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In passive microwave remote sensing of the Earth's surface, it is essential to relate the emission to geophysical parameters. The emissivity ranges between 0 and 1. Hence, a slight emissivity variation leads to a significant change in brightness temperature. Many sources of error contribute to such tiny variations in emission. This paper quantifies microwave emission variability from a rough surface through model simulation due to the non-Gaussianity in height probability density (HPD) and power spectrum density (PSD). We considered Gaussian and exponential distributions for surface height and correlation functions, representing two extremes of asperity and skewness. Additionally, the surface under consideration contains multiscale roughness. The impact of the HPD and multiscale roughness on the polarization index of the emissivity is evaluated as a function of frequency and roughness. In general, assuming that Gaussian-distributed height leads to an underestimation of the emissivity, with V polarization being less sensitive to the non-Gaussian HPD and PSD effects than H polarization, the emissions are enhanced at high roughness with small look angles but are reduced for smooth surfaces at large look angles under non-Gaussian PSD. In a specific scenario, the dynamic range of the difference between exponential and Gaussian HPD is 0~10%, and the difference in emissivity caused by non-Gaussian PSD ranges from −2% to 16%. These results should be helpful in interpreting the radiometric measurements that exhibit fluctuations and differences with model predictions. [ABSTRACT FROM AUTHOR]

Natural rough surfaces have inherent multiscale roughness. This article presents the modeling and analysis of microwave emission from a multiscale soil surface. Unlike the linear superposition of different correlation functions with various correlation lengths, we applied the frequency modulation concept to characterize the multiscale roughness, in which the modulation does not destroy the surface's curvature but only modifies it. The multiscale effect on emission under different observation geometries and surface parameters was examined using an AIEM model. The paper provides new insights into the dependence of polarized emissivity on multiscale roughness: V-polarized emissivity is much less sensitive to multiscale roughness across the moisture content from dry to wet (5–30%). The H-polarized is sensitive to multiscale roughness, especially at higher moisture content. The predicted emissivity will have considerable uncertainty, even for the same baseline correlation length, without accounting for the multiscale roughness effect. V-polarized emissivity is less sensitive to the multiscale effect than H-polarized and the higher modulation ratio indicates larger emissivity. The higher modulation ratio indicates larger emissivity. Multiscale roughness weakens the polarization difference, particularly in higher moisture conditions. In addition, ignoring the multiscale effect leads to underestimated emissivity to a certain extent, particularly at the larger RMS height region. Finally, when accounting for multiscale roughness, model predictions of emission from a soil surface are in good agreement with two independently measured data sets. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System interferometric reflectometry (GNSS-IR), a new and inexpensive technique, has become available to the broader scientific community for detecting surface environmental information, such as soil moisture, snow depth and vegetation growth. However, there have been limited experiments focusing on the potential of crop height retrieval, especially the performance evaluation of BeiDou Navigation Satellite System (BDS) with other GNSS. Accuracy and reliability are challenging to achieve with traditional methods utilizing a single GNSS, and few measured verification data. In this study, an improved method that includes segmentation processing and multi-GNSS fusion is proposed based on GPS/GLONASS/Galileo/BDS multi-frequency data. Furthermore, experiments were carried out on a farmland in Fengqiu County, Henan Province, China. The results show that the height retrievals from four GNSS were in good agreement with the in situ observations during the whole growth cycle of the wheat after overwintering. Meanwhile, the retrievals based on the proposed method exhibited greater correspondence than the single frequency results, the correlation coefficient was increased and the root-mean-square error (RMSE) was reduced, respectively. Therefore, this study illustrates the feasibility of the proposed method to precisely estimate wheat height and its potential for use in the early warning of wheat lodging based on GNSS-IR. [ABSTRACT FROM AUTHOR]

In modeling a rough surface, it is common to assume a Gaussian height distribution. This hypothesis cannot describe an eventual asymmetry between crests and troughs of natural surfaces. We analyzed the bistatic scattering from a rough surface with non-Gaussian height distributions using the Kirchhoff scattering theory. Two extreme cases of Gamma-distributed surfaces were compared in particular: exponential and Gaussian distributions. The bistatic angular dependence was examined under various root mean square (RMS) heights and power spectrum densities. Contribution sources to the coherent and incoherent scattering components were singled out relating to the surface height distribution. For an exponential height surface, the coherent scattering strengthens even as the surface becomes rough. The non-Gaussian effect on the incoherent scattering is connected with surface power spectrum density. The height distribution impacts differ in the different regions of the bistatic scattering plane and thus complicate the differentiation of the scattering patterns due to height distribution. [ABSTRACT FROM AUTHOR]

