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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

CSAPR2 cell-tracking data collected during TRACER

One of the challenges of analyzing convective cell properties is quick evolution of the individual convective cells. While the operational radar data provide great a data set to analyze the evolution of radar observables of convective precipitation clouds statistically, previous studies also suggested that, because of the quick evolution of cell life cycle, conventional radar volume scan strategies taking ~5-7 minutes might not capture the detailed evolution. The TRACER campaign deployed CSAPR2, which performed frequent update of RHI and sector PPI scans to track convective cells every < 2 minutes guided by a new cell-tracking framework, Multisensor Agile Adaptive Sampling (MAAS; Kollias et al. 2020). This allows for capturing fast-evolving radar observables. The submitted data files are CSAPR2 data in CfRadial format collected during the TRACER field campaign from June to September 2020. The data files include processed radar variables including: noise-masked reflectivity and differential reflectivity corrected for rain attenuation and systematic biases, noise-masked dealiased radial velocity, specific differential phase, locations of target cells (latitude, longitude, radar range), and radar-echo classification.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

One-Year Electric Field Study at the North Slope of Alaska Field Campaign Report

The Global Electric Circuit (GEC) of the atmosphere provides a unique prospective of the changing climate around the Earth. Monitoring this global electrical signature provides details of the global nature of electrified clouds and thunderstorms that are occurring. Using this very inexpensive measurement system, we can provide much-needed information about the vast electrical system that surrounds us, as well as gaining an understanding of how the electrical properties of global precipitation systems are changing over time. Prior to the OYESNSA field campaign (http://atmos.tamucc.edu/oyesnsa/; https://www.arm.gov/research/campaigns/nsa2017oyesnsa/), much of the electric field data used to compare to the physical properties of electrified clouds were collected in the Antarctica. With the inclusion of this high-quality data set in the Arctic, it allows for the simultaneous observation of the electric field at both poles. Preliminary results from the One-Year Electric Field Study-North Slope of Alaska (OYESNSA) field campaign already show that the GEC appears to be indeed a truly global phenomenon, with very similar fair-weather observations being taken at both poles. This first six months of data has already been uploaded to the ARM Data Center, and preliminary results have been presented at the American Geophysical Union (AGU) 2017 meeting with very positive feedback (http://atmos.tamucc.edu/oyesnsa/AGU_2017_Poster.pdf). The North Slope of Alaska provides a unique location for collecting these electric field measurements. Besides being at the opposite pole from many previous measurements, this site provides a rare opportunity to use the other instruments at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s Barrow, Alaska observatory, such as the Ka-band radar, upwardfacing lidar, vertical profile of meteorological measures, and other aerosol measurements. This allows for the unique chance to not only provide information about the global signature of the GEC, but also the physical inputs to the local electric field, by analyzing the physical properties of the simultaneous cloud, wind, and aerosol properties occurring with the electric field. Barrow, Alaska is home to some unique cloud formations that undoubtedly influence the electric field. This field campaign allows for the quantification of the influence to the electric field of these cloud types, as well as other unique phenomenon such as blowing snow. Already, the first six months of data have shown correlations of the electric field to radar reflectivity properties, cloud types, and increased wind (blowing snow and aerosol). Providing an understanding of these inputs to the local electric field, will improve modeling techniques of this system both locally and globally. <!-- Although the year of measurements from [sic] -->

North Slope of Alaska, micropulse lidar, Ka-band r↗
DOE OSTI2019

Albedo Data to Facilitate Bifacial PV System Planning

For mono-facial photovoltaic (PV) systems, the ground-reflected radiation typically comprises only 1% to 2% of the total radiation received by the PV module. Consequently, a rudimentary understanding of the ground albedo is adequate for predicting their performance. However, for bifacial PV modules where their benefit is determined by the additional radiation reflected by the ground to their backside, a better understanding of albedo values and characteristics is needed by both the PV and financial communities to better estimate performance and to reduce risk. This was identified as a key need at the 2018 Bifacial PV Workshop. Subsequently, the Department of Energy (DOE) has initiated work with the National Renewable Energy Laboratory (NREL) to provide albedo data for different locations and ground surfaces for both the simulation of bifacial PV systems and for summary information, including variations in albedo with respect to time of day, season, and year. This albedo data base will include data from measurement networks (SURFRAD and Ameriflux), and the PV industry is also asked to provide their albedo measurements and take part in a cooperative effort to provide an expansive and accurate data base of albedo values.

