Complex Surface Conditions Research Articles

Cloud Detection Fusion Algorithm for Complex and Variable Surface Conditions

Cloud Detection Fusion Algorithm for Complex and Variable Surface Conditions

Optimizing geological design of J-shaped deviation oil & gas well by deepening kickoff point

In the geological design of 2-D J-shaped deviated oil and gas wells, once the coordinates of wellhead and production layer target are determined (restricted by specific ground conditions and underground understanding, respectively), the position of the kickoff point (KOP) becomes very sensitive. By deepening the kickoff point (KOP) based on the current conventional fixed upper position and the dog leg severity (DLS) remaining unchanged within the penetrated range, a smaller drill axis and an angle between the production layer and axis can be obtained at the same designated target point to extend the section of penetrated oil-bearing layers, expand the oil drainage area, increase proven reserves, and ultimately enhance the oil production and recovery during the contract period. In addition, after optimization, it is expected to save 1-2% of well drilling costs by reducing drilling time. With the help of drilling engineering and production engineering, drilling KOP can be achieved easily in deeper and harder rock formations, which is able to improve the efficiency of the electric submersible pump (ESP) and to avoid eccentric wear of the ESP, resulting in extension of the service life of the ESP. This optimized design also runs in parallel with various well stimulation programs and projects without any confliction of each other. This design can be either used for comparison of the parameters of the completed inclined wells with assumed ones of deepening KOP design to show how the deepening KOP can be used to differentiate the increased thickness of the penetrated target layers and the drainage area of target zones, or it can be utilized to compare any pair of parameters between inclined production wells within a range. In the same drilling field, any random increase of 1° inclination angle in a stable inclination section can lead to a corresponding increase in the thickness of the penetrated oil-bearing layer and production. Our research results quantitatively indicate that in the geological design of J-shaped deviation oil and gas wells, the KOP can be re-set from a shallower fixed position along the current conventional trajectory to a reasonable position as deep as possible, which shall be suitable for most situations of optimizing well performance, especially for some restricted complex surface conditions and energy-deficient reservoirs, which assign the KOP a new geological significance in line with the business strategy of rapid oil recovery

Rice Identification Under Complex Surface Conditions with CNN and Integrated Remote Sensing Spectral-Temporal-Spatial Features

Accurate and effective rice identification has great significance for the sustainable development of agricultural management and food security. This paper proposes an accurate rice identification method that can solve the confused problem between fragmented rice fields and the surroundings in complex surface areas. The spectral, temporal, and spatial features extracted from the created Sentinel-2 time series were integrated and collaboratively displayed in the form of visual images, and a convolutional neural network model embedded with integrated information was established to further mine the key information that distinguishes rice from other types. The results showed that the overall accuracy, precision, recall, and F1-score of the proposed method for rice identification reached 99.4%, 99.5%, 99.5%, and 99.5%, respectively, achieving a better performance than the support vector machine classifier. Therefore, the proposed method can effectively reduce the confusion between rice and other types and accurately extract rice distribution information under complex surface conditions.

Cloud and Cloud Shadow Detection of GF-1 Images Based on the Swin-UNet Method

Cloud and cloud shadow detection in remote sensing images is an important preprocessing technique for quantitative analysis and large-scale mapping. To solve the problems of cloud and cloud shadow detection based on Convolutional Neural Network models, such as rough edges and insufficient overall accuracy, cloud and cloud shadow segmentation based on Swin-UNet was studied in the wide field of view (WFV) images of GaoFen-1 (GF-1). The Swin Transformer blocks help the model capture long-distance features and obtain deeper feature information in the network. This study selects a public GF1_WHU cloud and cloud shadow detection dataset for preprocessing and data optimization and conducts comparative experiments in different models. The results show that the algorithm performs well on vegetation, water, buildings, barren and other types. The average accuracy of cloud detection is 98.01%, the recall is 96.84% and the F1-score is 95.48%. The corresponding results of cloud shadow detection are 84.64%, 83.12% and 97.55%. In general, compared to U-Net, PSPNet and DeepLabV3+, this model performs better in cloud and cloud shadow detection, with clearer detection boundaries and a higher accuracy in complex surface conditions. This proves that Swin-UNet has great feature extraction capability in moderate and high-resolution remote sensing images.

