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All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Seasat over-land scatterometer data. I - Global overview of the Ku-band backscatter coefficients

Statistics on the backscatter coefficient sigma(0) from the Ku-band Seasat-A Satellite Scatterometer (SASS) collected over the world's land surfaces are presented. This spaceborne scatterometer provided data on sigma(0) between latitudes 80 deg S and 80 deg N at incidence angles up to 70 deg. The global statistics of vertical (V) and horizontal (H) polarization backscatter coefficients for 10 deg bands in latitude are presented for incidence angles between 20 deg and 70 deg and compared with the Skylab and ground spectrometer results. Global images of the time-averaged V polarization sigma(0) at a 45 deg incidence angle and its dependence on the incidence angle are presented and compared to a generalized map of the terrain type. Global images of the differences between the V and H polarization backscatter coefficients are presented and discussed. The most inhomogeneous region, which contains the deserts of North Africa and the Arabian Peninsula, is studied in greater detail and compared with the terrain type.

Kennett, Rosemary G.↗
NASA NTRS1987

Retrieval of surface roughness parameters from dual-frequency measurements of radar backscattering coefficients

The use of dual-frequency data of backscattering coefficients at a fixed angle to estimate surface roughness parameters is evaluated. Radar backscattering coefficients at 1.5 and 4.25 GHz are calculated using a model based on Kirchhoff approximation of electromagnetic wave scatttering from a rough soil surface. Plots of the calculated backscattering coefficients for Kansas soil moisture contents at the C- and L-band frequencies and HH polarization are analyzed. The effects of changes in correlation length on the backscattering coefficients are investigated. The calculated backscattering coefficients are compared with scatterometer data collected at 1.5 and 4.25 GHz, and it is detected that the model and field data correlate well. The data reveal that it is possible to retrieve the surface roughness parameters from measured radar data.

Mo, Tsan↗
NASA NTRS1993

Application of neural network to remote sensing of soil moisture using theoretical polarimetric backscattering coefficients

This paper investigates the potential application of neural network to inversion of soil moisture using polarimetric remote sensing data. The neural network used for the inversion of soil parameters is multi-layer perceptron trained with the back-propagation algorithm. The training data include the polarimetric backscattering coefficients obtained from theoretical surface scattering models together with an assumed nominal range of soil parameters which are comprised of the soil permittivity and surface roughness parameters. Soil permittivity is calculated from the soil moisture and the assumed soil texture based on an empirical formula at C-, L-, and P-bands. The rough surface parameters for the soil surface, which is described by the Gaussian random process, are the root-mean-square (rms) height and correlation length. For the rough surface scattering, small perturbation method is used for the L-band frequency, and Kirchhoff approximation is used for the C-band frequency to obtain the corresponding backscattering coefficients. During the training, the backscattering coefficients are the inputs to the neural net and the output from the net are compared with the desired soil parameters to adjust the interconnecting weights. The process is repeated for each input-output data entry and then for the entire training data until convergence is reached. After training, the backscattering coefficients are applied to the trained neural net to retrieve the soil parameters which are compared with the desired soil parameters to verify the effectiveness of this technique. Several cases are examined. First, for simplicity, the correlation length and rms height of the soil surface are fixed while soil moisture is varied. Soil moisture obtained using the neural networks with either L-band or C-band backscattering coefficients for the HH and VV polarizations as inputs is in good agreement with the desired soil moisture. The neural net output matches the desired output for the soil moisture range of 16 to 60 percent for the C-band case. The next case investigated is to vary both soil moisture and rms height while keeping the correlation length fixed. For this case, C-band backscattering coefficients are not sufficient for retrieving two parameters because the Kirchhoff approximation gives the same HH and VV backscattering coefficients. Therefore, the backscattering coefficients at two different frequency bands are necessary to find both the soil moisture and rms height. Finally, the neural nets are also applied to simultaneously invert soil moisture, rms height, and correlation length. Overall, the soil moisture retrieved from the neural network agrees very well with the desired soil moisture. This suggests that the neural network shows potential for retrieval of soil parameters from remote sensing data.

Wang, L.↗
NASA NTRS1986

Near-IR extinction and backscatter coefficient measurements in low- and mid-altitude clouds

Knowledge of the attenuation and backscattering properties of clouds is required to high resolution for several types of optical sensing systems. Such data was obtained in about 15 hours of flights through clouds in the vicinity of Washington, D.C. The flights were mainly through stratocumulus, altocumulus, stratus, and stratus fractus clouds and covered an altitude and temperature range of 300 to 3200 m and -13 to 17 C. Two instruments were flown, each of which measured the backscatter from close range in two range bins to independently determine both the extinction and backscatter coefficients. The extinction and backscatter coefficients can be obtained from the signals in the two channels of each instrument, provided that the aerosol is uniform over the measurement region. When this assumptions holds, the extinction coefficient is derived basically from the ratio of the signal in the two channels; the backscatter coefficient can then be obtained from the signal in either channel.

