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At least 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Spaceborne meteorological radar studies

Various radar designs and methods are studied for the estimation of rainfall parameters from space. An immediate goal is to support the development of the spaceborne radar that has been proposed for the Tropical Rain Measuring Mission (TRMM). The effort is divided into two activities: a cooperative airborne rain measuring experiment with the Radio Research Laboratory of Japan (RRL), and the modelling of spaceborne weather radars. An airborne rain measuring experiment was conducted at Wallops Flight Facility in 1985 to 1986 using the dual-wavelength radar/radiometer developed by RRL. The data are presently being used to test a number of methods that are relevant to spaceborne weather radars. An example is shown of path-averaged rain rates as estimated from three methods: the standard reflectivity rain rate method (Z-R), a dual-wavelength method, and a surface reference method. The results from the experiment shows for the first time the feasibility of using attenuation methods from space. The purposes of the modelling are twofold: to understand in a quantitative manner the relationships between a particular radar design and its capability for estimating precipitation parameters and to help devise and test new methods. The models are being used to study the impact of various TRMM radar designs on the accuracy of rain rate estimation as well as to test the performance of range-profiling algorithms, the mirror-image method, and some recently devised graphical methods for the estimation of the drop size distribution.

Meneghini, R.↗
NASA NTRS1992

Aerosol optical depth retrievals over the Konza Prairie

The aerosol optical depth over the Konza Prairie, near Manhattan, Kansas, was recorded at various locations by five separate teams. These measurements were made in support of the First ISLSCP Field Experiment (FIFE) and used to correct imagery from a variety of satellite and aircraft sensors for the effects of atmospheric scattering and absorption. The results from one instrument are reported here for 26 days in 1987 and for 7 in 1989. Daily averages span a range of 0.05 to 0.28 in the midvisible wavelengths. In addition, diurnal variations are noted in which the afternoon optical depths are greater than those of the morning by as much as 0.07. A comparison between instruments and processing techniques used to determine these aerosol optical depths is provided. The first comparisons are made using summer 1987 data. Differences of as much as 0.05 (midvisible) are observed. Although these data allow reasonable surface reflectance retrievals, they do not agree to within the performance limits typically associated with these types of instruments. With an accuracy goal of 0.02 a preseason calibration/comparison experiment was conducted at a mountain site prior to the final field campaign in 1989. Good calibration data were obtained, and good agreement (0.01, midvisible) was observed in the retrieved optical depth acquired over the Konza. By comparing data from the surface instruments at different locations, spatial inhomogeneities are determined. Then, data from the airborne tracking sunphotometer allow one to determine variations as a function of altitude. Finally, a technique is proposed for using the in situ data to establish an instrument calibration.

Bruegge, Carol J.↗
NASA NTRS1993

Performance assessment of low pressure nuclear thermal propulsion

A low pressure nuclear thermal propulsion (LPNTP) system, which takes advantage of hydrogen dissociation/recombination, was proposed as a means of increasing engine specific impulse (Isp). The effect of hydrogen dissociation/recombination on LPNTP Isp is examined. A two-dimensional computer model was used to show that the optimum chamber pressure is approximately 100 psia (at a chamber temperature of 3,000 K), with an Isp approximately 15 s higher than at 1,000 psia. At high chamber temperatures and low chamber pressures, the increase in Isp is due to both lower average molecular weights caused by dissociation and added kinetic energy from monatomic hydrogen recombination. Monatomic hydrogen recombination increases the Isp more then hydrogen dissociation. Variations in the mole fraction of monatomic hydrogen are similar to variations in static pressure along the axial nozzle position. Most recombination occurs close to the nozzle throat. Practical variations in nozzle geometry have minimal impact on recombination. Other models which can simulate a wider range of nozzle designs should be used in the future. The uncertainty of the hydrogen kinetic reaction rates at high temperatures (approximately 3,000 K) affects the accuracy of the analysis and should be verified with simple bench tests.

