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Noninvasive brain signal measurement techniques are crucial for understanding human brain function and brain–machine interface applications. Conventionally, noninvasive brain signal measurement techniques, such as electroencephalography, magnetoencephalography, functional magnetic resonance imaging, and near-infrared spectroscopy, have been developed. However, currently, there is no practical noninvasive technique to measure brain function with high temporal and spatial resolution using one instrument. We developed a novel noninvasive brain signal measurement technique with high temporal and spatial resolution by biasing a static magnetic field emitted from a coil on the head to the brain. In this study, we applied this technique to develop a groundbreaking system for noninvasive whole-head brain function measurement with high spatiotemporal resolution across the entire head. We validated this system by measuring movement-related brain signals evoked by a right index finger extension movement and demonstrated that the proposed system can measure the dynamic activity of brain regions involved in finger movement with high spatiotemporal accuracy over the whole brain. Full article

Miniaturization promotes the efficiency and exploration domain in scientific fields such as computer science, engineering, medicine, and biotechnology. In particular, the field of microfluidics is a flourishing technology, which deals with the manipulation of small volumes of liquid. Dispersed droplets or bubbles in a second immiscible liquid are of great interest for screening applications or chemical and biochemical reactions. However, since very small dimensions are characterized by phenomena that differ from those at macroscopic scales, a deep understanding of physics is crucial for effective device design. Due to small volumes in miniaturized systems, common measurement techniques are not applicable as they exceed the dimensions of the device by a multitude. Hence, image analysis is commonly chosen as a method to understand ongoing phenomena. Artificial Intelligence is now the state of the art for recognizing patterns in images or analyzing datasets that are too large for humans to handle. X-ray-based Computer Tomography adds a third dimension to images, which results in more information, but ultimately, also in more complex image analysis. In this work, we present the application of the U-Net neural network to extract certain states during droplet formation in a capillary, which forms a constantly repeated process that is captured on tens of thousands of CT images. The experimental setup features a co-flow setup that is based on 3D-printed capillaries with two different cross-sections with an inner diameter, respectively edge length of 1.6 mm. For droplet formation, water was dispersed in silicon oil. The classification into different droplet states allows for 3D reconstruction and a time-resolved 3D analysis of the present phenomena. The original U-Net was modified to process input images of a size of 688 × 432 pixels while the structure of the encoder and decoder path feature 23 convolutional layers. The U-Net consists of four max pooling layers and four upsampling layers. The training was performed on 90% and validated on 10% of a dataset containing 492 images showing different states of droplet formation. A mean Intersection over Union of 0.732 was achieved for a training of 50 epochs, which is considered a good performance. The presented U-Net needs 120 ms per image to process 60,000 images to categorize emerging droplets into 24 states at 905 angles. Once the model is trained sufficiently, it provides accurate segmentation for various flow conditions. The selected images are used for 3D reconstruction enabling the 2D and 3D quantification of emerging droplets in capillaries that feature circular and square cross-sections. By applying this method, a temporal resolution of 25–40 ms was achieved. Droplets that are emerging in capillaries with a square cross-section become bigger under the same flow conditions in comparison to capillaries with a circular cross section. The presented methodology is promising for other periodic phenomena in different scientific disciplines that focus on imaging techniques. Full article

