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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Open Rotor Aeroacoustic Modeling

Owing to their inherent fuel efficiency, there is renewed interest in developing open rotor propulsion systems that are both efficient and quiet. The major contributor to the overall noise of an open rotor system is the propulsor noise, which is produced as a result of the interaction of the airstream with the counter-rotating blades. As such, robust aeroacoustic prediction methods are an essential ingredient in any approach to designing low-noise open rotor systems. To that end, an effort has been underway at NASA to assess current open rotor noise prediction tools and develop new capabilities. Under this effort, high-fidelity aerodynamic simulations of a benchmark open rotor blade set were carried out and used to make noise predictions via existing NASA open rotor noise prediction codes. The results have been compared with the aerodynamic and acoustic data that were acquired for this benchmark open rotor blade set. The emphasis of this paper is on providing a summary of recent results from a NASA Glenn effort to validate an in-house open noise prediction code called LINPROP which is based on a high-blade-count asymptotic approximation to the Ffowcs-Williams Hawkings Equation. The results suggest that while predicting the absolute levels may be difficult, the noise trends are reasonably well predicted by this approach.

Envia, Edmane↗
NASA NTRS2021

IFAR Liner Benchmark Challenge #1 - DLR Impedance Eduction of Uniform and Axially Segmented Liners and Comparison with NASA Results

This paper presents the contribution from the German Aerospace Center (DLR) to the first liner benchmark challenge under the framework of the International Forum for Aviation Research (IFAR).Therefore, two sets of acoustically damping wall treatment, called ’liner samples’, have been produced by additive manufacturing based on the design data provided by NASA coordinating this benchmark. These liner samples have been integrated and acoustically characterized in the liner flow test facility DUCT-R at DLR Berlin as well as in the liner flow test facility GFIT at NASA Langley. Besides the dissipation coefficients and the axial pressure profiles, the liner wall impedance was educed by first determining the axial wave numbers and then applying a straightforward method based on the one-dimensional Convected Helmholtz Equation. Finally, the comparison of the liner impedance values to the NASA results show a fairly good agreement.

liner characterization↗
NASA NTRS2008

Statistical Estimation of Orbital Debris Populations with a Spectrum of Object Size

Orbital debris is a real concern for the safe operations of satellites. In general, the hazard of debris impact is a function of the size and spatial distributions of the debris populations. To describe and characterize the debris environment as reliably as possible, the current NASA Orbital Debris Engineering Model (ORDEM2000) is being upgraded to a new version based on new and better quality data. The data-driven ORDEM model covers a wide range of object sizes from 10 microns to greater than 1 meter. This paper reviews the statistical process for the estimation of the debris populations in the new ORDEM upgrade, and discusses the representation of large-size (greater than or equal to 1 m and greater than or equal to 10 cm) populations by SSN catalog objects and the validation of the statistical approach. Also, it presents results for the populations with sizes of greater than or equal to 3.3 cm, greater than or equal to 1 cm, greater than or equal to 100 micrometers, and greater than or equal to 10 micrometers. The orbital debris populations used in the new version of ORDEM are inferred from data based upon appropriate reference (or benchmark) populations instead of the binning of the multi-dimensional orbital-element space. This paper describes all of the major steps used in the population-inference procedure for each size-range. Detailed discussions on data analysis, parameter definition, the correlation between parameters and data, and uncertainty assessment are included.

Xu, Y. -l↗
NASA NTRS2019

Determination of Aerothermal Environment and Ablator Material Response Using Inverse Methods

The Mars Science Laboratory (MSL) was protected during entry into the Martian atmosphere by a thermal protection system that used NASA’s Phenolic Impregnated Carbon Ablator (PICA). The heat shield of the probe was instrumented with the Mars Entry Descent and Landing Instrument (MEDLI) suite of sensors. MEDLI Integrated Sensor Plugs (MISP) included thermocouples that measured in-depth temperatures at various locations on the heatshield. The flight data has been used as a benchmark for validating ablation codes within NASA. This work seeks to refine the estimate of the material properties for the MSL heat shield and the aerothermal environment during Mars entry using estimation methods in DAKOTA on the temperature data obtained from MEDLI.

