Comparative Analysis of Empirical and Machine Learning Models for Chl a Extraction Using Sentinel-2 and Landsat OLI Data: Opportunities, Limitations, and Challenges
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Amir M. Chegoonian↗
SEARCH · Engineering Papers
Results for “data- limited”
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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Discussion of the reliability of electron densities and energies interpreted from data and limits on the proton energy and density
Analysis of radio observations of Jupiter were changed to take into account the antenna resolution. A dipole magnetic field with a surface equatorial value of 7 gauss is assumed. The electron temperature is found to increase for r 2.5 Jupiter radii with decreasing r as 1/r cubed, reaching a peak of about 100 MeV at r = 2.5 Jupiter radii. For r 2.5 Jupiter radii, the electron temperature goes as r to the 6th power because of energy lost to radiation. The consequences of making an upper estimate on the proton flux by assuming the magnetic field is loaded with all the energetic protons it can hold are described. The upper limits of proton energy, density, flux, and energy flux are calculated for 1, 2, 2.5, 3, and 6 Jupiter radii. The proton energy and velocity estimates are considered to be fairly reliable; the upper limit to the number density is probably much higher than actuality.
Beard, D. B.↗
Amorphous magnetism in fcc Vicalloy 2
The results of magnetization and Mossbauer experiments are reported which give evidence for superparamagnetism in the quenched face centered cubic (gamma) phase of the ternary system Fe(0.34)C(0.52)V(0.14) (Vicalloy 2). while the magnetization data agree with the limited data of Nesbitt et al., this more complete study shows clearly that this alloy does not acquire long-range ferromagnetic order, nor does it indicate either antiferromagnetic long-range ordering or mictomagnetic behavior, down to 4.2 K. The data can best be described as resulting from ferromagnetic clusters in a weakly magnetic lattice. The Mossbauer data show a slightly broadened single line spectrum of 300 K which broadens with decreasing temperature without development of resolvable hyperfine splitting down to 4.2 K. The broadening is attributed to relaxation effects associated with magnetic cluster coalescence.
Cusick, J. P.↗
Solution convergence study during launch phase using limited tracking data
Orbit determination accuracies during critical phases at launch, when data are available from only one station, can be enhanced using angle data with conventional Doppler and range data. A study performed on the forthcoming Japanese ETS-5 mission indicates that this requires correct handling of the angle biases. This was verified using actual tracking data received from the BS-2B launch.
Premkumar, R. I.↗
Correlation of Flight Data on Limit Pressure Coefficients and Their Relation to High-Speed Burbling and Critical Tail Loads
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DIVING - PULL-OUTS↗
An experimental study of trace element partitioning between perovskite, hibonite and melt: Equilibrium values
The presence of perovskite (CATiO3) and hibonite (Ca Al12O19) within different regions of Calcium-, Aluminum-rich Inclusions (CAI) and the trace element concentrations of these minerals in each circumstance, constrain models of precursor formation, nebular condensation, the thermal history of inclusions with relict perovskite and hibonite, and the formation of the Wark-Lovering rim. At present mineral/melt partition coefficient data for hibonite are limited to a few elements in simple experimental systems, or to those derived from hibonite-glass pairs in hibonite/glass microspherules. Similarly, there is only limited data on perovskite D that are applicable to meteorite compositions. Apart from the importance of partitioning studies to meteorite research, D values also are invaluable in the development of thermodynamic models, especially when data is available for a large number of elements that have different ionic charge and radii. In addition, study of the effect of rapid cooling on partitioning is crucial to our understanding of meteorite inclusions. To expand our knowledge of mineral/melt D for perovskite and hibonite, a study was instituted where D values are obtained in both equilibrium and dynamic cooling experiments. As an initial phase of this study mineral/melt D was measured for major elements (Ca, Mg, Al, Ti, and Si), 15 rare earth elements (La-Lu) and 8 other elements (Ba, Sr, U, Th, Nb, Zr, Hf, and Ge) in perovskite and hibonite grown under equilibrium conditions, in bulk compositions that are respectively similar to Compact Type A (CTA) CAI and to a hibonite/glass microspherule. Experimental mixes were doped with REE at 20-50x chondritic (ch) abundances, Ba at 50 ppm, Sr, Hf, Nb, and Zr at 100 ppm and, U and Th at 200 ppm. Trace element abundances were measured with the PANURGE ion microprobe. Major element compositions were obtained by electron microprobe analysis.
