TOC Alert for Publication# 36
Updated: 3 years 8 weeks ago
Mon, 03/01/2021 - 00:00
We propose a stable ${Q}$ -estimation approach based on the compensation of amplitude spectra in the time–frequency domain after a synchrosqueezed wavelet transform (SSWT). SSWT employing a post-processing frequency reallocation method to the original representation of a continuous wavelet transform (CWT) for improving its readability provides the sharper time–frequency representation of a signal when compared with the other traditional time–frequency methods such as CWT or S-transform. For ${Q}$ -estimation, we transform a seismic trace into the time–frequency domain using SSWT at first. Then, we derive the amplitude compensation in the SSWT domain. By searching the predetermined ${Q}$ range, the comparison between the compensated amplitude spectrum and the reference one in the SSWT domain is carried out. The output optimized ${Q}$ -factor estimation is evaluated by the minimum of the mean square error. For the robust and fast stabilization form of the amplitude compensation in the SSWT domain with noise amplification damping, the seismic pulse is truncated with a limited length and the obtained time–frequency maps using an SSWT are smoothed. The synthetic vertical seismic profiling data and the real stacked seismic data applications illustrate the effectiveness and the ability of the proposed method.
Mon, 03/01/2021 - 00:00
Wave simulation in absorptive media using decoupled fractional Laplacian wave equation has received widespread attention in recent years, largely due to its precise description of frequency independent $Q$ and easy attenuation compensation in seismic processing. With many algorithms to solve the fractional Laplacian, $k$ -space pseudo-spectral method is predominantly used in academia, where the computing facilities support fast Fourier transforms. However, its global nature prevents the parallelization and computational efficiency of the forward solver, especially for large-scale applications. We propose to solve viscoacoustic wave equation using domain decomposition. A local Fourier basis is constructed around the truncated area to improve the periodicity and smoothness of the decomposed wave information. After independently simulating in the subdomains, a pointwise patching procedure is applied to maintain the continuity of the wavefield between subdomains. Numerical experiments show that this new algorithm obtains high computational efficiency without compromising the numerical stability condition of the traditional pseudo-spectral method. This work becomes more attractive for seismic inversion and imaging problems by improving its parallelization.
Mon, 03/01/2021 - 00:00
Investigating the relations between land surface temperature (LST) and biophysical compositions can help the understanding of the surface biophysical process. However, there are still uncertainties in determining the impacts of biophysical compositions on LST due to the atmospheric effects. In this article, four atmospheric correction algorithms were used to correct 12 Landsat 8 images in Xi’an, Beijing, Wuhan, and Guangzhou, China, including the Atmospheric Correction for Flat Terrain (ATCOR2), Quick Atmospheric Correction (QUAC), Fast Line-of-sight Atmospheric Analysis of Spectral Hypercube (FLAASH), and Second Simulation of Satellite Signal in the Solar Spectrum (6S). Then, geodetector was used to investigate the atmospheric correction differences in the spatial heterogeneity relationships between LST and normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and bare soil index (BSI). Results indicate that the selected composition factors were greatly improved after atmospheric correction, and the relations between LST and three factors were characterized by obvious atmospheric correction differences in four study areas. On the whole, the 6S algorithm performed the best in improving the factor values and impacting the spatial heterogeneity relations between LST and biophysical compositions, followed by FLAASH, QUAC, and ATCOR2 algorithms. Except for Wuhan, 6S, FLAASH, and QUAC algorithms significantly enhanced the correlation between LST and NDVI. However, all algorithms weakened the correlations between LST, NDVI, and BSI, except Guangzhou. These findings have been verified using the regression analysis. In addition, with geodetector, combinations of any two composition factors all had strongly enhanced impacts on LST, and a combination between NDVI and NDBI performed the strongest in most cases.