INVESTIGATION OF CRYOGENIC TREATED DRILL BIT

We look into brief introduction of cryogenic treatment. Our focus throughout the report is more on cryogenic treatment of tool steel and high speed steel. In metal forming industry tools are exposed to very complex and rough surface conditions, which are the result of different effects (mechanical, thermal and chemical) and thus require well defined mechanical properties. Different approaches are followed to increase the surface properties of tool steels. The surface hardening treatments of steel has shown significant improvement of various properties including wear and fatigue resistance. Cryogenic treatment is yet another approach acknowledged by some to extend the tool life of many cutting tools. We will describe the complete procedure and investigate the effects on the metallurgical changes in the tool steel. However real mechanisms behind the better performance of tools are still in doubt. Studies in the given references on cryogenically treated tool steel shows micro structural changes in material that can influence the tool life. However little is gained from the experimental results showing involvement of carbide precipitations. Cryogenic treatments of carbides has yet to be extensively studied. In this paper drill bits are treated with cryogenic method and to predict the hardness, wear strength and other results are analysing.

Correction of ICESat-2 terrain within urban areas using a water pump deployment criterion with the vertical contour of the terrain

Previous studies have shown that the terrain elevation of the ICESat-2's ATL08 product is of good accuracy, overall, but in urban areas under complex surface conditions, the terrain elevation is vulnerable to the influence of the effect of dense buildings, resulting in deviations and even outliers. However, the previous studies have not effectively corrected these deviations and outliers. To this end, we propose a method for correcting the terrain elevation of the ATL08 product in urban areas, which is an auto-refinement method for the terrain elevation. Based on the data characteristics of this type of terrain elevation data and the distribution characteristics of urban buildings, the proposed method innovatively corrects the deviated part of the terrain elevation by a water pump deployment criterion with the vertical contour of the terrain. To increase its applicability, the proposed method uses a strategy of mixing the terrain elevation data that are from the strong- and weak-beam observations, allowing the deviated terrain in one beam with a higher missing data rate to be corrected with the aid of the other beam with a lower missing data rate. We used airborne LiDAR data from several areas (New York, Amsterdam, and Palmerston North) with different city sizes to validate the corrected results. The results show that the root-mean-square error (RMSE) of the terrain elevation in urban areas is reduced from ∼4 m to ∼1 m after correction. The correction results also show that the proposed method can correct the deviated terrain elevation, while ensuring that the high-quality terrain elevation can be effectively retained.

Combination of effective color information and machine learning for rapid prediction of soil water content

Soil water content (SWC) is one of the critical indicators in various fields such as geotechnical engineering and agriculture. To avoid the time-consuming, destructive, and laborious drawbacks of conventional SWC measurements, the image-based SWC prediction is considered based on recent advances in quantitative soil color analysis. In this study, a promising method based on the Gaussian-fitting gray histogram is proposed for extracting characteristic parameters by analyzing soil images, aiming to alleviate the interference of complex surface conditions with color information extraction. In addition, an identity matrix consisting of 32 characteristic parameters from eight color spaces is constituted to describe the multi-dimensional information of the soil images. Meanwhile, a subset of 10 parameters is identified through three variable analytical methods. Then, four machine learning models for SWC prediction based on partial least squares regression (PLSR), random forest (RF), support vector machines regression (SVMR), and Gaussian process regression (GPR), are established using 32 and 10 characteristic parameters, and their performance is compared. The results show that the characteristic parameters obtained by Gaussian-fitting can effectively reduce the interference from soil surface conditions. The RGB, CIEXYZ, and CIELCH color spaces and lightness parameters, as the inputs, are more suitable for the SWC prediction models. Furthermore, it is found that 10 parameters could also serve as optimal and generalizable predictors without considerably reducing prediction accuracy, and the GPR model has the best prediction performance (R2 ≥ 0.95, RMSE ≤ 2.01%, RPD ≥ 4.95, and RPIQ ≥ 6.37). The proposed image-based SWC predictive models combined with effective color information and machine learning can achieve a transient and highly precise SWC prediction, providing valuable insights for mapping soil moisture fields.

