bluuebunny/crossref_metadata_2025 huggingface dataset:
Regulation of Root Exudation in Wheat Plants in Response to Alkali Stress
Authors: Wang Huan , Zhao Shuting , Qi Zexin , Yang Changgang , Ding Dan , Xiao Binbin , Wang Shihong , Yang Chunwu
Published: April 2024

Soil alkalization is an important environmental factor limiting crop production. Despite the importance of root secretion in the response of plants to alkali stress, the regulatory mechanism is unclear. In this study, we applied a widely targeted metabolomics approach using a local MS/MS data library constructed with authentic standards to identify and quantify root exudates of wheat under salt and alkali stresses. The regulatory mechanism of root secretion in alkali-stressed wheat plants was analyzed by determining transcriptional and metabolic responses. Our primary focus was alkali stress-induced secreted metabolites (AISMs) that showed a higher secretion rate in alkali-stressed plants than in control and salt-stressed plants. This secretion was mainly induced by high-pH stress. We discovered 55 AISMs containing –COOH groups, including 23 fatty acids, 4 amino acids, 1 amino acid derivative, 7 dipeptides, 5 organic acids, 9 phenolic acids, and 6 others. In the roots, we also discovered 29 metabolites with higher levels under alkali stress than under control and salt stress conditions, including 2 fatty acids, 3 amino acid derivatives, 1 dipeptide, 2 organic acids, and 11 phenolic acids. These alkali stress-induced accumulated carboxylic acids may support continuous root secretion during the response of wheat plants to alkali stress. In the roots, RNAseq analysis indicated that 5 6-phosphofructokinase (glycolysis rate-limiting enzyme) genes, 16 key fatty acid synthesis genes, and 122 phenolic acid synthesis genes have higher expression levels under alkali stress than under control and salt stress conditions. We propose that the secretion of multiple types of metabolites with a –COOH group is an important pH regulation strategy for alkali-stressed wheat plants. Enhanced glycolysis, fatty acid synthesis, and phenolic acid synthesis will provide more energy and substrates for root secretion during the response of wheat to alkali stress.
Overexpression of Larch SCL6 Inhibits Transitions from Vegetative Meristem to Inflorescence and Flower Meristem in Arabidopsis thaliana (L.) Heynh.
Authors: Xing Jun-Xia , Zang Qiao-Lu , Ye Zha-Long , Qi Li-Wang , Yang Ling , Li Wan-Feng
Published: April 2024

SCARECROW-LIKE6 (SCL6) plays a role in the formation and maintenance of the meristem. In Larix kaempferi (Lamb.) Carr., an important afforestation tree species in China, SCL6 (LaSCL6) has two alternative splicing variants—LaSCL6-var1 and LaSCL6-var2—which are regulated by microRNA171. However, their roles are still unclear. In this study, LaSCL6-var1 and LaSCL6-var2 were transformed into the Arabidopsis thaliana (L.) Heynh. genome, and the phenotypic characteristics of transgenic A. thaliana, including the germination percentage, root length, bolting time, flower and silique formation times, inflorescence axis length, and branch and silique numbers, were analyzed to reveal their functions. It was found that LaSCL6-var1 and LaSCL6-var2 overexpression shortened the root length by 41% and 31%, respectively, and increased the inflorescence axis length. Compared with the wild type, the bolting time in transgenic plants was delayed by approximately 2–3 days, the first flower and silique formation times were delayed by approximately 3–4 days, and the last flower and silique formation times were delayed by about 5 days. Overall, the life cycle in transgenic plants was prolonged by approximately 5 days. These results show that LaSCL6 overexpression inhibited the transitions from the vegetative meristem to inflorescence meristem and from the flower meristem to meristem arrest in A. thaliana, revealing the roles of LaSCL6-var1 and LaSCL6-var2 in the fate transition and maintenance of the meristem.
Echinacea: Bioactive Compounds and Agronomy
Authors: Ahmadi Fatemeh , Kariman Khalil , Mousavi Milad , Rengel Zed
Published: April 2024

