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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

Seasonal soybean crop reflectance

Data are presented from field measurements of 1980 including 5 acquisitions of handheld radiometer reflectance measurements, 7 complete sets of parameters for implementing the Suits mode, and other biophysical parameters to characterize the soybean canopy. LANDSAT calculations on the simulated Brazilian soybean reflectance are included along with data collected during the summer and fall on 1981 on soybean single leaf optical parameters for three irrigation treatments. Tests of the Suits vegetative canopy reflectance model for the full hemisphere of observer directions as well as the nadir direction show moderate agreement for the visible channels of the MSS and poor agreement in the near infrared channel. Temporal changes in the spectral characteristics of the single leaves were seen to occur as a function of maturity which demonstrates that the absorptance of a soybean single leaf is more a function of thetransmittancee characteristics than the seasonally consistent single leaf reflectance.

Lemaster, E. W.↗
DOE OSTI2020

A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5

Abstract. Land models are essential tools for understanding and predicting terrestrial processes and climate–carbon feedbacks in the Earth system, but uncertainties in their future projections are poorly understood. Improvements in physical process realism and the representation of human influence arguably make models more comparable to reality but also increase the degrees of freedom in model configuration, leading to increased parametric uncertainty in projections. In this work we design and implement a machine learning approach to globally calibrate a subset of the parameters of the Community Land Model, version 5 (CLM5) to observations of carbon and water fluxes. We focus on parameters controlling biophysical features such as surface energy balance, hydrology, and carbon uptake. We first use parameter sensitivity simulations and a combination of objective metrics including ranked global mean sensitivity to multiple output variables and non-overlapping spatial pattern responses between parameters to narrow the parameter space and determine a subset of important CLM5 biophysical parameters for further analysis. Using a perturbed parameter ensemble, we then train a series of artificial feed-forward neural networks to emulate CLM5 output given parameter values as input. We use annual mean globally aggregated spatial variability in carbon and water fluxes as our emulation and calibration targets. Validation and out-of-sample tests are used to assess the predictive skill of the networks, and we utilize permutation feature importance and partial dependence methods to better interpret the results. The trained networks are then used to estimate global optimal parameter values with greater computational efficiency than achieved by hand tuning efforts and increased spatial scale relative to previous studies optimizing at a single site. By developing this methodology, our framework can help quantify the contribution of parameter uncertainty to overall uncertainty in land model projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Data and scripts associated with the manuscript evaluating the hydrologic responses of the Pacific Northwest watersheds to wildfires (v2)

This data package is associated with the publication “Evaluating Post-fire Watershed Response to Varying Burn Severity and Precipitation Regimes Using Fully-distributed and Integrated Hydrologic Models” submitted to Journal of Hydrology (Li et al. 2025). In this study, we employed the Advanced Terrestrial Simulator (ATS), an integrated watershed model that couples surface flow, subsurface flow, and canopy biophysical processes, to investigate post-fire hydrologic responses in a few selected watersheds with varying burn severity.The data package contains the required input data (meteorological forcing, Leaf Area Index, wildfire burn severities, etc.) to run the model, configuration files, the Jupyter notebooks in Python to pre-process and post-process data, the figures in the manuscript, and the modeling output files. The variables include watershed-averaged evapotranspiration, watershed-averaged surface/subsurface/canopy water content, and river discharge at watershed outlet.The data package contains a file-level metadata that lists and describes all the files contained in the data package (ATS_flmd.csv), a data dictionary file that defines columns headers across all csv files contained in the data package (ATS_dd.csv), a data package level readme file (the current file), and four zipped folders.The ‘data’ folder provides data needed to run the model in .h5, .i2s, .xyz, .shp, and .exo formats. The sub-folders are for each data types. The ‘model’ folder provides input files (.xml format) and essential model outputs. Each sub-folder provides the files from each simulated watershed. The ‘notebooks’ folder provides the Jupyter notebooks (.ipynb format) for pre- and post- processing model files, and for producing the figures in the manuscript. The ‘figures’ folder provides the figures associated with manuscript in .pdf and .png formats.The ‘model’ folder and the ‘data’ folder have been split into 5GB-large pieces using the Linux command ‘split -b 5120m model.zip model.zip.’ and ‘split -b 5120m data.zip data.zip.’, respectively. They can be merged back using the Linux command ‘cat model.zip.* > model.zip’ and ‘cat data.zip.* > data.zip’, respectively.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Data-driven prediction of α IIb β 3 integrin activation paths using manifold learning and deep generative modeling

