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All sources NASA NTRS DOE OSTI

DOE OSTI2019

Improving Land Surface Temperature Simulation in CoLM Over the Tibetan Plateau through Fractional Vegetation Cover Derived from a Remotely Sensed Clumping Index and Model-Simulated Leaf Area Index

Parameterizations of fractional vegetation cover (FVC) in land surface models have important effects on simulations of surface energy budget, especially in arid and semiarid regions. This study uses a FVC scheme in which FVC is derived from leaf area index and a remotely sensed clumping index. The performance of the new scheme (SMFVC) is evaluated against Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and in situ soil temperature observations, together with two other FVC schemes, a general FVC scheme (CTL) based on land cover map and a climatology-based FVC scheme (RSFVC) that uses long-term remotely sensed Normalized Difference Vegetation Index of MODIS. The three FVC schemes were implemented in the Common Land Model (CoLM) and applied in the Tibetan Plateau using the same forcing data and default parameters. Our results demonstrate that FVC schemes have significant influence on the CoLM performance: (1) the RSFVC and SMFVC schemes significantly reduce the LST biases found in CTL, particularly in grassland and during summer; (2) soil temperature evaluation by in situ observations from three networks on the Tibetan Plateau corroborates the LST results; and (3) the improvements are mainly related to representing temporal (seasonal) variability and subgrid heterogeneity of FVC, which improves surface albedo and surface energy balance. In other words, by including more vegetation characteristics, such as using a clumping index, land surface models may better simulate surface vegetation condition and further better represent the land surface energy budget over the Tibetan Plateau.

Li, Chengwei↗

DOE OSTI2026

Data for A Hybrid Biophysical-Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81–0.94) and H (R2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

AI/ML↗

NASA NTRS1988

Fiber optic sensing systems using high frequency resonant sensing heads with intensity sensors

Optical fibers have an inherent capability of transmitting high bandwidth analog and digital signals. To apply this property of fiber optics to remote sensing, special sensing heads as well as signal processing electronics have to be developed. In systems employing intensity modulating sensors, there is also a need for a referencing technique to compensate for changes in the transmission of the connecting fibers and light source intensity. Fiber optic sensing systems incorporated in sensing heads of a special configuration are discussed. Different modes of operation as well as resonant conditions are explained. Theoretical and experimental analyses are also given.

Adamovsky, Grigory↗

NASA NTRS1989

Fiber optic sensing systems using high frequency resonant sensing heads with intensity sensors

Adamovsky, Grigory↗

NASA NTRS2008

The Citizens and Remote Sensing Observational Network (CARSON) Guide: Merging NASA Remote Sensing Data with Local Environmental Awareness

"Citizen science" generally refers to observatoinal research and data collection conducted by non-professionals, commonly as volunteers. In the environmental science field, citizen scientists may be involved with local nad regional issues such as bird and wildlife populations, weather, urban sprawl, natural hazards, wetlands, lakes and rivers, estuaries, and a spectrum of public health concerns. Some citizen scientists may be primarily motivated by the intellectual challenge of scientific observations. Citizen scientists may now examine and utilize remote-sensing data related to their particular topics of interest with the easy-to-use NASA Web-based tools Giovanni and NEO, which allow exploration and investigation of a wide variety of Earth remote sensing data sets. The CARSON (Citizens and Remote Sensing Observational Network) Guide will be an online resource consisting of chapters each demonstrating how to utilize Giovanni and NEO to access and analyze specific remote-sensing data. Integrated in each chapter will be descriptions of methods that citizen scientists can employ to collect, monitor, analyze, and share data related to the chapter topic which pertain to environmental and ecological conditions in their local region. A workshop held in August 2008 initiated the development of prototype chapters on water quality, air quality, and precipitation. These will be the initial chapters in the first release of the CARSON Guide, which will be used in a pilot project at the Maryland Science Center in spring 2009. The goal of the CARSON Guide is to augment and enhance citizen scientist environmental research with NASA satellite data by creating a participatory network consisting of motivated individuals, environmental groups and organizations, and science-focused institutions such as museuma and nature centers. Members of the network could potentially interact with government programs, academic research projects, and not-for-profit organizations focused on environmental issues.

