Fine Resolution Research Articles

Predicting wheat yield from 2001 to 2020 in Hebei Province at county and pixel levels based on synthesized time series images of Landsat and MODIS

To obtain seasonable and precise crop yield information with fine resolution is very important for ensuring the food security. However, the quantity and quality of available images and the selection of prediction variables often limit the performance of yield prediction. In our study, the synthesized images of Landsat and MODIS were used to provide remote sensing (RS) variables, which can fill the missing values of Landsat images well and cover the study area completely. The deep learning (DL) was used to combine different vegetation index (VI) with climate data to build wheat yield prediction model in Hebei Province (HB). The results showed that kernel NDVI (kNDVI) and near-infrared reflectance (NIRv) slightly outperform normalized difference vegetation index (NDVI) in yield prediction. And the regression algorithm had a more prominent effect on yield prediction, while the yield prediction model using Long Short-Term Memory (LSTM) outperformed the yield prediction model using Light Gradient Boosting Machine (LGBM). The model combining LSTM algorithm and NIRv had the best prediction effect and relatively stable performance in single year. The optimal model was then used to generate 30 m resolution wheat yield maps in the past 20 years, with higher overall accuracy. In addition, we can define the optimum prediction time at April, which can consider simultaneously the performance and lead time. In general, we expect that this prediction model can provide important information to understand and ensure food security.

Large-ensemble assessment of the Arctic stratospheric polar vortex morphology and disruptions

Abstract. The stratospheric polar vortex (SPV) comprises strong westerly winds during winter in each hemisphere. Despite ample knowledge on the SPV's high variability and its frequent disruptions by sudden stratospheric warmings (SSWs) in the Northern Hemisphere (NH), questions on how well current climate models can simulate these dynamics remain open. Specifically the accuracy in reproducing SPV morphology and the differentiation between split and displacement SSW events are crucial to assess the models in this regard. In this study, we evaluate the capability of climate models to simulate the NH SPV by comparing large ensembles of historical simulations to ERA5 reanalysis data. For this, we analyze geometric-based diagnostics at three pressure levels that describe SPV morphology. Our analysis reveals that no model exactly reproduces SPV morphology of ERA5 in all diagnostics at all altitudes. Concerning the SPV morphology as stretching (aspect ratio) and location (centroid latitude) parameters, most models are biased to some extent, but the strongest deviations can be found for the vortex-splitting parameter (excess kurtosis). Moreover, some models underestimate the variability of SPV strength. Assessing the reliability of the ensembles in distinguishing SSWs subdivided into SPV displacement and split events, we find large differences between the model ensembles. In general, SPV displacements are represented better than splits in the simulation ensembles, and high-top models and models with finer vertical resolution perform better. A good performance in representing the morphological diagnostics does not necessarily imply reliability and therefore a good performance in simulating displacements and splits. Assessing the model biases and their representation of SPV dynamics is needed to improve credibility of climate model projections, for example, by giving stronger weightings to better performing models.

Bayesian hierarchical modeling for bivariate multiscale spatial data with application to blood test monitoring

Public health spatial data are often recorded at different spatial scales (or geographic regions/divisions) and over different correlated variables. Motivated by data from the Dartmouth Atlas Project, we consider jointly analyzing average annual percentages of diabetic Medicare enrollees who have taken the hemoglobin A1c and blood lipid tests, observed at the hospital service area (HSA) and county levels, respectively. Capitalizing on bivariate relationships between these two scales is not immediate as counties are not nested within HSAs. It is well known that one can improve predictions by leveraging correlations across both variables and scales. There are very few methods available that simultaneously model multivariate and multiscale correlations. We propose three new hierarchical Bayesian models for bivariate multiscale spatial data, extending spatial random effects, multivariate conditional autoregressive (MCAR), and ordered hierarchical models through a multiscale spatial approach. We simulated data from each of the three models and compared the corresponding predictions, and found the computationally intensive multiscale MCAR model is more robust to model misspecification. In an analysis of 2015 Texas Dartmouth Atlas Project data, we produced finer resolution predictions (partitioning of HSAs and counties) than univariate analyses, determined that the novel multiscale MCAR and OH models were preferable via out-of-sample metrics, and determined the HSA with the highest within-HSA variability of hemoglobin A1c blood testing. Additionally, we compare the univariate multiscale models to the bivariate multiscale models and see clear improvements in prediction over univariate analyses.