The integrin heterodimer is a transmembrane protein critical for driving cellular process and is a therapeutic target in the treatment of multiple diseases linked to its malfunction. Activation of integrin involves conformational transitions between bent and extended states. Some of the conformations that are intermediate between bent and extended states of the heterodimer have been experimentally characterized, but the full activation pathways remain unresolved both experimentally due to their transient nature and computationally due to the challenges in simulating rare barrier crossing events in these large molecular systems. An understanding of the activation pathways can provide new fundamental understanding of the biophysical processes associated with the dynamic interconversions between bent and extended states and unveil new putative therapeutic targets. In this work, we apply nonlinear manifold learning to coarse-grained molecular dynamics simulations of bent, extended, and two intermediate states of αI I b β3 integrin to learn a low-dimensional embedding of the configurational phase space. We then train deep generative models to learn an inverse mapping between the low-dimensional embedding and high-dimensional molecular space and use these models to interpolate the molecular configurations constituting the activation pathways between the experimentally characterized states. Furthermore, this work furnishes plausible predictions of integrin activation pathways and reports a generic and transferable multi-scale technique to predict transition pathways for biomolecular systems.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2013

Galactic Cosmic Ray Event-Based Risk Model (GERM) Code

This software describes the transport and energy deposition of the passage of galactic cosmic rays in astronaut tissues during space travel, or heavy ion beams in patients in cancer therapy. Space radiation risk is a probability distribution, and time-dependent biological events must be accounted for physical description of space radiation transport in tissues and cells. A stochastic model can calculate the probability density directly without unverified assumptions about shape of probability density function. The prior art of transport codes calculates the average flux and dose of particles behind spacecraft and tissue shielding. Because of the signaling times for activation and relaxation in the cell and tissue, transport code must describe temporal and microspatial density of functions to correlate DNA and oxidative damage with non-targeted effects of signals, bystander, etc. These are absolutely ignored or impossible in the prior art. The GERM code provides scientists data interpretation of experiments; modeling of beam line, shielding of target samples, and sample holders; and estimation of basic physical and biological outputs of their experiments. For mono-energetic ion beams, basic physical and biological properties are calculated for a selected ion type, such as kinetic energy, mass, charge number, absorbed dose, or fluence. Evaluated quantities are linear energy transfer (LET), range (R), absorption and fragmentation cross-sections, and the probability of nuclear interactions after 1 or 5 cm of water equivalent material. In addition, a set of biophysical properties is evaluated, such as the Poisson distribution for a specified cellular area, cell survival curves, and DNA damage yields per cell. Also, the GERM code calculates the radiation transport of the beam line for either a fixed number of user-specified depths or at multiple positions along the Bragg curve of the particle in a selected material. The GERM code makes the numerical estimates of basic physical and biophysical quantities of high-energy protons and heavy ions that have been studied at the NASA Space Radiation Laboratory (NSRL) for the purpose of simulating space radiation biological effects. In the first option, properties of monoenergetic beams are treated. In the second option, the transport of beams in different materials is treated. Similar biophysical properties as in the first option are evaluated for the primary ion and its secondary particles. Additional properties related to the nuclear fragmentation of the beam are evaluated. The GERM code is a computationally efficient Monte-Carlo heavy-ion-beam model. It includes accurate models of LET, range, residual energy, and straggling, and the quantum multiple scattering fragmentation (QMSGRG) nuclear database.

Cucinotta, Francis A.↗
NASA NTRS2010

Overview of the Graphical User Interface for the GERMcode (GCR Event-Based Risk Model)

