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Results for “Bidirectional prediction”

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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

A physical model for predicting bidirectional reflectances over bare soil

While most previous attempts to retrieve soil surface optical property characteristics have proceeded through a fitting of empirical functions to the data, an optimization technique is presently applied to a physically-based surface reflectance model developed for the study of planetary surfaces. This inversion procedure is shown to allow the direct estimation of the single-scattering coefficient, two parameters describing the 'hot spot' phenomenon, and two parameters describing the scattering phase function. A comparison of inversion technique results with both synthetic data and actual observations shows the model to be capable of predicting the observed bidirectional reflectances as well as directional-hemispherical reflectances; it can also build the complete radiance field over the upward hemisphere.

Pinty, Bernard↗
NASA NTRS1985

Contrasts among bidirectional reflectance of leaves, canopies, and soils

Simple models are presented for predicting the bidirectional reflectance distribution functions (BRDFs) for soils and plant canopies viewed from various directions. BRDFs are predicted for bare soil, individual leaves, and plant canopies, and the results are compared with measurements and a three coefficient empirical equation. BRDF measurements for corn and soybean leaves are presented to contrast with canopy and soil distributions. Estimates of the soil, canopy, and leaf BRDFs are combined into a model called Cupid to predict BRDFs for complex natural surfaces.

Norman, J. M.↗
NASA NTRS1984

Bidirectional Reflectance Modeling of Non-homogeneous Plant Canopies

Efforts to develop a three dimensional model to predict canopy, bidirectional reflectance for heterogenous plant stands using incident radiation and canopy structural descriptions as inputs are described. Utility programs were developed to cope with the complex output from the 3 dimensional model. In addition an attempt was made to define leaf and soil properties, which are appropriate to the mode, by measuring leaf and soil bidirectional reflectance distribution functions; since almost no data exist on these distributions. In the process it was realized that most models probably are using the wrong leaf spectral properties, and that off-nadir reflectance measurements are difficult to make because of non-Lambertian properties of reference surfaces. Also, in the visible wavebands, rough soil may not be distinguishable from canopies when viewed from above.

Norman, J. M.↗
NASA NTRS1985

Bidirectional Reflectance Modeling of Non-homogeneous Plant Canopies

The objective of this research is to develop a 3-dimensional radiative transfer model for predicting the bidirectional reflectance distribution function (BRDF) for heterogeneous vegetation canopies. The model (named BIGAR) considers the angular distribution of leaves, leaf area index, the location and size of individual subcanopies such as widely spaced rows or trees, spectral and directional properties of leaves, multiple scattering, solar position and sky condition, and characteristics of the soil. The model relates canopy biophysical attributes to down-looking radiation measurements for nadir and off-nadir viewing angles. Therefore, inversion of this model, which is difficult but practical should provide surface biophysical pattern; a fundamental goal of remote sensing. Such a model also will help to evaluate atmospheric limitations to satellite remote sensing by providing a good surface boundary condition for many different kinds of canopies. Furthermore, this model can relate estimates of nadir reflectance, which is approximated by most satellites, to hemispherical reflectance, which is necessary in the energy budget of vegetated surfaces.

Norman, J. M.↗
NASA NTRS1986

Bidirectional reflectance modeling of non-homogeneous plant canopies

The objective of this research is to develop a 3-dimensional radiative transfer model for predicting the bidirectional reflectance distribution function (BRDF) for heterogeneous vegetation canopies. Leaf bidirectional reflectance and transmittance distribution functions were measured for corn and soybean leaves. The measurements clearly show that leaves are complex scatterers and considerable specular reflectance is possible. Because of the character of leaf reflectance, true leaf reflectance is larger than the nadir reflectances that are normally used to represent leaves. A 3-dimensional reflectance model, named BIGAR (Bidirectional General Array Model), was developed and compared with measurements from corn and soybean. The model is based on the concept that heterogeneous canopies can be described by a combination of many subcanopies, which contain all the foliage, and these subcanopy envelopes can be characterized by ellipsoids of various sizes and shapes. The model/measurement comparison results indicate that this relatively simple model captures the essential character of row crop BRDF's. Finally, two soil BDRF models were developed: one represents soil particles as rectangular blocks and the other represents soil particles as spheres. The sphere model was found to be superior.