The Chukchi Sea is an open estuary in the southwestern Arctic. Its near-surface salinities are higher than those of the surrounding open Arctic waters due to the key inflow of saltier and warmer Pacific waters through the Bering Strait. This salinity distribution may suggest that interannual changes in the Bering Strait mass transport are the sole and dominant factor shaping the salinity distribution in the downstream Chukchi Sea. Using satellite sea surface salinity (SSS) retrievals and altimetry-based estimates of the Bering Strait transport, the relationship between the Strait transport and Chukchi Sea SSS distributions is analyzed from 2010 onward, focusing on the ice-free summer to fall period. A comparison of five different satellite SSS products shows that anomalous SSS spatially averaged over the Chukchi Sea during the ice-free period is consistent among them. Observed interannual temporal change in satellite SSS is confirmed by comparison with collocated ship-based thermosalinograph transect datasets. Bering Strait transport variability is known to be driven by the local meridional wind stress and by the Pacific-to-Arctic sea level gradient (pressure head). This pressure head, in turn, is related to an Arctic Oscillation-like atmospheric mean sea level pattern over the high-latitude Arctic, which governs anomalous zonal winds over the Chukchi Sea and affects its sea level through Ekman dynamics. Satellite SSS anomalies averaged over the Chukchi Sea show a positive correlation with preceding months’ Strait transport anomalies. This correlation is confirmed using two longer (>40-year), separate ocean data assimilation models, with either higher- (0.1°) or lower-resolution (0.25°) spatial resolution. The relationship between the Strait transport and Chukchi Sea SSS anomalies is generally stronger in the low-resolution model. The area of SSS response correlated with the Strait transport is located along the northern coast of the Chukotka Peninsula in the Siberian Coastal Current and adjacent zones. The correlation between wind patterns governing Bering Strait variability and Siberian Coastal Current variability is driven by coastal sea level adjustments to changing winds, in turn driving the Strait transport. Due to the Chukotka coastline configuration, both zonal and meridional wind components contribute. Full article

A range of remote sensing platforms provide high spatial and temporal resolution insights which are useful for monitoring vegetation growth. Very few studies have focused on fruit orchards, largely due to the inherent complexity of their structure. Fruit trees are mixed with inter-rows that can be grassed or non-grassed, and there are no standard protocols for ground measurements suitable for the range of crops. The assessment of biophysical variables (BVs) for fruit orchards from optical satellites remains a significant challenge. The objectives of this study are as follows: (1) to address the challenges of extracting and better interpreting biophysical variables from optical data by proposing new ground measurements protocols tailored to various orchards with differing inter-row management practices, (2) to quantify the impact of the inter-row at the Sentinel pixel scale, and (3) to evaluate the potential of Sentinel 2 data on BVs for orchard development monitoring and the detection of key phenological stages, such as the flowering and fruit set stages. Several orchards in two pedo-climatic zones in southeast France were monitored for three years: four apricot and nectarine orchards under different management systems and nine cherry orchards with differing tree densities and inter-row surfaces. We provide the first comparison of three established ground-based methods of assessing BVs in orchards: (1) hemispherical photographs, (2) a ceptometer, and (3) the Viticanopy smartphone app. The major phenological stages, from budburst to fruit growth, were also determined by in situ annotations on the same fields monitored using Viticanopy. In parallel, Sentinel 2 images from the two study sites were processed using a Biophysical Variable Neural Network (BVNET) model to extract the main BVs, including the leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fraction of green vegetation cover (FCOVER). The temporal dynamics of the normalised FAPAR were analysed, enabling the detection of the fruit set stage. A new aggregative model was applied to data from hemispherical photographs taken under trees and within inter-rows, enabling us to quantify the impact of the inter-row at the Sentinel 2 pixel scale. The resulting value compared to BVs computed from Sentinel 2 gave statistically significant correlations (0.57 for FCOVER and 0.45 for FAPAR, with respective RMSE values of 0.12 and 0.11). Viticanopy appears promising for assessing the PAI (plant area index) and FCOVER for orchards with grassed inter-rows, showing significant correlations with the Sentinel 2 LAI (R² of 0.72, RMSE 0.41) and FCOVER (R² 0.66 and RMSE 0.08). Overall, our results suggest that Sentinel 2 imagery can support orchard monitoring via indicators of development and inter-row management, offering data that are useful to quantify production and enhance resource management. Full article