For centuries, medicinal plants have been used as sources of remedies and treatments for various disorders and diseases. Recently, there has been renewed interest in these plants due to their potential pharmaceutical properties, offering natural alternatives to synthetic drugs. Echinacea, among the world’s most important medicinal plants, possesses immunological, antibacterial, antifungal, and antiviral properties. Nevertheless, there is a notable lack of thorough information regarding the echinacea species, underscoring the vital need for a comprehensive review paper to consolidate existing knowledge. The current review provides a thorough analysis of the existing knowledge on recent advances in understanding the physiology, secondary metabolites, agronomy, and ecology of echinacea plants, focusing on E. purpurea, E. angustifolia, and E. pallida. Pharmacologically advantageous effects of echinacea species on human health, particularly distinguished for its ability to safeguard the nervous system and combat cancer, are discussed. We also highlight challenges in echinacea research and provide insights into diverse approaches to boost the biosynthesis of secondary metabolites of interest in echinacea plants and optimize their large-scale farming. Various academic databases were employed to carry out an extensive literature review of publications from 2001 to 2024. The medicinal properties of echinacea plants are attributed to diverse classes of compounds, including caffeic acid derivatives (CADs), chicoric acid, echinacoside, chlorogenic acid, cynarine, phenolic and flavonoid compounds, polysaccharides, and alkylamides. Numerous critical issues have emerged, including the identification of active metabolites with limited bioavailability, the elucidation of specific molecular signaling pathways or targets linked to echinacoside effects, and the scarcity of robust clinical trials. This raises the overarching question of whether scientific inquiry can effectively contribute to harnessing the potential of natural compounds. A systematic review and analysis are essential to furnish insights and lay the groundwork for future research endeavors focused on the echinacea natural products.
The Time from Submission to Publication in Primary Health Care Journals: A Cross-Sectional Study
Authors: Chen Tsung-An , Lin Ming-Hwai , Chen Yu-Chun , Chen Tzeng-Ji
Published: April 2024

Background: The time from submission to publication can significantly impact the speed of knowledge dissemination and is influenced by multiple factors. This research aims to investigate the time from submission to publication of journals of primary health care and to explore the factors that influence this duration. Methods: We selected journals of primary health care and extracted their impact factors, annual publication frequencies, and open access status. The time from submission to acceptance (SA lag), acceptance to publication (AP lag), and submission to publication (SP lag) were calculated. Additionally, we conducted statistical analyses to determine whether impact factors, annual publication frequencies, and journal open access status had an influence on publication time. Results: This study revealed the average SP lag was 243.4 days (interquartile range, IQR 159–306), the average SA lag was 177.8 days (IQR 99–229.3), and the average AP lag was 65.6 days (IQR 14–101). Variations were observed in SP lag, SA lag, and AP lag among different journals. SP lag generally decreased with higher impact factors. Journals with open access had longer SA lag but shorter AP lag. There was a general trend of decreasing SP lag and SA lag with an increasing number of annual publications, but no clear trend was observed for AP lag. Conclusions: Improvements are needed in reducing the duration from submission to publication for primary health care journals. Significant variation exists among journals. Additionally, factors such as the impact factor, open access status, and the number of annual publications may influence publication speed.
Considering Grouped or Individual Non-Methane Volatile Organic Compound Emissions in Life Cycle Assessment of Composting Using Three Life Cycle Impact Assessment Methods
Authors: Joseph Ben , Stichnothe Heinz
Published: April 2024