Norman, John M.↗
NASA NTRS1997

Utility of BRDF Models for Estimating Optimal View Angles in Classification of Remotely Sensed Images

Statistical classification of remotely sensed images attempts to discriminate between surface cover types on the basis of the spectral response recorded by a sensor. It is well known that surfaces reflect incident radiation as a function of wavelength producing a spectral signature specific to the material under investigation. Multispectral and hyperspectral sensors sample the spectral response over tens and even hundreds of wavelength bands to capture the variation of spectral response with wavelength. Classification algorithms then exploit these differences in spectral response to distinguish between materials of interest. Sensors of this type, however, collect detailed spectral information from one direction (usually nadir); consequently, do not consider the directional nature of reflectance potentially detectable at different sensor view angles. Improvements in sensor technology have resulted in remote sensing platforms capable of detecting reflected energy across wavelengths (spectral signatures) and from multiple view angles (angular signatures) in the fore and aft directions. Sensors of this type include: the moderate resolution imaging spectroradiometer (MODIS), the multiangle imaging spectroradiometer (MISR), and the airborne solid-state array spectroradiometer (ASAS). A goal of this paper, then, is to explore the utility of Bidirectional Reflectance Distribution Function (BRDF) models in the selection of optimal view angles for the classification of remotely sensed images by employing a strategy of searching for the maximum difference between surface BRDFs. After a brief discussion of directional reflect ante in Section 2, attention is directed to the Beard-Maxwell BRDF model and its use in predicting the bidirectional reflectance of a surface. The selection of optimal viewing angles is addressed in Section 3, followed by conclusions and future work in Section 4.

Valdez, P. F.↗
NASA NTRS1992

Prediction and measurement of soil bidirectional reflectance

The present model for soil bidirectional reflectance distribution functions in the visible and reflective IR ranges is based on a representation of soil surfaces by a collection of opaque spheres atop a Lambertian horizontal surface. The model is not sensitive to increases in the sphere area index beyond a value of 0.4. Observed soil reflectance factor data for a tilled field are noted to be consistent with those predicted by the model's expression of soil reflectance as a function of the horizontal area obscured by a sphere, the amount of surface in shadow, and relative brightness of a sphere, and the fraction of diffuse illumination.

Irons, James R.↗
NASA NTRS2020

Neural Network Reflectance Prediction Model for Both Open Ocean and Coastal Waters

Remote sensing of global ocean color is a valuable tool for understanding the ecology and biogeochemistry of the worlds oceans, and provides critical input to our knowledge of the global carbon cycle and the impacts of climate change. Ocean polarized reflectance contains information about the constituents of the upper ocean euphotic zone, such as colored dissolved organic matter (CDOM), sediments, phytoplankton, and pollutants. In order to retrieve the information on these constituents, remote sensing algorithms typically rely on radiative transfer models to interpret water color or remote-sensing reflectance; however, this can be resource-prohibitive for operational use due to the extensive CPU time involved in radiative transfer solutions. In this work, we report a fast model based on machine learning techniques, called Neural Network Reflectance Prediction Model (NNRPM), which can be used to predict ocean bidirectional polarized reflectance given inherent optical properties of ocean waters. This supervised model is trained using a large volume of data derived from radiative transfer simulations for coupled atmosphere and ocean systems using the successive order of scattering technique (SOS-CAOS). The performance of the model is validated against another large independent test dataset generated from SOS-CAOS. The model is able to predict both polarized and unpolarized reflectances with an absolute error (AE) less than 0.004 for 99% of test cases. We have also shown that the degree of linear polarization (DoLP) for unpolarized incident light can be predicted with an AE less than 0.002 for 99% of test cases. In general, the simulation time of SOS-CAOS depends on optical depth, and required accuracy. When comparing the average speeds of the NNRPM against the SOS-CAOS model for the same parameters, we see that the NNRPM is able to predict the Ocean BRDF 6000 times faster than SOS-CAOS. Both ultraviolet and visible wavelengths are included in the model to help differentiate between dissolved organic material and chlorophyll in the study of the open ocean and the coastal zone. The incorporation of this model into the retrieval algorithm will make the retrieval process more efficient, and thus applicable for operational use with global satellite observations.

radiative transfer↗
NASA NTRS1990

A physical model of the bidirectional reflectance of vegetation canopies. I - Theory. II - Inversion and validation