The Moon-based Synthetic Aperture Radar (Moon-Based SAR), using the Moon as a platform, has a great potential to offer global-scale coverage of the earth's surface with a high revisit cycle and is able to meet the scientific requirements for climate change study. However, operating in the lunar orbit, Moon-Based SAR imaging is confined within a complex geometry of the Moon-Based SAR, Moon, and Earth, where both rotation and revolution have effects. The extremely long exposure time of Moon-Based SAR presents a curved moving trajectory and the protracted time-delay in propagation makes the "stop-and-go" assumption no longer valid. Consequently, the conventional SAR imaging technique is no longer valid for Moon-Based SAR. This paper develops a Moon-Based SAR theory in which a signal model is derived. The Doppler parameters in the context of lunar revolution with the removal of -stop-and-go' assumption are first estimated, and then characteristics of Moon-Based SAR imaging's azimuthal resolution are analyzed. In addition, a signal model of Moon-Based SAR and its two-dimensional (2-D) spectrum are further derived. Numerical simulation using point targets validates the signal model and enables Doppler parameter estimation for image focusing. [ABSTRACT FROM AUTHOR]

We apply Shannon entropy, an information content measure, in sensitivity analysis (SA), stemming from the fact that the essence of SA is to preserve the maximum information content of the parameters of interest that are inverted from the radar response. Then, the sensitivity to the observation configuration and surface parameters is subsequently analyzed. Attempts are also made to explore advantages, by maximizing the information content, of dual-polarization and multi-angle in improving the parameter retrieval from radar sensing of rough surface. Simulation results show that the entropy is a good indicator of the sensitivity of the radar response to the surface parameter, as it contains information on not only the probability distribution of the scattering coefficient but also on its deviation. By information entropy, richer details, to large extent, on the scattering behavior are offered through quantitatively predicting the scattering signal saturation, evaluating the effect of using multi-polarization and multi-angle observation configuration, and identifying non-significant variables. It is found that Shannon entropy, compared to Renyi entropy, appears to better represent the sensitivity with respect to monotonic variation and narrower parameter ranges. The proposed entropy-based SA method helps to deepen our understanding of the microwave scattering behavior in response to surface parameters. [ABSTRACT FROM AUTHOR]

This paper investigates the radar image statistics of rough surfaces by simulating the scattered signal's dependence on the surface roughness. Statistically, the roughness characteristics include the height probability density (HPD) and, to the second-order, the power spectral density (PSD). We simulated the radar backscattered signal by computing the far-field scattered field from the rough surface within the antenna beam volume in the context of synthetic aperture radar (SAR) imaging. To account for the non-Gaussian height distribution, we consider microscopic details of the roughness on comparable radar wavelength scales to include specularly, singly, and multiply scatterers. We introduce surface roughness index (RSI) to distinguish the statistical characteristics of rough surfaces with different height distributions. Results suggest that increasing the RMS height does not impact the Gaussian HPD surface but significantly affects the Weibull surface. The results confirm that as the radar frequency increases, or reaches a relatively larger roughness, the surface's HPD causes significant changes in incoherent scattering due to more frequent multiple scattering contributions. As a result, the speckle move further away from the Rayleigh model. By examining individual RSI, we see that the Gaussian HPD surface is much less sensitive to RMS height than the Weibull HPD surface. We demonstrate that to retrieve the surface parameters (both dielectric and roughness) from the estimated RCS, less accuracy is expected for the non-Gaussian surface than the Gaussian surface under the same conditions. Therefore, results drawn from this study are helpful for system performance evaluations, parameters estimation, and target detection for SAR imaging of a rough surface. [ABSTRACT FROM AUTHOR]