Global Natural and Planted Forests Mapping at Fine Spatial Resolution of 30 m

Global Natural and Planted Forests Mapping at Fine Spatial Resolution of 30 m

A methodological framework to characterize the wildlife-livestock interface: The case of wild boar in mainland Spain

BackgroundThe representation of wildlife-livestock interface (WLI) at an accurate spatial resolution poses several challenges. Furthermore, there is a lack of published material providing detailed descriptions of geospatial techniques for the purpose of producing visual results that are interpretable and contrastable for epidemiological analysis. ObjectivesOur aim is to develop a standardized, applicable, and scalable methodological framework for describing and characterizing the WLI across a large spatial extent. Subsequently, we aim to employ this framework to depict specific WLI based on different epidemiological scenarios determined by the abundance of wild boar (Sus scrofa) and domestic ungulates as an illustrative case, specifically focusing on mainland Spain. MethodsTo establish a methodological framework, we merged data from both wild and domestic sources into a hexagonal grid. We utilized data on wild boar hunting and the locations of pig, cattle, sheep, and goat farms in mainland Spain. New variables were derived from this combined dataset to illustrate the overlapping abundance between wild boar and domestic species. Finally, a cluster analysis of the generated variables was carried out, with the aim of distinguishing and characterizing various scenarios of the wild boar-domestic ungulate interface in mainland Spain. ResultsThe hexagonal grid proved appropriate to represent and evaluate the WLI at fine spatial resolution over such broad extent. Despite the inability to ascribe a dominant livestock type and production system to a specific region, we were able to identify fifteen main areas of interest in terms of overlap. As for extensive livestock, normally at the highest risk of interaction with wild boar, the primary regions in Spain were those with dehesa agroecosystem and the Atlantic areas. Certain scenarios were particularly relevant in terms of risk for interaction and subsequent transmission of disease, namely, the case of extensive pig production in south western Spain (dehesa agroecosystem), which is especially concerned about the potential introduction of African Swine fever (ASF) in the Country. Discussion and conclusionsWe provide a basis for visualizing and understanding of different WLI scenarios, which is extensible to other regions and interfaces, and automatable where precise source of data from wildlife and livestock are available. This spatial statistics framework enables the utilization of high-resolution data, ensuring consistency on uniform grids. This aligns with the needs of high-resolution disease dissemination models based on wildlife behaviour. Such aspects are crucial for developing risk assessment and improving strategies for the prevention, control, and eradication of shared priority emerging diseases at national and international levels, such as ASF.

Melt electrowriting of poly(ϵ-caprolactone)—poly(ethylene glycol) backbone polymer blend scaffolds with improved hydrophilicity and functionality

Melt electrowriting (MEW) is an additive manufacturing technique that harnesses electro-hydrodynamic phenomena to produce 3D-printed fibres with diameters on the scale of 10s of microns. The ability to print at this small scale provides opportunities to create structures with incredibly fine resolution and highly defined morphology. The current gold standard material for MEW is poly(ϵ-caprolactone) (PCL), a polymer with excellent biocompatibility but lacking in chemical groups that can allow intrinsic additional functionality. To provide this functionality while maintaining PCL’s positive attributes, blending was performed with a Poly(Ethylene Glycol) (PEG)-based Acrylate endcapped Urethane-based Precursor (AUP). AUPs are a group of polymers, built on a backbone of existing polymers, which introduce additional functionality by the addition of one or more acrylate groups that terminate the polymer chain of a backbone polymer. By blending with a 20kDa AUP-PEG in small amounts, it is shown that MEW attributes are preserved, producing high-quality meshes. Blends were produced in various PCL:AUP weight ratios (100:0, 90:10 and 0:100) and processed into both solvent-cast films and MEW meshes that were used to characterise the properties of the blends. It was found that the addition of AUP-PEG to PCL significantly increases the hydrophilicity of structures produced with these polymers, and adds swelling capability compared to the non-swelling PCL. The developed blend (90:10) is shown to be processable using MEW, and the quality of manufactured scaffolds is evaluated against pure PCL scaffolds by performing scanning electron microscopy image analysis, with the quality of the novel MEW blend scaffolds showing comparable quality to that of pure PCL. The presence of the functionalisable AUP material on the surface of the developed scaffolds is also confirmed using fluorescence labelling of the acrylate groups. Biocompatibility of the MEW-processable blend was confirmed through a cell viability study, which found a high degree of cytocompatibility.