14 SOLAR ENERGY↗
DOE OSTI2022

Data Validation for Hosting Capacity Analyses

The usefulness of Hosting Capacity Analysis (HCA) is dependent on users' confidence that the results accurately reflect grid conditions. This report provides the findings and recommendations from research, conducted by The National Renewable Energy Lab (NREL) and The Interstate Renewable Energy Council (IREC), into HCA data validation best practices. The goal is to provide utilities, regulators, and stakeholders best practices for HCA data validation procedures so that future HCA deployments avoid uncertainties and potential mistakes from earlier rollouts and provide useful and accurate data from the day they are published. Utilities can use this report to develop or refine their HCA data validation procedures. Regulators can use the report to inform their oversight of utilities' HCA data validation practices, while stakeholders can use it to evaluate the effectiveness of utility efforts.

14 SOLAR ENERGY↗
DOE OSTI2019

The IEEE Reliability Test System: A Proposed 2019 Update

The evolving nature of electricity production, transmission and consumption necessitates an update to the IEEE's Reliability Test System, which was last modernized in 1996. The update introduced here introduces a generation mix more representative of modern power systems, with the removal of several nuclear and oil generating units and the addition of natural gas, wind, solar photovoltaics, concentrating solar power, and energy storage. The update includes assigning the test system a geographic location in the southwestern United States to enable the integration of spatio-temporally consistent wind, solar, and load data with forecasts. Further updates include common RTS transmission modifications in published literature, definitions for reserve product requirements and market simulation descriptions to enable benchmarking of multi-period power system scheduling problems. The final section reflects example results from a production cost modeling simulation on the updated RTS system data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Investigation of the impact of supplemental reflective films to improve micro-light climate within tomato plant canopy in solar greenhouses

The non-uniform growth and development of crops within Chinese Solar Greenhouses (CSG) is directly related to the micro-light climate within canopy. In practice, reflective films are used to improve micro-light climate within plant canopy by homogenizing light distribution and so increasing total plant light interception. However, as to our knowledge, the contributions to light distribution within canopy have not been investigated for passive reflector like reflective films. Field experiments dealing with light conditions and growth behavior over time, are complicated to carry out, time-consuming and hard to control, while however, accurate measurements of how reflective films influence the micro-light climate of canopy are an essential step to improve the growth conditions for any crop. Here, we propose a supplementary light strategy using reflective films to improve light distribution within plant canopy. Based on the example of CSG, a 3D greenhouse model including a detailed 3D tomato canopy structure was constructed to simulate the influence of supplementary reflective films to improve micro-light climate. Comparison of measured solar radiation intensity with predicted model data demonstrated that the model could precisely predict light radiation intensity over time with different time points and positions in the greenhouse. A series of reflective film configurations were investigated based on features analysis of light distribution in the tomato canopy on sunny days using the proposed model. The reflective film configuration scheme with the highest impact significantly improved the evenness of horizontal and vertical light distribution in tomato canopy. The strategy provided here can be used to configure reflective films that will enhance light conditions in CSG, which can be applied and extended in different scenarios.

Liu, Anhua↗
DOE OSTI2025

Tracking cropland transitions: A comparative analysis of U.S. land cover change data