Acker, James↗

DOE OSTI2015

Remote sensing-based estimation of annual soil respiration at two contrasting forest sites

Here, soil respiration (R s ), an important component of the global carbon cycle, can be estimated using remotely sensed data, but the accuracy of this technique has not been thoroughly investigated. In this study, we proposed a methodology for the remote estimation of annual R s at two contrasting FLUXNET forest sites (a deciduous broadleaf forest and an evergreen needleleaf forest). A version of the Akaike's information criterion was used to select the best model from a range of models for annual R s estimation based on the remotely sensed data products from the Moderate Resolution Imaging Spectroradiometer and root-zone soil moisture product derived from assimilation of the NASA Advanced Microwave Scanning Radiometer soil moisture products and a two-layer Palmer water balance model. We found that the Arrhenius-type function based on nighttime land surface temperature (LST-night) was the best model by comprehensively considering the model explanatory power and model complexity at the Missouri Ozark and BC-Campbell River 1949 Douglas-fir sites.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

An Ultra-Shapeable, Smart Sensing Platform Based on a Multimodal Ferrofluid-Infused Surface

Not provided.

Chemistry↗

DOE OSTI2020

Combined Plasmonic Au-nanoparticle and Conducting Metal Oxide High-temperature Optical Sensing with LSTO

<span style="color: rgba(0, 0, 0, 0.79); font-family: "Source Sans Pro", source-sans-pro, museo_sans300, museo-sans, Arial, sans-serif; font-size: 16px; background-color: rgb(255, 255, 255);">Fiber optic sensor technology offers several advantages for harsh-environment applications. However, the development of optical gas sensing layers that are stable under harsh environmental conditions is an ongoing research challenge. In this work, electronically conducting metal oxide lanthanum-doped strontium titanate (LSTO) films embedded with gold nanoparticles are examined as a sensing layer for application in reducing gas flows at high temperature (600–800 °C). A strong localized surface plasmon resonance (LSPR) based response to hydrogen is demonstrated in the visible region of the spectrum, while a Drude free electron-based response is observed in the near-IR. Characteristics of these responses are studied both on planar glass substrates and on silica fibers. Charge transfer between the oxide film and the gold nanoparticles is explored as a possible mechanism governing the Au LSPR response and is considered in terms of the corresponding properties of the conducting metal oxide-based matrix phase. Principal component analysis is applied to the combined plasmonic and free-carrier based response over a range of temperatures and hydrogen concentrations. It is demonstrated that the combined visible and near-IR response of these films provides improved versatility for multiwavelength interrogation, as well as improved discrimination of important process parameters (concentration and temperature) through application of multivariate analysis techniques.</span>

Wuenschell, Jeffrey K.↗

DOE OSTI2018

Changes in iron availability in Arabidopsis are rapidly sensed in the leaf vasculature and impaired sensing leads to opposite transcriptional programs in leaves and roots

The OLIGOPEPTIDE TRANSPORTER 3 (OPT3) has recently been identified as a component of the systemic network mediating iron (Fe) deficiency responses in Arabidopsis. Reduced expression of OPT3 induces an over accumulation of Fe in roots and leaves, due in part by an elevated expression of the IRON-REGULATED TRANSPORTER 1. Here we show however, that opt3 leaves display a transcriptional program consistent with an Fe overload, suggesting that Fe excess is properly sensed in opt3 leaves and that the OPT3-mediated shoot-to-root signaling is critical to prevent a systemic Fe overload. We also took advantage of the tissue-specific localization of OPT3, together with other Fe-responsive genes, to determine the timing and location of early transcriptional events during Fe limitation and resupply. Our results show that the leaf vasculature responds more rapidly than roots to both Fe deprivation and resupply, suggesting that the leaf vasculature is within the first tissues that sense and respond to changes in Fe availability. Our data highlight the importance of the leaf vasculature in Fe homeostasis by sensing changes in apoplastic levels of Fe coming through the xylem and relaying this information back to roots via the phloem to regulate Fe uptake at the root level.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

Compressive Sensing with Cross-Validation and Stop-Sampling for Sparse Polynomial Chaos Expansions