Thornton, John M.↗
NASA NTRS2002

Investigation of Error Patterns in Geographical Databases

The objective of the research conducted in this project is to develop a methodology to investigate the accuracy of Airport Safety Modeling Data (ASMD) using statistical, visualization, and Artificial Neural Network (ANN) techniques. Such a methodology can contribute to answering the following research questions: Over a representative sampling of ASMD databases, can statistical error analysis techniques be accurately learned and replicated by ANN modeling techniques? This representative ASMD sample should include numerous airports and a variety of terrain characterizations. Is it possible to identify and automate the recognition of patterns of error related to geographical features? Do such patterns of error relate to specific geographical features, such as elevation or terrain slope? Is it possible to combine the errors in small regions into an error prediction for a larger region? What are the data density reduction implications of this work? ASMD may be used as the source of terrain data for a synthetic visual system to be used in the cockpit of aircraft when visual reference to ground features is not possible during conditions of marginal weather or reduced visibility. In this research, United States Geologic Survey (USGS) digital elevation model (DEM) data has been selected as the benchmark. Artificial Neural Networks (ANNS) have been used and tested as alternate methods in place of the statistical methods in similar problems. They often perform better in pattern recognition, prediction and classification and categorization problems. Many studies show that when the data is complex and noisy, the accuracy of ANN models is generally higher than those of comparable traditional methods.

Dryer, David↗
NASA NTRS2013

Evaluating and Quantifying the Climate-Driven Interannual Variability in Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) at Global Scales

Satellite observations of surface reflected solar radiation contain informationabout variability in the absorption of solar radiation by vegetation. Understanding thecauses of variability is important for models that use these data to drive land surface fluxesor for benchmarking prognostic vegetation models. Here we evaluated the interannualvariability in the new 30.5-year long global satellite-derived surface reflectance index data,Global Inventory Modeling and Mapping Studies normalized difference vegetation index(GIMMS NDVI3g). Pearsons correlation and multiple linear stepwise regression analyseswere applied to quantify the NDVI interannual variability driven by climate anomalies, andto evaluate the effects of potential interference (snow, aerosols and clouds) on the NDVIsignal. We found ecologically plausible strong controls on NDVI variability by antecedent precipitation and current monthly temperature with distinct spatial patterns. Precipitation correlations were strongest for temperate to tropical water limited herbaceous systemswhere in some regions and seasons 40 of the NDVI variance could be explained byprecipitation anomalies. Temperature correlations were strongest in northern mid- to-high-latitudes in the spring and early summer where up to 70 of the NDVI variance was explained by temperature anomalies. We find that, in western and central North America,winter-spring precipitation determines early summer growth while more recent precipitation controls NDVI variability in late summer. In contrast, current or prior wetseason precipitation anomalies were correlated with all months of NDVI in sub-tropical herbaceous vegetation. Snow, aerosols and clouds as well as unexplained phenomena still account for part of the NDVI variance despite corrections. Nevertheless, this study demonstrates that GIMMS NDVI3g represents real responses of vegetation to climate variability that are useful for global models.

interference↗
NASA NTRS2005

Comparison of the Effects of using Tygon Tubing in Rocket Propulsion Ground Test Pressure Transducer Measurements

This paper documents acoustics environments data collected during liquid oxygen- ethanol hot-fire rocket testing at NASA Marshall Space Flight Center in November- December 2003. The test program was conducted during development testing of the RS-88 development engine thrust chamber assembly in support of the Orbital Space Plane Crew Escape System Propulsion Program Pad Abort Demonstrator. In addition to induced environments analysis support, coincident data collected using other sensors and methods has allowed benchmarking of specific acoustics test measurement methodologies during propulsion tests. Qualitative effects on data characteristics caused by using tygon sense lines of various lengths in pressure transducer measurements is discussed here.

