High Inversion Accuracy Research Articles

Adaptive weighted learning for vegetation contribution in soil moisture inversion using PolSAR data

ABSTRACT The high-resolution soil moisture inversion under vegetation from remote-sensing data is a challenging task. The key issue is to separate or eliminate the influence of vegetation. Therefore, the difficult problem is to select appropriate vegetation descriptors and accurately deal with them. With the great success of convolutional neural network (CNN) in PolSAR image classification, this paper intends to use CNN and introduce an adaptive weighted learning mechanism to calculate the new vegetation descriptors and eliminate the influence of vegetation on soil moisture inversion under vegetation by modifying the network structure. First, we propose an adaptive weighted learning module that can learn the adaptive weights of vegetation contribution which are the volume scattering components and the double-bounce scattering components in the Freeman-Durden decomposition to obtain new vegetation parameters. Second, we transform the inversion problem into a classification-regression problem, and use CNN to design two corresponding models to complete the inversion of soil moisture. In the proposed strategy, the adaptive weighted learning can effectively extract salient features from different components and combine them to obtain new vegetation descriptors and, the CNN can effectively utilize the spatial distribution of PolSAR data to automatically extract features that are useful for soil moisture inversion. The experimental results show that both classification network and retrospective network can achieve high inversion accuracy, whose inversion accuracy reaches up 96.66%, and root mean square error and the determination coefficient are 2.32% and 0.93, respectively. It suggests the great potential of combining the deep learning technique with traditional inversion model for soil moisture inversion from PolSAR data.

Cooperative multinetworks semi-supervised pre-stack seismic inversion

SUMMARY The elastic properties of the subsurface, such as density, P-velocity and S-velocity, can be estimated in pre-stack seismic inversion. In recent research, the deep neural network is widely used in pre-stack seismic inversion for its strong non-linear fitting and feature extraction ability. However, the label data is generally inadequate due to high drilling costs and strong data-sharing barriers in the field of exploration geophysics. In order to reduce the dependence of network performance on label data and ensure the accuracy of inversion mostly, semi-supervised learning is adopted. Here, we develop a cooperative multinetworks semi-supervised pre-stack seismic inversion method. In the cooperative multinetworks inversion framework, the inversion network, mapping network, and modification network are adopted to complete the inversion task cooperatively. A forward network is constructed to automatically generate seismic data from density, P-velocity and S-velocity, which can assist the above networks to complete semi-supervised learning. Compared with some published deep learning pre-stack inversion methods, the spatio-temporal correlation of data can be fully mined, the prior geological structure and low-frequency information can be utilized effectively, and reflectivity is adopted as an intermediate output parameter to improve the robustness of the method. The experiments on the Marmousi2 model demonstrate that cooperative multinetworks semi-supervised inversion strategy is superior to conventional semi-supervised inversion methods in both inversion accuracy and antinoise performance. In addition, the susceptibility experiments of the initial model indicate that the proposed method can maintain a high inversion accuracy with little effective information in the initial model. Finally, the proposed method is successfully applied to the field data and obtains a high-resolution inversion result.

Leaf Area Index Retrieval for Broadleaf Trees by Envelope Fitting Method Using Terrestrial Laser Scanning Data

Most conventional Leaf Area Index (LAI) retrieval methods using Terrestrial Laser Scanning (TLS) data are based on Beer’s law and are severely affected by the effects of leaf occlusion and aggregation. Moreover, the correction of LAI using the Clumping Index (CI) relies on assumptions and is generally not robust. This paper exploits the high spatial resolution and penetration capability of TLS to explore the physical meaning of point cloud data sampling and then model the leaf cluster envelope by the Alpha-shape algorithm. Subsequently, canopy LAI is obtained by counting the surface area of the envelope of each leaf cluster within the canopy and combining it with the projected area of the canopy. The entire process is physically based and introduces a new LAI inversion approach based on TLS. We tested the approach by simulating the TLS data of 25 synthetic trees with different leaf areas and morphologies to evaluate its robustness. Four strategies were adopted for parameter selection in the envelope modeling step to automate the process of finding the optimal envelope radius and improve the inversion accuracy of LAI. In comparison with the traditional LAI retrieval method based on Beer’s law (RMSE% is 47.3%), we found that the method proposed in this letter has a higher inversion accuracy with a minimum RMSE% of 27.7%. Our method also is significantly more robust for high LAI scenes and performs well in scenes with high occlusion and aggregation.