Gerrish, H. P., Jr.↗
NASA NTRS1995

Metafitting: Weight optimization for least-squares fitting of PTTI data

For precise time intercomparisons between a master frequency standard and a slave time scale, we have found it useful to quantitatively compare different fitting strategies by examining the standard uncertainty in time or average frequency. It is particularly useful when designing procedures which use intermittent intercomparisons, with some parameterized fit used to interpolate or extrapolate from the calibrating intercomparisons. We use the term 'metafitting' for the choices that are made before a fitting procedure is operationally adopted. We present methods for calculating the standard uncertainty for general, weighted least-squares fits and a method for optimizing these weights for a general noise model suitable for many PTTI applications. We present the results of the metafitting of procedures for the use of a regular schedule of (hypothetical) high-accuracy frequency calibration of a maser time scale. We have identified a cumulative series of improvements that give a significant reduction of the expected standard uncertainty, compared to the simplest procedure of resetting the maser synthesizer after each calibration. The metafitting improvements presented include the optimum choice of weights for the calibration runs, optimized over a period of a week or 10 days.

Douglas, Rob J.↗
NASA NTRS2015

Acceleration and Velocity Sensing from Measured Strain

A simple approach for computing acceleration and velocity of a structure from the strain is proposed in this study. First, deflection and slope of the structure are computed from the strain using a two-step theory. Frequencies of the structure are computed from the time histories of strain using a parameter estimation technique together with an autoregressive moving average model. From deflection, slope, and frequencies of the structure, acceleration and velocity of the structure can be obtained using the proposed approach. Simple harmonic motion is assumed for the acceleration computations, and the central difference equation with a linear autoregressive model is used for the computations of velocity. A cantilevered rectangular wing model is used to validate the simple approach. Quality of the computed deflection, acceleration, and velocity values are independent of the number of fibers. The central difference equation with a linear autoregressive model proposed in this study follows the target response with reasonable accuracy. Therefore, the handicap of the backward difference equation, phase shift, is successfully overcome.

shape sensing↗
NASA NTRS2021

Impact of environmental attributes on the uncertainty in MAIAC/MODIS AOD retrievals: A comparative analysis

This work examines the impact of different environmental attributes on the uncertainty in satellite-based Aerosol Optical Depth (AOD) retrieval against the benchmark Aerosol Robotic Network (AERONET) AOD measurements at 21 sites across North Africa, California and Germany, in the years 2007–2017. As a first step, we studied the effects of spatial averaging the Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD retrievals, and of temporal averaging the AERONET AOD around the satellite (Aqua) overpass, on the agreement between the two products. AERONET AOD averaging over a time-window of ±15 min around the satellite overpass and the 1 ×1 km2 spatial grid of MAIAC were found to provide the best AOD retrieval performance. Next, MAIAC AOD were stratified according to different co-measured environmental attributes (aerosol loading, dominant particle size, vegetation cover, and prevailing particle type) and analyzed against the AERONET AOD. The envelope of the expected retrieval error varied considerably among different environmental attributes categories, with more accurate AOD retrievals obtained over highly vegetated areas (i.e. less surface reflectance) than over arid areas. Moreover, the retrieval accuracy was found to be sensitive to the aerosol loading and particle size, with a large bias between the MAIAC and AERONET AOD during high aerosol loading of coarse particles. In addition, the retrieval accuracy of MAIAC AOD was found to depend on the aerosol type due to the aerosol model assumptions regarding their optical properties.

AOD RETRIEVAL↗
DOE OSTI2024

Validating automated resonance evaluation with synthetic data

The integrity and precision of nuclear data are crucial for a broad spectrum of applications, from national security and nuclear reactor design to medical diagnostics, where the associated uncertainties can significantly impact outcomes. A substantial portion of uncertainty in nuclear data originates from the subjective biases in the evaluation process, a crucial phase in the nuclear data production pipeline. Recent advancements indicate that automation of certain routines can mitigate these biases, thereby standardizing the evaluation process and enhancing reproducibility. This research aims to provide a methodology, framework, and metrics for the validation of automated nuclear data evaluation software leveraging high-quality synthetic data that closely mimic real experimental observables. An introduced error metric provides a scale and intuitive measure of the evaluation quality by quantifying the estimate’s accuracy and performance across the specified energy range. Synthetic data provides access to experimental observables and underlying resonance parameters, enabling comparison of different evaluations. The methodology is demonstrated using Ta-181 isotope data in the resolved resonance region. The Automated Resonance Identification Subroutine (ARIS), which operates without prior resonance information, was used to test and showcase the framework’s capabilities utilizing the proposed error metrics. The results demonstrate the effectiveness of the proposed approach and framework for optimizing software parameters and testing hypotheses through “what-if” controlled experiments, such as modifying assumptions about experimental conditions or average resonance parameters.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Flow pattern and void fraction characterization in nitrogen/water flows through a diamond-type triply periodic minimal surface lattice