Here, compressive sensing is a powerful technique for recovering sparse solutions of underdetermined linear systems, which is often encountered in uncertainty quantification analysis of expensive and high-dimensional physical models. We perform numerical investigations employing several compressive sensing solvers that target the unconstrained LASSO formulation, with a focus on linear systems that arise in the construction of polynomial chaos expansions. With core solvers l1_ls, SpaRSA, CGIST, FPC_AS, and ADMM, we develop techniques to mitigate overfitting through an automated selection of regularization constant based on cross-validation, and a heuristic strategy to guide the stop-sampling decision. Practical recommendations on parameter settings for these techniques are provided and discussed. The overall method is applied to a series of numerical examples of increasing complexity, including large eddy simulations of supersonic turbulent jet-in-crossflow involving a 24-dimensional input. Through empirical phase-transition diagrams and convergence plots, we illustrate sparse recovery performance under structures induced by polynomial chaos, accuracy, and computational trade-offs between polynomial bases of different degrees, and practicability of conducting compressive sensing for a realistic, high-dimensional physical application. Across test cases studied in this paper, we find ADMM to have demonstrated empirical advantages through consistent lower errors and faster computational times.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1985

International Symposium on Remote Sensing of Environment, Third Thematic Conference: Remote Sensing for Exploration Geology, Colorado Springs, CO, April 16-19, 1984, Proceedings. Volumes 1 & 2

A photogeologic and remote sensing model of porphyry type mineral sytems is considered along with a Landsat application to development of a tectonic model for hydrocarbon exploration of Devonian shales in west-central Virginia, remote sensing and the funnel philosophy, Landsat-based tectonic and metallogenic synthesis of the southwest United States, and an evolving paradigm for computer vision. Attention is given to the neotectonics of the Tibetan plateau deduced from Landsat MSS image interpretation, remote sensing in northern Arizona, the use of an airborne laser system for vegetation inventories and geobotanical prospecting, an evaluation of Thematic Mapper data for hydrocarbon exploration in low-relief basins, and an evaluation of the information content of high spectral resolution imagery. Other topics explored are related to a major source of new radar data for exploration research, the accuracy of geologic maps produced from Landsat data, and an approach for the geometric rectification of radar imagery.

Source record↗

NASA NTRS1997

Grapevine Remote Sensing Analysis of Phylloxera Early Stress (GRAPES): Remote Sensing Analysis Summary

High spatial resolution airborne imagery was acquired in California's Napa Valley in 1993 and 1994 as part of the Grapevine Remote sensing Analysis of Phylloxera Early Stress (GRAPES) project. Investigators from NASA, the University of California, the California State University, and Robert Mondavi Winery examined the application of airborne digital imaging technology to vineyard management, with emphasis on detecting the phylloxera infestation in California vineyards. Because the root louse causes vine stress that leads to grapevine death in three to five years, the infested areas must be replanted with resistant rootstock. Early detection of infestation and changing cultural practices can compensate for vine damage. Vineyard managers need improved information to decide where and when to replant fields or sections of fields to minimize crop financial losses. Annual relative changes in leaf area due to phylloxera infestation were determined by using information obtained from computing Normalized Difference Vegetation Index (NDVI) images. Two other methods of monitoring vineyards through imagery were also investigated: optical sensing of the Red Edge Inflection Point (REIP), and thermal sensing. These did not convey the stress patterns as well as the NDVI imagery and require specialized sensor configurations. NDVI-derived products are recommended for monitoring phylloxera infestations.