Farr, Rebecca A.↗
NASA NTRS2001

Infrared Sensing Aeroheating Flight Experiment: STS-96 Flight Results

Major elements of an experiment called the Infrared Sensing Aeroheating Flight Experiment are discussed. The primary experiment goal is to provide reentry global temperature images from infrared measurements to define the characteristics of hypersonic boundary-layer transition during flight. Specifically, the experiment is to identify, monitor, and quantity hypersonic boundary layer windward surface transition of the X-33 vehicle during flight. In addition, the flight data will serve as a calibration and validation of current boundary layer transition prediction techniques, provide benchmark laminar, transitional, and fully turbulent global aeroheating data in order to validate existing wind tunnel and computational results, and to advance aeroheating technology. Shuttle Orbiter data from STS-96 used to validate the data acquisition and data reduction to global temperatures, in order to mitigate the experiment risks prior to the maiden flight of the X-33, is discussed. STS-96 reentry midwave (3-5 micron) infrared data were collected at the Ballistic Missile Defense Organization/Innovative Sciences and Technology Experimentation Facility site at NASA-Kennedy Space Center and subsequently mapped into global temperature contours using ground calibrations only. A series of image mapping techniques have been developed in order to compare each frame of infrared data with thermocouple data collected during the flight. Comparisons of the ground calibrated global temperature images with the corresponding thermocouple data are discussed. The differences are shown to be generally less than about 5%, which is comparable to the expected accuracy of both types of aeroheating measurements.

Blanchard, Robert C.↗
NASA NTRS2001

Infrared Sensing Aeroheating Flight Experiement: STS-96 Flight Results

Major elements of an experiment called the Infrared Sensing Aeroheating Flight Experiment are discussed. The primary experiment goal is to provide reentry global temperature images from infrared measurements to define the characteristics of hypersonic boundary-layer transition during flight. Specifically, the experiment is to identify, monitor, and quantify hypersonic boundary layer windward surface transition of the X-33 vehicle during flight. In addition, the flight data will serve as a calibration and validation of current boundary layer transition prediction techniques, provide benchmark laminar, transitional, and fully turbulent global aeroheating data in order to validate existing wind tunnel and computational results, and to advance aeroheating technology. Shuttle Orbiter data from STS-96 used to validate the data acquisition and data reduction to global temperatures, in order to mitigate the experiment risks prior to the maiden flight of the X-33, is discussed. STS-96 reentry mid-wave (3-5 Pm) infrared data were collected at the Ballistic Missile Defense Organization/Innovative Sciences and Technology Experimentation Facility site at NASA-Kennedy Space Center and subsequently mapped into global temperature contours using ground calibrations only. A series of image mapping techniques have been developed in order to compare each frame of infrared data with thermocouple data collected during the flight. Comparisons of the ground calibrated global temperature images with the corresponding thermocouple data are discussed. The differences are shown to be generally less than about 5%, which is comparable to the expected accuracy of both types of aeroheating measurements.

Blanchard, Robert C.↗
NASA NTRSDate not supplied

NASA POWER: Providing Analysis-Ready, Cloud-Optimized Data for AI /ML Training and Applications in Earth Science

As global demand for sustainable development grows, the integration of Earth Observation (EO) data into decision making frameworks has become a primary objective for the scientific community. The NASA Prediction of Worldwide Energy Resources (POWER) project serves as a bridge between NASA EO data and the specialized needs of the renewable energy, sustainable infrastructure and agroclimatology communities. In this poster presentation we will present an overview of POWER data products and services along with its use in diverse research to decision-making workflows. By providing over 40 years of high-resolution historical, hourly and daily solar and meteorological data, POWER transforms satellite observations and global model reanalysis into actionable, Analysis-Ready Dataset (ARD). Currently, the project delivers over 250 industry-friendly parameters to the users from different NASA datasets like CERES SYN1Deg, MERRA-2, and IMERG alongside downscaled CMIP6 climate model data, fulfilling over 16 million requests from 50,000 unique users monthly. To ensure data quality and traceability, these parameters are rigorously validated against the ground-based observations from the Baseline Surface Radiation Network (BSRN) and the Global Surface Summary of the Day (GSOD) – these results will be discussed in the presentation. A newly introduced web-based PaRameter Uncertainty ViEwer (PRUVE) tool will be presented that provides an online validation platform to the users that benchmarks satellite-based and assimilation data products against these surface measurements. To reduce technical barriers to data adoption, POWER data is accessible through RESTful APIs, ESRI ArcGIS Image Services, a web-based Data Access Viewer tool, allowing users to visualize, validate and apply the dataset. For efficient data delivery POWER data is cloud-optimized into Zarr datastore accessible through NASA managed Amazon S3 ensures high-performance allowing users to integrate EO directly into operational pipelines. These customized services will be presented. Use cases from application will be presented from the energy sector - such as for design of generation systems, performance monitoring of solar power plants, in infrastructure sector- optimizing building energy efficiency and thermal comfort, in agriculture – such as driving crop simulation and yield forecasting models to enable climate resilient farming. Furthermore, the shift toward machine learning (ML) in EO research that has positioned POWER as a key provider for training datasets which will be discussed. Use-cases will be presented to showcase how NASA data is enabling the development of predictive tools for climate variability and resource management. The poster will present POWER’s future plans including technology development to enhance data traceability and reproducibility and improving I/O performance to support the rapid integration of new EO products, ensuring that POWER remains a robust scalable backend for the evolving landscape of AI-driven Earth Science. Additionally, POWER is developing an AI Agent and an MCP-Server to enable industry AI-Agentic workflows.