We present a GPU-based computation for simulating the synthetic aperture radar (SAR) image of the complex target. To be more realistic, we included the multiple scattering field and antenna pattern tracking in producing the SAR echo signal for both Stripmap and Spotlight modes. Of the signal chains, the computation of the backscattering field is the most computationally intensive. To resolve the issue, we implement a computation parallelization for SAR echo signal generation. By profiling, the overall processing was identified to find which is the heavy loading stage. To further accommodate the hardware structure, we made extensive modifications in the CUDA kernel function. As a result, the computation efficiency is much improved, with over 224 times the speed up. The computation complexity by comparing the CPU and GPU computations was provided. We validated the proposed simulation algorithm using canonical targets, including a perfectly electric conductor (PEC), dielectric spheres, and rotated/unrotated dihedral corner reflectors. Additionally, the targets can be a multi-layered dielectric coating or a layered medium. The latter case aimed to evaluate the polarimetric response quantitively. Then, we simulated a complex target with various poses relative to the SAR imaging geometry. We show that the simulated images have high fidelity in geometric and radiometric specifications. The decomposition of images from individual scattering bounce offers valuable exploitation of the scattering mechanisms responsible for imaging certain target features. [ABSTRACT FROM AUTHOR]

On 21 May 2021, an Mw 6.1 earthquake, causing considerable seismic damage, occurred in Yangbi County, Yunnan Province of China. To better understand the surface deformation pattern, source characteristics, seismic effect on nearby faults, and strong ground motion, we processed the ascending and descending SAR images using the interferometric synthetic aperture radar (InSAR) technique to capture the radar line-of-sight (LOS) directional and 2.5-dimensional deformation. The source model was inverted from the LOS deformation observations. We further analyzed the Coulomb failure stress (CFS) transfer and peak ground acceleration (PGA) simulation based on the preferred source model. The results suggest that the 2021 Yangbi earthquake was dextral faulting with the maximum slip of 0.9 m on an unknown blind shallow fault, and the total geodetic moment was 1.4 × 1018 Nm (Mw 6.06). Comprehensive analysis of the CFS transfer and geological tectonics suggests that the Dian–Xibei pull-apart basin is still suffering high seismic hazards. The PGA result demonstrates that the seismic intensity of this event reached up to VIII. The entire process from InSAR deformation to source modeling and strong ground motion simulation suggests that the InSAR technique will play an important role in the assessment of earthquake disasters in the case of the shortening of the SAR imaging interval. [ABSTRACT FROM AUTHOR]

Soil moisture monitoring using Global Navigation Satellite System (GNSS) multipath signals has gained continuous interests in recent years. However, traditional GNSS-interferometric reflectometry (GNSS-IR) soil moisture retrieval methods generally utilize a single frequency or single satellite, which fail to take full advantage of different and complementary of satellite signals with different frequencies. An improved algorithm for soil moisture retrieval based on principal component analysis (PCA) and entropy method using multi-frequency amplitude and phase offset fusion data was proposed in this research. The performance of the proposed soil moisture retrieval method was evaluated using data recorded by Plate Boundary Observatory (PBO) H2O networks and a self-built site in Henan, China. The results from GPS and BeiDou both showed that the retrieved soil moisture has a stronger correlation with in situ soil moisture, which can better reflect the fluctuation of ground truth measurements. Compared with the traditional method, the retrieval accuracy of the proposed method in terms of root-mean-square error (RMSE) was improved by 50.93%, and the average correlation coefficient were increased by 11.71%. This research proved that the proposed method could effectively improve retrieval accuracy due to the increasing number of frequencies and tracks clustering. Moreover, this study has illustrated the feasibility of BeiDou signals to precisely estimate surface soil moisture. [ABSTRACT FROM AUTHOR]

Fully bistatic radar scattering from rough surfaces is of vital importance in terrain remote sensing, but results in bulky data volume. The scattering is dependent on physical parameters of the media and is controlled by the radar observation geometry. Together, the two sets of parameters determine the scattering patterns in a bistatic plane confined by incident and polar angles in both incident and scattering directions. For radar remote sensing, it is desirable to infer surface parameters of interest, with satisfactory accuracy, from large volumes of measured data sets. This is essentially a task of data mining. In this paper, we present model-generated bistatic radar scattering data, followed by a sensitivity analysis, to identify a suitable configuration in terms of parameter inversion from fully bistatic measurements by a Kalman filter-trained dynamic learning neural network (DLNN). Results indicate that with bistatic observation, superior retrieval performance (as compared to backscattering observation) can be readily achieved. [ABSTRACT FROM AUTHOR]