Composting is a waste management practice that converts organic waste into a product that can be used safely and beneficially as a bio-fertiliser and soil amendment. Non-methane volatile organic compounds (NMVOCs) from composting are known to cause damage to human health and the environment. The impact of waste management on the environment and workers is recognised as a growing environmental and public health concern. Measurements of NMVOCs emitted during composting have been carried out only in a few studies. NMVOC emissions are typically reported as a group rather than as species or speciation profiles. Recognising the need to investigate the issues associated with NMVOCs, the objective of this study is to estimate variation in life cycle assessment (LCA) results when NMVOCs are considered individual emissions compared to grouped emissions and to compare midpoint and endpoint life cycle impact assessment (LCIA) methods. In general, the ReCiPe 2016 LCIA method estimated the highest impact from the composting process in comparison to IMPACT World+ and EF 3.0 for the impact categories of ozone formation, stratospheric ozone depletion, and particulate matter formation. For ReCiPe 2016 and IMPACT World+, the NMVOC emissions were not linked to human toxicity characterisation factors, meaning that the contribution from NMVOC towards human health risks in and around composting facilities could be underestimated. Using individual NMVOCs helps to additionally estimate the impacts of composting on freshwater ecotoxicity and human carcinogenic and non-carcinogenic toxicity potential. If ecotoxicity or toxicity issues are indicated, then LCA should be accompanied by suitable risk assessment measures for the respective life cycle stage.
Polarized Bidirectional Reflectance Distribution Function Matrix Derived from Two-Scale Roughness Theory and Its Applications in Active Remote Sensing
Authors: He Lingli , Weng Fuzhong , Wen Jinghan , Jia Tong
Published: April 2024

A polarized bidirectional reflectance distribution function (pBRDF) matrix was developed based on the two-scale roughness theory to provide consistent simulations of fully polarized microwave emission and scattering, required for the ocean–atmosphere-coupled radiative transfer model. In this study, the potential of the two-scale pBRDF matrix was explored for simulating ocean full-polarization backscattering and bistatic-scattering normalized radar cross sections (NRCSs). Comprehensive numerical simulations of the two-scale pBRDF matrix across the L-, C-, X-, and Ku-bands were carried out, and the simulations were compared with experimental data, classical electromagnetic, and GMFs. The results show that the two-scale pBRDF matrix demonstrates reasonable dependencies on ocean surface wind speeds, relative wind direction (RWD), geometries, and frequencies and has a reliable accuracy in general. In addition, the two-scale pBRDF matrix simulations were compared with the observations from the advanced scatterometer (ASCAT) onboard MetOP-C satellites, with a correlation coefficient of 0.9634 and a root mean square error (RMSE) of 2.5083 dB. In the bistatic case, the two-scale pBRDF matrix simulations were compared with Cyclone Global Navigation Satellite System (CYGNSS) observations, demonstrating a good correlation coefficient of 0.8480 and an RMSE of 1.2859 dB. In both cases, the two-scale pBRDF matrix produced fairly good simulations at medium-to-high wind speeds. The relatively large differences at low wind speeds (<5 m/s) were due probably to the swell effects. This study proves that the two-scale pBRDF matrix is suitable for the applications of multiple types of active instruments and can consistently simulate the ocean surface passive and active signals.
Sensitivity of Sentinel-1 Backscatter to Management-Related Disturbances in Temperate Forests
Authors: van der Woude Sietse , Reiche Johannes , Sterck Frank , Nabuurs Gert-Jan , Vos Marleen , Herold Martin
Published: April 2024

The rapid and accurate detection of forest disturbances in temperate forests has become increasingly crucial as policy demands and climate pressure on these forests rise. The cloud-penetrating Sentinel-1 radar constellation provides frequent and high-resolution observations with global coverage, but few studies have assessed its potential for mapping disturbances in temperate forests. This study investigated the sensitivity of temporally dense C-band backscatter data from Sentinel-1 to varying management-related disturbance intensities in temperate forests, and the influence of confounding factors such as radar backscatter signal seasonality, shadow, and layover on the radar backscatter signal at a pixel level. A unique network of 14 experimental sites in the Netherlands was used in which trees were removed to simulate different levels of management-related forest disturbances across a range of representative temperate forest species. Results from six years (2016–2022) of Sentinel-1 observations indicated that backscatter seasonality is dependent on species phenology and degree of canopy cover. The backscatter change magnitude was sensitive to medium- and high-severity disturbances, with radar layover having a stronger impact on the backscatter disturbance signal than radar shadow. Combining ascending and descending orbits and complementing polarizations compared to a single orbit or polarization was found to result in a 34% mean increase in disturbance detection sensitivity across all disturbance severities. This study underlines the importance of linking high-quality experimental ground-based data to dense satellite time series to improve future forest disturbance mapping. It suggests a key role for C-band backscatter time series in the rapid and accurate large-area monitoring of temperate forests and, in particular, the disturbances imposed by logging practices or tree mortality driven by climate change factors.
Urban Building Height Extraction from Gaofen-7 Stereo Satellite Images Enhanced by Contour Matching
Authors: Cui Yunfan , Zhao Shuangming , Jiang Wanshou , Yu Guorong
Published: April 2024

