Geoscience Applications Research Articles

DETERMINATION AND MAPPING OF THE TERRESTRIAL GRAVITY ANOMALIES IN THE MOUNTAINOUS AREAS OF IRAQ

Gravity data and computing gravity anomalies are regarded as vital for both geophysics and physical geodesy fields. The mountainous areas of Iraq are characterized by the lack of regional gravity data because gravity surveys are rarely performed in the past four decades due to the Iraq-Iran war and the internal unstable political situation of this particular region. In addition, the formal map of the available terrestrial gravity which was published by the French Database of Bureau Gravimetrique International (International Gravimetric Bureau-in English) (BGI), introduces Iraq and the study area as a remote area and in white color because of the unavailability of gravity data. However, a dense and local (not regional) gravity data is available which was conducted by geophysics researchers 13 years ago. Therefore, the regional gravity survey of 160 gravity points was performed by the authors at an average 11 km apart, which was covers the whole area of Sulaymaniyah Governorate (part of the mountainous areas of Iraq). In spite of Although the risk of mine fields within the study area, suitable safe routes as well as a helicopter was used for the gravity survey of several points on the top of mountains. The survey was conducted via Lacoste and Romberg geodetic gravimeter and GPS handheld. The objective of the study is to determine and map the gravity anomalies for the entire study area, the data of which would assist different geosciences applications.

Evaluation of the Apple iPhone 12 Pro LiDAR for an Application in Geosciences

Traditionally, topographic surveying in earth sciences requires high financial investments, elaborate logistics, complicated training of staff and extensive data processing. Recently, off-the-shelf drones with optical sensors already reduced the costs for obtaining a high-resolution dataset of an Earth surface considerably. Nevertheless, costs and complexity associated with topographic surveying are still high. In 2020, Apple Inc. released the iPad Pro 2020 and the iPhone 12 Pro with novel build-in LiDAR sensors. Here we investigate the basic technical capabilities of the LiDAR sensors and we test the application at a coastal cliff in Denmark. The results are compared to state-of-the-art Structure from Motion Multi-View Stereo (SfM MVS) point clouds. The LiDAR sensors create accurate high-resolution models of small objects with a side length > 10 cm with an absolute accuracy of ± 1 cm. 3D models with the dimensions of up to 130 × 15 × 10 m of a coastal cliff with an absolute accuracy of ± 10 cm are compiled. Overall, the versatility in handling outweighs the range limitations, making the Apple LiDAR devices cost-effective alternatives to established techniques in remote sensing with possible fields of application for a wide range of geo-scientific areas and teaching.

The use of particle size distribution integrated with consistency limits for experimentally simulating fine-grained sedimentary units

The current study hypothesizes that reproducing sediment units that might belong to areas difficult to reach, sample, and attest is experimentally possible. The research aims to employ available sediment samples named source soils that can easily approach and sample to mix, simulate, and produce the (non-available) sedimentary units named Target Sediment Units (TSU). The particle size distribution (PSD) percentages are essentially used in the mixture procedure to construct the non-available (target) units (TSU). Several sedimentary units were targeted, designed, and simulated; They include marsh, bay mud, and natural levee sediment units selected from the eastern Basrah side at the lower Mesopotamian plain, southern Iraq. The matching between particle size distribution (PSD) curves of simulated sediment units (SSU) and target sediment unit (TSU) was verified by applying a developed slope proximity ratio. For the three tested sediment units (marsh, buy mud, and natural levee), the ratios of slope proximity were 1.0, 0.93, and 0.99, respectively, which are well matched. For the mineralogy, the consistency limits were used as a proxy. The Casagrande plasticity chart was modified to reveal two empirical functions linking the plasticity indices to the clay mineral groups. The two predictions are reliable means to proxy the mineralogy of the fine-grained sediments. Reconstructing procedure allows the non-available sediment samples to be practically presented and it better characterizes the sediment for forensic geoscience applications; for instance, engineering geology and sedimentology applications concerning with studies of the physical and mechanical behaviors of sediments and the depositional conditions interpretations, respectively.