Mon, 03/01/2021 - 00:00
Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
Mon, 03/01/2021 - 00:00
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Mon, 03/01/2021 - 00:00
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Mon, 02/01/2021 - 00:00
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Mon, 02/01/2021 - 00:00
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Mon, 02/01/2021 - 00:00
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Mon, 02/01/2021 - 00:00
The Greenhouse Gas Monitoring Instrument (GMI) is a short-wavelength infrared (SWIR) hyperspectral-resolution spectrometer onboard the Chinese satellite GaoFen-5 that uses a spatial heterodyne spectroscopy (SHS) interferometer to acquire interferograms. The GMI was designed to measure and study the source and sink processes of carbon dioxide and methane in the troposphere where the greenhouse effect occurs. In this study, the processing and geometric correction algorithms of the GMI Level 1 product (radiance spectrum) are introduced. The spectral quality and greenhouse gas (GHG) inversion ability of the Level 1 products are analyzed, and the results illustrate that the specifications meet the mission’s requirements. An initial evaluation of the resolution, signal-to-noise ratio (SNR), and stability of the radiance spectrum reveals that the overall function and performance are within the design objectives. A comparison between our Level 1 products and the theoretical spectrum shows that the root mean square (rms) of the residual is approximately 0.8%, and the Level 1 products of the GMI captured within five months after observations have good spectral stability characteristics (less than 0.005 cm−1 for Band 1, 0.003 cm−1 for Band 2, 0.002 cm−1 for Band 3, and 0.004 cm−1 for Band 4). These results demonstrate that the GMI payload and the processing algorithm all work well and reliably. Furthermore, based on the Level 1 products, a GHG retrieval experiment is carried out, and the results are compared with data from Total Column Carbon Observing Network (TCCON) stations. The initial comparison of the XCO2 results yields a value of 0.869 for ${R}^{2}$ (goodness of fit), 0.51 ppm for bias (mean of absolute error), and 0.53 ppm for ${sigma }$ (standard deviation of error). Similarly, the XCH4 comparison yields values of 0.841 for ${R}^{2}$ , 4.64 ppb for bias, and 4.66 ppb for $ {sigma}$ .
Mon, 02/01/2021 - 00:00
Recent optical remote sensing satellite missions, such as Sentinel-2 with the MultiSpectral Imager (MSI) onboard, allow the estimation of coastal water key parameters with very high spatial resolutions (down to 10 m). In this article, multiple approaches are proposed for retrieving chlorophyll-a (Chl-a) and total suspended matter (TSM) along the Adriatic and Tyrrhenian coasts in Italy, using both empirical and model-based frameworks to design regressive and neural network (NN) estimation methods. The latter proves to be more accurate on a regional scale, where standard ocean color physical models exhibit high uncertainty in their local parameterization due to the complex spectral characteristics of the observed scene. Retrieval results are encouraging for Chl-a with a coefficient of determination R2 up to 0.72 with a root-mean-square error (RMSE) of 0.33 mg m-3, using an empirical NN. The TSM algorithms exhibit higher uncertainty, mainly due to scarcity of in situ measurements and model parameterizations, with R2 = 0.52 and RMSE = 1.95 g/m3 using NNs. The bio-optical model, used for the development of model-based algorithms, shows some inadequacies in representing the inherent and apparent optical properties for the case study areas, especially considering the different spectral features between the oligotrophic Tyrrhenian Sea and the eutrophic Adriatic Sea. This study confirms the potential of Sentinel-2 MSI products for coastal water monitoring, but it also highlights key issues to be further tackled such as the atmospheric correction impact, the need of reliable in situ measurements, and possible bathymetry effects near the shores.