Kennedy, A. K.↗
Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers
NASA has employed high-throughput molecular assays to identify sub-cellular changes impacting human physiology during spaceflight. Machine learning (ML) methods hold the promise to improve our ability to identify important signals within highly dimensional molecular data. However, the inherent limitation of study subject numbers within a spaceflight mission minimizes the utility of ML approaches. To overcome the sample power limitations, data from multiple spaceflight missions must be aggregated while appropriately addressing intra- and inter-study variabilities. Here we describe an approach to log transform, scale and normalize data from six heterogeneous, mouse liver derived transcriptomics datasets (ntotal=137) which enabled ML-methods to perform well (AUC ≥ 0.87) in classifying spaceflown vs ground control animals rather than mission-of-origin. Concordance was found between liver-specific biological processes identified from harmonized ML-based analysis and study-by-study classical omics analysis. This work demonstrates the feasibility of applying ML methods on integrated, heterogeneous datasets of small sample size.
Machine Learning↗
Comparative Analysis of Empirical and Machine Learning Models for Chla Extraction Using Sentinel-2 and Landsat OLI Data: Opportunities, Limitations, and Challenges
Remote retrieval of near-surface chlorophyll-a (Chla) concentration in small inland waters is challenging due to substantial optical interferences of various water constituents and uncertainties in the atmospheric correction (AC) process. Although various algorithms have been developed to estimate Chla from moderate-resolution terrestrial missions (∼10–60 m), the production of both accurate distribution maps and time series of Chla has proven challenging, limiting the use of remote analyses for lake monitoring. Here, we develop a support vector regression (SVR) model, which uses satellite-derived remote-sensing reflectance spectra () from Sentinel-2 and Landsat-8 images as input for Chla retrieval in a representative eutrophic prairie lake, Buffalo Pound Lake (BPL), Saskatchewan, Canada. Validated against in situ Chla from seven ice-free seasons (N ∼ 200; 2014–2020), the SVR model outperformed both locally tuned, -fed empirical models (Normalized Difference Chlorophyll Index, 2- and 3-band, and OC3) and Mixture Density Networks (MDNs) by 15–65%, while exhibiting comparable performance to a locally trained MDN, with an error of ∼35%. Comparison of Chla retrieval models, AC processors (iCOR, ACOLITE), and radiometric products (Rayleigh-corrected, surface, and top-of-atmosphere reflectance) showed that the best Chla maps and optimal time series (up to 100 mg m−3) were produced using a coupled SVR-iCOR system.
algal blooms↗
Spatiotemporal Limitations of Data‐Driven Modeling: An ISINGLASS Case Study
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M. Burleigh↗
Machine Learning Enhanced Beam Envelope Prediction Using Limited Diagnostic Data
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linear induction accelerator↗
A data-driven multiscale model for reactive wetting simulations
Here, we describe a data-driven, multiscale technique to model reactive wetting of a silver–aluminum alloy on a Kovar™ (Fe-Ni-Co alloy) surface. We employ molecular dynamics simulations to elucidate the dependence of surface tension and wetting angle on the drop’s composition and temperature. A design of computational experiments is used to efficiently generate training data of surface tension and wetting angle from a limited number of molecular dynamics simulations. The simulation results are used to parameterize models of the material’s wetting properties and compute the uncertainty in the models due to limited data. The data-driven models are incorporated into an engineering-scale (continuum) model of a silver–aluminum sessile drop on a Kovar™ substrate. Model predictions of the wetting angle are compared with experiments of pure silver spreading on Kovar™ to quantify the model-form errors introduced by the limited training data versus the simplifications inherent in the molecular dynamics simulations. The paper presents innovations in the determination of “convergence” of noisy MD simulations before they are used to extract the wetting angle and surface tension, and the construction of their models which approximate physio-chemical processes that are left unresolved by the engineering-scale model. Together, these constitute a multiscale approach that integrates molecular-scale information into continuum scale models.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Evaluation of hydrograph separation techniques with uncertain end-member composition