Geological Characteristics and Challenges of Marine Shale Gas in the Southern Sichuan Basin

After more than 10 years of exploration, development, research, and practical efforts, China has opened up new perspectives for the commercial exploitation of marine shale gas. While high shale gas production is a main driver for energy security and economic development in China, there have been few attempts to systemically scientific analysis the challenges, prospect, development strategies, and goals for shale gas. Here, we present a detailed comparison of the differences in shale gas between the Sichuan Basin and North America from multiple dimensions, explain how and to what extent recent advances have been made, discuss the current challenges, and provide strategies to deal with these challenges. We demonstrate that a total of 13 graptolite zones developed in the Wufeng–Longmaxi Formations, achieved by representative cores from 32 coring wells and 7 outcrop profiles, can establish the chronostratigraphic framework in the Sichuan Basin, which leads to the potential impact of high-quality reservoir distribution and shale gas production. Shale gas is still faced with the challenges of complex underground and surface conditions, low single-well EUR, and immature deep development engineering technology. To circumvent these issues, here, we propose several strategies, including sweet-spot optimization, low-cost drilling techniques, and efficient fracturing technologies. Our results strengthen the importance of adopting fundamental theoretical research and practical and feasible development goals to realize more commercial discoveries of shale gas of diverse types and higher growth of shale gas reserves and production.

Seismic random noise suppression using improved CycleGAN

Random noise adversely affects the signal-to-noise ratio of complex seismic signals in complex surface conditions and media. The primary challenges related to processing seismic data have always been reducing the random noise and increasing the signal-to-noise ratio. In this study, we propose an improved cycle-consistent generative adversarial network (CycleGAN) seismic random noise suppression method. First, the generator replaces the original cycle-consistent generative adversarial network generator network structure with the Unet structure combined with the Resnet structure in order to increase the diversity of seismic data feature extraction and decrease the loss of seismic data details. Second, in order to improve the network’s stability, the feature extraction effect, the event texture preservation effect, and the signal-to-noise ratio, the Least Square GAN (LSGAN) square difference loss is used in place of the conventional generative adversarial network cross-entropy loss. The feasibility of the proposed method was confirmed using model and real seismic data, both of which demonstrated that the improved cycle-consistent generative adversarial network method effectively suppressed random noise in seismic data. In addition, the denoising effect was superior to both the widely used FX deconvolution denoising method and original cycle-consistent generative adversarial network denoising method.

Adaptive Focused Beam Prestack Depth Migration Under the Condition of Rugged Topography

The complex topography is a challenging issue in onshore seismic exploration. The rugged terrain and lateral change of near-surface velocity pose a significant obstacle to the accurate imaging of seismic data. The adaptive focused beam migration method retains the good applicability of the ray methods for calculating the seismic wavefield under complex surface conditions. It can effectively solve the contradiction between the imaging accuracy of deep and shallow strata in traditional Gaussian beam migration. We extend the adaptive focused beam migration approach to the deep domain imaging of seismic data under complex surface conditions. Firstly, the basic principles of the adaptive focused beam are reviewed. Then, Green’s functions of the seismic source and the receiving point of rugged topography are characterized by the adaptive focused beam, and an adaptive focused beam prestack depth migration method based on cross-correlation imaging is proposed. The full-wave-arrival imaging strategy is applied to image all wave arrivals of subsurface imaging points. A single input seismic trace is adopted for imaging, which can directly emit the focused beam from the receiving point of rugged topography for wave field continuation, thus avoiding multiple focusing. As a result, the applicability of the migration approach to complex surface conditions was improved, and the imaging accuracy of the migration method was also effectively enhanced. The numerical model migration test of different rugged topography conditions and tectonic forms verified that the proposed method was an effective prestack depth migration applicable to accurately imaging seismic data under the rugged topography condition.