Lobitz, Brad↗

NASA NTRS2019

Evaluation of Wing Load Calibration and Sensing Methods Using Conventional Strain Gages and a Fiber Optic Sensing System Installed on a Straight Tapered Wing

State-of-the-art instrumentation techniques have provided an opportunity to obtain greater insight into structural wing loads during flight. An investigation was undertaken to research new wing instrumentation and load sensing techniques for measuring accurate in-flight spanwise load distributions on wing structures. A straight tapered wing was instrumented with both conventional foil strain gages at five spanwise wing stations and fiber optic strain sensors at every half inch along the entire wing span. Thirty-nine unique load cases were applied to the wing lower surface using hydraulic actuators to obtain various shear, bending moment, and torque load distributions on the wing. This paper will highlight three load calibration approaches. Conventional linear regression calibration methods were applied to foil strain gages providing a single wing station vertical shear, bending moment, and torque load. Linear regression methods were applied to a fiber optic sensing system to provide bending moment and torque spanwise load distributions. A load sensing scheme using strain derived wing shape information derived from a single load case provided vertical shear and bending moment spanwise load distribution information. Aspects of the three different approaches will be compared and contrasted to inform the reader of the benefits or disadvantages of each. Instrument installation, sensor characteristics, test execution aspects, and recommended calibration techniques will be discussed.

structural mechanics↗

DOE OSTI2017

Functionalized silica aerogels for gas-phase purification, sensing, and catalysis: A review

Silica aerogels have a rich history and a unique, fascinating gas-phase chemistry that has lent them to many diverse applications. This review starts with a brief discussion of the fundamental issues driving the movement of gases in silica aerogels and then proceeds to provide an overview of the work that has been done with respect to the purification of gases, sensing of individual gases, and uses of silica aerogels as catalysts for gas-phase reactions. Salient features of the research behind these different applications are presented, and, where appropriate, critical aspects that affect the practical use of the aerogels are noted. Specific sections under the gas-purification category focus on the removal of airborne nanoparticles, carbon dioxide, volatile organic compounds, sulfur gases and radioactive iodine from gas streams. The use of silica aerogels as sensors for humidity, oxygen, hydrocarbons, volatile acids and bases, various non-ammoniacal nitrogen gases, and viral particles is discussed. With respect to catalysis, the demonstrated use of silica aerogels as supports for oxidation, Fischer-Tropsch, alkane isomerization, and hydrogenation reactions is reviewed, along with a section on untested catalytic formulations involving silica aerogels. A short section focuses on recent developments in thermomolecular Knudsen compressor pumps using silica aerogel membranes. The review continues with an overview of the production methods, locations of manufacturing facilities globally, and a brief discussion of the economics before concluding with a few remarks about the present and future trends revealed by the work presented.

gas catalysis↗

DOE OSTI2018

Distinguishing one from many using super-resolution compressive sensing

We present that distinguishing whether a signal corresponds to a single source or a limited number of highly overlapping point spread functions (PSFs) is a ubiquitous problem across all imaging scales, whether detecting receptor-ligand interactions in cells or detecting binary stars. Super-resolution imaging based upon compressed sensing exploits the relative sparseness of the point sources to successfully resolve sources which may be separated by much less than the Rayleigh criterion. However, as a solution to an underdetermined system of linear equations, compressive sensing requires the imposition of constraints which may not always be valid. One typical constraint is that the PSF is known. However, the PSF of the actual optical system may reflect aberrations not present in the theoretical ideal optical system. Even when the optics are well characterized, the actual PSF may reflect factors such as non-uniform emission of the point source (e.g. fluorophore dipole emission). As such, the actual PSF may differ from the PSF used as a constraint. Similarly, multiple different regularization constraints have been suggested including the l 1 -norm, l 0 -norm, and generalized Gaussian Markov random fields (GGMRFs), each of which imposes a different constraint. Other important factors include the signal-to-noise ratio of the point sources and whether the point sources vary in intensity. In this work, we explore how these factors influence super-resolution image recovery robustness, determining the sensitivity and specificity. In conclusion, we determine an approach that is more robust to the types of PSF errors present in actual optical systems.