Mon, 02/01/2021 - 00:00
Based on the sea surface scattering mechanism of radio wave, compact high-frequency surface wave radar that employs the direction finding technology also shows promising potential for remotely mapping of ocean wave parameters. However, due to the low signal-to-noise ratio (SNR) of scattered echoes, the diverse external interference and clutter signals, other unresolved measurement uncertainties, the quality (such as accuracy, temporal, and spatial coverage rates) of wave maps are often limited. In this article, a novel, real-time, data quality control method is proposed to alleviate these issues. A comprehensive three-stage processing scheme is established, including the range-Doppler spectral processing, the spatial-grid processing, and the temporal-scale Kalman filtering. The first two stages aim to improve the echo signal quality and reduce the spatial gaps, respectively. The third stage is designed to mitigate the estimation error using an autoregression prediction model and to relate the observation error variance to the SNR of second-order Doppler spectral peak. A detailed verification and performance analysis between the field radar data and in situ ground truth data over one-month period is carried out, indicating that the proposed method can improve the reliability of wave maps with respect to the conventional Doppler spectral smoothing or averaging method, particularly in low sea state (i.e., low SNR) scenarios.
Mon, 02/01/2021 - 00:00
Waves much shorter than those measured by operational systems make significant contribution to the ocean surface roughness. This article describes a method to obtain the L-band tilting ocean surface roughness using wind speed and windsea dominant wave period coupled with a wind-wave spectrum model. Examples are presented with wind and dominant wave data from ocean buoys and hurricane hunters. Several related issues are discussed: high-frequency wave spectrum, integration limit, swell contribution, and measurements in extreme winds: 1) it is well known since the 1970s that with stationary sensors, extending the frequency range in measuring elevation spectrum does not yield useful short-wave information because of the low signal level and large Doppler frequency shift involved in measuring short waves. 2) Low-pass mean square slopes (LPMSSs) integrated to 5 and 11 rad/m are computed to quantify their difference as a function of wind speed and inverse wave age (IWA). The normalized difference decreases with increasing wind speed and decreasing IWA. 3) Swell contribution to the L-band LPMSS is almost negligible for wind speed greater than 5 m/s (less than 5% in 99% of observations). In low-wind conditions (wind speed less than 5 m/s), the swell contribution is difficult to assess because of inaccuracy in identifying the weak windsea system. 4) The coarse resolution in National Data Buoy Center (NDBC) wave spectra causes large data scatter in the computed LPMSS in very high winds (greater than 20 m/s). A mitigating solution is offered.
Mon, 02/01/2021 - 00:00
A new technique for automated retrieval of ice concentration from RADARSAT-2 dual-polarization HH-HV ScanSAR Wide images for subsequent assimilation in ice numerical models is presented. First, we extended our previously introduced ice and water detection approach operating at a 2.05 km x 2.05 km spatial scale to a set of 19 different spatial scales ranging from 2.05 km (41 pixels) down to 0.25 km (5 pixels). As the spatial resolution was increased, the overall accuracy of ice and water detection stayed at a very high level across all scales (between 99.5% and 99.8%), but the number of water retrievals substantially dropped. Second, we designed an approach for estimating ice concentration in a 2 km × 2 km (40 × 40 pixels) area consisting of 64 5 × 5 pixel blocks. The 5 × 5 pixel blocks which are initially classified as unknowns are iteratively combined in clusters with effective spatial scales larger than 5 pixels. The clusters are further classified as ice or water using the ice probability model corresponding to the effective spatial scale. The 40 x 40 pixel area becomes populated with high-resolution (5 x 5 pixels) ice and water retrievals, and the ice concentration is estimated based on the number of ice and water retrievals. The proposed approach produces a much better agreement with the Canadian Ice Service Image Analysis ice concentrations (rootmean-square error (RMSE) = 2.2%) compared to the original 2-km ice/water detection approach (RMSE = 19.9%). The developed technique will be adapted to the RADARSAT Constellation Mission data for data assimilation in Environment and Climate Change Canada Regional Ice-Ocean Prediction System.