Response of soil water movement to rainfall under different land uses in karst regions

Soil water is a critical factor closely related to hydrological and ecological processes. Owing to the complex surface conditions with heterogeneous soil thickness and abundant underlying fissures, soil water in the karst region has been a complicated issue. In this study, the dynamic changes of soil water in the vertical profile of selected grassland, farmland and bare land on a karst yellow soil hillslope in southwest China were monitored at five depths including 20, 40, 60, 80 and 100 cm with an interval of 15 min. Results showed that (1) there were spatial differences in the response to rainfall of the soil water content at different depths. When the rainfall amount was similar, the soil water replenishment amount and migration depth under the three land uses decreased with the rainfall intensity. In the case of light rainfall, the soil water content at 20 cm was the most sensitive to rainfall, and the response of soil water to rainfall mainly occurred at 0–40 cm. In the case of moderate and heavy rainfall, soil water could migrate down to 100 cm on grassland but less than 100 cm on farmland and bare land under heavy intensity rainfall. (2) The variation in the soil water content had interlayer differences over time. The response of soil water to rainfall in different soil layers showed multipeak fluctuations. In general, when the rainfall intensity was the same, the soil water fluctuation on grassland and farmland at the same depth was larger than that on bare land; however, the peak value of soil water decreased with soil depth. (3) Land use and the antecedent soil water content had important effects on soil water loss during the dry period. Soil water loss was faster at the beginning and before slowing down. The soil water loss rate on grassland and farmland increased with the length of the dry period, but decreased gradually on bare land. These results can support the utilization and protection of soil and water resources on karst yellow soil slopes and help to understand the temporal and spatial dynamics of soil water under natural rainfall.

Continuous monitoring of nighttime light changes based on daily NASA's Black Marble product suite

Monitoring nighttime light (NTL) change enables us to quantitatively analyze the dynamic patterns of human activity and socioeconomic features. NASA's Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) atmospheric- and Lunar-BRDF-corrected Black Marble product (VNP46A2) provides daily global nighttime radiances with high temporal consistency. However, timely and continuous monitoring of NTL changes based on the dense daily DNB time series is still lacking. In this study, we proposed a novel Viewing Zenith Angle (VZA) stratified COntinuous monitoring of Land Disturbance (COLD) algorithm (VZA-COLD) to detect NTL change at 15 arc-second spatial resolution with daily updating capability based on NASA's Black Marble products. Specifically, we divided the clear observations into four VZA intervals (0–20°, 20°–40°, 40°–60°, and 0–60°) to mitigate the temporal variation of the NTL data caused by the combined angular effects of viewing geometry and the complex surface conditions (e.g., building heights, vegetation canopy covers, etc.). Single-term harmonic models were continuously estimated for new observations from each VZA interval, and by comparing the model predictions with the actual DNB observations, a unified set of NTL changes can be captured continuously among the different VZA intervals. The final NTL change maps were generated after excluding the consistent dark pixels. Results showed that the VZA-COLD algorithm reduced the DNB data temporal variations caused by disparities among different viewing angles and surface conditions, and successfully detected NTL changes for six globally distributed test sites with an overall accuracy of 99.71%, a user's accuracy of 87.18%, and a producer's accuracy of 68.88% for the NTL change category.