Semi-supervised learning seismic inversion based on Spatio-temporal sequence residual modeling neural network

The Spatio-temporal sequence residual modeling neural network (STSRM-net) is built to address the geophysical problem of obtaining P-impedance of the subsurface from the zero-offset seismic data and initial model of P-impedance. The initial model is used as the initial value of STSRM-net, and the modification value of the initial model and the residual between the modified initial model and true P-impedance are learned during the training. The Spatio-temporal characteristics of the data could be fully dug by STSRM-net. Limited by the inadequacy of labeled data, the semi-supervised learning inversion framework is constructed to train the STSRM-net. The performance of the STSRM-net and some deep learning inversion methods are compared on the synthetic dataset. The test results indicate that STSRM-net has higher inversion accuracy, stronger continuity, and better anti-noise performance. In addition, the robustness experiments indicate that the STSRM-net has excellent fault tolerance to the initial model. Finally, the STSRM-net is applied to the field data and compared with the sparse pulse inversion method, which also proves high inversion resolution, inversion accuracy and robustness to the choice of training wells of the proposed method.

Seismic Acoustic Impedance Inversion via Optimization-Inspired Semisupervised Deep Learning

Seismic acoustic impedance inversion (SAII) aims at recovering the subsurface impedance to achieve lithology interpretation. However, its ill-posedness and nonlinearity pose a great challenge to find an optimal solution. Regularization is an effective method to solve SAII by imposing prior information, but it suffers from high computational complexity and limited inversion performance. To mitigate the above limitations, we propose an optimization-inspired semisupervised deep learning SAII approach that incorporates the advantages between the model-driven optimization algorithm and the data-driven deep learning method. Specifically, it is implemented by parameterizing the alternating iterative method (AIM) by splitting it into two parts where the convolutional neural networks are adopted to learn the regularization terms and a nonlinear mapping and thus called the proposed network as AIM-SAIINet. The proposed method can not only simultaneously invert the seismic wavelet and impedance but also obtain high-resolution data as an intermediate product to facilitate the training of AIM-SAIINet and enhance the inversion accuracy. In addition, we introduce a joint semisupervised training scheme in which the network is first jointly pretrained in a supervised manner using the synthetic training data to provide good initial values, and then, a semisupervised training scheme is adopted to fine-tune it using few labeled data pairs to achieve high inversion accuracy. The synthetic and field data examples are conducted to validate the effectiveness of AIM-SAIINet, which achieves higher inversion accuracy at a fast computational speed compared with the traditional methods.

Elastic isotropic and anisotropic full-waveform inversions using automatic differentiation for gradient calculations in a framework of recurrent neural networks

We have implemented multiparameter full-waveform inversions (FWIs) in the framework of recurrent neural networks in elastic isotropic and transversely isotropic media. A staggered-grid velocity-stress scheme is used to solve the first-order elastodynamic equations for forward modeling. The gradients of loss with respect to model parameters are obtained by automatic differentiation. Multiple elastic model parameters are simultaneously inverted with a minibatch optimizer. We prove the equivalency of full-batch automatic differentiation and the conventional adjoint-state method for inversions in elastic isotropic media. Synthetic tests on elastic isotropic models show that the minibatch configuration has a better convergence rate and higher inversion accuracy than full-batch elastic FWIs. Inversions with data that contain incoherent and coherent noise are tested, respectively. With automatic differentiation, we determine the ease of extension to anisotropic media with two parameterizations, and the potential to implement it for more general media.

Monitoring Sulfur Content in Marine Fuel Oil Using Ultraviolet Imaging Technology

The emission of SO2 from ships is an important source of atmospheric pollution. Therefore, the International Maritime Organization (IMO) has established strict requirements for the sulfur content of marine fuel oil. In this paper, a new optical noncontact detection technique for ship exhaust emissions analysis is studied. Firstly, the single-band simulation analysis model of the imaging detection technology for SO2 concentration in ship exhaust gas and the deep neural network model for the prediction of sulfur content were established. A bench test was designed to monitor the tail gas concentration simultaneously using online and imaging detection methods, so as to obtain the concentration data in the flue and the ultraviolet image data. The results showed that 300 nm had a higher inversion accuracy than the other two bands. Finally, a deep neural network model was trained with the SO2 concentration data from the inversion and the engine power, and the predictive model of sulfur content in marine fuel oil was thereby obtained. When the deep learning model was used to predict sulfur content, the prediction accuracy at 300, 310, and 330 nm was 73%, 94%, and 71%, respectively.