Here, this study presents, to the authors knowledge, the first experimental investigation on the void fraction and flow patterns in two-phase flows through a diamond-type Triply Periodic Minimal Surface (TPMS) lattice. An additively manufactured TPMS structure was tested under upward co-current flow of a water/nitrogen mixture. Superficial velocities varied for a total of 42 test conditions (gas: 0.01–2.4 m/s; liquid: 0.01–2.4 m/s; mass flux: 20–2370 kg/m 2 ·s). High-speed video and X-ray imaging enabled time-averaged void fraction measurements and identified six distinct flow regimes which were used to develop a flow pattern map. Comparison of the void fraction data with correlations from literature demonstrated the Rouhani and Axelsson (1970) [49] correlation modified by Steiner (1993) [52] provided the best agreement, which was improved with empirically fit coefficients. This approach predicted the void fraction with errors < ±20% for 64% of the data, with a mean absolute percent deviation (MAPD) of 23%. The measured frictional pressure drop was compared to correlations from literature which captured the observed trends but did not provide good accuracy. The best agreement was found after optimizing the empirical coefficients of the Muller-Steinhagen & Heck (1986) [56] correlation; this approach captured 33% of the data within ±20% with a MAPD of 46% over the full range and captured 71% of the data within ±20% a MAPD of 13.3% at mass fluxes >1100 kg/m 2 -s. These results provide foundational insight into TPMS two-phase flow behavior and inform modeling and design of advanced heat exchange components incorporating TPMS geometries.

42 - ENGINEERING↗
DOE OSTI2024

Energetics of Cu Adsorption on and Adhesion to Rutile-TiO 2 (100) Studied by Cu Vapor Adsorption Calorimetry

The heat of adsorption of Cu vapor versus coverage was measured on rutile-TiO 2 (100) at 300 and 100 K using single-crystal adsorption calorimetry. At these conditions, Cu nanoparticles nucleate and then grow larger. The average Cu particle size versus Cu coverage was measured using He + low-energy ion scattering spectroscopy. These data were analyzed with the recently introduced spherical cap model (SCM) to provide the heat of Cu adsorption and Cu chemical potential versus Cu particle size, as well as the contact angle (67°) and adhesion energy (2.50 J/m 2 ) at the Cu particle/rutile-TiO 2 (100) interface. The hemispherical cap model (HCM) was used to model the data first, and then those HCM results were converted to the more accurate SCM results using a recently published mathematical approximation whose high accuracy was validated here. Furthermore, the adhesion energy of Cu on rutile-TiO 2 (100) measured here is compared to prior values for Ag and Au on rutile-TiO 2 (100), showing a nearly proportional increase in adhesion energy with metal oxophilicity (per unit area).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Comparative analysis of atmospheric parameters from high-resolution spectroscopic sky surveys: APOGEE, GALAH, Gaia -ESO

SDSS-IV APOGEE-2, GALAH, and Gaia-ESO are high-resolution, ground-based, multi-object spectroscopic surveys providing fundamental stellar atmospheric parameters and multiple elemental abundance ratios for hundreds of thousands of stars of the Milky Way. Data from these and other surveys contribute to investigations of the history and evolution of the Galaxy. We undertake a comparison between the most recent data releases of these surveys to investigate the accuracy and precision of derived parameters by placing the abundances on an absolute scale. We also discuss the correlations in parameter and abundance differences as a function of main parameters. Uncovering the variants provides a basis to continue the efforts of future sky surveys. Quality samples from the APOGEE-GALAH (15 537 stars), APOGEE-GES (804 stars), and GALAH-GES (441 stars) overlapping catalogs were collected. We investigated the mean variants between the surveys, and linear trends were also investigated. We compared the slope of correlations and mean differences with the reported uncertainties. The average and scatter of v rad , T eff , log g, [M/H], and v micro , along with numerous species of elemental abundances in the combined catalogs, show that in general there is a good agreement between the surveys. We find large radial velocity scatters ranging from 1.3 km s -1 to 4.4 km s -1 when comparing the three surveys. We observe some weak trends (e.g., in ΔT eff vs. Δlog g for the APOGEE-GES stars) and a clear correlation in the v micro - Δv micro planes in the APOGEE-GALAH common sample. For [α/H], [Ti/H] (APOGEE-GALAH giants), and [Al/H] (APOGEE-GALAH dwarfs) potential strong correlations are discovered as a function of the differences in the main atmospheric parameters, and we also find weak trends for other elements. In general we find good agreement between the three surveys within their respective uncertainties. However, there are certain regimes in which strong variants exist, which we discuss. There are still offsets larger than 0.1 dex in the absolute abundance scales.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Predicting Biomass Yields of Advanced Switchgrass Cultivars for Bioenergy and Ecosystem Services Using Machine Learning