Structure tensor-based interpolation for the derivation of accurate digital elevation models

For many geoscience applications, the quality of digital elevation models (DEMs) is the first and foremost requirement. DEM quality generally depends on the interpolation procedure, especially in forested areas with complex terrain. However, classical interpolation methods do not always preserve terrain features, owing to their isotropic and local estimation nature. Therefore, this paper presents a feature-preserving interpolation method, where a structure tensor is integrated into the radial basis function to ensure its anisotropy. This method alleviates the influence of points located on different surface patches. The performance of the proposed method was compared with those of classical interpolation methods, including the inverse distance weighting (IDW), ordinary kriging (OK), topo to raster (ANUDEM), and natural neighbour (NN) methods. The performance of these methods was tested on benchmark data provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) Commission, and on one private dataset. Both datasets were collected via the airborne light detection and ranging (LiDAR) technique. The results from both datasets demonstrate that from a quantitative perspective, the proposed method produces more accurate DEMs than the classical interpolation methods. Specifically, in terms of root mean square error (RMSE), the proposed method is at least 15.8% and 7.6% more accurate when applied to the ISPRS and private data, respectively. Moreover, the proposed method produces more visually appealing surfaces with a good trade-off between terrain feature retention and noise removal.

A Parallel Approach for Multi-GNSS Ultra-Rapid Orbit Determination

Global Navigation Satellite System (GNSS) ultra-rapid orbit is critical for geoscience and real-time engineering applications. To improve the computational efficiency and the accuracy of predicted orbit, a parallel approach for multi-GNSS ultra-rapid orbit determination is proposed based on Message Passing Interface (MPI)/Open Multi Processing (OpenMP). This approach, compared with earlier efficient methods, can improve the efficiency of multi-GNSS ultra-rapid orbit solution without changing the original observation data and retaining the continuity and consistency of the original parameters to be estimated. To obtain high efficiency, three steps are involved in the approach. First and foremost, the normal equation construction is optimized in parallel based on MPI. Second, equivalent reduction of the estimated parameters is optimized using OpenMP parallel method. Third, multithreading is used for parallel orbit extrapolation. Thus, GNSS ultra-rapid orbit determination is comprehensively optimized in parallel, and the computation efficiency is greatly improved. Based on the data from MGEX and IGS stations, experiments are carried out to analyze the performance of the proposed approach in computational efficiency, accuracy and stability. The results show that the approach greatly improves the efficiency of satellite orbit determination. It can realize 1-h update frequency for the multi-GNSS ultra-rapid orbit determination using 88 stations with four-system observations. The accuracy of the GPS, GLONASS, Galileo and BDS ultra-rapid orbit with 1-h update frequency using the parallel approach is approximately 33.4%,31.4%,40.1% and 32.8% higher than that of the original orbit, respectively. The root mean squares (RMS) of GPS, GLONASS, Galileo and BDS predicted orbit are about 3.2 cm, 5.1 cm, 5.6 cm and 11.8 cm. Moreover, the orbit provided by the proposed method has a better stability. The precision loss of all parallel optimization can be negligible and the original correlation between the parameters is fully preserved.

Deep Learning Applications in Geosciences: Insights into Ichnological Analysis

Ichnological analysis, particularly assessing bioturbation index, provides critical parameters for characterizing many oil and gas reservoirs. It provides information on reservoir quality, paleodepositional conditions, redox conditions, and more. However, accurately characterizing ichnological characteristics requires long hours of training and practice, and many marine or marginal marine reservoirs require these specialized expertise. This adds more load to geoscientists and may cause distraction, errors, and bias, particularly when continuously logging long sedimentary successions. In order to alleviate this issue, we propose an automated technique to determine the bioturbation index in cores and outcrops by harnessing the capabilities of deep convolutional neural networks (DCNNs) as image classifiers. In order to find a fast and robust solution, we utilize ideas from deep learning. We compiled and labeled a large data set (1303 images) composed of images spanning the full range (BI 0–6) of bioturbation indices. We divided these images into groups based on their bioturbation indices in order to prepare training data for the DCNN. Finally, we analyzed the trained DCNN model on images and obtained high classification accuracies. This is a pioneering work in the field of ichnological analysis, as the current practice is to perform classification tasks manually by experts in the field.

