Earth Science Applications Research Articles

A Machine Learning-Based Approach to Clinopyroxene Thermobarometry: Model Optimization and Distribution for Use in Earth Sciences.

Thermobarometry is a fundamental tool to quantitatively interrogate magma plumbing systems and broaden our appreciation of volcanic processes. Developments in random forest‐based machine learning lend themselves to a data‐driven approach to clinopyroxene thermobarometry, allowing users to access large experimental data sets that can be tailored to individual applications in Earth Sciences. We present a methodological assessment of random forest thermobarometry using the R freeware package extraTrees. We investigate the model performance, the effect of hyperparameter tuning, and assess different methods for calculating uncertainties. Deviating from the default hyperparameters used in the extraTrees package results in little difference in overall model performance (<0.2 kbar and <3°C difference in standard error estimate, SEE). However, accuracy is greatly affected by how the final value from the distribution of trees in the random forest is selected (mean, median, or mode). Using the mean value leads to higher residuals between experimental and predicted P and T, whereas using median values produces smaller residuals. Additionally, this work provides two scripts for users to apply the methodology to natural data sets. The first script permits modification and filtering of the model calibration data set. The second script contains premade models, where users can rapidly input their data to recover PT estimates (SEE clinopyroxene‐only model: 3.2 kbar, 72.5°C and liquid‐clinopyroxene model: 2.7 kbar, 44.9°C). Additionally, the scripts allow the user to estimate the uncertainty for each analysis, which in some cases is significantly smaller than the reported SEE. These scripts are open source and can be accessed at https://github.com/corinjorgenson/RandomForest-cpx-thermobarometer.

Material Design for Enhancing Properties of 3D Printed Polymer Composites for Target Applications

Polymer composites are becoming an important class of materials for a diversified range of industrial applications due to their unique characteristics and natural and synthetic reinforcements. Traditional methods of polymer composite fabrication require machining, manual labor, and increased costs. Therefore, 3D printing technologies have come to the forefront of scientific, industrial, and public attention for customized manufacturing of composite parts having a high degree of control over design, processing parameters, and time. However, poor interfacial adhesion between 3D printed layers can lead to material failure, and therefore, researchers are trying to improve material functionality and extend material lifetime with the addition of reinforcements and self-healing capability. This review provides insights on different materials used for 3D printing of polymer composites to enhance mechanical properties and improve service life of polymer materials. Moreover, 3D printing of flexible energy-storage devices (FESD), including batteries, supercapacitors, and soft robotics using soft materials (polymers), is discussed as well as the application of 3D printing as a platform for bioengineering and earth science applications by using a variety of polymer materials, all of which have great potential for improving future conditions for humanity and planet Earth.

Terrain deformation measurements from optical satellite imagery: The MPIC-OPT processing services for geohazards monitoring

Measuring terrain deformation over several spatial and temporal scales is relevant for many applications in Earth Sciences (i.e. active faults, volcanoes, landslides or glaciers understanding). The growing volume of freely available data represents nowadays a challenge in terms of storage capacity and computing resources which, together with the complexity of the processing (code parameterization, combination of the image sequences, co-registration of the images) may prevent the exploitation of long time series. We propose here a new version of the Multiple-Pairwise Image Correlation toolbox for processing OPTical images (MPIC-OPT). The toolbox proposes an end-to-end solution to compute the horizontal sub-pixel ground deformation time series from large Sentinel-2 datasets. In addition to time series inversion, several corrections and filtering options are integrated to reduce the noise and improve the accuracy and precision of the measurements. In particular, an automatic jitter correction based on wavelet filtering is proposed. Moreover, the MPIC-OPT service is deployed on the Tier 1.5 High-Performance Computing cluster (e.g. Datacentre/EOST-A2S) of the University of Strasbourg and is accessible on-line through the ESA Geohazards Exploitation Platform (GEP) and the ForM@Ter Solid Earth computing infrastructure with a user-friendly environment to query the satellite data catalogues, parameterize the processing and visualize the outputs. We test the performances of MPIC-OPT on several use cases: the measurement of the co-seismic ground deformation of the 2019 Ridgecrest earthquake sequence (USA), of the rapid motion of the Slumgullion landslide (USA) and of the glaciers of the Mont-Blanc massif (France/Italy). We show that the results of MPIC-OPT are in agreement with in-situ data. The jitter correction significantly improves the precision (RMSEjitter=0.3m vs. RMSEnojitter=0.5m) and the accuracy (RMSEjitter=0.3m vs. RMSEnojitter=1.3m) of the measurement of the co-seismic displacement of the Rigdecrest seismic deformation. We show that the precision and accuracy of the terrain deformation estimation depend mainly on the correlation threshold and the temporal matching range parameters and we quantify and discuss their impacts. This work opens new perspectives to monitor automatically surface displacements/velocities of natural hazards over large scales and large periods of time.