The sea surface essentially contains multiscale roughness with capillary waves of many sizes riding on large-scale waves that are also of many sizes. It is instructive to exploit the effect of radar frequency and observation geometry on the effective roughness scales responsible for radar backscattering so that the scattering mechanism and the scattering source can be better understood and quantitated. Based on common sea spectra and a theoretical scattering model, an attempt is made to attain the above objective. Model predictions, with selective roughness scales, are compared with wide validation data, including L-band radar observations, and predictions from C-band and Ku-band empirical models: geophysical model function (CMOD7) and NASA scatterometer (NSCAT-4) for C- and Ku-bands at different incident angles. Numerical results indicate that effective roughness scales for radar backscattering vary with radar frequency and incidence angle and are related to a portion of sea spectral components; the low limit of which is linearly proportional to the Bragg wavenumber determined by frequency and incidence angle, and the scale factor of the linear relationship is about 0.05. In addition, the root mean square (RMS) height and the correlation length of the effective roughness (i.e., scattering source) derived from the effective roughness decrease gradually as incident angle increases. In particular, the correlation length also linearly depends on the effective wavelength with a coefficient of 3.2. Moreover, these two coefficients are both independent of wind speed, radar frequency, and incident angle. These findings also reveal the essential properties of the spectral components contributing to radar backscattering and its variation with radar frequency and incident angle. [ABSTRACT FROM AUTHOR]

In recent years, bistatic synthetic aperture radar (SAR) technique has attracted considerable and increasing attention. Compared to monostatic SAR for which only the backscattering is measured, bistatic SAR expands the scattering measurements in aspects of angular region and polarization, and greatly enhances the capability of remote sensing over terrain and sea. It has been pointed out in recent theoretical researches that bistatic scattering measured in the forward region is preferable to that measured in the backward region in lines of surface parameters retrieval. In the forward region, both dynamic range and signal sensitivity increase to a great extent. For these reasons, bistatic SAR imaging is desirable. However, because of the separated positions of the transmitter and receiver, the degrees of freedom in the parameter space is increased and the forward bistatic imaging is more complicated than the backward bistatic SAR in the aspects of bistatic range history, Doppler parameter estimation and motion compensation, et, al. In this study, we analyze bistatic SAR in terms of ground range resolution, azimuth resolution, bistatic range history and signal to noise ratio (SNR) in different bistatic configurations. Effects of system motion parameters on bistatic SAR imaging are investigated through analytical modeling and numerical simulations. The results indicate that the range resolution is extremely degraded in some cases in forward bistatic SAR imaging. In addition, due to the different imaging projection rules between backward and forward bistatic SAR, the ghost point is produced in the forward imaging. To avoid the above problems, the forward bistatic imaging geometry must be carefully considered. For a given application requirement with the desired imaging performances, the design of the motion parameters can be considered as a question of solving the nonlinear equation system (NES). Then the improved chaos particle swarm optimization (CPSO) is introduced to solve the NES and obtain the optimal solutions. And the simulated imaging results are used to test and verify the effectiveness of CPSO. The results help to deepen understanding of the constraints and properties of bistatic SAR imaging and provide the reference to the optimal design of the motion parameters for a specific requirement, especially in forward bistatic configurations. [ABSTRACT FROM AUTHOR]

Imaging simulation of synthetic aperture radar (SAR) is one of the potential tools in the field of remote sensing. The echo signal in imaging simulation based on the point target model cannot be linked to practical scenes due to the model being a simple mathematical form, stating only the synthetic process and lacking the physical process based on electromagnetic theory. In this paper, the full-wave method is applied to include the electromagnetic effects in raw data generation, and then a refined omega-K algorithm is used to perform image focusing. According to the proposed method, the focused images not only demonstrate the difference under dielectric constant variation but also present the diversified interaction among the targets with the spacing change. In addition, the images are simulated in different observation modes and bandwidths to provide a satisfactory reference for the design of system parameters. The simulation results from the full-wave method also compare well with chamber experiments. The simulation of SAR imaging based on a full-wave method offers more complete recovery of scattering information and is useful in designing future novel SAR systems and in speckle reduction analysis. [ABSTRACT FROM AUTHOR]