Testing the reliability of interpretable neural networks in geoscience using the Madden–Julian oscillation

Abstract. We test the reliability of two neural network interpretation techniques, backward optimization and layerwise relevance propagation, within geoscientific applications by applying them to a commonly studied geophysical phenomenon, the Madden–Julian oscillation. The Madden–Julian oscillation is a multi-scale pattern within the tropical atmosphere that has been extensively studied over the past decades, which makes it an ideal test case to ensure the interpretability methods can recover the current state of knowledge regarding its spatial structure. The neural networks can, indeed, reproduce the current state of knowledge and can also provide new insights into the seasonality of the Madden–Julian oscillation and its relationships with atmospheric state variables. The neural network identifies the phase of the Madden–Julian oscillation twice as accurately as a linear regression approach, which means that nonlinearities used by the neural network are important to the structure of the Madden–Julian oscillation. Interpretations of the neural network show that it accurately captures the spatial structures of the Madden–Julian oscillation, suggest that the nonlinearities of the Madden–Julian oscillation are manifested through the uniqueness of each event, and offer physically meaningful insights into its relationship with atmospheric state variables. We also use the interpretations to identify the seasonality of the Madden–Julian oscillation and find that the conventionally defined extended seasons should be shifted later by 1 month. More generally, this study suggests that neural networks can be reliably interpreted for geoscientific applications and may thereby serve as a dependable method for testing geoscientific hypotheses.

Project Vienna: A Novel Precell Mass Filter for Collision/Reaction Cell MC-ICPMS/MS.

The last decade has seen widespread adoption of triple quadrupole-based inductively coupled plasma-tandem mass spectrometry (ICPMS/MS) technique using a collision/reaction cell in combination with a precell bandpass mass analyzer to measure isotopes otherwise masked by spectral interferences. High-precision isotope ratio analysis containing such isotopes would benefit from a similar capability on a multicollector inductively coupled plasma mass spectrometry (MC-ICPMS) platform, but using a quadrupole-based precell mass analyzer for MC-ICPMS/MS has several limitations. To overcome these limitations, we developed a novel precell mass analyzer for MC-ICPMS/MS using sector field technology. The new precell mass analyzer, comprising two Wien filters and a selection aperture, and a hexapole collision/reaction cell were integrated together in a single module and added to the commercially available Thermo Scientific Neptune XT MC-ICPMS to create a prototype MC-ICPMS/MS we named Vienna. Vienna was proven to retain the same performance of the base MC-ICPMS in terms of sensitivity, accuracy, and precision. Using the Vienna mass filter to eliminate Ar-based species, the abundance sensitivity achievable was equivalent to TIMS at mass 237.05, which was used to accurately determine the low 236U/238U isotope ratio of the uranium reference material IRMM184 (certified value, 1.2446 × 10-7). The performance of Vienna was then tested for a variety of geoscience applications that were expected to benefit from MC-ICPMS/MS technique, including Ca, K, Si, and in situ Rb/Sr dating by laser ablation.

Exploring Biases in Atom Probe Tomography Compositional Analysis of Minerals

Atom probe tomography (APT) is emerging as a key nanogeochemistry technique for diverse geoscience applications. Estimating stoichiometric mineral compositions is particularly challenging for features at nanoscale. APT provides reliable measurements for metallic systems but the reliability for oxides is problematic, notably due to oxygen deficit. Here, we use laser‐assisted APT to compare results for spinel and garnet crystals. APT compositional results were compared with those by electron microprobe to determine the possible APT analytical inaccuracy. Extensive data processing was accomplished, including correlation histograms, 2D ion distribution maps and 1D elements concentration profiles, to disclose the possible mechanisms leading to mineral stoichiometry biases. Multiple events and neutral molecules formation are probable the main processes responsible for atom deficit. In particular, the amount of the same isotope–same charge state ion pairs correlated with aluminium and oxygen deficits suggests that the co‐evaporation in a dead space–dead time window could lead to a significant decrease of detected ions. Also, molecular species dissociation and direct current evaporation could partially account for further atom loss. Overall, better APT compositional estimation was obtained for spinel, which has lesser variation in lattice sites and greater overall lattice symmetry, higher thermal conductivity and lower band gap compared with garnet.