The descriptions of biophysical events from heavy ions are of interest in radiobiology, cancer therapy, and space exploration. The biophysical description of the passage of heavy ions in tissue and shielding materials is best described by a stochastic approach that includes both ion track structure and nuclear interactions. A new computer model called the GCR Event-based Risk Model (GERM) code was developed for the description of biophysical events from heavy ion beams at the NASA Space Radiation Laboratory (NSRL). The GERMcode calculates basic physical and biophysical quantities of high-energy protons and heavy ions that have been studied at NSRL for the purpose of simulating space radiobiological effects. For mono-energetic beams, the code evaluates the linear-energy transfer (LET), range (R), and absorption in tissue equivalent material for a given Charge (Z), Mass Number (A) and kinetic energy (E) of an ion. In addition, a set of biophysical properties are evaluated such as the Poisson distribution of ion or delta-ray hits for a specified cellular area, cell survival curves, and mutation and tumor probabilities. The GERMcode also calculates the radiation transport of the beam line for either a fixed number of user-specified depths or at multiple positions along the Bragg curve of the particle. The contributions from primary ion and nuclear secondaries are evaluated. The GERMcode accounts for the major nuclear interaction processes of importance for describing heavy ion beams, including nuclear fragmentation, elastic scattering, and knockout-cascade processes by using the quantum multiple scattering fragmentation (QMSFRG) model. The QMSFRG model has been shown to be in excellent agreement with available experimental data for nuclear fragmentation cross sections, and has been used by the GERMcode for application to thick target experiments. The GERMcode provides scientists participating in NSRL experiments with the data needed for the interpretation of their experiments, including the ability to model the beam line, the shielding of samples and sample holders, and the estimates of basic physical and biological outputs of the designed experiments. We present an overview of the GERMcode GUI, as well as providing training applications.

Kim, Myung-Hee Y.↗
NASA NTRS2010

Overview of the Graphical User Interface for the GERM Code (GCR Event-Based Risk Model

The descriptions of biophysical events from heavy ions are of interest in radiobiology, cancer therapy, and space exploration. The biophysical description of the passage of heavy ions in tissue and shielding materials is best described by a stochastic approach that includes both ion track structure and nuclear interactions. A new computer model called the GCR Event-based Risk Model (GERM) code was developed for the description of biophysical events from heavy ion beams at the NASA Space Radiation Laboratory (NSRL). The GERM code calculates basic physical and biophysical quantities of high-energy protons and heavy ions that have been studied at NSRL for the purpose of simulating space radiobiological effects. For mono-energetic beams, the code evaluates the linear-energy transfer (LET), range (R), and absorption in tissue equivalent material for a given Charge (Z), Mass Number (A) and kinetic energy (E) of an ion. In addition, a set of biophysical properties are evaluated such as the Poisson distribution of ion or delta-ray hits for a specified cellular area, cell survival curves, and mutation and tumor probabilities. The GERM code also calculates the radiation transport of the beam line for either a fixed number of user-specified depths or at multiple positions along the Bragg curve of the particle. The contributions from primary ion and nuclear secondaries are evaluated. The GERM code accounts for the major nuclear interaction processes of importance for describing heavy ion beams, including nuclear fragmentation, elastic scattering, and knockout-cascade processes by using the quantum multiple scattering fragmentation (QMSFRG) model. The QMSFRG model has been shown to be in excellent agreement with available experimental data for nuclear fragmentation cross sections, and has been used by the GERM code for application to thick target experiments. The GERM code provides scientists participating in NSRL experiments with the data needed for the interpretation of their experiments, including the ability to model the beam line, the shielding of samples and sample holders, and the estimates of basic physical and biological outputs of the designed experiments. We present an overview of the GERM code GUI, as well as providing training applications.

Kim, Myung-Hee↗
DOE OSTI2026

Contrasting Parametric Sensitivities in Two Global Vegetation Models Using Parameter Perturbation Ensembles

Uncertainty in land model projections remains high and the roles of parametric and structural uncertainty are difficult to disentangle. To compare parametric sensitivity across model structures we present two parameter perturbation ensembles using the Community Land Model (CLM) operating in satellite phenology mode. The ensembles contrast two vegetation modules: (a) the default CLM vegetation module and (b) the Functionally Assembled Terrestrial Ecosystem Simulator (CLM-FATES). We perturbed over 300 parameters and quantified their effects on biophysical fluxes globally and across biomes. Most parameters have minimal impact on biophysical fluxes, with only a few substantially influencing results. While both models exhibit similar parameter sensitivity for some fluxes, CLM-FATES shows larger spread in gross primary productivity (GPP), driven by strong sensitivity to carboxylation rate. CLM-FATES also shows a weaker GPP response to soil hydrology parameters and exhibits higher water use efficiency (WUE). Cross-model comparisons reveal similar sensitivities for some parameters (e.g., leaf dimension) but divergent responses to others (e.g., stomatal intercept), highlighting underlying structural differences. Differences in WUE and sensitivity to hydrology and stomatal conductance parameters underscore how model structure fundamentally alters parametric sensitivity. The data sets generated from these ensembles can be used to identify influential parameters and guide future calibration efforts.