Forty-Year Fire History Reconstruction from Landsat Data in Mediterranean Ecosystems of Algeria following International Standards

Algeria, the main fire hotspot on the southern rim of the Mediterranean Basin, lacks a complete fire dataset with official fire perimeters, and the existing one contains inconsistencies. Preprocessed global and regional burned area (BA) products provide valuable insights into fire patterns, characteristics, and dynamics over time and space, and into their impact on climate change. Nevertheless, they exhibit certain limitations linked with their inherent spatio-temporal resolutions as well as temporal and geographical coverage. To address the need for reliable BA information in Algeria, we systematically reconstructed, validated, and analyzed a 40-year (1984–2023) BA product (NEALGEBA; North Eastern ALGeria Burned Area) at 30 m spatial resolution in the typical Mediterranean ecosystems of this region, following international standards. We used Landsat data and the BA Mapping Tools (BAMTs) in the Google Earth Engine (GEE) to map BAs. The spatial validation of NEALGEBA, performed for 2017 and 2021 using independent 10 m spatial resolution Sentinel-2 reference data, showed overall accuracies > 98.10%; commission and omission errors < 8.20%; Dice coefficients > 91.90%; and relative biases < 3.44%. The temporal validation, however, using MODIS and VIIRS active fire hotspots, emphasized the limitation of Landsat-based BA products in temporal fire reporting accuracy terms. The intercomparison with five readily available BA products for 2017, by using the same validation process, demonstrated the overall outperformance of NEALGEBA. Furthermore, our BA product exhibited the highest correspondence with the ground-based BA estimates. NEALGEBA currently represents the most continuous and reliable time series of BA history at fine spatial resolution for NE Algeria, offering a significant contribution to further national and international fire hazard and impact assessments and acts as a reference dataset for contextualizing future weather extremes, such as the 2023 exceptional heat wave, which we show not to have led to the most extreme fire year over the last four decades.

Operational Forest-Fire Spread Forecasting Using the WRF-SFIRE Model

In the present research, the open-source WRF-SFIRE model has been used to carry out surface forest fire spread forecasting in the North Sikkim region of the Indian Himalayas. Global forecast system (GFS)-based hourly forecasted weather model data obtained through the National Centers for Environmental Prediction (NCEP) at 0.25 degree resolution were used to provide the initial conditions for running WRF-SFIRE. A landuse–landcover map at 1:10,000 scale was used to define fuel parameters for different vegetation types. The fuel parameters, i.e., fuel depth and fuel load, were collected from 23 sample plots (0.1 ha each) laid down in the study area. Samples of different categories of forest fuels were measured for their wet and dry weights to obtain the fuel load. The vegetation specific surface area-to-volume ratio was referenced from the literature. The atmospheric data were downscaled using nested domains in the WRF model to capture fire–atmosphere interactions at a finer resolution (40 m). VIIRS satellite sensor-based fire alert (375 m spatial resolution) was used as ignition initiation point for the fire spread forecasting, whereas the forecasted hourly weather data (time synchronized with the fire alert) were used for dynamic forest-fire spread forecasting. The forecasted burnt area (1.72 km2) was validated against the satellite-based burnt area (1.07 km2) obtained through Sentinel 2 satellite data. The shapes of the original and forecasted burnt areas matched well. Based on the various simulation studies conducted, an operational fire spread forecasting system, i.e., Sikkim Wildfire Forecasting and Monitoring System (SWFMS), has been developed to facilitate firefighting agencies to issue early warnings and carry out strategic firefighting.