The aim of this study is to investigate, by means of experimental measurements and full-wave simulations, the dominant factors for the very high-resolution (VHR) radar image speckles from exponentially correlated rough surfaces. A Ka-band radar system was used to collect the return signal from such a surface sample fabricated by 3D printing and that signal was further processed into images at different resolution scales, where the image samples were obtained by horizontally turning around the surface sample. To cross-validate the results and to further discuss the VHR speckle properties, full wave simulations by full 3D Finite Difference Time Domain (FDTD) method were conducted with 1600 realizations for the speckle analysis. At the considered very high resolution, speckle statistics show divergence from the fully developed Rayleigh distribution. The factors that impact on the high-resolution speckle properties from exponentially correlated rough surface, are analyzed in views of the equivalent number of scatterers theory and scattering scales, respectively. From the data results and extended discussions, it is evident that both of the above factors matter for VHR speckle of backscattering, from the exponentially correlated rough surface as a good representative for the ground surface. [ABSTRACT FROM AUTHOR]

Color enhancement of decomposed fully polarimetric synthetic aperture radar (PolSAR) image is vital for visual understanding and interpretation of the polarimetric information about the target. It is common practice to use RGB or HIS color space to display the chromatic information for polarization-encoded, Pauli-basis images, or model-based target decomposition of PolSAR images. However, to represent the chroma for multi-polarization SAR data, the region of basic RGB color space does not fully cover the human perceptual system, leading to information loss. In this paper, we propose a color-encoding framework based on the CIE-Lab, a perceptually uniform color space, aiming at a better visual perception and information exploration. The effective interpretability in increasing chromatic, and thus visual enhancement, is presented using extensive datasets. In particular, the four decomposed components—volume scattering, surface scattering, double bounce, and helix scattering—along with total return power, are simultaneously mapped into the color space to improve the discernibility among the scattering components. The five channels derived from the four-component decomposition method can be simultaneously mapped to CIE-Lab color space intuitively. Results show that the proposed color enhancement not only preserves the color tone of the polarization signatures, but also magnifies the target information embedded in the total returned power. [ABSTRACT FROM AUTHOR]

Conventional direction-of-arrival (DOA) estimation methods are primarily used in point source scenarios and based on array signal processing. However, due to the local scattering caused by sea surface, signals observed from radar antenna cannot be regarded as a point source but rather as a spatially dispersed source. Besides, with the advantages of flexibility and comparably low cost, synthetic aperture radar (SAR) is the present and future trend of space-based systems. This paper proposes a novel DOA estimation approach for SAR systems using the simulated radar measurement of the sea surface at different operating frequencies and wind speeds. This article's forward model is an advanced integral equation model (AIEM) to calculate the electromagnetic scattered from the sea surface. To solve the DOA estimation problem, we introduce a convolutional neural network (CNN) framework to estimate the transmitter's incident angle and incident azimuth angle. Results demonstrate that the CNN can achieve a good performance in DOA estimation at a wide range of frequencies and sea wind speeds. [ABSTRACT FROM AUTHOR]

Soil moisture is one of the vital environmental variables in the land–atmosphere cycle. A study of the sensitivity analysis of bistatic scattering coefficients from bare soil at the Ku-band is presented, with the aim of deepening our understanding of the bistatic scattering features and exploring its potential in soil moisture retrieval. First, a well-established advanced integral method was adopted for simulating the bistatic scattering response of bare soil. Secondly, a sensitivity index and a normalized weight quality index were proposed to evaluate the effect of soil moisture on the bistatic scattering coefficient in terms of polarization and angular diversity, and the combinations thereof. The results of single-polarized VV data show that the regions with the maximum sensitivity and high quality index, simultaneously, to soil moisture are in the forward off-specular direction. However, due to the effect of surface roughness and surface autocorrelation function (ACF), the single-polarized data have some limitations for soil moisture inversion. By contrast, the results of two different polarization combinations, as well as a dual-angular simulation of one transmitter and two receivers, show significant estimation benefits. It can be seen that they all provide better ACF suppression capabilities, larger high-sensitivity area, and higher quality indices compared to single-polarized estimation. In addition, dual polarization or dual angular combined measurement provides the possibility of retrieving soil moisture in backward regions. These results are expected to contribute to the design of future bistatic observation systems. [ABSTRACT FROM AUTHOR]