Neha Khadka↗
NASA NTRS1992

Preliminary efforts toward development of data handling and analysis software for unsteady flow measurements: An application for aeroelastic transonic flow configurations

A few years ago the Structural Dynamics Division at LaRC started ambitious experimental research efforts known as the Benchmark Models Program. The primary objective of this program was to provide experimental data that may serve as a calibration source for computational fluid dynamics (CFD) efforts that deal with aeroelastic unsteady flow configurations. It also focuses on the understanding of complex flow phenomenon associated with unsteady flow developments. The overall plan for the program has been described by Bennett, including a presentation of initial test results of flutter of a rigid wing mounted on flexible supports. An example of a test model employed to measure the dynamic response along with corresponding pressure distributions is shown. This model incorporates eighty pressure transducers distributed along two spanwise stations. In addition, the models are equipped with four accelerometers and two strain gages. The data handling system for the Benchmark Model Program is under development. Several interactive computer routines designed for the user interface, dynamic memory allocation, unsteady flow measurements data extraction, and further data processing were developed. To present a few examples of measured data, the unsteady pressure distributions and the wing model dynamic response were plotted.

Finaish, Fathi↗
NASA NTRS2019

The Arctic-Boreal vulnerability experiment model benchmarking system

NASA's Arctic-Boreal Vulnerability Experiment (ABoVE) integrates field and airborne data into modeling and synthesis activities for understanding Arctic and Boreal ecosystem dynamics. The ABoVE Benchmarking System (ABS) is an operational software package to evaluate terrestrial biosphere models against key indicators of Arctic and Boreal ecosystem dynamics, i.e.: carbon biogeochemistry, vegetation, permafrost, hydrology, and disturbance. The ABS utilizes satellite remote sensing data, airborne data, and field data from ABoVE as well as collaborating research networks in the region, e.g.: the Permafrost Carbon Network, the International Soil Carbon Network, the Northern Circumpolar Soil Carbon Database, AmeriFlux sites, the Moderate Resolution Imaging Spectroradiometer, the Orbiting Carbon Observatory 2, and the Soil Moisture Active Passive mission. The ABS is designed to be interactive for researchers interested in having their models accurately represent observations of key Arctic indicators: a user submits model results to the system, the system evaluates the model results against a set of Arctic-Boreal benchmarks outlined in the ABoVE Concise Experiment Plan, and the user then receives a quantitative scoring of model strengths and deficiencies through a web interface. This interactivity allows model developers to iteratively improve their model for the Arctic-Boreal Region by evaluating results from successive model versions. We show here, for illustration, the improvement of the Lund–Potsdam–Jena-Wald Schnee und Landschaft (LPJwsl) version model through the ABoVE ABS as a new permafrost module is coupled to the existing model framework. The ABS will continue to incorporate new benchmarks that address indicators of Arctic-Boreal ecosystem dynamics as they become available.

ABoVE↗
NASA NTRS2007

ShareSync: A Solution for Deterministic Data Sharing over Ethernet

As part of upgrading the Contact Dynamics Simulation Laboratory (CDSL) at the NASA Marshall Space Flight Center (MSFC), a simple, cost effective method was needed to communicate data among the networked simulation machines and I/O controllers used to run the facility. To fill this need and similar applicable situations, a generic protocol was developed, called ShareSync. ShareSync is a lightweight, real-time, publish-subscribe Ethernet protocol for simple and deterministic data sharing across diverse machines and operating systems. ShareSync provides a simple Application Programming Interface (API) for simulation programmers to incorporate into their code. The protocol is compatible with virtually all Ethernet-capable machines, is flexible enough to support a variety of applications, is fast enough to provide soft real-time determinism, and is a low-cost resource for distributed simulation development, deployment, and maintenance. The first design cycle iteration of ShareSync has been completed, and the protocol has undergone several testing procedures including endurance and benchmarking tests and approaches the 2001ts data synchronization design goal for the CDSL.