Estimation model and its trade-off strategy of Mangifera persiciforma Colletotrichum gloeosporioides degree based on leaf reflection spectrum.

Colletotrichum gloeosporioides is one of the most common and serious fungal diseases of the tree Mangifera persiciforma. Yet we lack an effective method to evaluate this ecological interaction accurately. Here, we measured the functional traits and leaf reflectance spectrum of the host plants under different disease degrees. The findings provide a fast and efficient method for large-scale and high-precision monitoring of C. gloeosporioides in M. persiciforma stands. Using the collected leaf reflection data, we set up a prediction model of the optimal disease degree. Firstly, we found that leaf functional traits of M. persiciforma generally consisted of low leaf thickness, low relative chlorophyll content, small specific leaf area, high leaf tissue density, high dry matter content, low stomatal density, and large stomatal area. Secondly, leaf reflectivity increases with damage of C. gloeosporioides, which corresponds to five main reflection peaks and five absorption valleys in the spectral reflectance curve of leaves at the same positions (350-1800 nm). Thirdly, with the increase of infection degree, red edge slope and yellow edge slope decrease, while green peak reflectance, red valley reflectance, and blue edge slope all increase. Blue shift was detected in the red edge, green peak, and red valley, while red shift appeared at the blue edge and yellow edge. Finally, the best predictive model was that based on green peak reflectance (y=3.6396-0.0693x, R2=0.5149, RMSE [root-mean-square error] =0.2735), with an R2=0.92 and RMSE=0.0042 between its predicted vs. observed values. Because of its high inversion accuracy, the model can be used to predict the invasion conditions of M. persiciforma by C. gloeosporioides. Our study demonstrated that when plants are infected by C. gloeosporioides, there was a strong trade-off relationship between leaf functional traits. On the global leaf economics spectrum, the leaves tended toward the "slow investment-return" end when infected by C. gloeosporioides.

Inversion of Fracture Parameters in Anisotropic Coal Seam

Inversion of coal seam fracture parameters is of great significance to identify and predict the distribution state of coal seam fractures, and also provides certain theoretical support for the detection and identification of coalbed methane. According to the formula of PP wave reflection coefficient, this paper uses genetic algorithm to carry out inversion of coal seam fracture parameters and Thomsen anisotropy parameters, and analyzes the accuracy and stability of inversion results. The results show that the genetic algorithm has high inversion accuracy for density, shear wave velocity and fracture density of background medium, and has certain reference significance for fracture identification.

Simultaneous inversion of concentrations of POC and its endmembers in lakes: A novel remote sensing strategy

Data on the concentration of particulate organic carbon (POC) and its endmembers provide a basis for the characterisation of lake biogeochemical cycles. Here, a novel remote sensing strategy (the SCPOC algorithm) was developed to determine total POC concentrations, as well as terrestrial and endogenous POC concentrations in lakes. This strategy provides a successful example for the combination of isotope tracer and remote sensing technology. First, we obtained the terrestrial and endogenous POC concentration at the sampling point based on isotope tracing technology. Afterwards, we established a relationship between the phytoplankton absorption coefficient and the endogenous POC concentration (Cend), and applied a semi-analytical algorithm to invert the Cend value. Finally, the POC source ratio model and Cend value were combined to obtain the POC concentration (CPOC) and terrestrial POC (Cter). The results of synchronisation verification based on ocean and land colour instrument (OLCI) images show that the SCPOC algorithm has high Cend, Cter, and CPOC inversion accuracy, with MAPE values of 26.07%, 30.43%, and 42.28%, respectively. In fact, the SCPOC algorithm not only improved the accuracy of lake POC mapping, but also fills the gap of optical retrieval of POC endmember concentrations. Additionally, data from the OLCI images indicated that the studied lakes were dominated by external POC. However, because of the greater contribution of algal blooms to POC, this dominant advantage weakens in summer, although the terrestrial organic carbon carried by rainfall runoff also affects lake POC composition. Different POC sources have different ecological roles in lakes, and the superior POC end-element estimation capability of the SCPOC algorithm can not only be used as a supplement to traditional tracing methods, but also provides accurate spatial data for lake management.