42 ENGINEERING↗

DOE OSTI2021

Quantum Sensing of Insulator‐to‐Metal Transitions in a Mott Insulator

Nitrogen vacancy (NV) centers, optically active atomic defects in diamond, have attracted tremendous interest for quantum sensing, network, and computing applications due to their excellent quantum coherence and remarkable versatility in a real, ambient environment. Taking advantage of these strengths, this paper reports on NV-based local sensing of the electrically driven insulator-to-metal transition (IMT) in a proximal Mott insulator. The resistive switching properties of both pristine and ion-irradiated VO 2 thin film devices are studied by performing optically detected NV electron spin resonance measurements. These measurements probe the local temperature and magnetic field in electrically biased VO 2 devices, which are in agreement with the global transport measurement results. In pristine devices, the electrically driven IMT proceeds through Joule heating up to the transition temperature while in ion-irradiated devices, the transition occurs nonthermally, well below the transition temperature. Here, the results provide direct evidence for nonthermal electrically induced IMT in a Mott insulator, highlighting the significant opportunities offered by NV quantum sensors in exploring nanoscale thermal and electrical behaviors in Mott materials.

McLaughlin, Nathan J.↗

DOE OSTI2023

Quantum Sensing of Electric Fields Using Spin-Correlated Radical Ion Pairs

Quantum sensing affords the possibility of using quantum entanglement to probe electromagnetic fields with exquisite sensitivity. In this work, we show that a photogenerated spin-correlated radical ion pair (SCRP) can be used to sense an electric field change created at one radical ion of the pair using molecular recognition. The SCRP is generated within a covalent donor–chromophore–acceptor system PXX–PMI–NDI, 1, where PXX = peri-xanthenoxanthene, PMI = 1,6-bis(p-t-butylphenoxy)perylene-3,4-dicarboximide, and NDI = naphthalene-1,8:4,5-bis(dicarboximide). The electron-rich PXX donor in 1 acts as a guest molecule that can be encapsulated selectively by a tetracationic cyclophane ExBox4+ host to give a supramolecular complex 1 ⊂ ExBox 4+ . Selective photoexcitation of the PMI chromophore results in ultrafast generation of the PXX •+ –PMI–NDI •– SCRP. When PXX is encapsulated by ExBox 4+ , the cyclophane generates an electric field that repels the positive charge on PXX •+ within PXX •+ –PMI–NDI •– , reducing the SCRP distance, i.e., the distance between the centers-of-charge on the donor and acceptor. Pulse-EPR measurements are used to measure the coherent oscillations created primarily by the electron–electron dipolar coupling in the SCRP, which yields the distance between the two charges (spins) of PXX •+ –PMI–NDI •– . Here, the experimental results show that the distance between PXX •+ and NDI •– decreases when ExBox 4+ encapsulates PXX •+ , which demonstrates that the SCRP can function as a quantum sensor to detect electric field changes in the vicinity of the radical ions.

Anions↗

DOE OSTI2021

Validation of the Community Land Model Version 5 over the Contiguous United States (CONUS) using in situ and remote sensing data sets

The Community Land Model (CLM) is an effective tool to simulate the biophysical and biogeochemical processes and their interactions with the atmosphere. Although CLM Version 5 (CLM5) constitutes various updates in these processes, its performance in simulating energy, water and carbon cycles over the Contiguous United States (CONUS) at scales which land surface changes and hydrometeorological and hydroclimatological applications are more locally relevant is yet to be assessed. In this study, we conducted three simulations at 0.125? during 1979-2018 over the CONUS using different configurations of CLM, namely CLM5-biogeochemistry (CLM5BGC), CLM4.5BGC, and CLM5-satellite phenology (CLM5SP). We validated and compared their simulations against multiple remote-sensed and in-situ datasets. Overall, the parametric and structural updates (e.g., carbon cost for nitrogen uptake, variable soil thickness, dry surface layer) in CLM5 improve its ability in capturing terrestrial biogeochemical dynamics. The low evapotranspiration in CLM5BGC is associated with biases in simulating vegetation phenological characteristics rather than soil water limitations. The mismatch between CLM5BGC-simulated peak leaf area index and reference data can be attributed to CLM5BGC's inability in simulating phenology of trees and grasses. The differences between CLM-simulated irrigation and reference estimates can be attributed to differences between processes represented in models and in reality, and uncertainties in input and validation datasets. Evaluation against observations at small catchments suggest that hydrologic parameters needed to be calibrated to improve simulations of runoff, especially subsurface runoff. Additional efforts are needed to incorporate spatially-distributed plant phenology and physiology parameters and regional-specific agricultural management practices (e.g., planting, harvest).

Cheng, Yanyan↗