A new physically based analytical model of the bidirectional reflectance of vegetation canopies is derived. The model expresses the bidirectional reflectance field of a semiinfinite canopy as a combination of functions describing (1) the optical properties of the leaves through their single-scattering albedo and their phase function, (2) the average distribution of leaf orientations, and (3) the architecture of the canopy. The model is validated against laboratory and ground-based measurements in the visible and IR spectral regions, taken over two vegetation covers. The intrinsic optical properties of leaves and the information on the geometrical canopy arrangements in space were obtained using an inversion procedure based on a nonlinear optimization technique. Model predictions of bidirectional reflectances obtained using the inversion procedure compare well with actual observations.

Verstraete, Michel M.↗
NASA NTRS2008

Mapping Snow Grain Size over Greenland from MODIS

This paper presents a new automatic algorithm to derive optical snow grain size (SGS) at 1 km resolution using Moderate Resolution Imaging Spectroradiometer (MODIS) measurements. Differently from previous approaches, snow grains are not assumed to be spherical but a fractal approach is used to account for their irregular shape. The retrieval is conceptually based on an analytical asymptotic radiative transfer model which predicts spectral bidirectional snow reflectance as a function of the grain size and ice absorption. The analytical form of solution leads to an explicit and fast retrieval algorithm. The time series analysis of derived SGS shows a good sensitivity to snow metamorphism, including melting and snow precipitation events. Preprocessing is performed by a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, which includes gridding MODIS data to 1 km resolution, water vapor retrieval, cloud masking and an atmospheric correction. MAIAC cloud mask (CM) is a new algorithm based on a time series of gridded MODIS measurements and an image-based rather than pixel-based processing. Extensive processing of MODIS TERRA data over Greenland shows a robust performance of CM algorithm in discrimination of clouds over bright snow and ice. As part of the validation analysis, SGS derived from MODIS over selected sites in 2004 was compared to the microwave brightness temperature measurements of SSM\I radiometer, which is sensitive to the amount of liquid water in the snowpack. The comparison showed a good qualitative agreement, with both datasets detecting two main periods of snowmelt. Additionally, MODIS SGS was compared with predictions of the snow model CROCUS driven by measurements of the automatic whether stations of the Greenland Climate Network. We found that CROCUS grain size is on average a factor of two larger than MODIS-derived SGS. Overall, the agreement between CROCUS and MODIS results was satisfactory, in particular before and during the first melting period in mid-June. Following detailed time series analysis of SGS for four permanent sites, the paper presents SGS maps over the Greenland ice sheet for the March-September period of 2004.

Lyapustin, Alexei↗
NASA NTRS1973

Optical modeling of agricultural fields and rough-textured rock and mineral surfaces

Review was made of past models for describing the reflectance and/or emittance properties of agricultural/forestry and geological targets in an effort to select the best theoretical models. An extension of the six parameter Allen-Gayle-Richardson model was chosen as the agricultural plant canopy model. The model is used to predict the bidirectional reflectance of a field crop from known laboratory spectra of crop components and approximate plant geometry. The selected geological model is based on Mie theory and radiative transfer equations, and will assess the effect of textural variations of the spectral emittance of natural rock surfaces.

Suits, G. H.↗
NASA NTRS1991

Surface properties of Galilean satellites from bistatic radar experiments

The icy moons of Jupiter were the first to show unusual radar backscatter behavior in Earth-based experiments. Studies of Europa, Ganymede, and Callisto revealed strong echoes and a reversed sense of circular polarization. No explanations were entirely satisfactory because of the difficult constraints imposed by the existing data. The (scalar) bidirectional coherence model predicts an opposition effect, or enhancement in the backscatter direction, resulting from coherent addition of backscatter from identical (but oppositely directed) ray paths. The mode decoupling model yields a similar, vector result in which the observed polarization properties of the backscattered wave can also be obtained. The possibilities were considered for conducting such experiments using the Galileo spacecraft. Both conventional oblique-forward bistatic experiments (to determine basic electrical and physical properties of the surface material on centimeter-meter scales) and near-backscatter experiments (to sample the enhanced backscatter lobe) were considered.