One‐ and two‐step semi‐Lagrangian integrators for arbitrary Lagrangian–Eulerian‐finite element two‐phase flow simulations

AbstractSemi‐Lagrangian (SL) schemes are of utmost relevance to simulate two‐phase flows where advection dominates. The combination of SL schemes with the finite element (FE) method and arbitrary Lagrangian–Eulerian (ALE) dynamic meshes yields a strong ingredient to deal with applications in Earth sciences, environmental engineering, and oil and gas industry whose numerical background is incipient. This article is intended to propose a second‐order time multistep SL/ALE/FE scheme to track particle trajectories traveling amidst single‐ or two‐phase incompressible flows both in fixed and moving mesh situations. The novel scheme is a BDF2‐like implementation that considers the mesh velocity as part of its backward‐in‐time integration. Trajectory departure points are searched through extrapolation and followed by a multistep interpolation corrector that works both for constant and varying time steps. A series of two‐phase benchmark flow simulations are carried out by using the cubic element to compare the performance of the new integrator against single‐step approximations as well as to analyze enhancements in representing the two‐phase flow dynamics. We use the 2D axisymmetric Navier–Stokes equations as underlying model. Error analyses, convergence tests, quantitative, and qualitative comparisons are presented and discussed to highlight the superior conservative feature of the novel scheme.

Developing Metrics for NASA Earth Science Interdisciplinary Data Products and Services

Metrics are measures that are able to produce quantifiable information. There are many applications of metrics in Earth science data and services; for example, metrics are frequently used to track service performance and progress. In short, developing, collecting and analyzing metrics are essential activities to better support Earth science research, applications, and education. As one of the largest repositories of Earth science data in the world, NASA’s Earth Science Data and Information System (ESDIS) Project supports twelve Distributed Active Archive Centers (DAACs). Standard metrics have been developed by the ESDIS Metrics System (EMS). These metrics are collected and analyzed routinely at each DAAC. As it is expected that the total data volume will continue to grow rapidly, and the timely developed technologies (e.g., cloud computing, AI/ML) will continue to improve data discovery and accessibility, opportunities for developing new data services for the Earth science community will also arise, especially in interdisciplinary research and applications. However, developing such metrics has become a challenge because multiple datasets are often needed. Current metrics are designed for a single predefined dataset or service, a disadvantage for collecting metrics for interdisciplinary data services. In this paper, we assess current metrics using one of the NASA DAACs, the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), as an example, to discuss challenges and opportunities, along with recommendations for developing metrics addressing interdisciplinary satellite data products and services. <strong>Highlights</strong> <ul><li>Overview of NASA GES DISC Earth science datasets and services</li><li>Overview of existing metrics collection methods and analysis tools with examples</li><li>Discuss challenges and opportunities in collecting metrics for Earth science interdisciplinary data and services</li></ul>

Evaluation of Agricultural Bare Soil Properties Retrieval from Landsat 8, Sentinel-2 and PRISMA Satellite Data