Hydrograph separation is one of many approaches used to analyse shifts in source water contributions to stream flow resulting from climate change in remote watersheds. Understanding these shifts is vital, as shifts in source water contributions to a stream can shape water management decisions. Because remote watersheds are often inaccessible and have poorly characterized contributing water sources, or end-members, it is critical to understand the implications of using different hydrograph separation techniques in these data-limited environments. To explore the uncertainty associated with different techniques, results from two hydrograph separation techniques, mass balance and principle component analysis, were compared using 3 years of aqueous geochemical data from the East River watershed located in the Elk Mountains of Central Colorado. Solute concentrations of the end-members were characterized by both a limited set of direct chemical measurements of different sources and detailed seasonal instream chemistry to examine the influences of uncertain end-member compositions in a data-limited environment. Annual volumetric end-member contributions to stream flow had relatively good agreement across separation techniques. Large variations in time were observed in the hydrograph separations, depending on the end-member type, and estimated flow contributions varied between the selected solutes. End-member concentrations characterized by stream chemistry showed several limitations including a reduced number of distinguishable end-members and differences in timing of flow contributions. Here the results highlight the benefits of using multiple hydrograph separation techniques by providing a ‘weight-of-evidence’ approach to environments with limited end-member concentration data.
54 ENVIRONMENTAL SCIENCES↗
Power System Modeling for the Study of High Penetration of Distributed Photovoltaic Energy
Many conventional power systems are evolving due to the growth of renewable energy and distributed energy resources (DERs). Modeling the interplay of transmission and distribution systems is critical to analyze how DERs impact a system’s conventional operation and which electric infrastructure improvements are needed to achieve a balance between centralized generation and DERs. This article describes the process, tools, and resources used to model electric power systems with a centralized infrastructure in an isolated context and limited access to actual utility data. Photovoltaic systems installed on residential rooftops were the main design option. This work broadened the typical power system modeling to include planning and social considerations. This integrative engineering-social method allows for interdisciplinary teams to work in the development of a model as part of broader design goals for a renewable-dominant energy system. The Puerto Rico electric power system was used as a case study to demonstrate the process. The integrative engineering-social perspective in developing the model and the actions to manage data limitations are aspects that could be followed in other locations with aggressive renewable energy goals and where utility data are not readily available.
Cuello-Polo, Gustavo↗
Remotely Sensed High‐Resolution Soil Moisture and Evapotranspiration: Bridging the Gap Between Science and Society
This paper reviews the current state of high‐resolution remotely sensed soil moisture (SM) and evapotranspiration (ET) products and modeling, and the coupling relationship between SM and ET. SM downscaling approaches for satellite passive microwave products leverage advances in artificial intelligence and high‐resolution remote sensing using visible, near‐infrared, thermal‐infrared, and synthetic aperture radar sensors. Remotely sensed ET continues to advance in spatiotemporal resolutions from MODIS to ECOSTRESS to Hydrosat and beyond. These advances enable a new understanding of bio‐geo‐physical controls and coupled feedback mechanisms between SM and ET reflecting the land cover and land use at field scale (3–30 m, daily). Still, the state‐of‐the‐science products have their challenges and limitations, which we detail across data, retrieval algorithms, and applications. We describe the roles of these data in advancing 10 application areas: drought assessment, food security, precision agriculture, soil salinization, wildfire modeling, dust monitoring, flood forecasting, urban water, energy, and ecosystem management, ecohydrology, and biodiversity conservation. We discuss that future scientific advancement should focus on developing open‐access, high‐resolution (3–30 m), sub‐daily SM and ET products, enabling the evaluation of hydrological processes at finer scales and revolutionizing the societal applications in data‐limited regions of the world, especially the Global South for socio‐economic development.