Plantation distribution information is of great significance to the government’s macro-control, optimization of planting layout, and realization of efficient agricultural production. Existing studies primarily relied on high spatiotemporal resolution remote sensing data to address same-spectrum, different-object classification by extracting phenological information from temporal imagery. However, the classification problem of orchard or artificial forest, where the spectral and textural features are similar and their phenological characteristics are alike, still presents a substantial challenge. To address this challenge, we innovatively proposed a multi-index entropy weighting DTW method (ETW-DTW), building upon the traditional DTW method with single-feature inputs. In contrast to previous DTW classification approaches, this method introduces multi-band information and utilizes entropy weighting to increase the inter-class distances. This allowed for accurate classification of orchard categories, even in scenarios where the spectral textures were similar and the phenology was alike. We also investigated the impact of fusing optical and Synthetic Aperture Radar (SAR) data on the classification accuracy. By combining Sentinel-1 and Sentinel-2 time series imagery, we validated the enhanced classification effectiveness with the inclusion of SAR data. The experimental results demonstrated a noticeable improvement in orchard classification accuracy under conditions of similar spectral characteristics and phenological patterns, providing comprehensive information for orchard mapping. Additionally, we further explored the improvement in results based on two different parcel-based classification strategies compared to pixel-based classification methods. By comparing the classification results, we found that the parcel-based averaging method has advantages in clearly defining orchard boundaries and reducing noise interference. In conclusion, the introduction of the ETW-DTW method is of significant practical importance in addressing the challenge of same-spectrum, different-object classification. The obtained orchard distribution can provide valuable information for the government to optimize the planting structure and layout and regulate the macroeconomic benefits of the fruit industry. Full article

Mixed Reality (MR) environments hold immense potential for inducing a sense of embodiment, where users feel like their bodies are present within the virtual space. This subjective experience has been traditionally assessed using subjective reports and behavioral measures. However, neurophysiological approaches offer unique advantages in objectively characterizing embodiment. This review article explores the current state of the art in utilizing neurophysiological techniques, particularly Electroencephalography (EEG), Photoplethysmography (PPG), and Electrodermal activity (EDA), to investigate the neural and autonomic correlates of embodiment in MR for out-of-the-lab applications. More specifically, it was investigated how EEG, with its high temporal resolution, PPG, and EDA, can capture transient brain activity associated with specific aspects of embodiment, such as visuomotor synchrony, visual feedback of a virtual body, and manipulations of virtual body parts. The potential of such neurophysiological signals to differentiate between subjective experiences of embodiment was discussed, with a particular regard to identify the neural and autonomic markers of early embodiment formation during MR exposure in real settings. Finally, the strengths and limitations of the neurophysiological approach in the context of MR embodiment research were discussed, in order to achieve a more comprehensive understanding of this multifaceted phenomenon. Full article

As a contribution to the Integrated Marine Observing System (IMOS), the Bureau of Meteorology introduces new reprocessed Himawari-8 satellite-derived Sea Surface Temperature (SST) products. The Radiative Transfer Model and a Bayesian cloud clearing method is used to retrieve SSTs every 10 min from the geostationary satellite Himawari-8. An empirical Sensor Specific Error Statistics (SSES) model, introduced herein, is applied to calculate bias and standard deviation for the retrieved SSTs. The SST retrieval and compositing method, along with validation results, are discussed. The monthly statistics for comparisons of Himawari-8 Level 2 Product (L2P) skin SST against in situ SST quality monitoring (iQuam) in situ SST datasets, adjusted for thermal stratification, showed a mean bias of −0.2/−0.1 K and a standard deviation of 0.4–0.7 K for daytime/night-time after bias correction, where satellite zenith angles were less than 60° and the quality level was greater than 2. For ease of use, these native resolution SST data have been composited using a method introduced herein that retains retrieved measurements, to hourly, 4-hourly and daily SST products, and projected onto the rectangular IMOS 0.02 degree grid. On average, 4-hourly products cover ≈$10\%$ more of the IMOS domain, while one-night composites cover ≈$25\%$ more of the IMOS domain than a typical 1 h composite. All available Himawari-8 data have been reprocessed for the September 2015–December 2022 period. The 10 min temporal resolution of the newly developed Himawari-8 SST data enables a daily composite with enhanced spatial coverage, effectively filling in SST gaps caused by transient clouds occlusion. Anticipated benefits of the new Himawari-8 products include enhanced data quality for applications like IMOS OceanCurrent and investigations into marine thermal stress, marine heatwaves, and ocean upwelling in near-coastal regions. Full article