Foster, A. C. [NSF National Center for Atmospheric↗
DOE OSTI2023

Editorial: Neuroscience, computing, performance, and benchmarks: Why it matters to neuroscience how fast we can compute

At the turn of the millennium the computational neuroscience community realized that neuroscience was in a software crisis: software development was no longer progressing as expected and reproducibility declined. The International Neuroinformatics Coordinating Facility (INCF) was inaugurated in 2007 as an initiative to improve this situation. The INCF has since pursued its mission to help the development of standards and best practices. In a community paper published this very same year, Brette et al. tried to assess the state of the field and to establish a scientific approach to simulation technology, addressing foundational topics, such as which simulation schemes are best suited for the types of models we see in neuroscience. In 2015, a Frontiers Research Topic “Python in neuroscience” by Muller et al. triggered and documented a revolution in the neuroscience community, namely in the usage of the scripting language Python as a common language for interfacing with simulation codes and connecting between applications. The review by Einevoll et al. documented that simulation tools have since further matured and become reliable research instruments used by many scientific groups for their respective questions. Open source and community standard simulators today allow research groups to focus on their scientific questions and leave the details of the computational work to the community of simulator developers. A parallel development has occurred, which has been barely visible in neuroscientific circles beyond the community of simulator developers: Supercomputers used for large and complex scientific calculations have increased their performance from ~10 TeraFLOPS (10 13 floating point operations per second) in the early 2000s to above 1 ExaFLOPS (10 18 floating point operations per second) in the year 2022. This represents a 100,000-fold increase in our computational capabilities, or almost 17 doublings of computational capability in 22 years. Moore's law (the observation that it is economically viable to double the number of transistors in an integrated circuit every other 18–24 months) explains a part of this; our ability and willingness to build and operate physically larger computers, explains another part. It should be clear, however, that such a technological advancement requires software adaptations and under the hood, simulators had to reinvent themselves and change substantially to embrace this technological opportunity. It actually is quite remarkable that—apart from the change in semantics for the parallelization—this has mostly happened without the users knowing. The current Research Topic was motivated by the wish to assemble an update on the state of neuroscientific software (mostly simulators) in 2022, to assess whether we can see more clearly which scientific questions can (or cannot) be asked due to our increased capability of simulation, and also to anticipate whether and for how long we can expect this increase of computational capabilities to continue.

biophysically detailed models↗
DOE OSTI2021

Editorial: Advances in computational molecular biophysics

This special issue of BBA is dedicated to Dr. Jeremy C. Smith on the occasion of his 60th birthday and his more than three decades as a practitioner of computational molecular biophysics. The special issue showcases cutting-edge work in computational molecular biophysics. The senior authors of the manuscripts in this issue are Dr. Smith’s previous or present collaborators. Their articles reflect Dr. Smith’s distinguished career integrating experimental and molecular simulation approaches into a unified description of the function, structure, and dynamics of biomolecules. Each of the articles presented here illustrates one aspect of the broad nature of computational molecular biophysics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Multidisciplinary studies with mutated HIV-1 capsid proteins reveal structural mechanisms of lattice stabilization

Abstract HIV-1 capsid (CA) stability is important for viral replication. E45A and P38A mutations enhance and reduce core stability, thus impairing infectivity. Second-site mutations R132T and T216I rescue infectivity. Capsid lattice stability was studied by solving seven crystal structures (in native background), including P38A, P38A/T216I, E45A, E45A/R132T CA, using molecular dynamics simulations of lattices, cryo-electron microscopy of assemblies, time-resolved imaging of uncoating, biophysical and biochemical characterization of assembly and stability. We report pronounced and subtle, short- and long-range rearrangements: (1) A38 destabilized hexamers by loosening interactions between flanking CA protomers in P38A but not P38A/T216I structures. (2) Two E45A structures showed unexpected stabilizing CA NTD -CA NTD inter-hexamer interactions, variable R18-ring pore sizes, and flipped N-terminal β-hairpin. (3) Altered conformations of E45A a α9-helices compared to WT, E45A/R132T, WT PF74 , WT Nup153 , and WT CPSF6 decreased PF74, CPSF6, and Nup153 binding, and was reversed in E45A/R132T. (4) An environmentally sensitive electrostatic repulsion between E45 and D51 affected lattice stability, flexibility, ion and water permeabilities, electrostatics, and recognition of host factors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Enhancement of reactive oxygen species production by ultra-short electron pulses

