Many popular deconvolution methods based on Robinson’s convolutional model have played an important role in improving the temporal resolution of seismic data. However, the outcomes of applying these deconvolution methods to real land seismic data are not always desirable due to the effect of noise in the deconvolution process. Although the noise in the seismogram can be minimized during the recording process, the effect of residual noise on deconvolution operators can result in poor deconvolution output. To address the shortcomings of conventional deconvolution methods, we developed a new deconvolution method based on a multichannel statistical principle. In the proposed method, we have extended the surface-consistent convolutional model to include a noise component, thus including the noise effect on deconvolution operators in the deconvolution process. According to the proposed multichannel statistical strategy, we first calculated the autocorrelation of the seismogram, in which the lateral variation effect on the wavelet is considered because of inhomogeneities in the vicinity of sources and receivers. Then, we adopted a local fitting technique to approximate the autocorrelation of the seismic wavelet. To obtain the seismic data with a broad bandwidth and low-noise level, we used the integral-Ricker wavelet as the desired output wavelet. Tests on synthetic data and real land seismic data demonstrate the effectiveness of the proposed method in increasing the resolution of seismic signals.
Reconstructing an accurate and high-resolution subsurface model is attractive in the fields of both geology and seismology. However, due to the band-limited characteristics of seismic data, the inversion greatly depends on the reliability of the initial model. A fairly acceptable initial model could lay a good foundation for seismic inversion. In this article, we first introduce a well-log interpolation method with the local slope as a constraint for building a high-fidelity starting model in prestack amplitude versus offset/angle (AVO/AVA) inversion. First, we briefly review the basic theory of general seismic inversion. Then, instead of using the conventional preconditioned least-squares method, we introduce shaping regularization theory into the geological structure-guided well-log interpolation to accelerate the convergence. We use the plane-wave destruction (PWD) algorithm to extract the slope attribute from seismic data, images, or velocity models. The slope is used as the constraint to solve the inverse problem based on the shaping regularization method. Numerical examples demonstrate that the proposed initial model building method performs better than the conventional ones. It greatly improves the accuracy of inversion results. Furthermore, we apply the proposed model building method to the inverse problems of AVO/AVA inversion and reservoir parameter estimation of several field data sets for the first time, which demonstrate encouraging performance.
In this study, two collection 6 (C6) Moderate Resolution Imaging Spectroradiometer (MODIS) level-2 land surface temperature (LST) products (MYD11_L2 and MYD21_L2) from the Aqua satellite were evaluated using temperature-based (T-based) and radiance-based (R-based) validation methods over barren surfaces in Northwestern China. The ground measurements collected at four barren surface sites from June 2012 to September 2018 during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment were used to perform the T-based evaluation. Ten sand dune sites were selected in six large deserts in Northwestern China to carry out an R-based validation from 2012 to 2018. The T-based validation results indicate that the C6 MYD21 LST product has a better accuracy than the C6 MYD11 product during both daytime and nighttime. The LST is underestimated by the C6 MYD11 products at the four T-based sites during the daytime, with a mean bias of -2.82 K and a mean RMSE of 3.82 K, whereas the MYD21 LST product has a mean bias and RMSE of -0.51 and 2.53 K, respectively. The LST is also underestimated at night by the C6 MYD11 products at the four T-based sites, with a mean bias of -1.40 K and a mean RMSE of 1.72 K, whereas the MYD21 LST product has a mean bias and RMSE of 0.23 and 1.01 K, respectively. For the R-based validation, the MYD11 results are associated with large negative biases during both daytime and nighttime at three sand dune sites and biases within 1 K at the other seven sites, whereas the MYD21 results are more consistent at all ten sand dune sites, with a mean bias of 0.45 and 0.70 K for daytime and nighttime, respectively. The emissivities for these two products in MODIS bands 31 and 32 were compared with each other and then compared with the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) emissivity and laboratory emissivity. The results indicate that the emissivities in MODIS bands 31 and 32 of MYD11 at the four T-based and thr-
e of the R-based validation sites are overestimated and result in LST underestimation, whereas the emissivities of MYD21 are more consistent with the laboratory emissivity. Besides, an experiment was carried out to demonstrate that the physically retrieved dynamic emissivity of the MYD21 product can be utilized to improve the accuracy of the split-window (SW) algorithm for barren surfaces, making it a valuable data source for retrieving LST from different remote sensing data.