The traditional method for extracting the heights of urban buildings involves utilizing dense matching algorithms on stereo images to generate a digital surface model (DSM). However, for urban buildings, the disparity discontinuity issue that troubles the dense matching algorithm makes the elevations of high-rise buildings and the surrounding areas inaccurate. The occlusion caused by trees in greenbelts makes it difficult to accurately extract the ground elevation around the building. To tackle these problems, a method for building height extraction from Gaofen-7 (GF-7) stereo images enhanced by contour matching is presented. Firstly, a contour matching algorithm was proposed to extract accurate building roof elevation from GF-7 images. Secondly, a ground filtering algorithm was employed on the DSM to generate a digital elevation model (DEM), and ground elevation can be extracted from this DEM. The difference between the rooftop elevation and the ground elevation represents the building height. The presented method was verified in Yingde, Guangzhou, Guangdong Province, and Xi’an, Shaanxi Province. The experimental results demonstrate that our proposed method outperforms existing methods in building height extraction concerning accuracy.
A Multi-Level Robust Positioning Method for Three-Dimensional Ground Penetrating Radar (3D GPR) Road Underground Imaging in Dense Urban Areas
Authors: Zhang Ju , Hu Qingwu , Zhou Yemei , Zhao Pengcheng , Duan Xuzhe
Published: April 2024

Three-Dimensional Ground Penetrating Radar (3D GPR) detects subsurface targets non-destructively, rapidly, and continuously. The complex environment around urban roads affects the positioning accuracy of 3D GPR. The positioning accuracy directly affects the data quality, as inaccurate positioning can lead to distortion and misalignment of 3D GPR data. This paper proposed a multi-level robust positioning method to improve the positioning accuracy of 3D GPR in dense urban areas in order to obtain more accurate underground data. In environments with good GNSS signals, fast and high-precision positioning can be achieved based on GNSS data using differential GNSS technology; in scenes with weak GNSS signals, high-precision positioning of subsurface data can be achieved by using GNSS and IMU as well as using GNSS/INS tightly coupled solution technology; in scenes with no GNSS signals, SLAM technology is used for positioning based on INS data and 3D point cloud data. In summary, this method ensures a positioning accuracy of 3D GPR better than 10 cm and high-quality 3D images of underground urban roads in any environment. This provides data support for urban road underground structure surveys and has broad application prospects in underground disease detection and prevention.
MEA-EFFormer: Multiscale Efficient Attention with Enhanced Feature Transformer for Hyperspectral Image Classification
Authors: Sun Qian , Zhao Guangrui , Fang Yu , Fang Chenrong , Sun Le , Li Xingying
Published: April 2024

Hyperspectral image classification (HSIC) has garnered increasing attention among researchers. While classical networks like convolution neural networks (CNNs) have achieved satisfactory results with the advent of deep learning, they are confined to processing local information. Vision transformers, despite being effective at establishing long-distance dependencies, face challenges in extracting high-representation features for high-dimensional images. In this paper, we present the multiscale efficient attention with enhanced feature transformer (MEA-EFFormer), which is designed for the efficient extraction of spectral–spatial features, leading to effective classification. MEA-EFFormer employs a multiscale efficient attention feature extraction module to initially extract 3D convolution features and applies effective channel attention to refine spectral information. Following this, 2D convolution features are extracted and integrated with local binary pattern (LBP) spatial information to augment their representation. Then, the processed features are fed into a spectral–spatial enhancement attention (SSEA) module that facilitates interactive enhancement of spectral–spatial information across the three dimensions. Finally, these features undergo classification through a transformer encoder. We evaluate MEA-EFFormer against several state-of-the-art methods on three datasets and demonstrate its outstanding HSIC performance.
A Deep-Learning-Based Error-Correction Method for Atmospheric Motion Vectors
Authors: Cao Hang , Leng Hongze , Zhao Jun , Zhao Yanlai , Zhao Chengwu , Li Baoxu
Published: April 2024