Investigating the Actuation of Sidewall Sprinkler in an Atrium Using CFD Simulation

This study investigates the actuation of sidewall sprinklers in large-scale buildings with high-ceilinged atriums, addressing the challenges of unique architectural configurations. Compliance with NFPA 101 requires automatic sprinkler systems, including atrium areas, in these buildings. To maintain aesthetic considerations, design engineers, particularly in the middle east, often propose sidewall sprinklers as an alternative to traditional ceiling sprinklers. This research assesses whether the sidewall sprinklers would actuate during a fire using Fire Dynamic Simulator (FDS). The findings indicate that sidewall sprinklers will fail to actuate if the fire is located at the centre of the atrium, even if the edge of the fire area is below the sprinklers. Furthermore, the study emphasizes the importance of using an FDS mesh resolution (D*/dx) of 6 or finer resolution when measuring temperatures near the flame or fire plume to ensure accurate evaluations of sprinkler activation. These findings provide valuable insights for design engineers and authorities, assisting in decision-making processes related to fire safety measures, system designs, and regulatory compliance.

Temporal Distribution of Extreme Precipitation in Barcelona (Spain) under Multi-Fractal n-Index with Breaking Point

Rainfall regimes are experiencing variations due to climate change, and these variations are adequately simulated by Earth System Models at a daily scale for most regions. However, there are not enough raw outputs to study extreme and sub-daily precipitation patterns on a local scale. To address this challenge, Monjo developed the n-index by characterizing the intensity and concentration of precipitation based on mono-fractal theory. In this study, we explore the use of a multi-fractal approach to establish a more accurate method of time scaling useful to study extreme precipitation events at a finer temporal resolution. This study was carried out on the reference station of Barcelona (Spain) and its surroundings in order to be representative of the Mediterranean climate. For return periods between 2 and 50 years, two variables were analyzed: the n-index and the reference intensity I0. Moreover, a new parameter, the so-called “breaking point”, was designed here to describe the reference intensity I0, which is predominant for low time ranges. The results showed that both parameters are dependent on the time steps and the return period, and the scores confirmed the validity of our approach. Finally, the n-index was projected under downscaled CMIP6 climate scenarios by 2100, showing a sustained increase of up to +10%.

Evaluating Distributed Snow Model Resolution and Meteorology Parameterizations Against Streamflow Observations: Finer Is Not Always Better

AbstractEstimating snow conditions is often done using numerical snowpack evolution models at spatial resolutions of 500 m and greater; however, snow depth in complex terrain often varies on sub‐meter scales. This study investigated how the spatial distribution of simulated snow conditions varied across seven model spatial resolutions from 30 to 1,000 m and over two meteorological data sets, coarser (≈12 km) and finer (4 km). Simulated snow covered area (SCA) was compared to remotely sensed SCA and simulated watershed mean peak snow water equivalent (SWE) was compared to four streamflow statistics representing different water management‐relevant aspects of the hydrograph using non‐parametric correlations. April 1 SWE tended to increase with model resolution, particularly below 4,000 masl. Finer meteorology simulations produced deeper April 1 SWE than coarser meteorology simulations. Finer resolution snow simulations tended to produce longer snowmelt durations and slower snowmelt rates than coarser resolution simulations. Finer resolution simulations had better agreement with SCA for both meteorology data sets, particularly at high and low elevations. However, finer resolution simulations did not generally outperform coarser simulations in snow versus streamflow statistic correlations. Snow versus streamflow correlations were most sensitive to meteorology, watershed properties, and then resolution. Watershed physiographic properties such as wetness index may increase snow versus streamflow metric correlations while elevation and slope may decrease correlations. At watershed scales, these results suggest that simulation resolution and choice of meteorology is less important than the physiographic properties of the watershed; however, if resolving snow distribution across the landscape is important, finer‐resolution simulations are useful.