A seismic petrophysical classification study of the 2-D SEAM model using semisupervised techniques and detrended attributes

SUMMARYFor many machine learning problems, there are sufficient data to train a wide range of algorithms. However, many geoscience applications are challenged with limited training data. Seismic petrophysical classification, mapping seismic data to litho-fluid classes, is one of these examples because the training data labels are based on data gathered from wells. Supervised machine learning algorithms are prone to overfitting in scarce training data situations, but semisupervised approaches are designed for these problems because the unlabelled data are also used to inform the learning process. We adopt label propagation (LP) and self-training methods to solve this problem, because they are semisupervised methods that are conceptually simple and easy to implement. The supervised method we consider for comparison is the popular extreme gradient boosting (XGBoost) classifier. The data set we use for our study is one we generate ourselves from the SEG Advanced Modelling (SEAM) Phase 1 model. We first synthesize seismic data from this model and then perform pre-stack seismic inversion to recover seismic attributes. We formulate a classification problem using the seismic attributes as unlabelled data, with training labels from a single well. The benefit of this being a synthetic problem is that we have full control and the ability to quantitatively assess the machine learning predictions. Our initial results reveal that the inherent depth-dependent background trends of the input attributes produce artefacts in each of the machine learning predictions. We address this problem by using a simple median filter to remove these background trends. The predictions using the detrended inputs improve the performance for all three algorithms, in some cases on the order of 10 to 20 per cent. XGBoost and LP perform similarly in some situations, but our results indicate that XGBoost is rather unstable depending on the attributes used. However, LP coupled with self-training outperforms XGBoost by up to 10 per cent in some instances. Through this synthetic study, our results support the premise that semisupervised algorithms can provide more robust, generalized predictions than supervised techniques in minimal training data scenarios.

HPC cluster-based user-defined data integration platform for deep learning in geoscience applications

Objective:Efficient and flexible data integration platforms are important to apply various geoscience data for deep learning applications. Recently, deep learning techniques have been applied to analyze and predict natural phenomena in Earth sciences using geoscience data. Because geoscience data are considered as “big data”, data driven approaches such as deep learning are promising tools for understanding natural phenomena. In this paper, we propose a Geoscience Data Integration Platform (GeoDIP) for managing big geoscience data based on High-performance Computing (HPC) cluster systems. Methodology:GeoDIP provides data pre-processing and analyzes modules according to user defined configurations when creating Artificial Intelligence (AI) ready datasets. To determine the application of GeoDIP, we demonstrated precipitation prediction performance using multiple datasets. We collected three datasets from different sources: two from satellite observations and one from reanalysis data for weather forecasting. We then compared the results obtained for each dataset and the results obtained for integrated datasets. Results:The results confirmed that the integrated dataset generated from GeoDIP provided 9% improved prediction performance for F1-score over 2 h. This suggests that various types of information on atmospheric conditions explaining precipitation genesis from multiple data sources are crucial for precipitation prediction. For understanding model performance, we conducted a permutation-based feature importance test, which confirmed that the upper level information is important over time. In addition, we evaluated the performance of GeoDIP by comparing sequential and parallel tasks and obtained a performance improvement of approximately 97%. Conclusions:The proposed geoDIP facilitates the utilization of multiple datasets to analyze geophysical phenomena using parallel processes of HPC clusters with reduced computational time for data pre-processing and analysis.