There are a growing number of land cover data available for the conterminous United States, supporting various applications ranging from biofuel regulatory decisions to habitat conservation assessments. These datasets vary in their source information, frequency of data collection and reporting, land class definitions, categorical detail, and spatial scale and time intervals of representation. These differences limit direct comparison, contribute to disagreements among studies, confuse stakeholders, and hamper our ability to confidently report key land cover trends in the U.S. Here we assess changes in cropland derived from the Land Change Monitoring, Assessment, and Projection (LCMAP) dataset from the U.S. Geological Survey and compare them with analyses of three established land cover datasets across the coterminous U.S. from 2008-2017: (1) the National Resources Inventory (NRI), (2) a dataset Lark et al. 2020 derived from the Cropland Data Layer (CDL), and (3) a dataset from Potapov et al. 2022. LCMAP reports more stable cropland and less stable noncropland in all comparisons, likely due to its more expansive definition of cropland which includes managed grasslands (pasture and hay). Despite these differences, net cropland expansion from all four datasets was comparable (5.18-6.33 million acres), although the geographic extent and type of conversion differed. LCMAP projected the largest cropland expansion in the southern Great Plains, whereas other datasets projected the largest expansion in the northwestern and central Midwest. Most of the pixel-level disagreements (86%) between LCMAP and Lark et al. 2020 were due to definitional differences among datasets, whereas the remainder (14%) were from a variety of causes. Cropland expansion in the LCMAP likely reflects conversions of more natural areas, whereas cropland expansion in other data sources also captures conversion of managed pasture to cropland. The particular research question considered (e.g., habitat versus soil carbon) should influence which data source is more appropriate.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Power-Law Scaling in the Internal Variability of Cumulus Cloud Size Distributions due to Subsampling and Spatial Organization

In this study, the spatial structure of cumulus cloud populations is investigated using three-dimensional snapshots from large-domain LES experiments. The aim is to understand and quantify the internal variability in cloud size distributions due to subsampling effects and spatial organization. A set of idealized shallow cumulus cases is selected with varying degrees of spatial organization, including a slowly organizing marine precipitating case and five more quickly organizing diurnal cases over land. A subdomain analysis is applied, yielding cloud number distributions at sample sizes ranging from severely undersampled to nearly complete. A strong power-law scaling is found in the relation between cloud number variability and subdomain size, reflecting an inverse linear relation. Scaling subdomain size by cloud size yields a data collapse across time points and cases, highlighting the role played by cloud spacing in controlling the stochastic variability. Spatial organization acts on top of this baseline model by increasing the maximum cloud size and by enhancing the variability in the number of smallest clouds. This reflects that the smaller clouds start to live on top of larger-scale thermodynamic structures, such as cold pools, which favor or inhibit their formation. Compositing all continental cumulus cases suggests the existence of a prototype diurnal time dependence in the spatial organization. A simple stochastic expression for cloud number variability is proposed that is formulated in terms of two dimensionless groups, which allows objective estimation of the degree of spatial organization in simulated and observed cumulus cloud populations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

UAS remote sensing (3DR SOLO platform): multispectral reflectance, canopy height model, normalized difference vegetation index, Seward Peninsula, Alaska, 2021

Airborne remote sensing data collected using a Parrot Sequoia+ multispectral sensor installed on a 3DR SOLO unoccupied aerial system (UAS) - operated by the Terrestrial Ecosystem Science Technology group at Brookhaven National Laboratory. This package includes data from 10 flights flown over the NGEE-Arctic Council Mile Marker (MM) 71, Kougarok MM64, and Teller MM27 sites on the Seward Peninsula, Alaska, in August 2021. Derived image products include point cloud, ortho-mosaiced multispectral image, ortho-mosaiced RGB image, a digital surface model (DSM) using the structure from motion (SfM) technique, a canopy height model (CHM), and a normalized difference vegetation index (NDVI) map. Unprocessed and processed data products are included in this package (processing levels 0-2). Data and metadata are provided as text (*.txt, *.json, *hdr,), tabular (*.dat, *.csv), point cloud (*.laz), Cloud Optimized GeoTIFF (COG, *.tif), and image (*.jpg, *.tif, *png) formats. 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↗
DOE OSTI2019

Incorporation of Melittin Enhances Interfacial Fluidity of Bicontinuous Microemulsions