The production of advanced perennial bioenergy crops within marginal areas of the agricultural landscape is gaining interest due to its potential to sustainably produce feedstocks for biofuels and bioproducts while also improving the sustainability and resilience of commodity crop production. However, predicting the biomass yields of this production system is challenging because marginal areas are often relatively small and spread around agricultural fields and are typically associated with various abiotic conditions that limit crop production. Machine learning (ML) offers a viable solution as a biomass yield prediction tool because it is suited to predicting relationships with complex functional associations. The objectives of this study were to (1) evaluate the accuracy of commonly applied ML algorithms in agricultural applications for predicting the biomass yields of advanced switchgrass cultivars for bioenergy and ecosystem services and (2) determine the most important biomass yield predictors. Datasets on biomass yield, weather, land marginality, soil properties, and agronomic management were generated from three field study sites in two U.S. Midwest states (Illinois and Iowa) over three growing seasons. The ML algorithms evaluated in the study included random forests (RFs), gradient boosting machines (GBMs), artificial neural networks (ANNs), K-neighbors regressor (KNR), AdaBoost regressor (ABR), and partial least squares regression (PLSR). Coefficient of determination (R 2 ) and mean absolute error (MAE) were used to evaluate the predictive accuracy of the tested algorithms. Results showed that the ensemble methods, RF (R 2 = 0.86, MAE = 0.62 Mg/ha), GBM (R 2 = 0.88, MAE = 0.57 Mg/ha), and GBM (R 2 = 0.78, MAE = 0.66 Mg/ha), were the most accurate in predicting biomass yields of the Independence, Liberty, and Shawnee switchgrass cultivars, respectively. This is in agreement with similar studies that apply ML to multi-feature problems where traditional statistical methods are less applicable and datasets used were considered to be relatively small for ANNs. Consistent with previous studies on switchgrass, the most important predictors of biomass yield included average annual temperature, average growing season temperature, sum of the growing season precipitation, field slope, and elevation. This study helps pave the way for applying ML as a management tool for alternative bioenergy landscapes where understanding agronomic and environmental performance of a multifunctional cropping system seasonally and interannually at the sub-field scale is critical.

09 BIOMASS FUELS↗
NASA NTRS1946

Cylinder-temperature correlation of a single-cylinder liquid-cooled engine

An analysis based on nonboiling forced-convection heat-transfer theory is made of the cooling processes in liquid-cooled engine cylinders. Semiempirical equations that relate the average head and barrel temperatures with the primary engine and coolant parameters are derived. A correlation method based on these equations is applied to data obtained from previously reported investigations, which were conducted over large ranges of engine and coolant conditions with two liquid-cooled cylinders using water and various aqueous ethylene glycol solutions as coolants. Upon evaluation of empirical factors, an equation for the cylinder-head temperature as a function of the engine operating conditions and the flow rate, temperature, and physical properties of the coolants is obtained, which represents the data with good accuracy.