The PRISMA satellite is equipped with an advanced hyperspectral Earth observation technology capable of improving the accuracy of quantitative estimation of bio-geophysical variables in various Earth Science Applications and in particular for soil science. The purpose of this research was to evaluate the ability of the PRISMA hyperspectral imager to estimate topsoil properties (i.e., organic carbon, clay, sand, silt), in comparison with current satellite multispectral sensors. To investigate this expectation, a test was carried out using topsoil data collected in Italy following two approaches. Firstly, PRISMA, Sentinel-2 and Landsat 8 spectral simulated datasets were obtained from the spectral resampling of a laboratory soil library. Subsequently, bare soil reflectance data were obtained from two experimental areas in Italy, using real satellites images, at dates close to each other. The estimation models of soil properties were calibrated employing both Partial Least Square Regression and Cubist Regression algorithms. The results of the study revealed that the best accuracies in retrieving topsoil properties were obtained by PRISMA data, using both laboratory and real datasets. Indeed, the resampled spectra of the hyperspectral imager provided the best Ratio of Performance to Inter-Quartile distance (RPIQ) for clay (4.87), sand (3.80), and organic carbon (2.59) estimation, for the spectral soil library datasets. For the bare soil reflectance obtained from real satellite imagery, a higher level of prediction accuracy was obtained from PRISMA data, with RPIQ ± SE values of 2.32 ± 0.07 for clay, 3.85 ± 0.19 for silt, and 3.51 ± 0.16 for soil organic carbon. The results for the PRISMA hyperspectral satellite imagery with the Cubist Regression provided the best performance in the prediction of silt, sand, clay and SOC. The same variables were better estimated using PLSR models in the case of the resampled hyperspectral data. The statistical accuracy in the retrieval of SOC from real and resampled PRISMA data revealed the potential of the actual hyperspectral satellite. The results supported the expected good ability of the PRISMA imager to estimate topsoil properties.

Quantitative Texture Analysis: Implementation on the SKAT Neutron Diffractometer and Application in Materials and Earth Sciences

Quantitative Texture Analysis: Implementation on the SKAT Neutron Diffractometer and Application in Materials and Earth Sciences

In situ-produced cosmogenic krypton in zircon and its potential for Earth surface applications

Abstract. Analysis of cosmogenic nuclides produced in surface rocks and sediments is a valuable tool for assessing the rates of processes and the timing of events that shaped the Earth surface. The various nuclides that are used have specific advantages and limitations that depend on the time range over which they are useful, the type of material they are produced in and not least the feasibility of the analytical effort. Anticipating novel applications in Earth surface sciences, we develop in situ-produced terrestrial cosmogenic krypton (Krit) as a new tool, the motivation being the availability of six stable and one radioactive isotope (81Kr, half-life 229 kyr) and of an extremely weathering-resistant target mineral (zircon). We provide proof of principle that terrestrial Krit can be quantified and used to unravel Earth surface processes.

Direct Georeferencing UAV-SfM in High-Relief Topography: Accuracy Assessment and Alternative Ground Control Strategies along Steep Inaccessible Rock Slopes

Steep rock slopes present key opportunities and challenges within Earth science applications. Due to partial or complete inaccessibility, high-precision surveys of these high-relief landscapes remain a challenge. Direct georeferencing (DG) of unoccupied aerial vehicles (UAVs) with advanced onboard GNSS receivers presents opportunities to generate high-resolution 3D datasets without ground-based access to the study area. However, recent research has revealed large vertical errors using DG that may prove problematic in near-vertical terrain. To address these concerns, we examined more than 75 photogrammetric UAV-datasets with various imaging angles (nadir, oblique, and combinations) and ground control scenarios, including DG, along a steep slope exposure. Results demonstrate that mean errors in DG scenarios are up to 0.12 m higher than datasets using integrated georeferencing with well-distributed GCPs. Inclusion of GCPs greatly reduced mean error values but had limited influence on precision (&lt;0.01 m) for any given imaging strategy. Use of multiple image angles resulted in the highest precisions, regardless of georeferencing strategy. These findings have implications for applications requiring the highest precision and accuracy (e.g., geotechnical engineering, hazard mitigation and mapping, and geomorphic change detection), which should consider using ground control whenever possible. However, for applications less concerned with absolute accuracy, our results show that DG datasets provide strong internal consistency and relative accuracy that may be suitable for high precision measurements within a model, without use of ground control.

