agricultural interests informed of weather and climate impacts on agriculture. Because both the amount and timing of precipitation significantly affect crop yields, WAOB often uses precipitation time series to identify growing seasons with similar weather patterns and help estimate crop yields for the current growing season, based on observed yields in analog years. Historically, these analog years are visually identified; however, the qualitative nature of this method sometimes precludes the definitive identification of the best analog year. Thus, one goal of this study is to derive a more rigorous, statistical approach for identifying analog years, based on a modified coefficient of determination, termed the analog index (AI). A second goal is to compare the performance of AI for time series derived from surface-based observations vs. satellite-based measurements (NASA TRMM and other data).

Teng, William↗