Pinkel, Benjamin↗
NASA NTRS2002

Inter-Comparison of CHARM Data and WSR-88D Storm Integrated Rainfall

A localized precipitation network has been established over a 4000 sq km region of northern Alabama in support of local weather and climate research at the Global Hydrology and Climate Center (GHCC) in Huntsville. This Cooperative Huntsville-Area Rainfall Measurement (CHARM) network is comprised of over 80 volunteers who manually take daily rainfall measurements from 85 sites. The network also incorporates 20 automated gauges that report data at 1-5 minute intervals on a 24 h a day basis. The average spacing of the gauges in the network is about 6 kin, however coverage in some regions benefit from gauges every 1-2 km. The 24 h rainfall totals from the CHARM network have been used to validate Stage III rainfall estimates of daily and storm totals derived from the WSR-88D radars that cover northern Alabama. The Stage III rainfall product is produced by the Lower Mississippi River Forecast Center (LMRFC) in support of their daily forecast operations. The intercomparisons between the local rain gauge and the radar estimates have been useful to understand the accuracy and utility of the Stage III data. Recently, the Stage III and CHARM rainfall measurements have been combined to produce an hourly rainfall dataset at each CHARM observation site. The procedure matches each CHARM site with a time sequence of Stage III radar estimates of precipitation. Hourly stage III rainfall estimates were used to partition the rain gauge values to the time interval over which they occurred. The new hourly rain gauge dataset is validated at selected points where 1-5 minute rainfall measurements have been made. This procedure greatly enhances the utility of the CHARM data for local weather and hydrologic modeling studies. The conference paper will present highlights of the Stage III intercomparison and some examples of the combined radar / rain gauge product demonstrating its accuracy and utility in deriving an hourly rainfall product from the 24 h CHARM totals.

Jedlovec, Gary J.↗
NASA NTRS2012

Assessment of Global Cloud Datasets from Satellites: Project and Database Initiated by the GEWEX Radiation Panel

Clouds cover about 70% of the Earth's surface and play a dominant role in the energy and water cycle of our planet. Only satellite observations provide a continuous survey of the state of the atmosphere over the whole globe and across the wide range of spatial and temporal scales that comprise weather and climate variability. Satellite cloud data records now exceed more than 25 years in length. However, climatologies compiled from different satellite datasets can exhibit systematic biases. Questions therefore arise as to the accuracy and limitations of the various sensors. The Global Energy and Water cycle Experiment (GEWEX) Cloud Assessment, initiated in 2005 by the GEWEX Radiation Panel, provided the first coordinated intercomparison of publically available, standard global cloud products (gridded, monthly statistics) retrieved from measurements of multi-spectral imagers (some with multiangle view and polarization capabilities), IR sounders and lidar. Cloud properties under study include cloud amount, cloud height (in terms of pressure, temperature or altitude), cloud radiative properties (optical depth or emissivity), cloud thermodynamic phase and bulk microphysical properties (effective particle size and water path). Differences in average cloud properties, especially in the amount of high-level clouds, are mostly explained by the inherent instrument measurement capability for detecting and/or identifying optically thin cirrus, especially when overlying low-level clouds. The study of long-term variations with these datasets requires consideration of many factors. A monthly, gridded database, in common format, facilitates further assessments, climate studies and the evaluation of climate models.

Stubenrauch, C. J.↗
DOE OSTI2020

VADER: A Tool for Criticality Safety Validation

The purpose of criticality safety is to prevent any inadvertent criticality from occurring during the handling or storage of fissile material. Calculations are frequently used to demonstrate that a sufficient subcritical margin exists. Validation is a key aspect of the evaluation process, establishing the suitability, accuracy, and associated uncertainty of the computational method and data to be used for the intended application. The validation process is performed by comparing the results of critical experiments with the calculated results from models of the experiments using the computational method to be validated. Laboratory critical experiments are controlled systems that achieve a k eff of approximately 1 in order to investigate the parameters at which such a critical condition is achieved. The validation parameters that are traditionally applied to safety analysis calculations are the bias and the bias uncertainty . The bias is the deviation of the average k eff of the validation suite from unity. The bias uncertainty accounts for the statistical uncertainty in the bias based on the standard deviation, sample size, and distribution of k eff values of the validation suite. The values of bias and bias uncertainty ensure that the systems predicted to be subcritical by the computational method will indeed be subcritical. The bias and bias uncertainty are often combined to determine an upper subcritical limit (USL) or computational margin that can then be applied to safety analysis calculations. Many methods have been developed by different organizations to calculate the bias and bias uncertainty for various types of criticality analyses. Each of these methods typically requires that the validity of various underpinning statistical assumptions be confirmed to demonstrate that the method is appropriate for the analysis of a given validation suite. An example of the validation decision making flow is shown in Fig.1. As shown in Fig. 1, the analyst performing the validation fits a trend line to the data and performs a test to determine if the trend was a statistically better representation of the data than if it were treated as an uncorrelated sample. If the trend line is a better representation of the data, then the analyst uses any one of a number of trending techniques to determine the bias and bias uncertainty. If a trend is not an appropriate representation of the data, then the analyst proceeds to perform a normality assessment for the data. If the normal assumption can be shown to be acceptable, then the analyst calculates the bias and bias uncertainty with the parametric technique. If the assumption of normality cannot be justified, then the nonparametric technique is used. Once the decision flow has been followed and the appropriate technique has been selected, the bias and bias uncertainty is typically combined with an administrative margin to determine a USL below which calculated values of k eff for safety analysis models can be considered subcritical. The calculations used in each decision are often performed with spreadsheets or with small programs available at various sites performing criticality analyses. Expertise in understanding and interpreting the results must be maintained to perform these calculations. This can often be an error-prone process. Oak Ridge National Laboratory (ORNL) is currently developing the Validation and Data Evaluation Resource (VADER) to simplify and automate the criticality safety validation process and to provide a software quality assurance pedigree to the calculational methods used. This paper discusses the use of the Fulcrum user interface with VADER, the anticipated initial capabilities of VADER to perform validation analyses, and the output from the code.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1980