Application of Time-Domain Airborne Electromagnetic Method to the Study of Qingchengzi Ore Concentration Area in China

Qingchengzi is an important polymetallic mineral concentration zone in China. However, long-term mining has led to the depletion of proven reserves in the Qingchengzi ore concentration zone. Therefore, it is necessary to carry out prospecting in the periphery of the existing ore bodies. Taking into account the complex surface conditions of the Qingchengzi mining area, we carried out an airborne time-domain electromagnetic survey in this region and obtained a high-precision underground resistivity model which covers the whole area. The quasi-3D inversion result shows a good agreement with the surface geological information. After analyzing the relationship between the underground geological structures and the resistivity model, we constructed a geological model with the distribution of the main strata, the rock masses and the faults. The reconstructed resistivity distribution of the entire region added new knowledge to understand the geology of the Qingchengzi survey area.

Study of the Automatic Recognition of Landslides by Using InSAR Images and the Improved Mask R-CNN Model in the Eastern Tibet Plateau

The development of landslide hazards is spatially scattered, temporally random, and poorly characterized. Given the advantages of the large spatial scale and high sensitivity of InSAR observations, InSAR is becoming one of the main techniques for active landslide identification. The difficult problem is how to quickly extract landslide information from extensive InSAR image data. Since the instance segmentation model (Mask R-CNN) in deep learning can provide highly robust target recognition, we select the landslide-prone eastern edge of the Tibetan Plateau as a specific test area. Introducing and optimizing this model achieves high-speed and accurate recognition of InSAR observations. First, the InSAR patch landslide instance segmentation dataset (SLD) is established by developing a common object in context (COCO) annotation format conversion code based on InSAR observations. The Mask R-CNN+++ is found by adding three functions of the ResNext module to increase the fineness of the network segmentation results and enhance the noise resistance of the model, the DCB (deformable convolutional block) to improve the feature extraction ability of the network for geometric morphological changes of landslide patches, and an attention mechanism to selectively enhance usefully and suppress features less valuable to the native Mask R-CNN network. The model achieves 92.94% accuracy on the test set, and the active landslide recognition speed based on this model under ordinary computer hardware conditions is 72.3 km2/s. The overall characteristics of the results of this study show that the optimized model effectively enhances the perceptibility of image morphological changes, thereby resulting in smoother recognition boundaries and further improvement of the generalization ability of segmentation detection. This result is expected to serve to identify and monitor active landslides in complex surface conditions on a large spatial scale. Moreover, active landslides of different geometric features, motion patterns, and intensities are expected to be further segmented.

Improving the information content of seismic data and increasing the depth of investigation by choosing the optimal length of the amplitude adjustment operator

The article is devoted to the role of digital automatic amplitude control in increasing the depth of seismic exploration. It is noted that the depth of productive strata is gradually increasing, at the same time, more powerful explosive sources are being replaced by relatively weak non-explosive ones. Naturally, during processing, it is necessary to pay close attention to the choice of parameters for adjusting the amplitudes. The article provides basic information about the form and amplitude of seismic vibrations, lists the main empirical formulas that characterize the dependence of the amplitudes of seismic vibrations on time. It is noted that when registering seismic vibrations, one has to deal with minimal soil displacements caused by the arrival of a seismic wave at the observation point, which must be amplified millions of times and their adjustment. In this regard, in the digital processing of seismic data, a technique was developed for recovering the true amplitudes, more precisely, the true amplitude ratio. Adjustment while maintaining the true ratio of amplitudes is used only when it is required to study the dynamics of the wave field (dynamic digital processing). The optimal choice of the length of the adjustment operator (or window) is of paramount importance, since with a small adjustment interval (less than 0.1 s), a loss of dynamic expressiveness of the recording may occur, and this is clearly shown in model studies. The article considers the efficiency of choosing the length of the optimal adjustment operator on the example of the Kurovdag area, which has complex both surface and deep seismogeological conditions. A summary of the Kurovdag field is given. An amplitude compensation function for spherical divergence is given, and to compensate for the effects associated with changes in reception and excitation conditions, a surface-matched amplitude adjustment was performed after removing amplitude bursts. A fragment of the time section is given before and after the optimal adjustment of the amplitudes according to the seismograms of the Kurovdag area, which clearly demonstrates how the information content of the time section increases and, thereby, the depth of the seismic exploration: before the optimal digital automatic adjustment of the amplitudes at times of 3.25 - 3.5 sec, no seismic horizons, while after this procedure, dynamically well-defined seismic horizons appear at the same times, reflecting the structure of the medium at great depths.