Elucidating the mechanism of overcuring in microchannels fabricated via vat photopolymerization (VPP) for precise microfluidic chip printing

Vat Photopolymerization (VPP) technology is highly regarded for precision micromachining applications like microfluidic chip fabrication, owing to its fine resolution, adaptability, and rapid prototyping capabilities. However, the challenge of resin over-curing resulting from UV energy penetration poses a significant obstacle in microchannel printing. This study addresses this challenge by investigating the intricate relationship between resin properties and over-curing. Diverse microfluidic chips were printed utilizing VPP technology, and the overall cross-sectional over-curing characteristics of the channels were systematically examined. A novel combination of the classical UV light curing model and computational Fluid-Structure Interaction (FSI) model with the VPP printing process was proposed to examine the complex hydrodynamics and exfoliation dynamics within VPP-printed microchannels. The simulation results suggest that resins with higher viscosities and surface roughness are better suited for microchannel printing. Subsequent experiments using five different resins verified these findings and showed that the microchannels obtained with resins of lower viscosity and surface roughness resins have an irregular trapezoidal cross-section, which is uncontrollable and unpredictable and seriously affects the microchannel printing accuracy. Conversely, resins with higher viscosity and surface roughness have an ideal rectangular cross-section, which can be compensated by the model further refined printing to print microchannel. Finally, microfluidic chips were printed using "high precision 8 K resin" and "8 K-clay resin" with higher viscosity and surface roughness. The results proved that the microchannels printed by these resins had the desired rectangular cross-section, which was consistent with the "DLP light-curing" model. This study elucidates, for the first time, the intricate interplay between resin properties, printing structure, and microchannel over-curing, and this provides a solid groundwork for advancing high-precision microfluidic chip fabrication via DLP printing.

Integrating Mobile and Fixed-Site Black Carbon Measurements to Bridge Spatiotemporal Gaps in Urban Air Quality.

Urban air pollution can vary sharply in space and time. However, few monitoring strategies can concurrently resolve spatial and temporal variation at fine scales. Here, we present a new measurement-driven spatiotemporal modeling approach that transcends the individual limitations of two complementary sampling paradigms: mobile monitoring and fixed-site sensor networks. We develop, validate, and apply this model to predict black carbon (BC) using data from an intensive, 100-day field study in West Oakland, CA. Our spatiotemporal model exploits coherent spatial patterns derived from a multipollutant mobile monitoring campaign to fill spatial gaps in time-complete BC data from a low-cost sensor network. Our model performs well in reconstructing patterns at fine spatial and temporal resolution (30 m, 15 min), demonstrating strong out-of-sample correlations for both mobile (Pearson's R ∼ 0.77) and fixed-site measurements (R ∼ 0.95) while revealing features that are not effectively captured by a single monitoring approach in isolation. The model reveals sharp concentration gradients near major emission sources while capturing their temporal variability, offering valuable insights into pollution sources and dynamics.

Spatial disaggregation of a legacy soil map to support digital soil and land evaluation assessments in Scotland

