Improved Open Data Sharing of NASA Airborne and Field Investigation Data
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Abstract not provided.
The Jemez Pueblo, a community located in the semi-arid high desert region of northcentral New Mexico, faces challenges with drought mitigation and livestock management. NASA DEVELOP partnered with the Pueblo of Jemez Natural Resources Department and The Nature Conservancy to provide recent and historical rangeland conditions to aid present decision-making, which relies on cultural practices, site familiarity, available resources, and physical data. This partnership offers the opportunity to support management practices by leveraging remote sensing and virtual fencing technologies. Our project explored Rangeland Analysis Platform (RAP) products incorporating NASA Earth observation Landsat 5 TM, Landsat 7 ETM+, Landsat 8 OLI, and Landsat 9 OLI-2 remote-sensed imagery as an agricultural land management tool. We performed linear regression analysis to evaluate RAP data to field data and explored RAP annual herbaceous biomass, ground cover trends, and climatic factors at the pasture level. We then computed study area summary statistics, performed pixel-by-pixel trend analysis across the study period, and evaluated ground cover trends near water sources. Based on available data, RAP was poorly correlated to bare ground and herbaceous biomass, generally underestimating the former and overestimating the latter. To improve the evaluation of RAP products, we suggest increasing the spatiotemporal distribution of the field dataset. According to RAP, from 1986 to 2023, the site became hotter and drier, bare ground increased, and annual forbs and grasses increased while perennials decreased. RAP products did not agree with field-collected data; however, the trends over time may be useful for informing rangeland management actions.
ISEE 3 spacecraft measurements within and near the Earth's magnetic tail is presented. Plots are shown of ISEE 3 magnetic field data. The plots in this volume showing observations from the ISEE 3 vector helium magnetometer experiment were produced from a tape of merged plasma/magnetic field data. The magnetometer produced six vector measurements/sec that were subsequently averaged over one minute intervals before merging with plasma data. Merging was accomplished by associating the nearest 1 minute field average with each plasma sample.
Earth system models must predict forest responses to global change in order to simulate future global climate, hydrology, and ecosystem dynamics. These models are increasingly adopting vegetation demographic approaches that explicitly represent tree growth, mortality, and recruitment, enabling advances in the projection of forest vulnerability and resilience, as well as evaluation with field data. To date, simulation of regeneration processes has received far less attention than simulation of processes that affect growth and mortality, in spite of their critical role maintaining forest structure, facilitating turnover in forest composition over space and time, enabling recovery from disturbance, and regulating climate-driven range shifts. Here, our critical review of regeneration process representations within current Earth system vegetation demographic models reveals the need to improve parameter values and algorithms for reproductive allocation, dispersal, seed survival and germination, environmental filtering in the seedling layer, and tree regeneration strategies adapted to wind, fire, and anthropogenic disturbance regimes. These improvements require synthesis of existing data, specific field data-collection protocols, and novel model algorithms compatible with global-scale simulations. Vegetation demographic models offer the opportunity to more fully integrate ecological understanding into Earth system prediction; regeneration processes need to be a critical part of the effort.
We present an extensible, object-oriented data model designed for field data entitled Field Model (FM). FM objects can represent a wide variety of fields, including fields of arbitrary dimension and node type. FM can also handle time-series data. FM achieves generality through carefully selected topological primitives and through an implementation that leverages the potential of templated C++. FM supports fields where the nodes values are paired with any cell type. Thus FM can represent data where the field nodes are paired with the vertices ("vertex-centered" data), fields where the nodes are paired with the D-dimensional cells in R(sup D) (often called "cell-centered" data), as well as fields where nodes are paired with edges or other cell types. FM is designed to effectively handle very large data sets; in particular FM employs a demand-driven evaluation strategy that works especially well with large field data. Finally, the interfaces developed for FM have the potential to effectively abstract field data based on adaptive meshes. We present initial results with a triangular adaptive grid in R(sup 2) and discuss how the same design abstractions would work equally well with other adaptive-grid variations, including meshes in R(sup 3).
Results obtained by Forbes et al. (1981) on the basis of time delay measurements between ISEE 1 and ISEE 2 imply that the plasma flow and the boundary contracting velocity were nearly the same, whereas the expanding boundary velocity was not accompanied by any significant plasma sheet plasma motion. In the present study, this observation is discussed in conjunction with electric field data. The study is based on electric field data from the spherical double probe experiment on ISEE 1. Electric field data from GEOS 2 are used to some extent to monitor the electric fields near the geostationary orbit during the considered eve nts. Electric field data during CDAW 6 events are discussed, taking into account positions of ISEE 1/ISEE 2 and GEOS 2; March 22, 0600-1300 UT; and March 22, UT; and March 31, 1400-2400 UT.