On 28 September 2018, an Mw 7.4 earthquake, followed by a tsunami, struck central Sulawesi, Indonesia. It resulted in serious damage to central Sulawesi, especially in the Palu area. Two descending paths of the Advanced Land Observation Satellite 2 (ALOS-2) synthetic aperture radar (SAR) data were processed with interferometric synthetic aperture radar (InSAR) and pixel tracking techniques to image the coseismic deformation produced by the earthquake. The deformation measurement was used to determine the fault geometry and the coseismic distributed slip model with a constrained least square algorithm based on the homogeneous elastic half-space model. We divided the fault into four segments (named AS, BS, CS and DS, from the north to the south) in the inversion. The BS segment was almost parallel to the DS segment, the CS segment linked the BS and DS segments, and these three fault segments formed a fault step-over system. The Coulomb failure stress (CFS) change on the causative fault was also calculated. Results show that the maximum SAR line-of-sight (LOS) and horizontal deformation were −1.8 m and 3.6 m, respectively. The earthquake ruptured a 210-km-long fault with variable strike angles. The ruptured pattern of the causative fault is mainly a sinistral slip. Almost-pure normal characteristics could be identified along the fault segment across the Palu bay, which could be one of the factors resulting in the tsunami. The main slip area was concentrated at the depths of 0–20 km, and the maximum slip was 3.9 m. The estimated geodetic moment of the earthquake was 1.4 × 1020 Nm, equivalent to an earthquake of Mw 7.4. The CFS results demonstrate that the fault step-over of 5.3 km did not terminate the rupture propagation of the main shock to the south. Two M>6 earthquakes (the 23 January 2005 and the 18 August 2012) decreased CFS along CS segment and the middle part of DS segment of the 2018 main shock. This implies that the stress release during the previous two earthquakes may have played a vital role in controlling the coseismic slip pattern of the 2018 earthquake. [ABSTRACT FROM AUTHOR]

A good knowledge of the quality of the satellite soil moisture products is of great importance for their application and improvement. This paper examines the performance of eight satellite-based soil moisture products, including the Soil Moisture Active Passive (SMAP) passive Level 3 (L3), the Soil Moisture and Ocean Salinity (SMOS) Centre Aval de Traitement des Données SMOS (CATDS) L3, the Japan Aerospace Exploration Agency (JAXA) Advanced Microwave Scanning Radiometer 2 (AMSR2) L3, the Land Parameter Retrieval Model (LPRM) AMSR2 L3, the European Space Agency (ESA) Climate Change Initiative (CCI) L3, the Chinese Fengyun-3B (FY3B) L2 soil moisture products at a coarse resolution of ~0.25°, and the newly released SMAP enhanced passive L3 and JAXA AMSR2 L3 soil moisture products at a medium resolution of ~0.1°. The ground soil moisture used for validation were collected from two well-calibrated and dense networks, including the Little Washita Watershed (LWW) network in the United States and the REMEDHUS network in Spain, each with different land cover. The results show that the SMAP passive soil moisture product outperformed the other products in the LWW network region, with an unbiased root mean square (ubRMSE) of 0.027 m3 m-3, whereas the FY3B soil moisture performed the best in the REMEDHUS network region, with an ubRMSE of 0.025 m3 m-3. The JAXA product performed much better at 0.25° than at 0.1°, but at both resolutions it underestimated soil moisture most of the time (bias < -0.05 m3 m-3). The SMAP-enhanced passive soil moisture product captured the temporal variation of ground measurements well, with a correlation coefficient larger than 0.8, and was generally superior to the JAXA product. The LPRM showed much larger amplitude and temporal variation than the ground soil moisture, with a wet bias larger than 0.09 m3 m-3. The underestimation of surface temperature may have contributed to the general dry bias found in the SMAP (-0.018 m3 m-3 for LWW and 0.016 m3 m-3 for REMEDHUS) and SMOS (-0.004 m3 m-3 for LWW and -0.012 m3 m-3 for REMEDHUS) soil moisture products. The ESA CCI product showed satisfactory performance with acceptable error metrics (ubRMSE < 0.045 m3 m-3), revealing the effectiveness of merging active and passive soil moisture products. The good performance of SMAP and FY3B demonstrates the potential in integrating them into the existing long-term ESA CCI product, in order to form a more reliable and useful product. [ABSTRACT FROM AUTHOR]