In recent years, the importance of soils and soil functions has been recognised for supporting the delivery of ecosystem services and for the realisation of international initiatives, such as the UN Sustainable Development Goals. At the same time, Digital Soil Mapping (DSM) has emerged as a modelling technique that can satisfy increased end-user needs for new soil datasets by producing fine resolution soils and soil property maps to support complex digital soil and land evaluation assessments. Spatial disaggregation is a popular DSM technique that is used to transform legacy soil maps to more spatially-explicit soils datasets, which can also be used in conjunction with soil databases to generate digital soil property maps. In this study, we performed spatial disaggregation of the National Soil Map of Scotland (originally published at 1:250,000 scale) at the taxonomic level of Soil Series, with the specific objective to facilitate the production of harmonised digital soil property maps to support soil and land evaluation assessments in Scotland through linking to the Scottish Soil Database. We divided Scotland into Landscape Units of similar soil and landform characteristics and trained probability random forest models within each Landscape Unit using area-proportion random sampling of both single- and multiple- (complex) Soil Series map units and selected environmental covariates to produce Soil Series probability layers at 50 m grid resolution. The performance of the disaggregated Soil Series maps was evaluated using prediction uncertainties of individual soil types and independent soil profile classifications. Evaluation results indicated that the random forest algorithm was successful in promoting effective spatial disaggregation of both single soil and complex soil polygons and provided good prediction accuracies for most soil types with the exception of some of the least extensive soil types typically found within complex map units. This was attributed mainly to algorithm's tendency to favour dominant, more extensive classes, along with its difficulty to distinguish between similar soils within spatially diverse areas. However, training Soil Series models at a Landscape Unit level instead of nationally helped to limit both the underestimation of these minority soil types and the overestimation of the dominant ones. In addition, map evaluation results showed the usefulness of using the generated conditional Soil Series probabilities for exploring soil spatial variability, especially within complex areas such as river floodplains covered by multiple alluvial and non-alluvial soils. Overall, this study demonstrates the potential of using spatial disaggregation to extract pedological knowledge embedded in legacy soil maps and use it to generate new dynamic and harmonised soil and soil property maps by effectively using readily-available and easily-updated soils information from existing databases.

Simulation of group of droplets evaporation

Droplet evaporation occurs in many natural phenomena and industrial processes; hence, several studies have been conducted on droplet evaporation. In many applications, a group of droplets evaporate and the interaction between them affects the evaporation process. In this paper, the front-tracking method is used to simulate the droplets groups evaporation. Since the front-tracking method uses a Lagrangian grid for each droplet, this method offers good accuracy in predicting the shape, the displacement and the evaporation rate of droplets. The numerical method has been developed to simulate the evaporation of binary droplets. The fluid surrounding the droplets is modeled as a gas mixture, so the numerical method can be used to simulate multiphase-multicomponent problems. The front-tracking method requires very fine grid resolution to simulate flows at high-density ratios; therefore, the method is rarely used at high-density ratios. In this paper, a two-step method is used to move the front at high-density ratios without requiring a very fine grid resolution. First, a static droplet evaporation is simulated, and the results are compared with the analytical solutions; evaporation of a Decane droplet is then simulated, and the results are compared with the experimental data. Subsequently, the evaporation of a binary droplet is modeled. The evaporation of a group of static droplets is also simulated, and the effect of droplets interaction is investigated. Next, the evaporation of three injected droplets is simulated, and the effect of some parameters on droplets interaction is probed. The evaporation rate and displacement of each droplet are calculated and compared with the single droplet. Finally, the evaporation of the groups of droplets is simulated, and the effect of different arrangements of droplets on the evaporation rate is studied. Understanding the droplets interactions is helpful in predicting droplet spray behavior and developing numerical methods. Thus, the presented results are useful to achieve a better understanding of the droplets interaction phenomenon, its outcomes, and the parameters affecting evaporation rate.

Quantifying NOx point sources with Landsat and Sentinel-2 satellite observations of NO2 plumes

We show that the Landsat and Sentinel-2 satellites can detect NO2 plumes from large point sources at 10 to 60 m pixel resolution in their blue and ultrablue bands. We use the resulting NO2 plume imagery to quantify nitrogen oxides (NOx) emission rates for several power plants in Saudi Arabia and the United States, including a 13-y analysis of 132 Landsat plumes from Riyadh power plant 9 from 2009 through 2021. NO2 in the plumes initially increases with distance from the source, likely reflecting recovery from ozone titration. The fine pixel resolutions of Landsat and Sentinel-2 enable separation of individual point sources and stacks, including in urban background, and the long records enable examination of multidecadal emission trends. Our inferred NOx emission rates are consistent with previous estimates to within a precision of about 30%. Sources down to ~500 kg h-1 can be detected over bright, quasi-homogeneous surfaces. The 2009 to 2021 data for Riyadh power plant 9 show a strong summer peak in emissions, consistent with increased power demand for air conditioning, and a marginal slow decrease following the introduction of Saudi Arabia's Ambient Air Standard 2012.

Does Increasing Horizontal Resolution Improve the Simulation of Intense Tropical Rainfall in GFDL's AM4 Model?