Due to the tradeoff between spatial and temporal resolutions commonly encountered in remote sensing, no single satellite sensor can provide fine spatial resolution land surface temperature (LST) products with frequent coverage. This situation greatly limits applications that require LST data with fine spatiotemporal resolution. Here, a deep learning-based spatiotemporal temperature fusion network (STTFN) method for the generation of fine spatiotemporal resolution LST products is proposed. In STTFN, a multiscale fusion convolutional neural network is employed to build the complex nonlinear relationship between input and output LSTs. Thus, unlike other LST spatiotemporal fusion approaches, STTFN is able to form the potentially complicated relationships through the use of training data without manually designed mathematical rules making it is more flexible and intelligent than other methods. In addition, two target fine spatial resolution LST images are predicted and then integrated by a spatiotemporal-consistency (STC)-weighting function to take advantage of STC of LST data. A set of analyses using two real LST data sets obtained from Landsat and moderate resolution imaging spectroradiometer (MODIS) were undertaken to evaluate the ability of STTFN to generate fine spatiotemporal resolution LST products. The results show that, compared with three classic fusion methods [the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the spatiotemporal integrated temperature fusion model (STITFM), and the two-stream convolutional neural network for spatiotemporal image fusion (StfNet)], the proposed network produced the most accurate outputs [average root mean square error (RMSE) <; 1.40 °C and average structural similarity (SSIM) > 0.971].
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This article describes the first results obtained from the Surface Waves Investigation and Monitoring (SWIM) instrument carried by the China France Oceanography Satellite (CFOSAT), which was launched on October 29, 2018. SWIM is a Ku-band radar with a near-nadir scanning beam geometry. It was designed to measure the spectral properties of surface ocean waves. First, the good behavior of the instrument is illustrated. It is then shown that the nadir products (significant wave height, normalized radar cross section, and wind speed) exhibit an accuracy similar to standard altimeter missions, thanks to a new retracking algorithm, which compensates a lower sampling rate compared to standard altimetry missions. The off-nadir beam observations are analyzed in detail. The normalized radar cross section varies with incidence and wind speed as expected from previous studies presented in the literature. We illustrate that, in order to retrieve the wave spectra from the radar backscattering fluctuations, it is crucial to apply a speckle correction derived from the observations. Directional spectra of ocean waves and their mean parameters are then compared to wave model data at the global scale and to in situ data from a selection of case studies. The good efficiency of SWIM to provide the spectral properties of ocean waves in the wavelength range [70–500 m] is illustrated. The main limitations are discussed, and the perspectives to improve the data quality are presented.
The detection of small metallic objects buried in mineralized soil poses a challenge for metal detectors, especially when the response from the metallic objects is orders of magnitude below the response from the soil. This article describes a new, handheld, detector system based on magnetic induction spectroscopy (MIS), which can be used to detect buried metallic objects, even in challenging soil conditions. Experimental results consisting of 1669 passes across either buried objects or empty soil are presented. Fourteen objects were buried at three different depths in three types of soil including nonmineralized and mineralized soils. A novel processing algorithm is proposed to demonstrate how spectroscopy can be used to detect metallic objects in mineralized soils. The algorithm is robust across all types of soil, objects, and depths used in this experiment and achieves a true positive rate over 99% at a false-positive rate of less than 5%, based on just a single pass over the object. It has also been shown that the algorithm does not have to be trained separately for each soil type. The data gathered in the experiment are also published to enable more research on the processing algorithms for MIS-based detectors.