Sztankay, Z. G.↗
NASA NTRS1992

Dependence of the surface backscattering coefficients on roughness, frequency and polarization states

This paper summarizes the properties of the surface backscattering coefficient as a function of roughness, incidence angle, frequency and polarization state. Results are presented in the form of like- and cross-polarized backscattering coefficient curves versus the incidence angle and the polarization state for different values of the surface roughness parameters and frequency. It is seen that there is a gradual transition from the standard small perturbation scattering model into the Kirchhoff scattering model as frequency increases. It is also shown that in the intermediate frequency region neither the small perturbation nor the Kirchhoff model is applicable. The maximum value of the polarized backscattering coefficient occurs at VV polarization and its two minimum values occur at zero ellipticity and orientation angles between 0 deg and 45 deg and between 135 deg and 180 deg. The cross-polarized scattering coefficient has two maximum values which occur in the same polarization state regions as the minima of the polarized coefficients.

Fung, A. K.↗
NASA NTRS2016

Retrieval of Ocean Subsurface Particulate Backscattering Coefficient from Space-Borne CALIOP Lidar Measurement

A new approach has been proposed to determine ocean subsurface particulate backscattering coefficient bbp from CALIOP 30deg off-nadir lidar measurements. The new method also provides estimates of the particle volume scattering function at the 180deg scattering angle. The CALIOP based layer-integrated lidar backscatter and particulate backscattering coefficients are compared with the results obtained from MODIS ocean color measurements. The comparison analysis shows that ocean subsurface lidar backscatter and particulate backscattering coefficient bbp can be accurately obtained from CALIOP lidar measurements, thereby supporting the use of space-borne lidar measurements for ocean subsurface studies.

Lu, Xiaomei↗
NASA NTRS1983

Calculations of radar backscattering coefficient of vegetation-covered soils

A model for simulating the measured backscattering coefficient of vegetation-covered soil surfaces includes both coherent and incoherent components of the backscattered radar pulses from a rough sil surface. The effect of vegetation canopy scattering is also incorporated into the model by making the radar pulse subject to two-way attenuation and volume scattering when it passes through the vegetation layer. Model results agree well with the measured angular distributions of the radar backscattering coefficient for HH polarization at the 1.6 GHz and 4.75 GHz frequencies over grass-covered fields. It was found that the coherent scattering component is very important at angles near nadir, while the vegetation volume scattering is dominant at incident angles 30 degrees.

Mo, T.↗
NASA NTRS2019

EARLINET Evaluation of the CATS L2 Aerosol Backscatter Coefficient Product

We present the evaluation activity of the European Aerosol Research Lidar Network (EARLINET) for the quantitative assessment of the Level 2 aerosol backscatter coefficient product derived by the Cloud-Aerosol Transport System (CATS) onboard the International Space Station (ISS). The study employs correlative CATS and EARLINET backscatter measurements within 50km distance between the ground station and the ISS overpass and as close in time as possible, typically within 90min, from February 2015 to September 2016. The results demonstrate the good agreement of CATS Level 2 backscatter coefficient and EARLINET. Three ISS overpasses close to the EARLINET stations of Leipzig-Germany, Évora-Portugal and Dushanbe-Tajikistan are analysed here to demonstrate the performance of CATS lidar system under different conditions. The results show that under cloud-free, relative homogeneous aerosol conditions CATS is in good agreement with EARLINET, independently of daytime/nighttime conditions. CATS low negative biases, partially attributed to the deficiency of lidar systems to detect tenuous aerosol layers of backscatter signal below the minimum detection thresholds, may lead to systematic deviations and slight underestimations of the total Aerosol Optical Depth (AOD) in climate studies. In addition, CATS misclassification of aerosol layers as clouds, and vice versa, in cases of coexistent and/or adjacent aerosol and cloud features, may lead to non-representative, unrealistic and cloud contaminated aerosol profiles. The distributions of backscatter coefficient biases show the relatively good agreement between the CATS and EARLINET measurements, although on average underestimations are observed, 22.3% during daytime and 6.1% during nighttime.