The development of laser-driven accelerators-on-chip has provided an opportunity to miniaturize devices for electron radiotherapy delivery. Laser-driven accelerators produce highly time-compressed electron pulses, on the 100 fs to 1 ps scale. This delivers electrons at high peak power yet low average beam current compared with conventional delivery devices, which generate pulses of approximately 3 µs. The biophysical effects of this time structure, however, are unclear. Here, we use a Monte Carlo simulation approach to explore the effects of the electron beam time structure on the production of reactive oxygen species (ROS) in water. Our results show a power law increase in the generation of hydroxyl ions per deposited electron with decreasing pulse length over the pulse length range of 10 µs to 100 fs. Similar trends were observed for hydrogen peroxide, superoxide, hydroperoxyl, hydronium and solvated electrons. In practical terms, this indicates a fourfold increase in the efficiency of free radical production for sub-picosecond pulses, relative to that of conventional microsecond pulses, for the same number of deposited electrons.

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

Attention for Causal Relationship Discovery from Biological Neural Dynamics

This paper explores the potential of the transformer models for learning Granger causality in networks with complex nonlinear dynamics at every node, as in neurobiological and biophysical networks. Our study primarily focuses on a proof-of-concept investigation based on simulated neural dynamics, for which the ground-truth causality is known through the underlying connectivity matrix. For transformer models trained to forecast neuronal population dynamics, we show that the cross-attention module effectively captures the causal relationship among neurons, with an accuracy equal to or superior to that of the most popular Granger causality discovery method. While we acknowledge that real-world neurobiology data will bring further challenges, including dynamic connectivity and unobserved variability, this research offers an encouraging preliminary glimpse into the utility of the transformer model for causal representation learning in neuroscience.

Lu, Ziyu↗
NASA NTRS1989

Effects of implementing the Simple Biosphere Model in a general circulation model

The Simple Biosphere Model (SiB) of Sellers et al., was designed to simulate the interactions between the earth's land surface and the atmosphere by treating the vegetation explicitly and realistically, thereby incorporating the biophysical controls on the exchanges of radiation, momentum, sensible and latent heat between the two systems. This paper describes the steps taken to implement SiB in a modified version of the National Meteorological Center's global spectral general circulation model (GCM) and explores the impact of the implementation on the simulated land surface fluxes and near-surface meteorological conditions. The coupled model (SiB-GCM) was used to produce summer and winter simulations. The same GCM was used with a conventional hydrological model (Ctl-GCM) to produce comparable 'control' summer and winter simulations for comparison. It was found that SiB-GCM produced a more realistic partitioning of energy at the land surface than Ctl-GCM. Generally, SiB-GCM, produced more sensible heat flux and less latent heat flux over vegetated land than did Ctl-GCM and this resulted in a much deeper daytime planetary boundary layer and reduced precipitation rates over the continents in SiB-GCM. In the summer simulation, the 200 mb jet stream was slightly weakened in the SiB-GCM relative to the Ctl-GCM results and analyses made from observations.

Sato, N.↗
NASA NTRS1989

Implementing the Simple Biosphere Model (SiB) in a general circulation model: Methodologies and results

The Simple Biosphere MOdel (SiB) of Sellers et al., (1986) was designed to simulate the interactions between the Earth's land surface and the atmosphere by treating the vegetation explicitly and relistically, thereby incorporating biophysical controls on the exchanges of radiation, momentum, sensible and latent heat between the two systems. The steps taken to implement SiB in a modified version of the National Meteorological Center's spectral GCM are described. The coupled model (SiB-GCM) was used with a conventional hydrological model (Ctl-GCM) to produce summer and winter simulations. The same GCM was used with a conventional hydrological model (Ctl-GCM) to produce comparable 'control' summer and winter variations. It was found that SiB-GCM produced a more realistic partitioning of energy at the land surface than Ctl-GCM. Generally, SiB-GCM produced more sensible heat flux and less latent heat flux over vegetated land than did Ctl-GCM and this resulted in the development of a much deeper daytime planetary boundary and reduced precipitation rates over the continents in SiB-GCM. In the summer simulation, the 200 mb jet stream and the wind speed at 850 mb were slightly weakened in the SiB-GCM relative to the Ctl-GCM results and equivalent analyses from observations.

