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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
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↗
DOE OSTI2022

Feasibility of enhancing carbon sequestration and stock capacity in temperate and boreal European forests via changes to management regimes

Forest management practices might act as nature-based methods to remove CO 2 from the atmosphere and slow anthropogenic climate change and thus support an EU forest-based climate change mitigation strategy. However, the extent to which diversified management actions could lead to quantitatively important changes in carbon sequestration and stocking capacity at the tree level remains to be thoroughly assessed. To that end, we used a state-of-the-science bio-geochemically based forest growth model to simulate effects of multiple forest management scenarios on net primary productivity (NPP) and potential carbon woody stocks (pCWS) under twenty scenarios of climate change in a suite of observed and virtual forest stands in temperate and boreal European forests. Previous modelling experiments indicated that the capacity of forests to assimilate and store atmospheric CO 2 in woody biomass is already being attained under business-as-usual forest management practices across a range of climate change scenarios. Nevertheless, we find that on the long-term, with increasing atmospheric CO 2 concentration and warming, managed forests show both higher productivity capacity and a larger potential pool size of stored carbon than unmanaged forests as long as thinning and tree harvesting are of moderate intensity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

How deep is your soil? Quantifying and spatially analyzing understudied deep soil in the United States

Deep soil is largely understudied and important in understanding biogeochemical processes in soil. Here, understudied soil is defined as the difference between soil studied to a known depth and the estimated bedrock depth. To understand more about deep soil, the understudied soil in the US was quantified and spatially analyzed using soil survey data and model estimates of bedrock depth. An equation was derived to find understudied soil using the dataset parameters “max lower depth studied”, “depth to bedrock”, and “likelihood of bedrock in the top 200 cm”. The survey data and bedrock model revealed that soil has been studied to an average depth of 1-2 meters, and the average depth to bedrock is 20 meters. Soil data density in the soil surveys was greatest in the West Coast, Midwest, and areas historically managed for agricultural, while the non-contiguous US and interior West were underrepresented. The soil had been studied deeper than the estimated soil depth in 455 out of 56,889 observation points concentrated in Alaska, California, Texas, Florida, Puerto Rico, and the US Virgin Islands. To understand the diversity and any taxonomic bias of the global soil data available, soil order was compared to US-based National Resource Conservation Service percentages and it was found that Oxisols, Alfisols, Ultisols, Andisols, and Histosols were overrepresented while Gelisols, Aridisols, Vertisols, Entisols, and Spodosols are underrepresented. Soil depth is important in exploring the complexity of biogeochemical processes that take place in soil.

Bedrock↗
DOE OSTI2020

Comparison of four learning-based methods for predicting groundwater redox status

Knowing the location where groundwater denitrification occurs, or by proxy the groundwater redox status (oxic, mixed, and anoxic), is valuable information for assessing and managing potential agricultural land-use impacts on freshwater quality. We compare the efficacy of supervised (Linear Discriminant Analysis LDA; Boosted Regression Trees, BRT; and Random Forest, RF) and unsupervised (Modified Self-Organizing Map, MSOM) learning-based methods to predict groundwater redox status in the agriculturally dominated Tasman, Waikato, and Wellington regions of New Zealand. Thresholds applied to regional groundwater-quality samples provide redox status variables and learn heuristics constrained by these variables and applied to spatial factors (climate, elevation, geologic, hydrology soils, and well depth) identify optimal sets of regional predictor variables. A split-sample approach is used to train and test the learning methods ability to predict redox status using the optimal predictor variables. Overall, the supervised methods demonstrate a prediction bias toward oxic conditions and inability to perform statistically well when using independent regional data; for example, consider kappa statistics for BRT (Tasman: 0.42, Waikato: 0.38, Wellington: 0.17), RF (Tasman: 0.42, Waikato: 0.47, Wellington: 0.17 and LDA (Tasman: 0.46, Waikato: 0.32, Wellington: 0.17). By contrast, the unsupervised method performs statistically well when predicting oxic, mixed, and anoxic conditions and corresponding depths when using independent regional data; for example, consider MSOM kappa statistics for Tasman: 0.78, Waikato: 0.80, Wellington: 0.76. The unsupervised learning method provides the added benefits of being (1) able to combine predictions into 3D regional anoxic probability plots for interpreting the spatial influence of paleosols and groundwater flowpaths on redox status, and (2) readily extended to map 3D redox status across New Zealand and other countries despite data bias and sparsity.