This article is devoted to investigating the microwave scattering mechanisms of oil-free and oil-covered sea surfaces for an incidence angle smaller than 30° in a backscattering configuration. The Elfouhaily spectrum is used to simulate an oil-free sea surface, whereas the Elfouhaily spectrum combined with the Jenkins damping model is applied to the simulation of an oil-covered sea surface. Then, the Kirchhoff approximation-stationary phase approximation (KA-SP) and the first order of small-slope approximation (SSA-1) are employed to simulate the scattering coefficients induced by specular scattering and total scattering, respectively. Importantly, a new parameter defined as specular scattering to total scattering ratio (STR) is proposed in this article, which can be used to measure the ratio of specular backscattered power to total backscattered power. The dependencies of the scattering coefficient and the STR on incidence angles, wind speeds, wind directions, oil thicknesses, and so on, are investigated. This article provides new insights for a better understanding of the evolution of microwave scattering mechanisms from oil-free and oil-covered sea surfaces in the transition region of incidence angles (from about 15° to 30°).
This article presents a new index, polarization-conversion ratio (PCR) to characterize depolarized bistatic scattering from rough surfaces with dielectric inhomogeneity and spatial anisotropy. We then investigate the dependence of PCR on both surface and radar parameters. Numerical results show that the distribution of PCR on the scattering plane varies with the polarization state of the incident wave and incident angle. The PCR clusters more in the cross-plane for horizontally polarized incidence. However, for vertically polarized incidence, the PCR disperses as “triangular shape” on the whole scattering plane with a sharp valley occurring in the incident plane. The following points can be drawn: 1) the inhomogeneity effectively enhances the PCR in the cross-plane; 2) the effect of anisotropy on the PCR is relatively weak, because the scattering is less affected by correlation length; 3) the impacts of surface rms height on the PCR are negative on the whole scattering plane; and 4) as the background permittivity increases, at the horizontally polarized incidence, the PCR is enhanced in the backward and forward regions, while at vertically polarized incidence, it is enhanced in the incident plane and the forward region. As is demonstrated, the PCR is an effective measure of the sensitivity of depolarization, making it potentially useful as a new reliable index for surface parameter inversion.
Many similarity measures (SMs) were proposed to measure the similarity between multimodal remote sensing (RS) images. Each SM is efficient to a different degree in different registration cases (we consider visible-to-infrared, visible-to-radar, visible-to-digital elevation model (DEM), and radar-to-DEM ones), but no SM was shown to outperform all other SMs in all cases. In this article, we investigate the possibility of deriving a more powerful SM by combining two or more existing SMs. This combined SM relies on a binary linear support vector machine (SVM) classifier trained using real RS images. In the general registration case, we order SMs according to their impact on the combined SM performance. The three most important SMs include two structural SMs based on modality independent neighborhood descriptor (MIND) and scale-invariant feature transform-octave (SIFT-OCT) descriptors and one area-based logarithmic likelihood ratio (logLR) SM: the former ones are more robust to structural changes of image intensity between registered modes, the latter one is to image noise. Importantly, we demonstrate that a single combined SM can be applied in the general case as well as in each particular considered registration case. As compared to existing multimodal SMs, the proposed combined SM [based on five existing SMs, namely, MIND, logLR, SIFT-OCT, phase correlation (PC), histogram of orientated phase congruency (HOPC)] increases the area under the curve (AUC) by from 1% to 21%. From a practical point of view, we demonstrate that complex multimodal image pairs can be successfully registered with the proposed combined SM, while existing single SMs fail to detect enough correspondences for registration. Our results demonstrate that MIND, SIFT, and logLR SMs capture essential aspects of the similarity between RS modes, and their properties are complementary for designing a new more efficient multimodal SM.