A simplified method for the calculation of diurnally averaged photodissociation rates

Expressions are given for the calculation of the diurnally averaged transmission of solar flux from a series summation involving relatively few terms and readily available mathematical functions. It is shown that these expressions can be scaled to remove much of the dependence on latitude and solar declination so that at large optical depth, they become asymptotic to a function which depends only on the optical depth at noon and displays an inverse-square-root variation. From this variation, a simple analytic form containing one adjustable parameter is proposed. The functional dependence of this parameter on latitude and solar declination is determined by numerical fitting procedures. The accuracy of the analytic approximation is better than 4.5% for all altitudes, latitudes and seasons of the year.

Boughner, R. E.↗
NASA NTRS1985

Blended SST fields combined SMMR/ship data (summary of technique)

All remote sensors have bias problems which vary in space and time. The infrared measurements are very sparse in cloudy areas: the SMMR has coverage limitations due to the interference of land areas and only half time operation. If one simply averaged the data from these various sources, the combination of the biases and varying sampling would introduce artifacts into the analyzed product which would reflect the sampling and which could be quite misleading. The remote sensors provide reasonable estimates of gradients of SST over length scales greater than the sensor resolution but smaller than hemispheric. Ships can be considered unbiased but are poorly distributed on small scales except in the densest shipping lanes. An analysis scheme based on a suggestion by Holl was developed which exploits just this approximately complementarity to provide an improved SST analysis. The resulting accuracy appears to be in the required approx. 0.5 C range for the particular case of using SMMR and ship data to produce two degree latitude by two degrees longitude monthly analyses.

Wilheit, T. T.↗
NASA NTRS2020

Evaluation of GEOS-Simulated L-Band Microwave Brightness Temperature using Aquarius Observations over Non-Frozen Land across North America

L-band brightness temperature (T(sub b)) is one of the key remotely-sensed variables that provides information regarding surface soil moisture conditions. In order to harness the information in T(sub b) observations, a radiative transfer model (RTM) is investigated for eventual inclusion into a data assimilation framework. In this study, T(sub b) estimates from the RTM implemented in the NASA Goddard Earth Observing System (GEOS) were evaluated against the nearly four-year record of daily T(sub b) observations collected by L-band radiometers onboard the Aquarius satellite. Statistics between the modeled and observed T(sub b) were computed over North America as a function of soil hydraulic properties and vegetation types. Overall, statistics showed good agreement between the modeled and observed T(sub b) with a relatively low, domain-average bias (0.79 K (ascending) and -2.79 K (descending)), root mean squared error (11.0 K (ascending) and 11.7 K (descending)), and unbiased root mean squared error (8.14 K (ascending) and 8.28 K (descending)). In terms of soil hydraulic parameters, large porosity and large wilting point both lead to high uncertainty in modeled T(sub b) due to the large variability in dielectric constant and surface roughness used by the RTM. The performance of the RTM as a function of vegetation type suggests better agreement in regions with broadleaf deciduous and needleleaf forests while grassland regions exhibited the worst accuracy amongst the five different vegetation types.

L-band radiometry↗