A Spatiotemporal Fusion Method Based on Multiscale Feature Extraction and Spatial Channel Attention Mechanism

Remote sensing satellite images with a high spatial and temporal resolution play a crucial role in Earth science applications. However, due to technology and cost constraints, it is difficult for a single satellite to achieve both a high spatial resolution and high temporal resolution. The spatiotemporal fusion method is a cost-effective solution for generating a dense temporal data resolution with a high spatial resolution. In recent years, spatiotemporal image fusion based on deep learning has received wide attention. In this article, a spatiotemporal fusion method based on multiscale feature extraction and a spatial channel attention mechanism is proposed. Firstly, the method uses a multiscale mechanism to fully utilize the structural features in the images. Then a novel attention mechanism is used to capture both spatial and channel information; finally, the rich features and spatial and channel information are used to fuse the images. Experimental results obtained from two datasets show that the proposed method outperforms existing fusion methods in both subjective and objective evaluations.

Potential and Limitation of PlanetScope Images for 2-D and 3-D Earth Surface Monitoring With Example of Applications to Glaciers and Earthquakes

The Planet PlanetScope cubesat constellation acquires high-resolution optical images which cover the entire surface of the Earth daily, enabling an unprecedented capability to monitor the Earth surface changes. However, our analysis reveals artifacts of the geometry of PlanetScope images related to the imaging system and processing issues, limiting the usability of these data for various earth science applications, including the monitoring of glaciers, dune motion, or the measurement of ground deformation due to earthquakes and landslides. Here we analyze these artifacts and propose ways to remediate them. We use two examples to evaluate the data and assess the performance of our proposed approaches. The first is the ground deformation caused by the 2019 Ridgecrest earthquake sequence, California, and the second is the 2018-2019 surge of the Shisper glacier in the Karakorum. Using an image correlation technique, we show that PlanetScope images exhibit several geometric artifacts, such as scene-to-scene misregistration, inconsistence geolocation accuracy between spectral bands, and topographic artifacts. Altogether these artifacts make a quantitative analysis of ground displacement difficult and inaccurate. We present a method that remediates most of these geometric artifacts. In addition, we propose a framework for selecting the most appropriate images and a procedure for refining the Rational Function Model (RFM) of unrectified images to monitor surface displacements and topography changes in 3D. These tools should enhance the use of PlanetScope images for Earth Science applications.

Riparian Zone DEM Generation From Time-Series Sentinel-1 and Corresponding Water Level: A Novel Waterline Method

Topography data are essential for land management, hydrology, and earth science applications. The topography in a riparian zone varies with time. Available global digital elevation models (GDEMs) do not satisfy the hydrological and environmental modeling requirements for riparian zones. In this study, a novel waterline method was proposed to acquire the digital elevation model (DEM) of riparian zones. Synthetic aperture radar (SAR) images are used to extract waterlines and corresponding water level is applied to acquire the elevation. The uneven water surfaces are considered to acquire the elevation of waterlines and Shuttle Radar Topography Mission (SRTM) DEM is used to interpolate the elevation of pixels without waterlines. The proposed method is applied in the riparian zone of Three Gorges Reservoir and Dongting Lake to test its feasibility. More than 80 Sentinel-1 SAR images at different water levels within three years are used to detect waterlines. Based on corresponding water level of hydrological stations, the elevation of waterlines are acquired via inverse distance weighting method. The distance layer and slope derived from the original SRTM DEM are used to interpolate the elevation of pixels between waterlines. The DEM generated by the proposed method is compared against the DEM derived in 2017, SRTM DEM, the DEM which does not include the uneven effects, and Ice, Cloud and land Elevation Satellite -2 (ICESat-2) ATL08. The good performance of the proposed method implies its high efficiency in revealing riparian zone topography.

Landsat 9 Thermal Infrared Sensor 2 On-Orbit Calibration and Initial Performance

The Thermal Infrared Sensor 2 (TIRS-2) on Landsat 9 launched September 27, 2021 and underwent a variety of tests during its commissioning phase to establish its postlaunch performance. We report on the calibration updates performed to maintain its calibration and generate high quality imagery. This is done by transferring the SI-traceable pre-launch calibration to on-orbit while accounting for changes in the TIRS-2 response as detected through on-board calibrator observations. Additional empirical corrections were implemented to mitigate image striping observed on-orbit. The detector arrays were monitored through its commissioning phase to ensure that stable detectors were chosen for operations. TIRS-2 has demonstrated &#x007E;0.025% instability over its orbit, &#x007E;80 mK noise equivalent delta temperature, and an absolute radiometric uncertainty &#x003C;1.4% in its nominal temperature range enabling a wide array of Earth science applications.