Simpson, R. A.↗
NASA NTRS1993

Modeling Spectralon's Bidirectional Reflectance for In-flight Calibration of Earth-Orbiting Sensors

The in-flight calibration of the EOS Multi-angle Imaging SpectroRadiometer (MISR) will be achieved, in part, by observing deployable Spectralon panels. This material reflects light diffusely, and allows all cameras to view a near constant radiance field. This is particularly true when a panel is illuminated near the surface normal. To meet the challenging MISR calibration requirements, however, very accurate knowledge of the panel reflectance must be known for all utilized angles of illumination, and for all camera and monitoring photodiode view angles. It is believed that model predictions of the panels Bidirectional Reflectance Distribution Function (BRDF) can be used in conjunction with a measurements program to provide the required characterization. This paper describes the results of a model inversion which was conducted using measured Spectralon BRDF data at several illumination angles. Four physical parameters of the material were retrieved, and are available for use with the model to predict reflectance for any arbitrary illumination or view angle. With these data the root mean square difference between the model and the observations is currently of the order of the noise in the data, at about +/- l%. With this success the model will now be used in a variety of future studies, including the development of a measurements test plan, the validation of these data, and the prediction of a new BRDF profile, should the material degrade in space.

Flasse, Stephane P.↗
NASA NTRS1987

Analysis of a fixed-pitch X-wing rotor employing lower surface blowing

Lower surface blowing (LSB) is investigated as an alternative to the variable blade pitch requirement for the X-wing Circulation Control (CC) rotor concept. Addition trailing edge blowing slots on the lower surfaces of CC airfoils provide a bidirectional lift capability that effectively doubles the control range. The operational requirements of this rotor system are detailed and compared to the projected performance attributes of LSB airfoils. Analysis shows that, aerodynamically, LSB supplies a fixed pitch rotor system with the equivalent lift efficiency and rotor control of present CC rotor designs that employ variable blade pitch. Aerodynamic demands of bidirectional lift production are predicted to be within the capabilities of current CC airfoil design methodology. Emphasis in this analysis is given to the high speed rotary wing flight regime unique to stoppable rotor aircraft. The impact of a fixed pitch restriction in hover and low speed flight is briefly discussed.

Schwartz, Alan W.↗
NASA NTRS1982

Method for evaluating moisture tensions of soils using spectral data

A method is disclosed which permits evaluation of soil moisture utilizing remote sensing. Spectral measurements at a plurality of different wavelengths are taken with respect to sample soils and the bidirectional reflectance factor (BRF) measurements produced are submitted to regression analysis for development therefrom of predictable equations calculated for orderly relationships. Soil of unknown reflective and unknown soil moisture tension is thereafter analyzed for bidirectional reflectance and the resulting data utilized to determine the soil moisture tension of the soil as well as providing a prediction as to the bidirectional reflectance of the soil at other moisture tensions.

Peterson, John B.↗
NASA NTRS1990

The Nimbus-7 Earth Radiation Budget (ERB) data set and its uses

The Nimbus-7 Earth Radiation Budget (ERB) instrument began recording data on November 16, 1978 and it is now in its 12th data year. The principle products are nine years of global albedo, outgoing longwave, and net radiation plus continuing solar irradiance measurements. The presence of an associated six-year Nimbus-7 global cloud data set enhances the usefulness of the ERB products. The chief purpose of this paper is to describe some uses that have been made of the data. These include studies in: regional energy heat budgets, the improvement of climate and weather prediction models, interannual climate variations, shortwave bidirectional reflectance from earth atmosphere scenes, and solar physics. The evolution of the data products with time and some algorithm and calibration problems are also briefly discussed. Despite numerous problems, the experiment continues to prove its usefulness.

Kyle, H. Lee↗
NASA NTRS1985

Spectral estimation of green leaf area index of oats

Green leaf area index (LAI) is a measure of vegetative growth and development and is frequently used as an input parameter in yield estimation and evapotranspiration models. Extensive destructive sampling is usually required to achieve accurate estimates of green LAI in natural situations. In this investigation, a statistical modeling approach was used to predict the green LAI of oats from bidirectional reflectance data collected with multiband radiometers. Stepwise multiple regression models based on two sets of spectral reflectance factors accounted for 73 percent and 65 percent of the variance in green LAI of oats. Exponential models of spectral data transformations of greenness, normalized difference, and near-infrared/red ratio accounted for more of the variance in green LAI than the multiple regression models.

Best, R. G.↗