Local Stress Field Correction Method Based on a Genetic Algorithm and a BP Neural Network for In Situ Stress Field Inversion

The in situ stress field is the fundamental factor causing deformation and damage in geotechnical engineering, so it is the main basis for underground engineering design and excavation. However, it is difficult to accurately obtain the in situ stress through most existing inversion methods in areas with complex geological conditions. For the problem of a relatively discrete and nonlinear relationship of measured stress in the Yebatan Hydropower Station area, a new in situ stress inversion method called the local stress field correction (LSFC) method combining a genetic algorithm (GA), backpropagation (BP) neural network, and submodel method is proposed. The inverted in situ stress results produced by this method show that the distribution of in situ stress is greatly influenced by tectonic movements in the Yebatan area, there is no obvious linear relationship with depth, and the stress release phenomenon occurs at the faults. By comparison with the multiple regression method, it is found that the method still has high inversion accuracy under complex geological conditions, and the average relative error of LSFC inversion results is 17.05%, which is much lower than the value of 43.58% via the multiple regression method. Therefore, the LSFC method can be used for the inversion of in situ stress in complex geological regions and provide a reference for engineering design and construction.

Remote sensing inversion of cultivated land fertility at county scale based on SWCI-NDVI feature space

It is objective needs during utilization and management of regional cultivated land resource to use remote sensing to accurately and efficiently retrieve the status of cultivated land fertility at county level and realize the gradation of cultivated land rapidly. In this study, with Dongping County as a case, using Landsat TM satellite imagery and cultivated land fertility evaluation data, the moisture vegetation fertility index (MVFI) was constructed based on surface water capacity index (SWCI) and normalized difference vegetation index (NDVI), and then the optimal inversion model was optimized to obtain the best inversion model, which was further applied and verified at the county scale. The results showed that the correlation coefficient between MVFI and integrated fertility index (IFI) was -0.753, which could comprehensively reflect the growth of winter wheat, soil moisture and land fertility, and had clear biophysical significance. The best inversion model was the quadratic model, with high inversion accuracy. This model was suitable for the inversion of cultivated land fertility in the county. The spatial distribution and uniformity of the inversion results were similar to the results of soil fertility evaluation. The area differences between the high, medium and low grades were all less than 2.9%. This study provided a remote sensing inversion method of cultivated land fertility based on the feature space theory, which could effectively improve the evaluation efficiency and prediction accuracy of cultivated land fertility at the county scale.

MODIS Aerosol Optical Thickness Product Algorithm Verification and Analysis

ABSTRACT A Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical thickness inversion algorithm has been rapidly developed in recent years, forming various representative aerosol optical thickness remote sensing products. Typical inversion algorithms include Dark Target, Dark Target–Deep Blue (DTB), and the high-spatial-resolution aerosol retrieval algorithm based on a priori land surface (HARLS). Both DTB and HARLS can realise aerosol optical depth inversion over land worldwide, but their accuracy and space–time adaptability vary. In this study, 80 Aerosol Robotic Network data worldwide were selected as the evaluation basis to quantitatively analyse and verify the adaptability and accuracy of different aerosol inversion algorithms on various underlying surfaces. The results indicate that the DTB algorithm has a higher inversion accuracy in vegetation area, whereas HARLS algorithm performs better in urban area; the HARLS and DTB algorithm accuracies in desert area type were found to be similar. The results provide a basis for selecting appropriate aerosol optical thickness remote sensing products for different applications.

Estimating salt content of vegetated soil at different depths with Sentinel-2 data.