machine learning algorithms, Random Forest, booste↗
DOE OSTI2021

Accounting for uncertainty in complex alluvial aquifer modeling by Bayesian multi-model approach

Alluvial aquifers by nature are complex caused by varied depositional environments. Developing a reliable groundwater model to represent an alluvial aquifer is non-trivial. Also, relying on a single best calibrated model may not be sufficient because of an inadequate choice of model parameter values. To better understand groundwater dynamics and improve model prediction reliability, this study presents a Bayesian multi-model uncertainty quantification (BMMUQ) framework to account for model parameter uncertainty in complex alluvial groundwater modeling. The methodology was applied to the agriculturally intensive Mississippi River alluvial aquifer (MRAA), Northeast Louisiana. An aquifer architecture was first constructed using 7,259 well logs in the MRAA area which covers three fluvial deposits (alluvium, braided-stream terrace, and braided-stream terrace-loess). A 12-layer MODFLOW model was then developed to address the alluvial aquifer complexity and well calibrated through a genetic algorithm. This study quantified model parameter uncertainty in hydraulic conductivity and specific storage of sand facies. Bayesian model averaging (BMA) with the Expectation Maximization (EM) algorithm was adopted to derive posterior model weights and head variances of 50 alternative conceptual groundwater flow models, and thereby obtains BMA ensemble model predictions instead of only relying on the best calibrated conceptual model. Overall, results show that an estimated around 950 million m3 of groundwater storage loss occurs in 2015 with respect to the beginning of 2004, due to high groundwater demand for irrigation in the MRAA area. Explicitly quantifying model uncertainty can produce more reliable groundwater level predictions from BMA ensemble model. The presented groundwater modeling framework improves our understanding of the MRAA and provides a valuable tool to assist agricultural water management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Biological Carbon Sequestration for Climate and Soil Health

The United States recently launched the Net Zero World Initiative on decarbonization. As temperatures and sea levels rise, strong action is needed to mitigate high atmospheric CO 2 levels. In response to this need, a joint workshop between the US Department of Energy’s Oak Ridge National Laboratory (ORNL) and Lawrence Berkeley National Laboratory (LBNL) was convened to assess the challenges and opportunities within biological carbon (C) capture. The virtual workshop on Biological Carbon Sequestration for Climate and Soil Health focused on three aspects of biological C capture research: natural systems, engineered systems, and modeling. For this workshop, the scope of natural systems included unmanaged (e.g., grasslands, wetlands, natural forests) and managed (e.g., agricultural lands, commercial forests, forest biofuels) ecosystems. The engineered systems group covered topics in genetically modified plants and microbes, synthetic microbiomes, and altered plant–microbe interactions for enhancing C capture, storage, and utilization. The modeling group focused on computational tools and data collection to predict and assess the effectiveness of new biocarbon sequestration advances. Participant expertise included plant and microbial systems biology, synthetic biology, soil biogeochemistry, and techno-economic analysis. Together, participants outlined key scientific advances in support of biological C capture and storage. Among the major findings of the meeting were the need to better measure C flux within natural, agricultural, and fabricated ecosystems for experimentation; better understand the role of soil composition, plant roots, and the rhizosphere (i.e., the soil surrounding the plant root) in the underground storage of organic C; and design plant–microbe systems for maximal C storage.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Summary Report from the 2025 Interfaces for Energy and the Environment Conference

The inaugural Interfaces for Energy and the Environment Conference (IEEC) took place on May 19-23, 2025, at Pacific Northwest National Laboratory (PNNL), Richland, Washington (USA). The aim of this first-of-its-kind interdisciplinary meeting was to provide a forum for participants to share the latest cutting edge experimental and computational advances in interfacial science across energy and environmental applications. The sessions below (elaborated further in the report summaries) highlighted fundamental and applied collaborative research aimed at understanding the interactions occurring at interfaces in aqueous environments, including, but not limited to, the fields of geochemistry, atmospheric chemistry, agriculture, environmental management, and catalysis. They were organized to stimulate and provide opportunities to create, renew, and deepen collaborations. The conference included activities such as oral and poster presentations, honoree mentoring session, and a team building exercise to support all career stages (detailed summaries of these activities are in the Appendices).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Science & Technology Review June 2026: Predicting Tsunamis