Sudden commencement events are examined in terms of available auroral-zone and low-latitude magnetic field, data, interplanetary plasma and magnetic field data, and magnetospheric electron flux and magnetic field data from the geostationary satellite ATS 1.
This submittal includes the field-test data collected during stress tests conducted in the Utah FORGE 16B(78)-32 wellbore to measure/characterize the stresses in the geothermal reservoir. The type of stress test performed is referred to as a mini-frac test or a micro-frac test. The test is a hydraulic fracture test that involves injecting a small volume of water into a short interval of the reservoir that is isolated by a straddle packer to create a fracture. The test provides information about the minimum and maximum horizontal stress at the test depths. A total of seven mini-frac tests were performed within the upper (vertical or nearly vertical) section of the 9-5/8-inch diameter uncased wellbore, between depths of 5,202 ft MD [5,202 ft TVD] and 5,980 ft MD [5966.18 ft TVD]. Two types of data were generated as part of the mini-frac tests. The first type of data is time-series data for each mini-frac test (e.g., injection-rate, test-interval pressure), and are contained in the "Mini-Frac Test Data.zip" file below. The second type is geophysical log data, attached below in the "Geophysical Logs.zip" file. Here, two types of image logs and acoustic logs were obtained before and after the mini-frac tests to examine each test depth for visual evidence of induced fracture(s) and to measure the azimuth (orientation) of any observed fractures. The attached Readme file includes the test depth information and contents of each data file included here. Analysis of the field-test data was conducted and is documented in the attached report below.
The availability of satellite remote sensing observations at multiple spatial and temporal scales, coupled with advances in climate modeling and information technologies offer new opportunities for the application of mechanistic models to predict how continental scale bird migration patterns may change in response to environmental change. In earlier studies, we explored the phenotypic plasticity of a migratory population of Pectoral sandpipers by simulating the movement patterns of an ensemble of 10,000 individual birds in response to changes in stopover locations as an indicator of the impacts of wetland loss and inter-annual variability on the fitness of migratory shorebirds. We used an individual based, biophysical migration model, driven by remotely sensed land surface data, climate data, and biological field data. Mean stop-over durations and stop-over frequency with latitude predicted from our model for nominal cases were consistent with results reported in the literature and available field data. In this study, we take advantage of new computing capabilities enabled by recent GP-GPU computing paradigms and commodity hardware (general purchase computing on graphics processing units). Several aspects of our individual based (agent modeling) approach lend themselves well to GP-GPU computing. We have been able to allocate compute-intensive tasks to the graphics processing units, and now simulate ensembles of 400,000 birds at varying spatial resolutions along the central North American flyway. We are incorporating additional, species specific, mechanistic processes to better reflect the processes underlying bird phenotypic plasticity responses to different climate change scenarios in the central U.S.
Seismic monitoring of geological CO 2 storage (GCS) involves highly nonlinear seismic inversion and petrophysical inversion, making it challenging to estimate CO 2 volume efficiently and detect possible early CO 2 leakages. Deep learning (DL) using convolutional neural networks (CNNs) has shown promise in solving highly nonlinear seismic inversion problems. However, direct estimation of CO 2 plume extent/saturation from time-lapse seismic gathers using DL is still underexplored, with no reported field applications to date. The investigation of field data is primarily hindered by scarcity of field data for neural network training. Other obstacles include highly nonlinear seismic-petrophysics inverse relationship, and presence of noise in field seismic data. We introduce SeisCO2Net, a deep CNN that predicts CO 2 saturation maps directly from time-lapse full waveform shot gathers. For training, we use site-specific geological information, fluid flow physics, rock physics, and seismic modeling to generate synthetic datasets that closely resemble the CO 2 storage site. Synthetic tests show promising results, inspiring us to apply SeisCO2Net's trained weights on field data collected at Frio-II GCS site by leveraging transfer learning principles. As reference, we compare SeisCO2Net's predicted CO 2 saturation maps with results obtained from physics-based inversion. Our analyses show both methods display similar CO 2 plume shapes, reasonable CO 2 plume characteristics, and comparable saturation values. Our results suggest pre-training CNNs on physics-informed synthetic datasets and then applying the learned weights to field data is a viable approach to estimating field CO 2 saturation. This method effectively addresses the scarcity of field training data, thus encouraging the feasibility of long-term GCS monitoring.