The accurate and timely monitoring of the soil salt content (SSC) at different depths is the prerequisite for the solution to salinization in the arid and semiarid areas. Sentinel-2 has demonstrated significant superiority in SSC inversion for its higher temporal, spatial and spectral resolution, but previous research on SSC inversion with Sentinel-2 mainly focused on the unvegetated surface soil. Based on Sentinel-2 data, this study aimed to build four machine learning models at five depths (0∼20 cm, 20∼40 cm, 40∼60 cm, 0∼40 cm, and 0∼60 cm) in the vegetated area, and evaluate the sensitivity of Sentinel-2 to SSC at different depths and the inversion capability of the models. Firstly, 117 soil samples were collected from Jiefangzha Irrigation Area (JIA) in Hetao Irrigation District (HID), Inner Mongolia, China during August, 2019. Then a set of independent variables (IVs, including 12 bands and 32 spectral indices) were obtained based on the Sentinel-2 data (released by the European Space Agency), and the full subset selection was used to select the optimal combination of IVs at five depths. Finally, four machine learning algorithms, back propagation neural network (BPNN), support vector machine (SVM), extreme learning machine (ELM) and random forest (RF), were used to build inversion models at each depth. The model performance was assessed using adjusted coefficient of determination (R2adj), root mean square error (RMSE) and mean absolute error (MAE). The results indicated that 20∼40 cm was the optimal depth for SSC inversion. All the models at this depth demonstrated a good fitting (R2adj≈ 0.6) and a good control of the inversion errors (RMSE < 0.16%, MAE < 0.12%). At the depths of 40∼60 cm and 0∼20 cm the inversion performance showed a slight and a great decrease respectively. The sensitivity of Sentinel-2 to SSC at different depths was as follows: 20∼40 cm > 40∼60 cm > 0∼40 cm > 0∼60 cm > 0∼20 cm. All four machine learning models demonstrated good inversion performance (R2adj > 0.46). RF was the best model with high fitting and inversion accuracy. Its R2adj at five depths were between 0.5 to 0.68. The SSC inversion capabilities of all the four models were as follows: RF model > ELM model > SVM model > BPNN model. This study can provide a reference for soil salinization monitoring in large vegetated area.

Suitability Evaluation of Typical Drought Index in Soil Moisture Retrieval and Monitoring Based on Optical Images

As a basic agricultural parameter in the formation, transformation, and consumption of surface water resources, soil moisture has a very important influence on the vegetation growth, agricultural production, and healthy operation of regional ecosystems. The Aksu river basin is a typical semi-arid agricultural area which seasonally suffers from water shortage. Due to the lack of knowledge on soil moisture change, the water management and decision-making processes have been a difficult issue for local government. Therefore, soil moisture monitoring by remote sensing became a reasonable way to schedule crop irrigation and evaluate the irrigation efficiency. Compared to in situ measurements, the use of remote sensing for the monitoring of soil water content is convenient and can be repetitively applied over a large area. To verify the applicability of the typical drought index to the rapid acquisition of soil moisture in arid and semi-arid regions, this study simulated, compared, and validated the effectiveness of soil moisture inversion. GF-1 WFV images, Landsat 8 OLI images, and the measured soil moisture data were used to determine the Perpendicular Drought Index (PDI), the Modified Perpendicular Drought Index (MPDI), and the Vegetation Adjusted Perpendicular Drought Index (VAPDI). First, the determination coefficients of the correlation analyses on the PDI, MPDI, VAPDI, and measured soil moisture in the 0–10, 10–20, and 20–30 cm depth layers based on the GF-1 WFV and Landsat 8 OLI images were good. Notably, in the 0–10 cm depth layers, the average determination coefficient was 0.68; all models met the accuracy requirements of soil moisture inversion. Both indicated that the drought indices based on the Near Infrared (NIR)-Red spectral space derived from the optical remote sensing images are more sensitive to soil moisture near the surface layer; however, the accuracy of retrieving the soil moisture in deep layers was slightly lower in the study area. Second, in areas of vegetation coverage, MPDI and VAPDI had a higher inversion accuracy than PDI. To a certain extent, they overcame the influence of mixed pixels on the soil moisture spectral information. VAPDI modified by Perpendicular Vegetation Index (PVI) was not susceptible to vegetation saturation and, thus, had a higher inversion accuracy, which makes it performs better than MPDI’s in vegetated areas. Third, the spatial heterogeneity of the soil moisture retrieved by the GF-1 WFV and Landsat 8 OLI image were similar. However, the GF-1 WFV images were more sensitive to changes in the soil moisture, which reflected the actual soil moisture level covered by different vegetation. These results provide a practical reference for the dynamic monitoring of surface soil moisture, obtaining agricultural information and agricultural condition parameters in arid and semi-arid regions.