Rapid Site Selection of Shale Gas Multi-Well Pad Drilling Based on Digital Elevation Model

Drilling and completion platform construction is a fundamental part of oil and gas reservoir development, and the location of construction directly affects the whole process of shale gas drilling and development. Due to the complex surface conditions and fragile ecological environment in mountainous areas, having an appropriate platform location can significantly reduce shale gas development and environmental costs. The DEM (digital elevation model) includes geographic elevation, surface complexity, land use type, and other data, so it can be used for rapid site selection for shale gas multi-well pad drilling. In this study, first, research results related to drilling platform site selection were analyzed and summarized, and then a platform site selection method aiming to minimize the total well construction cost was developed. Second, the well construction costs were decomposed into the surface construction costs and the underground construction costs, and the site selection model with the lowest total multi-well pad construction costs was established. Third, ground feature data obtained from DEM (digital elevation model) processing were substituted into the site selection model and solved using the genetic clustering algorithm. Finally, two practical cases were used to verify the research method developed in this study. The results show that the platform site selection results can be used to not only guide the formulation of development plans, but also to reduce the scope of the field investigation in the process of site selection, reduce the intensity of field work, and improve the work efficiency.

The exploration of gravel layer with 3D magnetotelluric data

The Horgos area of the thrust belt on the southern margin of the Junggar Basin has a good prospect for oil and gas exploration. However, because of the complex surface conditions and the coverage of the gravel layer, the seismic data on the southern margin thrust belt have some poor imaging, which makes the three-row structural pattern unclear. It is urgent to use 3D magnetotelluric (MT) exploration to investigate the distribution of gravel layer for seismic processing to build shallow model. In this paper, a 3D geoelectric model is established using MT data and logging data collected in the study area. Through the forward modeling and inversion calculation, the feasibility of using 3D MT data to explore the high resistivity gravel layer of various thickness is verified. Phase tensor analyses indicate the shallow (> 0.1 Hz) electrical structure is mainly 1D or 2D, while the deep structure is affected by 3D structure. The 3D conjugate gradients inversion method is used to process the MT data of the study area, the forward responses of the inversion result fit the observed data well and the electrical layers of the 3D inversion is consistent with the electrical characteristic revealed by the electric logging data. All these indicate the reliability of the 3D inversion results. The 3D MT results are first used to interpret the distribution and thickness of two sets of gravel layers in the study area. It is mainly used to assist the seismic exploration to solve problems involving exploration of complex surface carbonate rocks, exploration of deep complex structures and identification of shallow lithology. It proves that 3D MT is an effective technique for the gravel and shallow lithology exploration in similar regions.

Noniterative f -x-y Streaming Prediction Filtering for Random Noise Attenuation on Seismic Data

Random noise is unavoidable in seismic exploration, especially under complex-surface conditions and in deep-exploration environments. The current problems in random noise attenuation include preserving the nonstationary characteristics of the signal and reducing computational cost of broadband, wide-azimuth, and high-density data acquisition. To obtain high-quality images, traditional prediction filters (PFs) have proved effective for random noise attenuation, but these methods typically assume that the signal is stationary. Most nonstationary PFs use an iterative strategy to calculate the coefficients, which leads to high computational costs. In this study, we extended the streaming prediction theory to the frequency domain and proposed the <inline-formula> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula> streaming prediction filter (SPF) to attenuate random noise. Instead of using the iterative optimization algorithm, we formulated a constraint least-squares problem to calculate the SPF and derived an analytical solution to this problem. The multidimensional streaming constraints are used to increase the accuracy of the SPF. We also modified the recursive algorithm to update the SPF with the snaky processing path, which takes full advantage of the streaming structure to improve the effectiveness of the SPF in high dimensions. In comparison with 2-D <inline-formula> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula> SPF and 3-D <inline-formula> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula> regularized nonstationary autoregression (RNA), we tested the practicality of the proposed method in attenuating random noise. Numerical experiments show that the 3-D <inline-formula> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula> SPF is suitable for large-scale seismic data with the advantages of low computational cost, reasonable nonstationary signal protection, and effective random noise attenuation.