Nonlinear Detector Response of Aqua MODIS Land Imaging Bands

The detectors in the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) reflective solar bands (RSBs) are calibrated assuming a linear relationship between the observed top-of-atmosphere radiance and the instrument output response. Any nonlinear behavior in the system response can contribute to errors in NASA's Level 1B MODIS reflectance and radiance products, which are used in a wide variety of Earth science applications. While most RSB detectors continue to have very linear behavior even after more than 18 years of operation, we present evidence that the detectors in Aqua MODIS bands 1 (645 nm) and 2 (858 nm) have deviations from gain linearity that change over the mission by up to 3%. We show a clear divergence between gain measurements made with the onboard solar diffuser (SD) at two different radiance levels, achieved by collecting data both with and without an attenuation screen. Radiance trends from lunar calibrations and typical desert and ocean scenes are also compared. We present a simple method to characterize the response nonlinearity using a quadratic function by combining the results of the two sets of SD calibrations. Applying this quadratic calibration algorithm to the desert and lunar data results in improved agreement in their long-term radiance trends. A clear understanding of the magnitude of the gain nonlinearity could be useful to help improve the quality of numerous MODIS science products, including the land imaging applications that rely heavily on these bands.

The novel pushing gravity model and volcanic activity. Is alignment of planets with compact stars a possible cause of natural phenomena?

We developed the model, and carried out its discussion at the PIRT-2021 conference, within the framework of the research topic “External Forcing on Volcanoes and Volcanic Processes: Observations, Analysis and Implications” announced by the journal “Frontiers in Earth Science” in October 2020. Besides other, external processes considered in this Research Topic included astronomical. In this study, in the category “Hypothesis and Theory”, we investigate how changes in the position of large bodies of the Solar system can cause natural phenomena, associated with the movement of free masses, such as volcanism, earthquakes and landslides in the lithosphere, as well as various catastrophic events in the atmosphere and hydrosphere. The analysis has shown that the discovered phenomena of celestial bodies’ alignments accompanying manifestations of natural phenomena require going beyond the standard cosmological model and clarify the fundamental mechanism of gravity. We propose the novel Bidirectional Pushing Gravitation model (BPG), which, in addition to application in Earth Sciences, may occur useful in Astrophysics, Cosmology and Gravitation research.

Based on Atmospheric Physics and Ecological Principle to Assess the Accuracies of Field CO2/H2O Measurements From Infrared Gas Analyzers in Closed‐Path Eddy‐Covariance Systems

AbstractField CO2/H2O measurements from infrared gas analyzers in closed‐path eddy‐covariance systems have wide applications in earth sciences. Knowledge about exactness of these measurements is required to assess measurement applicability. Although the analyzers are specified with uncertainty components (zero drift, gain drift, cross‐sensitivities, and precision), exactness for individual measurements is unavailable due to an absence of methodology to comprehend the components as an overall uncertainty. Adopting an advanced definition of accuracy as a range of all measurement uncertainty sources, the specified components are composited into a model formulated for studying analyzers’ CO2/H2O accuracy equations. Based on atmospheric physics and environmental parameters, the analyzers are evaluated using the equations for CO2 accuracy (±0.78 µmolCO2 mol−1, relatively ±0.18%) and H2O accuracy (±0.15 mmolH2O mol−1). Evaluation shows that precision and cross‐sensitivity are minor uncertainties while zero and gain drifts are major uncertainties. Both drifts need adjusting through zero/span procedures during field maintenance. The equations provide rationales to guide and assess the procedures. H2O span needs more attentions under humid conditions. Under freezing conditions while H2O span is impractical, this span is fortunately unnecessary. Under the same conditions, H2O zero drift dominates H2O measurement uncertainty. Therefore, automatic zero becomes a more applicable and necessary tactic. In general cases of atmospheric CO2 background, automatic CO2 zero/span procedures can narrow CO2 accuracy by 36% (±0.74 to ± 0.47 µmolCO2 mol−1). Automatic/manual H2O zero/span procedures can narrow H2O accuracy by 27% (±0.15 to ±0.11 mmolH2O mol−1). While ensuring system specifications, the procedures guided by equations improve measurement accuracies.