Changes of mean annual net accumulation at the surface on the grounded ice sheets of East Antarctica, West Antarctica, and Greenland in response to variations in sea ice extent are estimated using grid-point values 100 km apart. The data bases are assembled principally by bilinear interpolation of remotely sensed brightness temperature (Nimbus-5 ESMR, Nimbus-7 SMMR), surface temperature (Nimbus-7 THIR), and surface elevation (ERS-1 radar altimeter). These data, complemented by field data where remotely sensed data are not available, are used in multivariate analyses in which mean annual accumulation (derived from firn emissivity) is the dependent variable; the independent variables are latitude, surface elevation, mean annual surface temperature, and mean annual distance to open ocean (as a source of energy and moisture). The last is the shortest distance measured between a grid point and the mean annual position of the 10% sea ice concentration boundary, and is used as an index of changes in sea ice extent as well as of mean concentration. Stepwise correlation analyses indicate that variations in sea ice extent of +/-50 km would lead to changes in accumulation inversely of +/-4% on East Antarctica, +/- 10% on West Antarctica, and +4% on Greenland. These results are compared with those obtained in a previous study using visually interpolated values from contoured compilations of field data; they substantiate the findings for the Antarctic ice sheets (+/-4% on East Antarctica, +/-9% in West Antarctica), and suggest a reduction by one half of the probable change of accumulation on Greenland (from +/-8%). The results also suggest a reduction of the combined contribution to sea level variability to +/- 0.19 mm/a (from +/- 0.22 mm/a).
ESnet’s Watershed Field Science Data Backhaul Campaign is intended to improve the ability to gather data from sensors deployed across the East River and Trail Creek watersheds, with a special focus on the Snodgrass distributed sensor area, the Pumphouse instruments with co-located U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility Surface Atmosphere Integrated Field Laboratory (SAIL) tethered balloon setup, and the former ARM SAIL campaign’s second ARM Mobile Facility (AMF2) site by the Watershed Function Science Focus Area (WFSFA) program. This program is not funded by ARM, but collaborates with ARM as part of the SAIL campaign, collocated with the WFSFA field sites in Colorado’s East River Watershed.
Technique of extrapolating measured near-field overpressure data to larger altitude applied to wind tunnel overpressure data
Data from a wind tunnel investigation of the flow fields around helicopter rotors were presented. A two component laser velocimeter was used to measure the velocity fields of two 2.1 m diameter rotors. A minicomputer-based online data system is described which monitored, reduced, and plotted the results. Tip vortices constitute the primary disturbances in the flow field, but present theories do not predict vortex positions and velocity distributions with sufficient accuracy.
The Johns Hopkins University Applied Physics Laboratory (JHU/APL) Magnetic Field Experiment Data Analysis System (MFEDAS) has been developed to process and analyze satellite magnetic field experiment data from the TRIAD, MAGSAT, AMPTE/CCE, Viking, Polar BEAR, DMSP, HILAT, UARS, and Freja satellites. The MFEDAS provides extensive data management and analysis capabilities. The system is based on standard data structures and a standard user interface. The MFEDAS has two major elements: (1) a set of satellite unique telemetry processing programs for uniform and rapid conversion of the raw data to a standard format and (2) the program Magplot which has file handling, data analysis, and data display sections. This system is an example of software reuse, allowing new data sets and software extensions to be added in a cost effective and timely manner. Future additions to the system will include the addition of standard format file import routines, modification of the display routines to use a commercial graphics package based on X-Window protocols, and a generic utility for telemetry data access and conversion.
Data from the first year of ESA's Swarm constellation mission are used to derive the Swarm Initial Field Model (SIFM), a new model of the Earth's magnetic field and its time variation. In addition to the conventional magnetic field observations provided by each of the three Swarm satellites, explicit advantage is taken of the constellation aspect by including east-west magnetic intensity gradient information from the lower satellite pair. Along-track differences in magnetic intensity provide further information concerning the north-south gradient. The SIFM static field shows excellent agreement (up to at least degree 60) with recent field models derived from CHAMP data, providing an initial validation of the quality of the Swarm magnetic measurements. Use of gradient data improves the determination of both the static field and its secular variation, with the mean misfit for east-west intensity differences between the lower satellite pair being only 0.12 nT.
This dataset represents geolocation data associated with field observations and sampling from the Colorado Headwaters Ecological Spectroscopy Study (CHESS) during June and July of 2025. Location data were collected using Trimble DA2 Global Navigation Satellite System (GNSS) receivers with Trimble Catalyst 2 centimeter (cm) positioning service and the Environmental Systems Research Institute (Esri) Field Maps mobile app. Files in this data package include meadow site polygons, shrub site polygons, tree site polygons and stem point locations, and Leaf Area Index (LAI) plot polygons (.geojson). The geojson files can be opened with open-source GIS software (e.g, QGIS). A csv file is also provided with point coordinates for all locations. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgment: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.