Mon, 02/01/2021 - 00:00
Process-based ecosystem models are increasingly used to simulate the effects of a changing environment on vegetation growth in the past, present, and future. To improve the simulation, the multimodel ensemble mean (MME) and ensemble Bayesian model averaging (EBMA) methods are often used in optimizing the integration of ecosystem model ensemble. These two methods were compared with four other optimization techniques, including genetic algorithm (GA), particle swarm optimization (PSO), cuckoo search (CS), and interior-point method (IPM), to evaluate their efficiency in this article. Here, we focused on eight commonly used ecosystem models to simulate vegetation growth, represented by the growing season leaf area index (LAIgs), collected globally from 2000 to 2014. The performances of the multimodel ensembles and individual models were compared using the satellite-observed LAI products as the reference. Generally, ensemble simulations provide more accurate estimates than individual models. There were significant performance differences among the six tested methods. The IPM ensemble model simulated LAIgs more accurately than the other tested models, as the reduction in the root-mean-square error was 84.99% higher than the MME results and 61.50% higher than the EBMA results. Thus, IPM optimization can reproduce LAIgs trends accurately for 91.62% of the global vegetated area, which is double the area of the results from MME. Furthermore, the contributions and uncertainties of the individual models in the final simulated IPM LAIgs changes indicated that the best individual model (CABLE) showed the greatest area fraction for the maximum IPM weight (32.49%), especially in the low-lalitude to midlatitude areas.
Mon, 02/01/2021 - 00:00
The pathogenesis of wheat powdery mildew (WPM) is from the bottom to upper layers of the plant, and the vertical observation angle limits the early monitoring of WPM status. Multiangle remote sensing could effectively extract spatial structural information from different plant layers. The objectives of this study were to improve the monitoring accuracy of WPM severity and to screen suitable observation angles by developing a novel vegetation index. The monitoring capacities of the novel parameters [Normalized Powdery Mildew Index (NPMI) and Ratio Powdery Mildew Index (RPMI)] and 14 optimized traditional spectral parameters were compared at 13 observation angles and different angle ranges. The correlation between all spectral parameters and disease severity was superior in the forward observation direction than in the backward observation direction in the principal plane of the Sun, and the correlation between the two observation directions decreased with an increase in observation angle. The new spectral parameter suitable for inversion of disease index was RPMI (R744/R762 - 0.5 × R710), and the best optimal observation angle was +10°, with a 0.852 coefficient, which was 31.74% higher than that of the two-band spectral parameter, R744/R762. The fitting accuracy of the new parameter in the range of 0° to +30° in the forward direction was 0.704. RPMI could not only improve the monitoring accuracy of powdery mildew severity at a single angle but also achieve a more stable monitoring accuracy in the 0° to +30° range in the forward direction, which significantly expands the application scope of remote sensing monitoring and enhances the flexibility of the technology in actual production environments.
Mon, 02/01/2021 - 00:00
Prediction of large-scale water-related natural disasters such as droughts, floods, wildfires, landslides, and dust outbreaks can benefit from the high spatial resolution soil moisture (SM) data of satellite and modeled products because antecedent SM conditions in the topsoil layer govern the partitioning of precipitation into infiltration and runoff. SM data retrieved from Soil Moisture Active Passive (SMAP) have proved to be an effective method of monitoring SM content at different spatial resolutions: 1) radiometer-based product gridded at 36 km; 2) radiometer-only enhanced posting product gridded at 9 km; and 3) SMAP/Sentinel-1A/B products at 3 and 1 km. In this article, we focused on 9-, 3-, and 1-km SM products: three products were validated against in situ data using conventional and triple collocation analysis (TCA) statistics and were then merged with a Noah-Multiparameterization version-3.6 (NoahMP36) land surface model (LSM). An exponential filter and a cumulative density function (CDF) were applied for further evaluation of the three SM products, and the maximize-R method was applied to combine SMAP and NoahMP36 SM data. CDF-matched 9-, 3-, and 1-km SMAP SM data showed reliable performance: R and ubRMSD values of the CDF-matched SMAP products were 0.658, 0.626, and 0.570 and 0.049, 0.053, and 0.055 m3/m3, respectively. When SMAP and NoahMP36 were combined, the R-values for the 9-, 3-, and 1-km SMAP SM data were greatly improved: R-values were 0.825, 0.804, and 0.795, and ubRMSDs were 0.034, 0.036, and 0.037 m3/m3, respectively. These results indicate the potential uses of SMAP/Sentinel data for improving regional-scale SM estimates and for creating further applications of LSMs with improved accuracy.