Atmospheric motion vectors, which can be used to infer wind speed and direction based on the trajectory of cloud movement, are instrumental in enhancing atmospheric wind-field insights, contributing notably to wind-field optimization and forecasting. However, a widespread problem with vector data is their inaccuracy, which, when coupled with the mediocre effectiveness of existing correction methods, limits their practical utility in forecasting, often falling short of expectations. Deep-learning techniques are used to refine atmospheric motion vector data from the FY-4A satellite, notably enhancing data quality. Post-training data undergoes a thorough analysis using a quality evaluation function, followed by its integration into a numerical weather prediction system in order to conduct forecasting experiments. Results indicate a marked improvement in data quality post-error correction by the model, characterized by a significant reduction in root mean square error and a notable increase in correlation coefficients. Furthermore, refined data demonstrate a considerable enhancement in the accuracy of meteorological element forecasts, particularly for Asian and Western Pacific regions.
The Sensitivity of Polar Mesospheric Clouds to Mesospheric Temperature and Water Vapor
Authors: Lee Jae N. , Wu Dong L. , Thurairajah Brentha , Hozumi Yuta , Tsuda Takuo
Published: April 2024

Polar mesospheric cloud (PMC) data obtained from the Aeronomy of Ice in the Mesosphere (AIM)/Cloud Imaging and Particle Size (CIPS) experiment and Himawari-8/Advanced Himawari Imager (AHI) observations are analyzed for multi-year climatology and interannual variations. Linkages between PMCs, mesospheric temperature, and water vapor (H2O) are further investigated with data from the Microwave Limb Sounder (MLS). Our analysis shows that PMC onset date and occurrence rate are strongly dependent on the atmospheric environment, i.e., the underlying seasonal behavior of temperature and water vapor. Upper-mesospheric dehydration by PMCs is evident in the MLS water vapor observations. The spatial patterns of the depleted water vapor correspond to the PMC occurrence region over the Arctic and Antarctic during the days after the summer solstice. The year-to-year variabilities in PMC occurrence rates and onset dates are highly correlated with mesospheric temperature and H2O. They show quasi-quadrennial oscillation (QQO) with 4–5-year periods, particularly in the southern hemisphere (SH). The combined influence of mesospheric cooling and the mesospheric H2O increase provides favorable conditions for PMC formation. The global increase in mesospheric H2O during the last decade may explain the increased PMC occurrence in the northern hemisphere (NH). Although mesospheric temperature and H2O exhibit a strong 11-year variation, little solar cycle signatures are found in the PMC occurrence during 2007–2021.
Soil Salinity Inversion in Yellow River Delta by Regularized Extreme Learning Machine Based on ICOA
Authors: Wang Jiajie , Wang Xiaopeng , Zhang Jiahua , Shang Xiaodi , Chen Yuyi , Feng Yiping , Tian Bingbing
Published: April 2024