Technical Note: Temporal disaggregation of spatial rainfall fields with generative adversarial networks

Abstract. Creating spatially coherent rainfall patterns with high temporal resolution from data with lower temporal resolution is necessary in many geoscientific applications. From a statistical perspective, this presents a high- dimensional, highly underdetermined problem. Recent advances in machine learning provide methods for learning such probability distributions. We test the usage of generative adversarial networks (GANs) for estimating the full probability distribution of spatial rainfall patterns with high temporal resolution, conditioned on a field of lower temporal resolution. The GAN is trained on rainfall radar data with hourly resolution. Given a new field of daily precipitation sums, it can sample scenarios of spatiotemporal patterns with sub-daily resolution. While the generated patterns do not perfectly reproduce the statistics of observations, they are visually hardly distinguishable from real patterns. Limitations that we found are that providing additional input (such as geographical information) to the GAN surprisingly leads to worse results, showing that it is not trivial to increase the amount of used input information. Additionally, while in principle the GAN should learn the probability distribution in itself, we still needed expert judgment to determine at which point the training should stop, because longer training leads to worse results.

From consumer to enterprise grade: How the choice of four UAS impacts point cloud quality

AbstractUncrewed aerial systems (UAS), combined with structure‐from‐motion photogrammetry, has already proven to be very powerful for a wide range of geoscience applications and different types of UAS are used for scientific and commercial purposes. However, the impact of the UAS used on the accuracy of the point clouds derived is not fully understood, especially for the quantitative analysis of geomorphic changes in complex terrain. Therefore, in this study, we aim to quantify the magnitude of systematic and random error in digital elevation models derived from four commonly used UAS (XR6/Sony α6000, Inspire 2/X4s, Phantom 4 Pro+, Mavic Pro) following different flight patterns. The vertical error of each elevation model is evaluated through comparison with 156 GNSS reference points and the normal distribution and spatial correlation of errors are analysed. Differences in mean errors (−0.4 to −1.8 cm) for the XR6, Inspire 2 and Phantom 4 Pro are significant but not relevant for most geomorphological applications. The Mavic Pro shows lower accuracies with mean errors up to 4.3 cm, thus showing a higher influence of random errors. QQ plots revealed a deviation of errors from a normal distribution in almost all data. All UAS data except Mavic Pro exhibit a pure nugget semivariogram, suggesting spatially uncorrelated errors. Compared to the other UAS, the Mavic Pro data show trends (i.e. differences increase with distance across the survey—doming) and the range of semivariances is 10 times greater. The lower accuracy of Mavic Pro can be attributed to the lower GSD at the same flight altitude and most likely, the rolling shutter sensor has an effect on the accuracy of the camera calibration. Overall, our study shows that accuracies depend highly on the chosen data sampling strategy and that the survey design used here is not suitable for calibrating all types of UAS camera equally.

Diffusion‐to‐Imbibition Transition in Water Sorption in Nanoporous Media: Theoretical Studies

AbstractThe ability to predict multiphase fluid transport in nanoporous rocks such as shales is critical for many geoscience applications, for example unconventional hydrocarbon production, geologic carbon sequestration, and nuclear waste disposal. When the pore sizes approach nanoscales, the impact of the molecular interaction forces between fluids and solids becomes increasingly important. These forces can alter macroscopic fluid phase behavior and control transport. Recent experimental studies have shown that capillary condensation and subsequent imbibition of liquid water can occur in hydrophilic nanoporous media even if the vapor phase is at a critical relative humidity (rhcrit) well below vapor saturation. This study presents a theoretical investigation of the processes controlling adsorption, capillary condensation and imbibition in nanoporous media, using the square‐gradient classical density functional theory. The proposed theoretical model explicitly includes the relevant interaction forces among fluids and solids in macroscopic porous media. Application of the model to a relative‐humidity‐controlled water adsorption experiment is presented to demonstrate the impact of water‐pore wall attractive forces on multiphase water behavior in a hydrophilic silicon nanoporous medium. The model represents well the measured time‐dependent evolution of the water imbibition front inside the nanoporous medium and also explains the diffusion‐like water transport regimes observed at rh < rhcrit and the imbibition‐like flow regimes observed at rh > rhcrit. The study furthermore gives an insight on hysteresis phenomenon in adsorption and desorption isotherms.