Remote sensing is essential in construction management by providing valuable information and insights throughout the project lifecycle. Due to the rapid advancement of remote sensing technologies, their use has been increasingly adopted in the architecture, engineering, and construction industries. This review paper aims to advance the understanding, knowledge base, and practical implementation of remote sensing technologies in the construction industry. It may help support the development of robust methodologies, address challenges, and pave the way for the effective integration of remote sensing into construction management processes. This paper presents the results of a comprehensive literature review, focusing on the challenges faced in using remote sensing technologies in construction management. One hundred and seventeen papers were collected from eight relevant journals, indexed in Web of Science, and then categorized by challenge type. The results of 44 exemplary studies were reported in the three types of remote sensing platforms (satellite, airborne, and ground-based remote sensing). The paper provides construction professionals with a deeper understanding of remote sensing technologies and their applications in construction management. The challenges of using remote sensing in construction were collected and classified into eleven challenges. According to the number of collected documents, the critical challenges were shadow, spatial, and temporal resolution issues. The findings emphasize the use of unmanned airborne systems (UASs) and satellite remote sensing, which have become increasingly common and valuable for tasks such as preconstruction planning, progress tracking, safety monitoring, and environmental management. This knowledge allows for informed decision-making regarding integrating remote sensing into construction projects, leading to more efficient and practical project planning, design, and execution. Full article

By utilizing top-of-atmosphere radiation (TOAR) data from China’s new generation of geostationary satellites (FY-4A and FY-4B) along with interpretable machine learning models, near-surface particulate matter concentrations in China were estimated, achieving hourly temporal resolution, 4 km spatial resolution, and 100% spatial coverage. First, the cloudless TOAR data were matched and modeled with the solar radiation products from the ERA5 dataset to construct and estimate a fully covered TOAR dataset under assumed clear-sky conditions, which increased coverage from 20–30% to 100%. Subsequently, this dataset was applied to estimate particulate matter. The analysis demonstrated that the fully covered TOAR dataset (R² = 0.83) performed better than the original cloudless dataset (R² = 0.76). Additionally, using feature importance scores and SHAP values, the impact of meteorological factors and air mass trajectories on the increase in PM₁₀ and PM_2.5 during dust events were investigated. The analysis of haze events indicated that the main meteorological factors driving changes in particulate matter included air pressure, temperature, and boundary layer height. The particulate matter concentration products obtained using fully covered TOAR data exhibit high coverage and high spatiotemporal resolution. Combined with data-driven interpretable machine learning, they can effectively reveal the influencing factors of particulate matter in China. Full article

Garlic constitutes a significant small-scale agricultural commodity in China. A key factor influencing garlic prices is the planted area, which can be accurately and efficiently determined using remote sensing technology. However, the spectral characteristics of garlic and winter wheat are easily confused, and the widespread intercropping of these crops in the study area exacerbates this issue, leading to significant challenges in remote sensing image analysis. Additionally, remote sensing data are often affected by weather conditions, spatial resolution, and revisit frequency, which can result in delayed and inaccurate area extraction. In this study, historical data were utilized to restore Sentinel-2 remote sensing images, aimed at mitigating cloud and rain interference. Feature combinations were devised, incorporating two vegetation indices into a comprehensive time series, along with Sentinel-1 synthetic aperture radar (SAR) time series and other temporal datasets. Multiple classification combinations were employed to extract garlic within the study area, and the accuracy of the classification results was systematically analyzed. First, we used passive satellite imagery to extract winter crops (garlic, winter wheat, and others) with high accuracy. Second, we identified garlic by applying various combinations of time series features derived from both active and passive remote sensing data. Third, we evaluated the classification outcomes of various feature combinations to generate an optimal garlic cultivation distribution map for each region. Fourth, we developed a garlic fragmentation index to assess the impact of landscape fragmentation on garlic extraction accuracy. The findings reveal that: (1) Better results in garlic extraction can be achieved using active–passive time series remote sensing. The performance of the classification model can be further enhanced by incorporating short-wave infrared bands or spliced time series data into the classification features. (2) Examination of garlic cultivation fragmentation using the garlic fragmentation index aids in elucidating variations in accuracy across the study area’s six counties. (3) Comparative analysis with validation samples demonstrated superior garlic extraction outcomes from the six primary garlic-producing counties of the North China Plain in 2021, achieving an overall precision exceeding 90%. This study offers a practical exploration of target crop identification using multi-source remote sensing data in mixed cropping areas. The methodology presented here demonstrates the potential for efficient, cost-effective, and accurate garlic classification, which is crucial for improving garlic production management and optimizing agricultural practices. Moreover, this approach holds promise for broader applications, such as nationwide garlic mapping. Full article