Mon, 02/01/2021 - 00:00
Agricultural drought is a frequent global phenomenon. Solar-induced chlorophyll fluorescence (SIF) is a by-product of photosynthesis that can be used to monitor vegetation growth and agricultural drought. The global 0.05° spatial resolution data set has been obtained using the data-driven algorithm method. However, the broken farmland is not conducive to regional agricultural drought monitoring. Hence, 0.05° SIF products should be downscaled. On this basis, a convolutional neural network (CNN) downscaled work was conducted in this article to obtain 0.008° spatial resolution SIF results. The downscaled SIF and land surface temperature (LST) data were used to establish the temperature fluorescence dryness index (TFDI). The new TFDI was subsequently used for monitoring agricultural drought in Henan province (China) during the corn-growing season (from June to October 2013-2017). Results showed that the downscaled SIF data exhibit a good correlation with gross primary productivity (GPP) from the Moderate Resolution Imaging Spectroradiometer (MODIS) than 0.05° SIF products. During the study period, the soil moisture fluctuation corresponded well with precipitation, and the value of TFDI had an opposite fluctuation with soil moisture. Meanwhile, the annual averaged TFDI had a high correlation with summer corn yield (R = -0.84). In conclusion, the SIF results through the CNN-based downscaled method were reliable, and the new TFDI was suitable for region agricultural drought monitoring.
Mon, 02/01/2021 - 00:00
Accurate land use/land cover (LULC) mapping over a large area is essential to environmentally sustainable development. Recently, the Chinese government established a new national economic zone called the Xiongan New Area, and along with the upcoming large-scale urban construction, this area will inevitably experience a dramatic LULC change, which will threaten the local ecological balance. In this article, we proposed a two-stage approach for LULC mapping in the Xiongan New Area ahead of the forthcoming dense urban construction. The first stage is to obtain base-class maps through a supervised imagery classification. Specifically, we designed a new object-based framework consisting of automatic image segmentation, pixel-based probabilistic estimation, and area-weighted probability statistics for Sentinel-2 multiresolution imagery classification. The second stage is an LULC map refinement process in which the temporal features of each land use category are extracted to refine the LULC classification. Through the implementation of the proposed two-stage approach, an LULC map containing permanent water, temporal water, natural vegetation, barren land, built-up land, and cropland categories can be produced. Through an accuracy assessment, the proposed multiresolution imagery classification method achieved the highest overall accuracy of 88.58% and an average accuracy (AA) of 87.78% compared with conventional classification methods. After obtaining the refined LULC map, we find that the current Xiongan New Area is in a less developed state, that is, cropland accounts for the highest proportion of 51.59%, which is followed by natural vegetation (22.38%) and built-up land (15.69%).
Mon, 02/01/2021 - 00:00
In this article, an efficient algorithm for the reconstruction of a 1-D random rough surface profile separating two lossy dielectric half-spaces is presented. First, the general scattering problem is formulated by the use of surface integral equations (SIEs). Then, the synthetic scattering field data are obtained through the use of these conventional SIEs. In the inverse problem, the same SIEs together with the data equation are solved in an iterative fashion to reconstruct the surface variation. In the numerical implementation, the so-called ill-posed inverse problem is regularized in the sense of Tikhonov, and a least squares solution is obtained by the use of appropriate basis functions. A very detailed numerical assessment of the presented approach is provided which shows that the method is very effective and promising.