Assessment of Close-Range Remote Sensing Methods for DTM Estimation in a Lowland Deciduous Forest

Digital terrain models (DTMs) are important for a variety of applications in geosciences as a valuable information source in forest management planning, forest inventory, hydrology, etc. Despite their value, a DTM in a forest area is typically lower quality due to inaccessibility and limited data sources that can be used in the forest environment. In this paper, we assessed the accuracy of close-range remote sensing techniques for DTM data collection. In total, four data sources were examined, i.e., handheld personal laser scanning (PLShh, GeoSLAM Horizon), terrestrial laser scanning (TLS, FARO S70), unmanned aerial vehicle (UAV) photogrammetry (UAVimage), and UAV laser scanning (ULS, LS Nano M8). Data were collected within six sample plots located in a lowland pedunculate oak forest. The reference data were of the highest quality available, i.e., total station measurements. After normality and outliers testing, both robust and non-robust statistics were calculated for all close-range remote sensing data sources. The results indicate that close-range remote sensing techniques are capable of achieving higher accuracy (root mean square error < 15 cm; normalized median absolute deviation < 10 cm) than airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) data that are generally understood to be the best data sources for DTM on a large scale.

Automated Quality Assessment of Interferometric Ring Laser Data.

In seismology, an increased effort to observe all 12 degrees of freedom of seismic ground motion by complementing translational ground motion observations with measurements of strain and rotational motions could be witnessed in recent decades, aiming at an enhanced probing and understanding of Earth and other planetary bodies. The evolution of optical instrumentation, in particular large-scale ring laser installations, such as G-ring and ROMY (ROtational Motion in seismologY), and their geoscientific application have contributed significantly to the emergence of this scientific field. The currently most advanced, large-scale ring laser array is ROMY, which is unprecedented in scale and design. As a heterolithic structure, ROMY’s ring laser components are subject to optical frequency drifts. Such Sagnac interferometers require new considerations and approaches concerning data acquisition, processing and quality assessment, compared to conventional, mechanical instrumentation. We present an automated approach to assess the data quality and the performance of a ring laser, based on characteristics of the interferometric Sagnac signal. The developed scheme is applied to ROMY data to detect compromised operation states and assign quality flags. When ROMY’s database becomes publicly accessible, this assessment will be employed to provide a quality control feature for data requests.

Simulation of Fluvial Patterns With GANs Trained on a Data Set of Satellite Imagery

AbstractModels that can generate realistic Earth surface patterns are important both for geomorphological applications and as prior models for underdetermined inverse problems. Generative machine learning methods such as GANs and the increasing availability of large remote sensing data sets represents an exciting combination for this purpose. Several studies show promising results for GANs trained on artificial data sets in geostatistics, but it is necessary to further quantify how well such models reproduce and generalize real data. The conditioning ability of GANs is often evaluated based on output which originates from a trained generator. In reality, geophysical data necessarily arises from elsewhere. Here, we use more realistic training data than in previous studies and evaluate performance using an extensive set of metrics and real images outside the training data set. The data set consists of multispectral satellite imagery of 38 large river deltas, a type of Earth surface pattern which is limited in number. The channel network is used to create training images with four sedimentary facies, which are subsequently used to train a Wasserstein GAN of deltaic 2D patterns. GANs successfully reproduce all training data characteristics and produce manifold the number of combinations with respect to the training data. However, there does not seem to be an infinite number of discrete combinations of facies, and the posterior landscapes are not well‐shaped for efficient exploration in the presence of so‐called hard data. Thus, GANs should have many exciting applications in geosciences, but it will depend on the type of measurement data.

Gravitational field modelling near irregularly shaped bodies using spherical harmonics: a case study for the asteroid (101955) Bennu