Satellite aerosol optical depth (AOD) is a quantitative parameter frequently used to estimate ground-level fine particulate matters (PM2.5)at regional to global scales. In this article, Himawari-8 apparent reflectance (top-of-atmosphere reflectance) data were used to estimate the hourly ground-level PM2.5 concentrations (Ref-PM2.5) using deep neural networks (DNNs), and comparison was conducted with the AOD-based PM2.5 estimation method (AOD-PM2.5). In high-density site areas, the Ref-PM2.5 method was closer to the actual situation and more capable of PM2.5 estimation compared with the AOD-PM2.5 method. The PM2.5 samples used in the AOD-PM2.5 method were less than one-half of the Ref-PM2.5 method due to unavailability of AOD observations, which might be due to strict surface assumptions, cloud detection, and error in the aerosol scheme used in the AOD inversion method. This led to many missing values of AOD-derived PM2.5 in the spatial distribution map of a single day. Moreover, similar hourly variations in PM2.5 were observed for both the methods, and the highest concentration of PM2.5 appeared at the junction of Jiangsu, Anhui, and Shandong at 08:00, 09:00, and 10:00 in local time, which gradually decreased at 11:00 and reached to a minimum value at 16:00 and 18:00.
The application of empirical mode decomposition (EMD) in the analysis and processing of lightning electric field waveforms acquired by the low-frequency e-field detection array (LFEDA) in China has significantly improved the capabilities of the low-frequency/very-low-frequency (LF/VLF) time-of-arrival technique for studying the lightning discharge processes. However, the inherent mode mixing and the endpoint effect of EMD lead to certain problems, such as an inadequate noise reduction capability, the incorrect matching of multistation waveforms, and the inaccurate extraction of pulse information, which limit the further development of the LFEDA’s positioning ability. To solve these problems, the advanced ensemble EMD (EEMD) technique is introduced into the analysis of LF/VLF lightning measurements, and a double-sided bidirectional mirror (DBM) extension method is proposed to overcome the endpoint effect of EMD. EEMD can effectively suppress mode mixing, and the DBM extension method proposed in this article can effectively suppress the endpoint effect, thus greatly improving the accuracy of a simulated signal after a 25–500-kHz bandpass filter. The resulting DBM_EEMD algorithm can be used in the LFEDA system to process and analyze the detected electric field signals to improve the system’s lightning location capabilities, especially in terms of accurate extraction and location of weak signals from lightning discharges. In this article, a 3-D image of artificially triggered lightning obtained from an LF/VLF location system is reported for the first time, and methods for further improving the location capabilities of the LF/VLF lightning detection systems are discussed.
Changing a logic switch threshold in the linear fit sulfur dioxide (LFSO2) algorithm improves the performance based on the evaluation of the NOAA operational atmospheric SO2 near-real-time (NRT) retrieval. The LFSO2 is used to create estimates from measurements made by the Suomi NPP (S-NPP) Ozone Mapping and Profiler Suite (OMPS). We evaluate the LFSO2 and compare the results to those from a principal component analysis (PCA) offline algorithm. Twenty independent volcanic scenarios and one environmental disaster scenario spread over eight years are selected for this comparison. More than three months of Kilauea volcanic activity in 2018 are monitored and are included in this evaluation and comparison. We found that the operational LFSO2 retrievals at lower troposphere (TRL), mid-troposphere (TRM), and lower stratosphere (STL) exhibited a discontinuity and have a saturation-like relationship if compared with PCA results. Using the new retrieval logic, the discontinuity in LFSO2 retrievals and the saturation appearance in comparisons vanished and a close to a linear relationship with the matchup data from the PCA retrieval products is demonstrated. The minimum detectable values for all three SO2 layer products and the planetary boundary layer (PBL) products are estimated with the updated LFSO2 algorithm. Results for a volcanic cloud over Colombia for the updated LFSO2 for OMPS and a Differential Optical Absorption Spectroscopy (DOAS) algorithm for the Tropospheric Monitoring Instrument (TROPOMI) measurements are also examined. Similar SO2 total mass estimates over the region are obtained from the two products.