Sato, N.↗
DOE OSTI2022

Structure and dynamics of SARS-CoV-2 proofreading exoribonuclease ExoN

High-fidelity replication of the large RNA genome of coronaviruses (CoVs) is mediated by a 3'-to-5' exoribonuclease (ExoN) in nonstructural protein 14 (nsp14), which excises nucleotides including antiviral drugs misincorporated by the low-fidelity viral RNA-dependent RNA polymerase (RdRp) and has also been implicated in viral RNA recombination and resistance to innate immunity. Here, we determined a 1.6-Å resolution crystal structure of severe acute respiratory syndrome CoV 2 (SARS-CoV-2) ExoN in complex with its essential cofactor, nsp10. The structure shows a highly basic and concave surface flanking the active site, comprising several Lys residues of nsp14 and the N-terminal amino group of nsp10. Modeling suggests that this basic patch binds to the template strand of double-stranded RNA substrates to position the 3' end of the nascent strand in the ExoN active site, which is corroborated by mutational and computational analyses. We also show that the ExoN activity can rescue a stalled RNA primer poisoned with sofosbuvir and allow RdRp to continue its extension in the presence of the chain-terminating drug, biochemically recapitulating proofreading in SARS-CoV-2 replication. Molecular dynamics simulations further show remarkable flexibility of multidomain nsp14 and suggest that nsp10 stabilizes ExoN for substrate RNA binding to support its exonuclease activity. Our high-resolution structure of the SARS-CoV-2 ExoN–nsp10 complex serves as a platform for future development of anticoronaviral drugs or strategies to attenuate the viral virulence.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2010

Application of the Tor Vergata Scattering Model to L Band Backscatter During the Corn Growth Cycle

At the USDA's Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) experimental site in Beltsville, Maryland, USA) a field campaign took place throughout the 2002 corn growth cycle from May 10th (emergence of corn crops) to October 2nd (harvest). One of the microwave instruments deployed was the multi-frequency (X-, C- and L-band) quad-polarized (HH, HV, VV, VH) NASA GSFC/George Washington University (GWU) truck mounted radar. During the field campaign, this radar system provided once a week fully polarized C- and L-band (4.75 and 1.6 GHz) backscatter measurements from incidence angle of 15, 35, and 55 degrees. In support of microwave observations, an extensive ground characterization took place, which included measurements of surface roughness, soil moisture, vegetation biomass and morphology. The field conditions during the campaign are characterized by several dry downs with a period of drought in the month of August. Peak biomass the corn canopies was reached on July 24th with a total biomass of approximately 6.5 kg/sq m. This dynamic range in both soil moisture and vegetation conditions within the data set is ideal for the validation of discrete medium vegetation scattering models. In this study, we compare the L band backscatter measurements with simulations by the Tor Vergata model (ferrazzoli and Guerriero 1996). The measured soil moisture, vegetation biomass and most reliably measured vegetation morphological parameters (e.g. number of leaves, number of stems and stem height) were used as input for the Tor Vergata model. The more uncertain model parameters (e.g. surface roughness, leaf thickness) and the stem diameter were optimized using a parameter estimation routine based on the Levenberg-Marquardt algorithm. As cost function for this optimization, the HH and VV polarized backscatter measured and stimulated by the TOR Vergata model for incidence angle of 15, 35, and 55 degrees were used (6 measurements in total). The calibrated Tor Vergata model simulations are in excellent agreement with the measurements of Root Mean Squared Differences (RMSD's) of 0.8, 0.9 and 1.4 dB for incidences of 15, 35 and 55 degrees, respectively. The results from this study that a physically based scattering model with the appropriate parameterization can accurately simulate backscatter measurements and, as such, have the potential of being used for the retrieval of biophysical variables (e.g. soil moisture and vegetation biomass).

Joseph, A. T.↗