The effect of an antimicrobial peptide, melittin, on the long-range collective and local undulation motions of bicontinuous microemulsions (BμEs) was evaluated using dynamic light scattering (DLS) and neutron spin echo (NSE) spectroscopy. Small-angle neutron scattering (SANS) was also used to study the effect of melittin on the structure and the correlation length of BμEs. BμEs were isolated from Winsor-III systems formed by mixing aqueous melittin solutions containing sodium dodecyl sulfate with dodecane/1-pentanol mixtures at optimal salinity. An increase of melittin concentration linearly increased the diffusivity of the microemulsions (D DLS ) and decreased the bending rigidity (κ NSE ) up to an aqueous melittin concentration of 2 g/L, thus indicating an increase of interfacial fluidity. Further increase of melittin concentration above 2 g/L did not change D DLS and slightly increased κ NSE . The changes reflect differences of interaction between melittin and BμE surfactant monolayers at different melittin concentrations. At low concentrations, melittin is highly associated with the surfactant monolayers, likely ion-paired to the sulfate surfactant head groups and perhaps partially penetrating into the surfactant tail region. Above 2 g/L, an apparent saturation concentration, added melittin is less strongly associated with the monolayers. In contrast, the bare bending constant derived from SANS data (κ bare ) underwent only a minor decrease. Noting that κ bare reflects contributions from both the elasticity and saddle-splay moduli, this suggests that κ bare is not an effective indicator of dynamics for the BμEs investigated. Our study demonstrates that amphiphilic solubilizates can have a complex impact on dynamics of surfactant monolayers in microemulsions, with implications for transmembrane diffusion and the kinetics of release from the microemulsion phase, which are important characteristics for the use of BμEs as delivery vehicles and host systems for (bio)chemical reactions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Emulation Framework for Distributed Large-Scale Systems Integration

Recent trends in systems engineering include integration of very large-scale systems, which entails significant challenges when they are geographically dispersed. In these scenarios, intelligent integration of distributed large-scale systems requires significant coordination among hardware elements as well as all software components. The approach of integrated systems (both computing platform and experimental equipment) for end-to-end orchestration is called federation. Virtual frameworks can aid in the testing, assessment, and implementation of a functional system of interconnected resources. We present an emulation framework that replicates the software environments of multi-site federations of computing systems and instruments. Our emulation framework allows systems engineers to reduce developmentcost and avoid disruptions to production infrastructure. Our framework was effectively used to develop and test software modules for various tasks including container orchestration and instrument access. For performance assessment, however, the emulated framework is severely limited in providing accurate network and IO measurements at 10 Gbps and higher data rates. The data transfer performance profiles estimated using these emulated measurements are usually inaccurate for high bandwidth and high latency connections, since emulation does not accurately reflect the critical network transport dynamics.We utilize measurements from a physical testbed with hardware network emulators to obtain data transfer profiles that closely match the expected profiles for the emulated federations. We show the effectiveness of our approach by an illustrative example of integrated (federated) multi-site ultra large-scale systems that are connected via high speed wide area networks.

Imam, Neena↗
DOE OSTI2018

Application of modern autoradiography to nuclear forensic analysis

Modern autoradiography techniques based on phosphorimaging technology using image plates (IPs) and digital scanning can identify heterogeneities in activity distributions and reveal material properties, serving to inform subsequent analyses. Here, we have adopted these advantages for applications in nuclear forensics, the technical analysis of radioactive or nuclear materials found outside of legal control to provide data related to provenance, production history, and trafficking route for the materials. IP autoradiography is a relatively simple, non-destructive method for sample characterization that records an image reflecting the relative intensity of alpha and beta emissions from a two-dimensional surface. Such data are complementary to information gathered from radiochemical characterization via bulk counting techniques, and can guide the application of other spatially resolved techniques such as scanning electron microscopy (SEM) and secondary ion mass spectrometry (SIMS). IP autoradiography can image large 2-dimenstional areas (up to 20 × 40 cm), with relatively low detection limits for actinides and other radioactive nuclides, and sensitivity to a wide dynamic range (10 5 ) of activity density in a single image. Distributions of radioactivity in nuclear materials can be generated with a spatial resolution of approximately 50 μm using IP autoradiography and digital scanning. While the finest grain silver halide films still provide the best possible resolution (down to ~10 μm), IP autoradiography has distinct practical advantages such as shorter exposure times, no chemical post-processing, reusability, rapid plate scanning, and automated image digitization. Sample preparation requirements are minimal, and the analytical method does not consume or alter the sample. These advantages make IP autoradiography ideal for routine screening of nuclear materials, and for the identification of areas of interest for subsequent micro-characterization methods. Here in this article we present a summary of our setup, as modified for nuclear forensic sample analysis and related research, and provide examples of data from select samples from the nuclear fuel cycle and historical nuclear test debris.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2025