At Lawrence Livermore National Laboratory, we focus on science and technology research to ensure our nation’s security. We also apply that expertise to solve other important national problems in energy, bioscience, and the environment. Science & Technology Review is published eight times a year to communicate, to a broad audience, the Laboratory’s scientific and technological accomplishments in fulfilling its primary missions. The publication’s goal is to help readers understand these accomplishments and appreciate their value to the individual citizen, the nation, and the world.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Machine Learning Models for Mapping Groundwater Pollution Risk: Advancing Water Security and Sustainable Development Goals in Georgia, USA

The widespread use of pesticides, such as atrazine and malathion, in agricultural systems raises significant concerns regarding the contamination of groundwater, which serves as a critical resource for drinking water. This study applies machine learning techniques to predict the concentrations of atrazine and malathion in groundwater across Georgia, USA, using 2019 data. A Random Forest classifier was employed to integrate various environmental and demographic factors, including pesticide application rates, precipitation, lithology, and population density, to predict pesticide contamination in groundwater. The models demonstrated high training accuracies of 100% and moderate average testing accuracy of 55% for atrazine and 60% for malathion across five iterations. The low test accuracy of the model, ranging from 50% to 75%, is likely due to overfitting, which can be attributed to the small dataset size and the complex nature of pesticide-contamination patterns, making it challenging for the model to generalize to unseen data. Feature importance analysis revealed that average pesticide usage emerged as the most influential factor for atrazine, while aquifer lithology and precipitation played crucial roles in both models. These results provide valuable insights into the dynamics of pesticide contamination, highlighting areas at greater risk of contamination. The findings underscore the importance of integrating environmental, geological, and agricultural variables for more effective groundwater management and sustainable agricultural practices, contributing to the protection of water resources and public health.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

DayCent_CABBI_energy_farm_sorghum_BNI_Ecosphere

This repository contains call source code and input files that are part of the DayCent_CABBI BNI model experiments described in the manuscript below. Hartman et al. 2022. In Silico Evaluation of Plant Nitrification Suppression Effects on Agroecosystem Nitrogen Loss. Ecosphere . In Review.

70101 Agricultural Land Management↗
NASA NTRS1972

ERTS-1 applications to California resource inventory

There are no author-identified significant results in this report. ERTS-1 information will be utilized by resource management groups working in the fields of forestry, hydrology, range management, and agriculture to develop resource inventories of the state of California. Five examples are given of the use of ERTS-1 imagery and aerial photography in identifying different crops and field conditions.

Colwell, R. N.↗
NASA NTRS1973

Remote sensing applications in agriculture and forestry. Applications of aerial photography and ERTS data to agricultural, forest and water resources management

Remote sensing techniques are being used in Minnesota to study: (1) forest disease detection and control; (2) water quality indicators; (3) forest vegetation classification and management; (4) detection of saline soils in the Red River Valley; (5) corn defoliation; and (6) alfalfa crop productivity. Results of progress, and plans for future work in these areas, are discussed.

Source record↗
NASA NTRS1977

Post LANDSAT D Advanced Concept Evaluation (PLACE)

An outline is given of the mission objectives and requirements, system elements, system concepts, technology requirements and forecasting, and priority analysis for LANDSAT D. User requirements and mission analysis and technological forecasting are emphasized. Mission areas considered include agriculture, range management, forestry, geology, land use, water resources, environmental quality, and disaster assessment.

Source record↗
NASA NTRS1978

Post LANDSAT D advanced concept evaluation

Preliminary results of a past LANDSAT D advanced concept evaluation study to forecast and identify key technologies required for earth resources satellite systems in the 1985 to 2000 time period are presented. Mission categories discussed include agriculture, range management, forestry, geological resources, land use, water resources, environmental quality, and disaster assessment. Sensor and system concepts are described for a passive L-band radiometer (microsat), a texturometer to measure ground surface texture, and a ferris wheel radar configuration that relies on cable tension for support.