We apply the Canny edge algorithm to imagery from the Utqiaġvik coastal sea ice radar system (CSIRS) to identify regions of open water and sea ice and quantify ice concentration. The radar-derived sea ice concentration (SIC) is compared against the (closest to the radar field of view) 25 km resolution NSIDC Climate Data Record (CDR) and the 1 km merged MODIS-AMSR2 sea ice concentrations within the ∼11 km field of view for the year 2022–2023, when improved image contrast was first implemented. The algorithm was first optimized using sea ice concentration from 14 different images and 10 ice analysts (140 analyses in total) covering a range of ice conditions with landfast ice, drifting ice, and open water. The algorithm is also validated quantitatively against high-resolution MODIS-Terra in the visible range. Results show a correlation coefficient and mean bias error between the optimized algorithm, the CDR and MODIS-AMSR2 daily SIC of 0.18 and 0.54, and ∼−1.0 and 0.7%, respectively, with an averaged inter-analyst error of ±3%. In general, the CDR captures the melt period correctly and overestimates the SIC during the winter and freeze-up period, while the merged MODIS-AMSR2 better captures the punctual break-out events in winter, including those during the freeze-up events (reduction in SIC). Remnant issues with the detection algorithm include the false detection of sea ice in the presence of fog or precipitation (up to 20%), quantified from the summer reconstruction with known open water conditions. The proposed technique allows for the derivation of the SIC from CSIRS data at spatial and temporal scales that coincide with those at which coastal communities members interact with sea ice. Moreover, by measuring the SIC in nearshore waters adjacent to the shoreline, we can quantify the effect of land contamination that detracts from the usefulness of satellite-derived SIC for coastal communities. Full article

Previous studies were mostly conducted based on sparse time series and different phenological images, which often ignored the dramatic changes in salinization evolution throughout the year. Based on Landsat and moderate-resolution-imaging spectroradiometer (MODIS) images from 2000 to 2020, this study applied the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) algorithm to obtain similar phenological images for the month of April for the past 20 years. Based on the random forest algorithm, the surface parameters of the salinization were optimized, and the feature space index models were constructed. Combined with the measured ground data, the optimal monitoring index model of salinization was determined, and then the spatiotemporal evolution patterns of salinization and its driving mechanisms in the Yellow River Delta were revealed. The main conclusions were as follows: (1) The derived long-time-series and similar phenological-fusion images enable us to reveal the patterns of change in the dramatic salinization in the year that we examined using the ESTARFM algorithm. (2) The NDSI-TGDVI feature space salinization monitoring index model based on point-to-point mode had the highest accuracy of 0.92. (3) From 2000 to 2020, the soil salinization in the Yellow River Delta showed an aggravating trend. The average value of salinization during the past 20 years was 0.65, which is categorized as severe salinization. The degree of salinization gradually decreased from the northeastern coastal area to the southwestern inland area. (4) The dominant factors affecting soil salinization in different historical periods varied. The research results could provide support for decision-making regarding the precise prevention and control of salinization in the Yellow River Delta. Full article