Toward equitable environmental exposure modeling through convergence of data, open, and citizen sciences: an example of air pollution exposure modeling amidst increasing wildfire smoke

Exposure modeling is critical in environmental epidemiology and human health but may face challenges (e.g., skewed data, unequal error, context-insensitive validation, and computational demands). Modeling decisions reflect the intended use of the models and the values that modelers prioritize. We aimed to provide a conceptual framework and machine learning (ML) modeling protocols that address these issues. With 500m-gridded hourly PM 2.5 and O 3 levels in Illinois before, during, and after the 2023 Canadian wildfire season as a motivating example, we conducted modeling experiments to evaluate modeling methods, guided by three domains we propose based on theories of science: 1) Data Diversity, leveraging open and citizen science data to enhance inclusivity, parsimony, and representativeness; 2) Equitable Accuracy, ensuring fairly distributed uncertainties across subpopulations; and 3) Sustainable Modeling, balancing accuracy with reducing computational demands to promote accessibility for under-resourced researchers. Here, we found that ML with publicly available data can achieve high accuracy. Depending on methods, performance may vary substantially, even with identical input data. Large but skewed data may reduce performance. Misuse of cross-validation protocols can underestimate prediction error; although we observed R 2 s of ∼98 %, the modeled estimates varied significantly, indicating the need for careful model validation. By using new modeling protocols including representativeness-considered training and validation data and a new loss function, we achieved high agreement between estimates and ground-based measurements (e.g., R 2 = ∼90 % for PM 2.5 ; ∼80 % for O 3 ), equally distributed errors across sociodemographic strata and urban–rural divides, and reduction in computation time—from several weeks or months to a few days.

Exposure assessment↗
DOE OSTI2024

Andreev reflection of quantum Hall states through a quantum point contact

Here, we investigate the interplay between the quantum Hall (QH) effect and superconductivity in InAs surface quantum well (SQW)/NbTiN heterostructures using a quantum point contact (QPC). We use QPC to control the proximity of the edge states to the superconductor. By measuring the upstream and downstream resistances of the device, we investigate the efficiency of Andreev conversion at the InAs/NbTiN interface. Our experimental data is analyzed using the Landauer Büttiker formalism, generalized to allow for Andreev reflection processes. We show that by varying the voltage of the QPC, V QPC , the average Andreev reflection, A, at the QH-SC interface can be tuned from 50% to ~ 10%. The evolution of A with V QPC extracted from the measurements exhibits plateaus separated by regions for which A varies continuously with V QPC . The presence of plateaus suggests that for some ranges of V QPC the QPC might be pinching off almost completely from the QH-SC interface some of the edge modes. Our work shows a new experimental setup to control and advance the understanding of the complex interplay between superconductivity and QH effect in two-dimensional gas systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Recent Metallic Fuel Data Recovery in FIPD