Dunn, Daniel J., II↗
NASA NTRS2017

Prediction of Cognitive States During Flight Simulation Using Multimodal Psychophysiological Sensing

The Commercial Aviation Safety Team found the majority of recent international commercial aviation accidents attributable to loss of control inflight involved flight crew loss of airplane state awareness (ASA), and distraction was involved in all of them. Research on attention-related human performance limiting states (AHPLS) such as channelized attention, diverted attention, startle/surprise, and confirmation bias, has been recommended in a Safety Enhancement (SE) entitled "Training for Attention Management." To accomplish the detection of such cognitive and psychophysiological states, a broad suite of sensors was implemented to simultaneously measure their physiological markers during a high fidelity flight simulation human subject study. Twenty-four pilot participants were asked to wear the sensors while they performed benchmark tasks and motion-based flight scenarios designed to induce AHPLS. Pattern classification was employed to predict the occurrence of AHPLS during flight simulation also designed to induce those states. Classifier training data were collected during performance of the benchmark tasks. Multimodal classification was performed, using pre-processed electroencephalography, galvanic skin response, electrocardiogram, and respiration signals as input features. A combination of one, some or all modalities were used. Extreme gradient boosting, random forest and two support vector machine classifiers were implemented. The best accuracy for each modality-classifier combination is reported. Results using a select set of features and using the full set of available features are presented. Further, results are presented for training one classifier with the combined features and for training multiple classifiers with features from each modality separately. Using the select set of features and combined training, multistate prediction accuracy averaged 0.64 +/- 0.14 across thirteen participants and was significantly higher than that for the separate training case. These results support the goal of demonstrating simultaneous real-time classification of multiple states using multiple sensing modalities in high fidelity flight simulators. This detection is intended to support and inform training methods under development to mitigate the loss of ASA and thus reduce accidents and incidents.

Harrivel, Angela R.↗
NASA NTRS1998

Buckling and Damage Resistance of Transversely-Loaded Composite Shells

Experimental and numerical work was conducted to better understand composite shell response to transverse loadings which simulate damage-causing impact events. The quasi-static, centered, transverse loading response of laminated graphite/epoxy shells in a [+/-45(sub n)/O(sub n)](sub s) layup having geometric characteristics of a commercial fuselage are studied. The singly-curved composite shell structures are hinged along the straight circumferential edges and are either free or simply supported along the curved axial edges. Key components of the shell response are response instabilities due to limit-point and/or bifurcation buckling. Experimentally, deflection-controlled shell response is characterized via load-deflection data, deformation-shape evolutions, and the resulting damage state. Finite element models are used to study the kinematically nonlinear shell response, including bifurcation, limit-points, and postbuckling. A novel technique is developed for evaluating bifurcation from nonlinear prebuckling states utilizing asymmetric spatial discretization to introduce numerical perturbations. Advantages of the asymmetric meshing technique (AMT) over traditional techniques include efficiency, robustness, ease of application, and solution of the actual (not modified) problems. The AMT is validated by comparison to traditional numerical analysis of a benchmark problem and verified by comparison to experimental data. Applying the technique, bifurcation in a benchmark shell-buckling problem is correctly identified. Excellent agreement between the numerical and experimental results are obtained for a number of composite shells although predictive capability decreases for stiffer (thicker) specimens which is attributed to compliance of the test fixture. Restraining the axial edge (simple support) has the effect of creating a more complex response which involves unstable bifurcation, limit-point buckling, and dynamic collapse. Such shells were noted to bifurcate into asymmetric deformation modes but were undamaged during testing. Shells in this study which were damaged were not observed to bifurcate. Thus, a direct link between bifurcation and atypical damage could not be established although the mechanism (bifurcation) was identified. Recommendations for further work in these related areas are provided and include extensions of the AMT to other shell geometries and structural problems.