EARLINET/CATS evaluation↗
NASA NTRS2020

EARLINET evaluation of the CATS Level 2 aerosol backscatter coefficient product

We present the evaluation activity of the European Aerosol Research Lidar Network (EARLINET) for the quantitative assessment of the Level 2 aerosol backscatter coefficient product derived by the Cloud-Aerosol Transport System (CATS) aboard the International Space Station (ISS; Rodier et al., 2015). The study employs correlative CATS and EARLINET backscatter measurements within a 50 km distance between the ground station and the ISS overpass and as close in time as possible, typically with the starting time or stopping time of the EARLINET performed measurement time window within 90 min of the ISS overpass, for the period from February 2015 to September 2016. The results demonstrate the good agreement of the CATS Level 2 backscatter coefficient and EARLINET. Three ISS overpasses close to the EARLINET stations of Leipzig, Germany; Évora, Portugal; and Dushanbe, Tajikistan, are analyzed here to demonstrate the performance of the CATS lidar system under different conditions. The results show that under cloud-free, relative homogeneous aerosol conditions, CATS is in good agreement with EARLINET, independent of daytime and nighttime conditions. CATS low negative biases are observed, partially attributed to the deficiency of lidar systems to detect tenuous aerosol layers of backscatter signal below the minimum detection thresholds; these are biases which may lead to systematic deviations and slight underestimations of the total aerosol optical depth (AOD) in climate studies. In addition, CATS misclassification of aerosol layers as clouds, and vice versa, in cases of coexistent and/or adjacent aerosol and cloud features, occasionally leads to non-representative, unrealistic, and cloud-contaminated aerosol profiles. Regarding solar illumination conditions, low negative biases in CATS backscatter coefficient profiles, of the order of 6.1 %, indicate the good nighttime performance of CATS. During daytime, a reduced signal-to-noise ratio by solar background illumination prevents retrievals of weakly scattering atmospheric layers that would otherwise be detectable during nighttime, leading to higher negative biases, of the order of 22.3 %.

Emmanouil Proestakis↗
NASA NTRS1983

Method for retrieving the true backscattering coefficient from measurements with a real antenna

Coherent and incoherent scattering produce contributions to the power backscattered by a surface at angles near normal incidence, and these contributions are further weighed by the antenna pattern. On the basis of a theoretical model describing the backscattering from a rough soil surface, a procedure is developed for retrieving the true angular pattern of the backscattering coefficient from its measured estimates, where these estimates are based on the usual assumption that the coefficient is approximately constant over the angular extent of the antenna beam for narrow beam systems. The retrieved backscattering coefficient patterns were then used to evaluate the dependence of the coefficient on soil surface roughness at 1.5, 4.25, and 7.25 GHz.

Ulaby, F. T.↗
NASA NTRS1998

Intercomparison of Remote and Flight Level Measured Aerosol Backscatter Coefficient During GLOBE 2 Pacific Survey Mission

Aerosol backscatter coefficient data are examined from two local flights undertaken during NASA's GLObal Backscatter Experiment (GLOBE) in May - June, 1990. During each of these two flights the aircraft traversed different altitudes within a region of the atmosphere defined by the same set of latitude and longitude coordinates. This provides an ideal opportunity to allow flight level measured or modeled aerosol backscafter to be compared with pulsed lidar aerosol backscafter data that were obtained at these same altitudes either earlier or later than the flight level measurements. Aerosol backscafter comparisons were made at 1.06-, 9.11- and 9.25-mm wavelengths, using data from three lidar systems and two aerosol optical counters. The best agreement between all sensor's was found in the altitude region below 7 km where backscafter values were moderately high at all three wavelengths. Above this altitude the pulsed lidar backscafter data at 1.06- and 9.25-mm wavelengths were higher than the flight level data obtained from the CW lidar or derived from the optical counters. Possible reasons are offered to explain this discrepancy. During the Japan local flight, microphysics analysis revealed: (1) evidence of a strong advected seasalt aerosol plume from the marine boundary layer, and (2) where backscatter was low, the large lidar sampling volume included many large particles which were of different chemical composition to the small particle category sampled by the particle counters.

Cutten, D. R.↗
NASA NTRS2001

A Multi-Band Analytical Algorithm for Deriving Absorption and Backscattering Coefficients from Remote-Sensing Reflectance of Optically Deep Waters

A multi-band analytical (MBA) algorithm is developed to retrieve absorption and backscattering coefficients for optically deep waters, which can be applied to data from past and current satellite sensors, as well as data from hyperspectral sensors. This MBA algorithm applies a remote-sensing reflectance model derived from the Radiative Transfer Equation, and values of absorption and backscattering coefficients are analytically calculated from values of remote-sensing reflectance. There are only limited empirical relationships involved in the algorithm, which implies that this MBA algorithm could be applied to a wide dynamic range of waters. Applying the algorithm to a simulated non-"Case 1" data set, which has no relation to the development of the algorithm, the percentage error for the total absorption coefficient at 440 nm a (sub 440) is approximately 12% for a range of 0.012 - 2.1 per meter (approximately 6% for a (sub 440) less than approximately 0.3 per meter), while a traditional band-ratio approach returns a percentage error of approximately 30%. Applying it to a field data set ranging from 0.025 to 2.0 per meter, the result for a (sub 440) is very close to that using a full spectrum optimization technique (9.6% difference). Compared to the optimization approach, the MBA algorithm cuts the computation time dramatically with only a small sacrifice in accuracy, making it suitable for processing large data sets such as satellite images. Significant improvements over empirical algorithms have also been achieved in retrieving the optical properties of optically deep waters.