54 ENVIRONMENTAL SCIENCES↗
The Global Spectra-Trait Initiative: A database of paired leaf spectroscopy and functional traits associated with leaf photosynthetic capacity
Accurate assessment of leaf functional traits is crucial for a diverse range of applications from crop phenotyping to parameterizing global climate models. Leaf reflectance spectroscopy offers a promising avenue to advance ecological and agricultural research by complementing traditional, time-consuming gas exchange measurements. However, the development of robust hyperspectral models for predicting leaf photosynthetic capacity and associated traits from reflectance data has been hindered by limited data availability across species and environments. Here we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. The GSTI repository currently encompasses over 7500 observations from 397 species and 41 sites gathered from 36 published and unpublished studies, thereby offering a key resource for developing and validating hyperspectral models of leaf photosynthetic capacity. The GSTI database is developed on GitHub (https://github.com/plantphys/gsti, last access: 4 January 2026) and published to ESS-DIVE https://doi.org/10.15485/2530733, Lamour et al., 2025). It includes gas exchange data, derived photosynthetic parameters, and key leaf traits often associated with traditional gas exchange measurements such as leaf mass per area and leaf elemental composition. By providing a standardized repository for data sharing and analysis, we present a critical step towards creating hyperspectral models for predicting photosynthetic traits and associated leaf traits for terrestrial plants.
Lamour, Julien [Université of Toulouse (France); U↗
LDEF meteoroid and debris special investigation group investigations and activities at the Johnson Space Center
Since the return of the Long Duration Exposure Facility (LDEF) in January, 1990, members of the Meteoroid and Debris Special Investigation Group (M&D SIG) at the Johnson Space Center (JSC) in Houston, Texas have been examining LDEF hardware in an effort to expand the knowledge base regarding the low-Earth orbit (LEO) particulate environment. In addition to the various investigative activities, JSC is also the location of the general Meteoroid & Debris database. This publicly accessible database contains information obtained from the various M&D SIG investigations, as well as limited data obtained by individual LDEF Principal Investigators. LDEF exposed approximately 130 m(exp 2) of surface area to the LEO particulate environment, approximately 15.4 m(exp 2) of which was occupied by structural frame components (i.e., longerons and intercoastals) of the spacecraft. The data reported here was obtained as a result of detailed scans of LDEF intercoastals, 68 of which reside at JSC. The limited amount of data presently available on the A0178 thermal control blankets was reported last year and will not be reiterated here. The data presented here are limited to measurements of crater diameters and their frequency of occurrence (i.e., flux).
See, Thomas H.↗
Basin-Scale Structural Features Database
The Basin-Scale Structural Features database provides spatial datasets of faults, fractures, folds, and earthquakes compiled from public, authoritative sources (e.g., U.S. Geological Survey and State Geological Surveys) and aggregated into derivative forms to support subsurface assessments. Recognizing that characterizing basin-scale structural features requires interpreting data that are often ambiguous or lack key information, the source data were evaluated using a knowledge-data framework and geospatial fuzzy logic method (Justman et al., 2020) to represent both measured (observed) and predicted (inferred or potential) structural features as derivative datasets. This workflow employs conceptual models for known structural features and predicted structural features, incorporating geospatial data to estimate potential, even with limited data. The aim is to aid and support an understanding of basin-scale features and identify potential gaps in data and knowledge. As of 4/30/2025, the database includes resources for nine sedimentary basins: Appalachian, Denver, U.S. Gulf Coast, Illinois, Michigan, Permian, Sacramento, San Joquin and Williston. The database is organized by basin and then data category: 1) Faults, fractures, folds, 2) Earthquakes, 3) Topographic, 4) Structural contours and isopachs, 5) Geophysical, and 6) Structural feature density assessment maps.
basin scale↗
Remote sensing of tornadic storms from geosynchronous satellite infrared digital data
Two cases of GOES digital infrared data were analyzed during the three-hour period immediately prior to the tornado touchdown times. Clouds associated with tornadoes were compared to those without tornadoes using a combination of satellite infrared and rawinsonde data. On the basis of this limited data sample, it appears as if the altitude to which the overshooting cloud top penetrated above the tropopause is the factor which determines whether or not a tornado is formed. In these cases, the overshooting cloud top collapsed about 15 to 30 min before the tornado touchdown.
Hung, R. J.↗