Aurora Detection from Nighttime Lights for Earth and Space Science Applications

Aurora Detection from Nighttime Lights for Earth and Space Science Applications

Sliding Windows Method Based on Terrain Self-Similarity for Higher DEM Resolution in Flood Simulating Modeling

A digital elevation model (DEM) is a quantitative representation of terrain and an important tool for Earth science and hydrological applications. A high-resolution DEM provides accurate basic Geodata and plays a crucial role in related scientific research and practical applications. However, in reality, high-resolution DEMs are often difficult to obtain. Due to the self-similarity present within terrains, we proposed a method using the original DEM itself as a sample to expand the DEM using sliding windows method (SWM) and generate a higher resolution DEM. The main processes of SWM include downsampling the original DEM and constructing mapping sets, searching for the optimal matching, window replacement. Then, we repeat these processes with the small-scale expansion factor. In this paper, the grid resolution of the Taitou Basin was expanded from 30 to 10 m. Overall, the superresolution reconstruction results showed that the method could achieve better outcomes than other commonly used techniques and exhibited a slight deviation (root mean square error (RMSE) = 3.38) from the realistic DEM. The generated high-resolution DEM prove to be significant in the application of flood simulation modeling.

Resolving Wave Propagation in Anisotropic Poroelastic Media Using Graphical Processing Units (GPUs)

AbstractBiot's equations describe the physics of hydromechanically coupled systems establishing the widely recognized theory of poroelasticity. This theory has a broad range of applications in Earth and biological sciences as well as in engineering. The numerical solution of Biot's equations is challenging because wave propagation and fluid pressure diffusion processes occur simultaneously but feature very different characteristic time scales. Analogous to geophysical data acquisition, high resolution and three dimensional numerical experiments lately redefined state of the art. Tackling high spatial and temporal resolution requires a high‐performance computing approach. We developed a multi‐ graphical processing units (GPU) numerical application to resolve the anisotropic elastodynamic Biot's equations that relies on a conservative numerical scheme to simulate, in a few seconds, wave fields for spatial domains involving more than 1.5 billion grid cells. We present a comprehensive dimensional analysis reducing the number of material parameters needed for the numerical experiments from ten to four. Furthermore, the dimensional analysis emphasizes the key material parameters governing the physics of wave propagation in poroelastic media. We perform a dispersion analysis as function of dimensionless parameters leading to simple and transparent dispersion relations. We then benchmark our numerical solution against an analytical plane wave solution. Finally, we present several numerical modeling experiments, including a three‐dimensional simulation of fluid injection into a poroelastic medium. We provide the Matlab, symbolic Maple, and GPU CUDA C routines to reproduce the main presented results. The high efficiency of our numerical implementation makes it readily usable to investigate three‐dimensional and high‐resolution scenarios of practical applications.

Application of Topological Data Analysis to Multi-Resolution Matching of Aerosol Optical Depth Maps

Topological data analysis (TDA) combines concepts from algebraic topology, machine learning, statistics, and data science which allow us to study data in terms of their latent shape properties. Despite the use of TDA in a broad range of applications, from neuroscience to power systems to finance, the utility of TDA in Earth science applications is yet untapped. The current study aims to offer a new approach for analyzing multi-resolution Earth science datasets using the concept of data shape and associated intrinsic topological data characteristics. In particular, we develop a new topological approach to quantitatively compare two maps of geophysical variables at different spatial resolutions. We illustrate the proposed methodology by applying TDA to aerosol optical depth (AOD) datasets from the Goddard Earth Observing System, Version 5 (GEOS-5) model over the Middle East. Our results show that, contrary to the existing approaches, TDA allows for systematic and reliable comparison of spatial patterns from different observational and model datasets without regridding the datasets into common grids.