Wardle, Brian L.↗
NASA NTRSDate not supplied

Geopolymer Lunar Concrete under Reduced-Pressure Curing and Vacuum Exposure

Geopolymer concrete has emerged as a promising material for constructing habitats, roads, landing pads, and other necessary infrastructure on the lunar surface. Proper in-situ resource utilization (ISRU) is an important aspect of any candidate lunar construction material. Geopolymer concrete is effective at ISRU, as the lunar regolith can be used in the “as found” condition and mixed with an alkaline solution, such as sodium silicate and sodium hydroxide, which could also be mostly produced in-situ. It is envisioned that any concrete material on the lunar surface would be deposited with additive manufacturing techniques and would require a temporary environmental enclosure to mitigate the harsh lunar environment during construction and curing, such as pressure and temperature. Data are needed to set a benchmark for the conditions to be achieved within the deployable environmental enclosure during printing, as well as the resultant material properties after the enclosure is removed. This novel study investigated the influence of reduced atmospheric pressures during the initial curing period on the compressive strength of geopolymer lunar concrete created with two types of lunar regolith simulant. The simulants used in the study represent the regolith for the lunar mare (OPRL2N) and lunar highlands (OPRH2N) regions. Results show a noticeable decline in strength with a reduction in pressure and visible degradation. After the deployable environmental enclosure is removed from the printed segment of infrastructure on the lunar surface, it would then be exposed to the harsh vacuum environment. To understand the importance of curing time required within the deployable enclosure before it is removed, samples were cast and cured for different lengths of time and then exposed to a vacuum chamber capable of achieving 100 mTorr. Control samples were also cast at the same time but left in a standard laboratory environment after curing. The weight change and compressive strength as a function of time in the vacuum chamber were monitored for up to 56 days. The results showed that exposure to the vacuum limits further reaction and strength gain in comparison to control samples left in the laboratory environment. This research is in strong alignment with the 2020 NASA Technology Taxonomy materials area (12.1) and the in-situ resource utilization area (7.1), as it provides necessary data on the geopolymer lunar concrete material to support safe and sustainable options for maintaining a human presence on the Moon.

Moon to Mars Planetary Autonomous Construction Tec↗
NASA NTRSDate not supplied

Space Flown Rodent Liver RNA Sequencing Data for Machine Learning in Space Biology Research

High-throughput nucleic acid sequencing (DNA-seq, RNA-seq) has become widespread in biomedical research due to the growing availability and affordability of these assays. Data analysis has been accelerated in recent years by the adoption of artificial intelligence (AI) and machine learning (ML) techniques by biomedical researchers. In space biology research, RNAseq datasets from space-flown experimental samples are critical for characterizing the gene expression aberrations associated with exposure to spaceflight stressors. However, space biological experiments tend to be very low sample size, so identifying proper AI/ML algorithms for sequencing data analysis is an ongoing challenge since these algorithms typically require large sample size. The NASA Science Mission Directorate (SMD) has started the “Benchmark Initiative for AI/ML”, focused on creating datasets meant for three main applications: 1) scientific benchmarking, which finds the best algorithm for a specific problem; 2) application benchmarking, which measures algorithm performance against a set of parameters; and 3) system benchmarking, which evaluates performance of hardware and software architecture. These scientific benchmarks consist of an AI-ready dataset and a reference implementation on a specific scientific question. In this work, we focused on generating standardized datasets to allow the scientific community to benchmark AI/ML algorithms in the domain of space biology. We present here a standardized, AI-ready, publicly available benchmark dataset for space biology RNA-seq data as a collaboration between the NASA AI4LS (Artificial Intelligence for Life Sciences) working group. and NASA’s SMD. This dataset consists of space-flown and ground control mouse liver found in the NASA GeneLab omics database. However, to amplify the small sample number (n=112 samples) for ML purposes, we employ Gaussian noise and a generative adversarial network to extend this dataset to 6,000 synthetic samples, matching the original gene expression characteristics.

James Casaletto↗
NASA NTRS1974

The effects of the physical and chemical properties of soils on the spectral reflectance of soils

The effects of organic matter, free iron oxides, texture, moisture content, and cation exchange capacity on the spectral reflectance of soils were investigated along with techniques for differentiating soil orders by computer analysis of multispectral data. By collecting soil samples of benchmark soils from the different climatic regions within the United States and using the extended wavelength field spectroradiometer to obtain reflectance values and curves for each sample, average curves were constructed for each soil order. Results indicate that multispectral analysis may be a valuable tool for delineating and quantifying differences between soils.

Montgomery, O. L.↗