The Metallic Fuels Irradiation and Physics Database (FIPD) [1] is an organized collection of metallic fuel test pin data (U-xPu-yZr, = 0 ~ 28; y = 2 ~ 10) and documentation available to industry. FIPD mainly contains three types of data: (1) Fuel pin fabrication data, including fuel slug diameter, fuel slug length, cladding diameter, smear density, etc. (2) Fuel pin operation conditions, including axial distributions for power, temperatures, fluences, burnup, and isotopic densities, etc. and (3) Fuel pin post-irradiation examination (PIE) data, including fission gas release and gas chemistry, profilometry, and neutron radiography, etc. The operating conditions for pins with PIE data available in FIPD span significant ranges across key parameters. The fuel peak burnup extends from less than 5% up to 20 at%. The cladding peak temperature varies from about 490°C to 660°C. Finally, the cladding peak DPA shows a wide range from less than 5 to 120. These broad ranges reflect the diverse testing conditions and operational parameters captured in the available PIE data. More detail about FIPD can be found in ref. [2]. The database development is an ongoing effort covering metallic fuel experiments from the Experimental Breeder Reactor II (EBR-II) and the Fast Flux Test Facility (FFTF). As reported in the ref. [3, 4], most of the PIE data generated during the IFR program [5] has been collected, reviewed, processed, and integrated into FIPD. The most recently added PIE data can be found in ref. [4], which shows the collection of over 95% of the PIE data by the time of this paper. The recent improvements to the database are summarized in this paper.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Capturing the full benefits of bifacial modules to approach an LCOE of 3c/kWh through a regional optimization of the electrical architecture (Final Technical Report)

This is the final technical report for the Cypress Creek Renewables project focused on bifacial plant performance. The project consisted of building a heavily instrumented Bifacial test sites with several cell technologies; instrumenting >1 MW scale commercial sites with both monofacial and bifacial arrays in order to understand real-world differences; and to engage with the performance modeling and financial stakeholder community to improve accuracy and increase investor confidence. The data from the test and commercial sites has been used to validate and improve the accuracy of both physics-based and reduced order modeling software. The heavily instrumented nature has been used to understand how light contributions from the sky and reflected from the ground impact different portions of the module and array while the data from the commercial sites has directly impacted how independent engineers model production from bifacial systems. The latter point has led to an increase in over 2% compared to earlier predictions. Even this modest increase has had significant downward pressure on project LCOE. As the data and results are both public, this refined modeling and empirical evidence for a higher bifacial gain than modeled, the benefits to the broader US solar community are assured.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Airborne hyperspectral imaging of cover crops through radiative transfer process-guided machine learning

Cover cropping between cash crop growing seasons is a multifunctional conservation practice. Timely and accurate monitoring of cover crop traits, notably aboveground biomass and nutrient content, is beneficial to agricultural stakeholders to improve management and understand outcomes. Currently, there is a scarcity of spatially and temporally resolved information for assessing cover crop growth. Remote sensing has a high potential to fill this need, but conventional empirical regression operated with coarse-resolution multispectral data has large uncertainties. Therefore, this study utilized airborne hyperspectral imaging techniques and developed new process-guided machine learning approaches (PGML) for cover crop monitoring. Specifically, we deployed an airborne hyperspectral system covering visible to shortwave-infrared wavelengths (400–2400 nm) to acquire high spatial (0.5 m) and spectral (3–5 nm) resolution reflectance over 23 cover crop fields across Central Illinois in March and April of 2021. Airborne hyperspectral surface reflectance with high spectral and spatial resolution can be well matched with field data to quantify cover crop traits. Furthermore, the PGML models were pre-trained by synthetic data from soil-vegetation radiative transfer modeling (one million records), and then fine-tuned with field data of cover crop biomass and nutrient content. Results show that airborne hyperspectral data with PGML can achieve high accuracy to predict cover crop aboveground biomass (R 2 = 0.72, relative RMSE = 15.16%) and nitrogen content (R 2 = 0.69, relative RMSE = 16.59%) through leave-one-field-out cross-validation. Unlike the pure data-driven approach (e.g., partial least-squares regression), PGML incorporated radiative transfer knowledge and obtained higher predictive performance with fewer field data. Meanwhile, with field data for model fine-tuning, PGML predicted biomass more accurately than the inversion of radiative transfer models. Here we also found that the red edge has a high contribution in quantifying aboveground biomass and nitrogen content, followed by green and shortwave spectra. This study demonstrated the first attempt of utilizing hyperspectral remote sensing to accurately quantify cover crop traits. We highlight the strength of PGML in exploiting sensing data to quantify ecosystem variables to advance agroecosystem monitoring for sustainable agricultural management.

60 APPLIED LIFE SCIENCES↗