Back analysis of permeability coefficient of earth-rock dam based on EMD-RVM

Since contemporary information-retrieval systems rely heavily on the content of titles and abstracts to identify relevant articles in literature searches, great care should be taken in constructing both. Since the permeability coefficient of the dam material of the earth-rock dam directly affects the seepage flow of the dam, in order to improve the accuracy of retrieving the permeability coefficient from the measured value of the seepage flow, the empirical mode decomposition method (EMD) is used to monitor the actual seepage flow of the weir. After decomposition, multiple sets of intrinsic modal functions (IMF) were generated to fit the relationship between water level and seepage flow; then the finite element software was used to calculate the seepage flow corresponding to the randomly distributed infiltration parameters at different water levels in order to the RVM model performs inverse analysis on the permeability coefficient. The training sample group is constructed by finite element calculation, the calculated seepage volume of the sample group is used as the input value, and the corresponding permeability coefficient is used as the output value. Based on EMD-RVM, a nonlinear mapping relationship between the permeability coefficient and seepage volume of each section of the face rockfill dam is established. Using the model inversion, the results show that the EMD-RVM model has high inversion accuracy and is significantly higher than the conventional RVM model and GA-BP model. The infiltration pressure of the face rockfill dam can be calculated from the inversion result of the permeability coefficient, and the error with the measured value is small, which verifies the feasibility of the model.

Hyperspectral Inversion of Chromium Content in Soil Using Support Vector Machine Combined with Lab and Field Spectra

Chromium is not only an essential trace element for the growth and development of living organisms; it is also a heavy metal pollutant. Excessive chromium in farmland soil will not only cause harm to crops, but could also constitute a serious threat to human health through the cumulative effect of the food chain. The determination of heavy metals in tailings of farmland soil is an essential means of soil environmental protection and sustainable development. Hyperspectral remote sensing technology has good characteristics, e.g., high speed, macro, and high resolution, etc., and has gradually become a focus of research to determine heavy metal content in soil. However, due to the spectral variation caused by different environmental conditions, the direct application of the indoor spectrum to conduct field surveys is not effective. Soil components are complex, and the effect of linear regression of heavy metal content is not satisfactory. This study builds indoor and outdoor spectral conversion models to eliminate soil spectral differences caused by environmental conditions. Considering the complex effects of soil composition, we introduce a support vector machine model to retrieve chromium content that has advantages in solving problems such as small samples, non-linearity, and a large number of dimensions. Taking a mining area in Hunan, China as a test area, this study retrieved the chromium content in the soil using 12 combination models of three types of spectra (field spectrum, lab spectrum, and direct standardization (DS) spectrum), two regression methods (stepwise regression and support vector machine regression), and two factors (strong correlation factor and principal component factor). The results show that: (1) As far as the spectral types are concerned, the inversion accuracy of each combination of the field spectrum is generally lower than the accuracy of the corresponding combination of other spectral types, indicating that field environmental interference affects the modeling accuracy. Each combination of DS spectra has higher inversion accuracy than the corresponding combination of field spectra, indicating that DS spectra have a certain effect in eliminating soil spectral differences caused by environmental conditions. (2) The inversion accuracy of each spectrum type of SVR_SC (Support Vector Regression_Strong Correlation) is the highest for the combination of regression method and inversion factor. This indicates the feasibility and superiority of inversion of heavy metals in soil by a support vector machine. However, the inversion accuracy of each spectrum type of SVR_PC (Support Vector Regression_Principal Component) is generally lower than that of other combinations, which indicates that, to obtain superior inversion performance of SVR, the selection of characteristic factors is very important. (3) Through principal component regression analysis, it is found that the pre-processed spectrum is more stable for the inversion of Cr concentration. The regression coefficients of the three types of differential spectra are roughly the same. The five statistically significant characteristic bands are mostly around 384–458 nm, 959–993 nm, 1373–1448 nm, 1970–2014 nm, and 2325–2400 nm. The research results provide a useful reference for the large-scale normalization monitoring of chromium-contaminated soil. They also provide theoretical and technical support for soil environmental protection and sustainable development.

Inversion of the Equivalent Electric Dipole Moment of Ship’s Corrosion-Related Static Electric Field in Frequency Domain

When evaluating the ship’s underwater electric field stealth, the underwater electric potential or the underwater electric field is often used, but it is easily affected by the environment conditions. As a result, the evaluating accuracy is not high. To solve this problem, the equivalent electric dipole moment is used as the evaluating factor in this paper. Firstly, the method of inverting the equivalent electric dipole moment in the frequency domain is proposed. However, the limited measuring range will also lead to some errors based on the proposed method. As a result, we improve the proposed method by applying an integral correction which uses a standard dipole source. To test the effectiveness of this method, a simulation experiment is carried out, and the results show that the method has high inversion accuracy even in a low signal-to-noise ratio (SNR) environment. This method has provided a new technological approach for evaluating the ship’s corrosion-related static electric field.