Simulations and Analysis of GNSS Multipath Observables for Frozen and Thawed Soil under Complex Surface Conditions

The transition of the freeze–thaw state of the land surface soil occurs every year with the season and is closely related to the human living environment. The freezing and thawing changes of the ground surface have important effects on hydrological activities, meteorological conditions, and ecological gas dynamics. Traditional monitoring methods have their limitations. In the past two decades, the emerging GNSS-R/IR (Global Navigation Satellite System-Reflectometry/Interference Reflectometry) technology has provided a new method for monitoring the surface f state; however, fewer works have paid attention to the scattering mechanism models in the current study. In this paper, a forward GNSS multipath model suitable for a complex cold surface is developed. The dielectric constant model with different surface parameters is added. The calculation of snow layer attenuation is employed to take the snow cover into consideration. Based on the first-order radiation transfer equation model, a polarization synthesis method is used to obtain the circularly and linearly polarized vegetation specular scattering characteristics. The surface characteristics and antenna model are coupled. A more detailed forward GNSS multipath model of frozen and thawed soil under complex surface conditions is established. The model is used to simulate and analyze the forward GNSS multipath (Signal to Noise Ratio (SNR), phase and pseudorange) responses of frozen and thawed soil under complex surface conditions (soil salinity, snow and vegetation coverage). Studies have shown that when the soil changes from freezing to thawing due to the change in the phase of the water in the soil, the dielectric constant and BRCS (bi-static radar cross-section) increase, causing the increase in the amplitude of the multipath observation. The higher the salinity content, the larger the amplitude of the multipath observation. The attenuation of the snow cover and the vegetation layer will lead to the reduction of the multipath observation amplitude. For the first time, the model developed by this paper reveals the GNSS multipath observation response of frozen and thawed soil under complex surface conditions in detail, which can provide some theoretical support for subsequent experimental design and data analysis.

Evaluation of satellite land surface albedo products over China using ground-measurements

ABSTRACT Land surface albedo (LSA) is an important parameter in surface energy balance and global climate change. It has been used in the fields of energy budgets, climate dynamics, and land surface processes. To apply satellite LSA products more widely, the product accuracy needs to be evaluated at different scales and under atmospheric and surface conditions. This study validates and analyzes the errors of the LSA datasets from the Global LAnd Surface Satellites (GLASS) product, the European Space Agency’s Earth Observation Envelope Programme (GlobAlbedo), the Quality Assurance for Essential Climate Variables (QA4ECV) project, the Gap-filled Snow-free Bidirectional Reflectance Distribution Function (BRDF) parameters product (MCD43GF), and the Satellite Application Facility on Climate Monitoring (CM SAF) Albedo dataset from the AVHRR data (CLARA-SAL) against the Chinese Ecosystem Research Network (CERN) measurements at different spatiotemporal scales over China from 2005 to 2015. The results show that LSA estimated by GLASS agrees well with the CERN measurements on a continental scale. The GLASS product is characterized by a correlation coefficient of 0.80, a root-mean-square error of 0.09, and a mean absolute error of 0.06. The consistency between GLASS, GlobAlbedo, and CLARA-SAL is slightly lower over the regions with high aerosol optical depth (AOD) (e.g. Sichuan Basin, northern China) and high cloud cover compared with that in regions with lower AOD and low cloud cover. The estimation errors are related to varying atmospheric and surface conditions and increase with increasing AOD and cloud cover and decreasing enhanced vegetation index. Therefore, algorithms under complex atmospheric and surface conditions (e.g. high AOD, sparse vegetation) should be optimized to improve the accuracy of LSA products.