Flatow, F. S.↗
NASA NTRS1982

A legislator's guide to LANDSAT

The LANDSAT satellite is an effective tool in meeting the natural resources data requirements of state and federal legislation. The availability of data from the satellite is beginning to have an impact on state legislature activities. An overview of the history, operation, and data analysis techniques, is presented as well as a discussion of the advantages and limitations of this method of remote sensing. Applications are discussed in the areas of (1) land resource planning and management; (2) coastal zone management; (3) agriculture; (4) forestry; (5) routing and siting; (6) environmental monitoring; and (7) geological exploration. National and state sources from which information about LANDSAT technology is available are listed.

Source record↗
NASA NTRS2011

CEOS Land Surface Imaging Constellation Mid-Resolution Optical Guidelines

The LSI community of users is large and varied. To reach all these users as well as potential instrument contributors this document has been organized by measurement parameters of interest such as Leaf Area Index and Land Surface Temperature. These measurement parameters and the data presented in this document are drawn from multiple sources, listed at the end of the document, although the two primary ones are "The Space-Based Global Observing System in 2010 (GOS-2010)" that was compiled for the World Meteorological Organization (WMO) by Bizzarro Bizzarri, and the CEOS Missions, Instruments, and Measurements online database (CEOS MIM). For each measurement parameter the following topics will be discussed: (1) measurement description, (2) applications, (3) measurement spectral bands, and (4) example instruments and mission information. The description of each measurement parameter starts with a definition and includes a graphic displaying the relationships to four general land surface imaging user communities: vegetation, water, earth, and geo-hazards, since the LSI community of users is large and varied. The vegetation community uses LSI data to assess factors related to topics such as agriculture, forest management, crop type, chlorophyll, vegetation land cover, and leaf or canopy differences. The water community analyzes snow and lake cover, water properties such as clarity, and body of water delineation. The earth community focuses on minerals, soils, and sediments. The geo-hazards community is designed to address and aid in emergencies such as volcanic eruptions, forest fires, and large-scale damaging weather-related events.

Keyes, Jennifer P.↗
NASA NTRS2017

Synthetic Scenarios from CMIP5 Model Simulations for Climate Change Impact Assessments in Managed Ecosystems and Water Resources: Case Study in South Asian Countries

Increasing population, urbanization, and associated demand for food production compounded by climate change and variability have important implications for the managed ecosystems and water resources of a region. This is particularly true for south Asia, which supports one quarter of the global population, half of whom live below the poverty line. This region is largely dependent on monsoon precipitation for water. Given the limited resources of the developing countries in this region, the objective of our study was to empirically explore climate change in south Asia up to the year 2099 using monthly simulations from 35 global climate models (GCMs) participating in the fifth phase of the Climate Model Inter-comparison Project (CMIP5) for two future emission scenarios (representative concentration pathways RCP4.5 and RCP8.5) and provide a wide range of potential climate change outcomes. This was carried out using a three-step procedure: calculating the mean annual, monsoon, and non-monsoon precipitation and temperatures; estimating the percent change from historical conditions; and developing scenario funnels and synthetic scenarios. This methodology was applied for the entire south Asia region; however, the percent change information generated at 1.5deg grid scale can be used to generate scenarios at finer spatial scales. Our results showed a high variability in the future change in precipitation (-23% to 52%, maximum in the non-monsoon season) and temperature (0.8% to 2.1%) in the region. Temperatures in the region consistently increased, especially in the Himalayan region, which could have impacts including a faster retreat of glaciers and increased floods. It could also change rivers from perennial to seasonal, leading to significant challenges in water management. Increasing temperatures could further stress groundwater reservoirs, leading to withdrawal rates that become even more unsustainable. The high precipitation variability (with higher propensity for localized intense rainfall events) observed in the region can be a key factor for managed ecosystems and water management and could also lead to more incidence of severe urban flooding. The results could be used to assess both mitigation and adaptation alternatives to reduce vulnerabilities in managed ecosystems (agricultural and urban) and water resources.

agriculture↗