AbstractWe examine tropical rainfall from the Geophysical Fluid Dynamics Laboratory's Atmosphere Model version 4 (GFDL AM4) at three horizontal resolutions of 100 km, 50 km, and 25 km. The model produces more intense rainfall at finer resolutions, but a large discrepancy still exists between the simulated and the observed frequency distribution. We use a theoretical precipitation scaling diagnostic to examine the frequency distribution of the simulated rainfall. The scaling accurately produces the frequency distribution at moderate‐to‐high intensity (≥10 mm day−1). Intense tropical rainfall at finer resolutions is produced primarily from the increased contribution of resolved precipitation and enhanced updrafts. The model becomes more sensitive to the grid‐scale updrafts than local thermodynamics at high rain rates as the contribution from the resolved precipitation increases.

Optimizing Micro-CT Resolution for Geothermal Reservoir Characterization in the Pannonian Basin

In the context of global efforts to transition toward renewable energy and reduce greenhouse gas emissions, geothermal energy is increasingly recognized as a viable and sustainable option. This paper presents a comprehensive assessment derived from a subset of a larger sample collection within the Dunántúli Group of the Pannonian Basin, Hungary, focusing on optimizing micro-computed tomography (µ-CT) resolution for analyzing pore structures in sandstone formations. By categorizing samples based on geological properties and selecting representatives from each group, the study integrates helium porosity and gas permeability measurements with µ-CT imaging at various resolutions (5 µm, 2 µm, and 1 µm). The findings reveal that µ-CT resolution significantly affects the discernibility and characterization of pore structures. Finer resolutions (2 µm and 1 µm) effectively uncovered interconnected pore networks in medium- to coarse-grained sandstones, suggesting favorable properties for geothermal applications. In contrast, fine-grained samples showed limitations in geothermal applicability at higher resolutions due to their compact nature and minimal pore connectivity, which could not be confidently imaged at 1 µm. Additionally, this study acknowledges the challenges in delineating the boundaries within the Dunántúli Group formations, which adds a layer of complexity to the characterization process. The research highlights the importance of aligning µ-CT findings with geological backgrounds and laboratory measurements for accurate pore structure interpretation in heterogeneous formations. By contributing vital petrophysical data for the Dunántúli Group and the Pannonian Basin, this study provides key insights for selecting appropriate µ-CT imaging resolutions to advance sustainable geothermal energy strategies in the region. The outcomes of this research form the basis for future studies aimed at developing experimental setups to investigate physical clogging and enhance geothermal exploitation methods, crucial for the sustainable development of geothermal resources in the Pannonian Basin.

Determinants of Maximum Magnetic Anomaly Detection Distance.

The maximum detection distance is usually the primary concern of magnetic anomaly detection (MAD). Intuition tells us that larger object size, stronger magnetization and finer measurement resolution guarantee a further detectable distance. However, the quantitative relationship between detection distance and the above determinants is seldom studied. In this work, unmanned aerial vehicle-based MAD field experiments are conducted on cargo vessels and NdFeB magnets as typical magnetic objects to give a set of visualized magnetic field flux density images. Isometric finite element models are established, calibrated and analyzed according to the experiment configuration. A maximum detectable distance map as a function of target size and measurement resolution is then obtained from parametric sweeping on an experimentally calibrated finite element analysis model. We find that the logarithm of detectable distance is positively proportional to the logarithm of object size while negatively proportional to the logarithm of resolution, within the ranges of 1 m~500 m and 1 pT~1 μT, respectively. A three-parameter empirical formula (namely distance-size-resolution logarithmic relationship) is firstly developed to determine the most economic sensor configuration for a given detection task, to estimate the maximum detection distance for a given magnetic sensor and object, or to evaluate minimum detectable object size at a given magnetic anomaly detection scenario.

Correction: Wu et al. Accumulatively Increasing Sensitivity of Ultrawide Instantaneous Bandwidth Digital Receiver with Fine Time and Frequency Resolution for Weak Signal Detection. Electronics 2022, 11, 1018

In the original publication [...]