Since its launch in October 2011, the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite has provided high quality global ocean color products, which include normalized water-leaving radiance spectra $nL_{w}$ ( $lambda $ ) of six moderate (M) bands (M1–M6) at the wavelengths of 410, 443, 486, 551, 671, and 745 nm with a spatial resolution of 750-m, and one imagery (I) band at a wavelength of 638 nm with a spatial resolution of 375-m. Because the high-resolution I-band measurements are highly correlated spectrally to those of M-band data, it can be used as a guidance to super-resolve the M-band $nL_{w}$ ( $lambda $ ) imagery from 750- to 375-m spatial resolution. Super-resolving images from coarse spatial resolution to finer ones have been a field of very active research in recent years. However, no previous studies have been applied to satellite ocean color remote sensing, in particular, for VIIRS ocean color applications. In this study, we employ the deep convolutional neural network (CNN) technique to glean the high-frequency content from the VIIRS I1 band and transfer to super-resolved M-band ocean color images. The network is trained to super-resolve each of the VIIRS six M-bands $nL_{w}$ ( $lambda $ ) separately. In our results, the super-resolved (375-m) $nL_{w}$ ( $lambda $ ) images are much sharper and sh-
w finer spatial structures than the original images. Quantitative evaluations show that biases between the super-resolved and original $nL_{w}$ ( $lambda $ ) images are small for all bands. However, errors in the super-resolved $nL_{w}$ ( $lambda $ ) images are wavelength-dependent. The smallest error is found in the super-resolved $nL_{w}$ (551) and $nL_{w}$ (671) images, and error increases as the wavelength decreases from 486 to 410 nm. The results show that the networks have the capability to capture the correlations of the M-band and the I1 band images to super-resolved M-band images.
As interferometric synthetic aperture radar (InSAR) data improve in their global coverage and temporal sampling, studies of ground deformation using InSAR are becoming feasible even in heavily vegetated regions such as the American Pacific Northwest (PNW) and Sumatra. However, ongoing forest disturbance due to logging, wildfires, or disease can introduce time-variable signals which could be misinterpreted as ground displacements. This study constrains the error introduced into InSAR time series in the presence of time-variable forest disturbance using synthetic data. For satellite platforms with randomly distributed orbital positions in time (e.g., Sentinel-1), mid-time series forest disturbance results in random error on the order of 0.2 and 10 cm/year for 1-year secular and time-variable velocities, respectively. If the orbital positions are not randomly distributed in time (e.g., ALOS-1), a biased error on the order of 10 cm/year is introduced to the inferred secular velocity. A time series using real ALOS-1 data near Eugene, OR, USA, shows agreement with the bias estimated by synthetic models. Mitigation of time-variable land cover change effects can be achieved if their timing is known, either through independent observations of surface properties (e.g., Landsat/Sentinel-2) or through the use of more computationally expensive, nonlinear inversions with additional terms for the timing of height changes. Inclusion of these additional terms reduces the potential for misinterpretation of InSAR signals associated with land surface change as ground deformation.
Imbalanced training sets are known to produce suboptimal maps for supervised classification. Therefore, one challenge in mapping land cover is acquiring training data that will allow classification with high overall accuracy (OA) in which each class is also mapped onto similar user’s accuracy. To solve this problem, we integrated local adaptive region and box-and-whisker plot (BP) techniques into an iterative algorithm to expand the size of the training sample for selected classes in this article. The major steps of the proposed algorithm are as follows. First, a very small initial training sample (ITS) for each class set is labeled manually. Second, potential new training samples are found within an adaptive region by conducting local spectral variation analysis. Lastly, three new training samples are acquired to capture information regarding intraclass variation; these samples lie in the lower, median, and upper quartiles of BP. After adding these new training samples to the ITS, classification is retrained and the process is continued iteratively until termination. The proposed approach was applied to three very high-resolution (VHR) remote-sensing images and compared with a set of cognate methods. The comparison demonstrated that the proposed approach produced the best result in terms of OA and exhibited superiority in balancing user’s accuracy. For example, the proposed approach was typically 2%-10% more accurate than the compared methods in terms of OA and it generally yielded the most balanced classification.