We apply three spherical-harmonic-based techniques to deliver external gravitational field models of the asteroid (101955) Bennu within its circumscribing sphere. This region is known to be peculiar for external spherical harmonic expansions, because it may lead to a divergent series. The studied approaches are (i) spectral gravity forward modelling via external spherical harmonics, (ii) the least-squares estimation from surface gravitational data using external spherical harmonics and (iii) the combination of internal and external series expansions. While the first method diverges beyond any reasonable doubts, we show that the other two methods may ensure relative accuracy from $${\sim }10^{-6}$$ to $$10^{-8}$$ in the vicinity of Bennu. This is possible even with the second method, despite the fact that it relies on a single series of external spherical harmonics. Our main motivation was to study conceptual differences between spherical harmonic coefficients from satellite data (analogy to the first method) and from surface gravitational data (the second method). Such coefficients are available through the popular spherical-harmonic-based models of the Earth’s gravitational field and often are combined together. We show that the coefficients from terrestrial data may lead to a divergence effect of partial sums, though excellent accuracy can be achieved when such model is used in full. Under (presently) extreme but realistic conditions, the divergence effect of partial sums may affect many near-surface geoscientific applications, such as the geoid/quasigeoid computation or residual terrain modelling. Computer codes (Fortran, MATLAB) and data produced within the study are made freely available at http://edisk.cvt.stuba.sk/~xbuchab/ .

RETRACTED ARTICLE: Spatiotemporal Geospatial Modeling for 3D City Based on Edge Computing Using Statistical Framework Linear Injection Model Based on Multi-data Source Fusion

In recent years, data on individual spatiotemporal levels have been increasingly available, especially due to the utilization of modern GPS tracking devices. Cloud pollution significantly restricts the potential use for geoscience applications of optical remote sensing images. This paper presents a spatial and time-based melting process for remote imaging using a model of linear injection and information for neighborhood with edge computing assistance. The detailed data have proven helpful for inferring a more precise degree of transmission than conventional methods. Nevertheless, a lack of statistically sound frames remains for modeling mechanistically for the underlying transmission process and this development is particularly important to provide an individual general predictive epidemic framework. In this paper, a New Statistical Framework Linear Injection Model Based on Multi-Data Source Fusion method is proposed to determine the transmission of cloud-free image and to enhance the noise robustness. To improve its noise strength further, the local neighborhood information is introduced for refining initial fused stage image and to obtain more accurate prediction results, derived simultaneously from resolution fine image and fused result. To improve the spectral cohesion of recovered and remaining areas, correction residual strategy based on the Poisson equation has been analyzed. The tests demonstrated that the methods proposed for cases with substantial land cover modifications can work very well; they have less biases and more reliable performance compared with several cutting-edge approaches. To sum up, our solution is a major technical improvement to the current cloud removal system and offers the possibility of handling scenes with substantial land coverage changes.

SoilKsatDB: global database of soil saturated hydraulic conductivity measurements for geoscience applications

Abstract. The saturated soil hydraulic conductivity (Ksat) is a key parameter in many hydrological and climate models. Ksat values are primarily determined from basic soil properties and may vary over several orders of magnitude. Despite the availability of Ksat datasets in the literature, significant efforts are required to combine the data before they can be used for specific applications. In this work, a total of 13 258 Ksat measurements from 1908 sites were assembled from the published literature and other sources, standardized (i.e., units made identical), and quality checked in order to obtain a global database of soil saturated hydraulic conductivity (SoilKsatDB). The SoilKsatDB covers most regions across the globe, with the highest number of Ksat measurements from North America, followed by Europe, Asia, South America, Africa, and Australia. In addition to Ksat, other soil variables such as soil texture (11 584 measurements), bulk density (11 262 measurements), soil organic carbon (9787 measurements), moisture content at field capacity (7382), and wilting point (7411) are also included in the dataset. To show an application of SoilKsatDB, we derived Ksat pedotransfer functions (PTFs) for temperate regions and laboratory-based soil properties (sand and clay content, bulk density). Accurate models can be fitted using a random forest machine learning algorithm (best concordance correlation coefficient (CCC) equal to 0.74 and 0.72 for temperate area and laboratory measurements, respectively). However, when these Ksat PTFs are applied to soil samples obtained from tropical climates and field measurements, respectively, the model performance is significantly lower (CCC = 0.49 for tropical and CCC = 0.10 for field measurements). These results indicate that there are significant differences between Ksat data collected in temperate and tropical regions and Ksat measured in the laboratory or field. The SoilKsatDB dataset is available at https://doi.org/10.5281/zenodo.3752721 (Gupta et al., 2020) and the code used to extract the data from the literature and the applied random forest machine learning approach are publicly available under an open data license.