Mapping seafloor topography of gulf of Guinea using an adaptive meshed gravity-geologic method

An adaptive mesh form of the gravity-geologic method, known as improved Gravity-Geologic Method (iGGM), was implemented on free-air gravity anomalies and shipborne depths to obtain an improved 1′ × 1′ bathymetry model of the Gulf of Guinea (15°W–5°E, 4°S–4°N). An optimal density contrast of 8000 kg/m3 was used for the whole area. The iGGM model compared well with NGDC, ETOPO1, and SIO models; with difference standard deviations and correlation coefficients being 180.20 m, 0.9248, 184.34 m, 0.9551, and 179.84 m, 0.8886, respectively. These prove generally that iGGM is efficient for estimating bathymetry with limited shipborne depths. The influence of shipborne depths quantity and optimal density contrast on bathymetry inversion are analysed, respectively, for the whole region and three subregions (15°W–8°W, 4°S–2°N; 7°W–2°E, 2°S–2°N; and 1°E–5°E, 4°S–0°N). Results showed that, compared with the mountainous areas, higher inversion accuracy (standard deviation of test differences less than 50 m) is achievable in the low-lying region using fewer shipborne depths. With 75% of shipborne depths used for the entire model, the standard deviation of differences between iGGM and shipborne depths at test points was 184.74 m. This indicates that to further improve the region’s bathymetry, more ship sounding is required in the mountainous areas. Numerical results showed different optimal densities should be selected for different areas, especially for the mountainous areas. Using a common density contrast in the whole region may limit the accuracy of the bathymetry inversion.

Retrieval of Fine-Resolution Aerosol Optical Depth (AOD) in Semiarid Urban Areas Using Landsat Data: A Case Study in Urumqi, NW China

The aerosol optical depth (AOD) represents the light attenuation by aerosols and is an important threat to urban air quality, production activities, human health, and sustainable urban development in arid and semiarid regions. To some extent, the AOD reflects the extent of regional air pollution and is often characterized by significant spatiotemporal dynamics. However, detailed local AOD information is ambiguous at best due to limited monitoring techniques. Currently, the availability of abundant satellite data and constantly updated AOD extraction algorithms offer unprecedented perspectives for high-resolution AOD extraction and long-time series analysis. This study, based on the long-term sequence MOD09A1 data from 2010 to 2018 and lookup table generation, uses the improved deep blue algorithm (DB) to conduct fine-resolution (500 m) AOD (at 550 nm wavelength) remote sensing (RS) estimation on Landsat TM/OLI data from the Urumqi region, analyzes the spatiotemporal AOD variation characteristics in Urumqi and combines gray relational analysis (GRA) and the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to analyze AOD influence factors and simulate pollutant propagation trajectories in representative periods. The results demonstrate that the improved DB algorithm has a high inversion accuracy for continuous AOD inversion at a high spatial resolution in urban areas. The spatial AOD distribution in Urumqi declines from urban to suburban areas, and higher AODs are concentrated in cities and along roads. Among these areas, Xinshi District has the highest AOD, and Urumqi County has the lowest AOD. The seasonal AOD variation characteristics are distinct, and the AOD order is spring (0.411) &gt; summer (0.285) &gt; autumn (0.203), with the largest variation in spring. The average AOD in Urumqi is 0.187, and the interannual variation generally shows an upward trend. However, from 2010 to 2018, AOD first declined gradually and then declined significantly. Thereafter, AOD reached its lowest value in 2015 (0.076), followed by a significant AOD increase, reaching a peak in 2016 (0.354). This shows that coal to natural gas (NG) project implementation in Urumqi promoted the improvement of Urumqi’s atmospheric environment. According to GRA, the temperature has the largest impact on the AOD in Urumqi (0.699). Combined with the HYSPLIT model, it was found that the aerosols observed over Urumqi were associated with long-range transport from Central Asia, and these aerosols can affect the entire northern part of China through long-distance transport.