Lee, Zhong-Ping↗
NASA NTRS1984

Calculations of radar backscattering coefficient of vegetation-covered soils

The present investigation has the objective to develop a simple 'user's' model for simulating the measured radar backscattering coefficients from vegetation-covered fields in conjunction with the data obtained by Jackson et al. (1980, 1982). The theoretical work reported by Fung and Eom (1981) provides the basis for the model. Certain modifications are related to a consideration of the effect of a vegetation canopy. The first part of the model is concerned with a description of scatter from rough bare soil, while the second part takes into account the effect of a vegetation cover. It is shown that the measured angular distribution of the backscattering coefficient of vegetation-covered fields can be satisfactory reproduced by using the developed model.

Mo, T.↗
NASA NTRS1983

Effects of vegetation canopy on the radar backscattering coefficient

Airborne L- and C-band scatterometer data, taken over both vegetation-covered and bare fields, were systematically analyzed and theoretically reproduced, using a recently developed model for calculating radar backscattering coefficients of rough soil surfaces. The results show that the model can reproduce the observed angular variations of radar backscattering coefficient quite well via a least-squares fit method. Best fits to the data provide estimates of the statistical properties of the surface roughness, which is characterized by two parameters: the standard deviation of surface height, and the surface correlation length. In addition, the processes of vegetation attenuation and volume scattering require two canopy parameters, the canopy optical thickness and a volume scattering factor. Canopy parameter values for individual vegetation types, including alfalfa, milo and corn, were also determined from the best-fit results. The uncertainties in the scatterometer data were also explored.

Mo, T.↗
NASA NTRS1983

A comparison between backscattering coefficients using Gaussian and non-Gaussian surface statistics

Backscattering coefficients computed using Gaussian and non-Gaussian surface statistics are compared. The computations are performed for a class of surface-height distributions and surface correlation functions. Results indicate that the coherent component of the scattering coefficient is strongly dependent upon the surface-height distribution. The noncoherent component also depends on the surface-height distribution and the surface root-mean-square (rms) slope when the rms surface height is large relative to the incident wavelength. On the other hand, when the surface rms height is moderate or small, the noncoherent component is sensitive mainly to the surface-height correlation function.

Eom, H. J.↗
NASA NTRS2021

Development and Validation of an Empirical Ocean Color Algorithm with Uncertainties: A Case Study with the Particulate Backscattering Coefficient

We explored how algorithm (model) and in situ measurement (observation) uncertainties can effectively be incorporated into empirical ocean color model development and assessment. In this study we focused on methods for deriving the particulate backscattering coefficient at 555 nm, b(bp)(555)/(m). We developed a simple empirical algorithm for deriving b(bp)(555) as a function of a remote sensing reflectance line height (LH) metric. Model training was performed using a high-quality bio-optical dataset that contains coincident in situ measurements of the spectral remote sensing reflectances, R(rs)(λ)/(sr), and the spectral particulate backscattering coefficients, b(bp)(λ). The LH metric used is defined as the magnitude of Rrs(555) relative to a linear baseline drawn between R(rs)(490) and R(rs)(670). Using an independent validation dataset, we compared the skill of the LH-based model with two other models. We used contemporary validation metrics, including bias and mean absolute error (MAE), that were corrected for model and observation uncertainties. The results demonstrated that measurement uncertainties do indeed impact contemporary validation metrics such as mean bias and MAE. Zeta-scores and z-tests for overlapping confidence intervals were also explored as potential methods for assessing model skill.

ocean color↗
NASA NTRS1986

Wavelength dependence of aerosol backscatter coefficients obtained by multiple wavelength Lidar measurements

Aerosols are often classified into several general types according to their origins and composition, such as maritime, continental, and stratospheric aerosols, and these aerosol types generally have different characteristics in chemical and physical properties. The present study aims at demonstrating the potential for distinguishing these aerosol types by the wavelength dependence of their backscatter coefficients obtained from quantitative analyses of multiple wavelength lidar signals. Data from the NASA Airborne Differential Abosrption lidar (DIAL) S ystems, which can measure aerosol backscatter profiles at wavelenghts of 300, 600, and 1064 nm and ozone profiles of backscatter coefficients for these three wavelength were derived from the observations of aerosols of different types. Observations were performed over the Atlantic Ocean, the Southwestern United States, and French Guyana.

Sasano, Y.↗