Cultivated land is crucial for food production and security. In complex environments like mountainous regions, the fragmented nature of the cultivated land complicates rapid and accurate information acquisition. Deep learning has become essential for extracting cultivated land but faces challenges such as edge detail loss and limited adaptability. This study introduces a novel approach that combines geographical zonal stratification with the temporal characteristics of medium-resolution remote sensing images for identifying cultivated land. The methodology involves geographically zoning and stratifying the study area, and then integrating semantic segmentation and edge detection to analyze remote sensing images and generate initial extraction results. These results are refined through post-processing with medium-resolution imagery classification to produce a detailed map of the cultivated land distribution. The method achieved an overall extraction accuracy of 95.07% in Tongnan District, with specific accuracies of 92.49% for flat cultivated land, 96.18% for terraced cultivated land, 93.80% for sloping cultivated land, and 78.83% for forest intercrop land. The results indicate that, compared to traditional methods, this approach is faster and more accurate, reducing both false positives and omissions. This paper presents a new methodological framework for large-scale cropland mapping in complex scenarios, offering valuable insights for subsequent cropland extraction in challenging environments. Full article

The source region of the Yellow River (SRYR), known as the “Chinese Water Tower”, is currently grappling with severe soil erosion, which jeopardizes the sustainability of its alpine grasslands. Large-scale soil erosion monitoring poses a significant challenge, complicating global efforts to study soil erosion and land cover changes. Moreover, conventional methods for assessing soil erosion do not adequately address the variety of erosion types present in the SRYR. Given these challenges, the objectives of this study were to develop a suitable assessment and prediction model for soil erosion tailored to the SRYR’s needs. By leveraging soil erosion data measured by ¹³⁷Cs from 521 locations and employing the random forest (RF) algorithm, a new soil erosion model was formulated. Key findings include that: (1) The RF soil erosion model significantly outperformed the revised universal soil loss equation (RUSLE) model and revised wind erosion equation (RWEQ) model, achieving an R² of 0.52 and an RMSE of 5.88. (2) The RF model indicated that from 2001 to 2020, the SRYR experienced an average annual soil erosion modulus (SEM) of 19.32 t·ha⁻¹·y⁻¹ with an annual total erosion in the SRYR of 225.18 × 10⁶ t·y⁻¹. Spatial analysis revealed that 78.64% of the region suffered low erosion, with erosion intensity declining from northwest to southeast. (3) The annual SEM in the SRYR demonstrated a downward trend from 2001 to 2020, with 83.43% of the study area showing improvement. Based on these findings, measures for soil erosion prevention and control in the SRYR were proposed. Future studies should refine the temporal analysis to better understand the influence of extreme climate events on soil erosion, while leveraging high-resolution data to enhance model accuracy. Insights into the drivers of soil erosion in the SRYR will support more effective policy development. Full article

Accurate PV power prediction is crucial for enhancing grid planning, optimizing dispatch operations, and advancing management strategies. In pursuit of this objective, this study proposes a short-term distributed PV power prediction method that incorporates temporal and spatial feature extraction as well as similar day analysis. Firstly, to address the poor adaptability of traditional clustering methods to time-series data, the K-shape clustering algorithm is employed to categorize the time series into different weather types. Secondly, to overcome the challenges posed by varying time resolutions in similar day analysis, a novel method based on Dynamic Time Warping (DTW) is proposed. This method calculates the similarity between the target days and the days to be collected, considering both the time of day and the day of the week. Subsequently, a PV power generation prediction model based on a convolutional long short-term memory (CNN-LSTM) network is developed to enhance prediction accuracy. To tackle the difficulty of manual hyperparameter tuning, the chaos reverse sparrow search algorithm (CRSSA) is introduced. Finally, a case study is conducted on the measured data of a distributed photovoltaic power station in a certain region of China. By comparing RMSE and MAPE, compared with other prediction models, the proposed prediction model and solving algorithm effectively reduced the relative error by more than 1%, verifying the effectiveness of the proposed method. Full article