Soil salinization has seriously affected agricultural production and ecological balance in the Yellow River Delta region. Rapid and accurate monitoring of soil salinity has become an urgent need. Traditional machine learning models tend to fall into local optimal values during the learning process, which reduces their accuracy. This paper introduces Circle map to enhance the crayfish optimization algorithm (COA), which is then integrated with the regularized extreme learning machine (RELM) model, aiming to improve the accuracy of soil salinity content (SSC) inversion in the Yellow River Delta region. We employed Landsat5 TM remote sensing images and measured salinity data to develop spectral indices, such as the band index, salinity index, vegetation index, and comprehensive index, selecting the optimal modeling variable group through Pearson correlation analysis and variable projection importance analysis. The back propagation neural network (BPNN), RELM, and improved crayfish optimization algorithm–regularized extreme learning machine (ICOA-RELM) models were constructed using measured data and selected variable groups for SSC inversion. The results indicate that the ICOA-RELM model enhances the R2 value by an average of about 0.1 compared to other models, particularly those using groups of variables filtered by variable projection importance analysis as input variables, which showed the best inversion effect (test set R2 value of 0.75, MAE of 0.198, RMSE of 0.249). The SSC inversion results indicate a higher salinization degree in the coastal regions of the Yellow River Delta and a lower degree in the inland areas, with moderate saline soil and severe saline soil comprising 48.69% of the total area. These results are consistent with the actual sampling results, which verify the practicability of the model. This paper’s methods and findings introduce an innovative and practical tool for monitoring and managing salinized soils in the Yellow River Delta, offering significant theoretical and practical benefits.
Ship Detection in Maritime Scenes under Adverse Weather Conditions
Authors: Zhang Qiuyu , Wang Lipeng , Meng Hao , Zhang Zhi , Yang Chunsheng
Published: April 2024

Point cloud-based detection focuses on land traffic, rarely marine, facing issues with ships: it struggles in bad weather due to reliance on adverse weather data and fails to detect ships effectively due to overlooking size and appearance differences. Addressing the above challenges, our work introduces point cloud data of marine scenarios under realistically simulated adverse weather conditions and a dedicated Ship Detector tailored for marine environments. To adapt to various maritime weather conditions, we simulate realistic rain and fog in collected marine scene point cloud data. Additionally, addressing the issue of losing geometric and height information during feature extraction for large objects, we propose a Ship Detector. It employs a dual-branch sparse convolution layer for extracting multi-scale 3D feature maps, effectively minimizing height information loss. Additionally, a multi-scale 2D convolution module is utilized, which encodes and decodes feature maps and directly employs 3D feature maps for target prediction. To reduce dependency on existing data and enhance model robustness, our training dataset includes simulated point cloud data representing adverse weather conditions. In maritime point cloud ship detection, our Ship Detector, compared to adjusted small object detectors, demonstrates the best performance.
Annual and Seasonal Variations in Aerosol Optical Characteristics in the Huai River Basin, China from 2007 to 2021
Authors: Deng Xu , Xie Chenbo , Liu Dong , Wang Yingjian
Published: April 2024

Over the past three decades, China has seen aerosol levels substantially surpass the global average, significantly impacting regional climate. This study investigates the long-term and seasonal variations of aerosols in the Huai River Basin (HRB) using MODIS, CALIOP observations from 2007 to 2021, and ground-based measurements. A notable finding is a significant decline in the annual mean Aerosol Optical Depth (AOD) across the HRB, with MODIS showing a decrease of approximately 0.023 to 0.027 per year, while CALIOP, which misses thin aerosol layers, recorded a decrease of about 0.016 per year. This downward trend is corroborated by improvements in air quality, as evidenced by PM2.5 measurements and visibility-based aerosol extinction coefficients. Aerosol decreases occurred at all heights, but for aerosols below 800 m, with an annual AOD decrease of 0.011. The study also quantifies the long-term trends of five major aerosol types, identifying Polluted Dust (PD) as the predominant frequency type (46%), which has significantly decreased, contributing to about 68% of the total AOD reduction observed by CALIOP (0.011 per year). Despite this, Dust and Polluted Continental (PC) aerosols persist, with PC showing no clear trend of decrease. Seasonal analysis reveals aerosol peaks in summer, contrary to surface measurements, attributed to variations in the Boundary Layer (BL) depth, affecting aerosol distribution and extinction. Furthermore, the study explores the influence of seasonal wind patterns on aerosol type variation, noting that shifts in wind direction contribute to the observed changes in aerosol types, particularly affecting Dust and PD occurrences. The integration of satellite and ground measurements provides a comprehensive view of regional aerosol properties, highlighting the effectiveness of China’s environmental policies in aerosol reduction. Nonetheless, the persistence of high PD and PC levels underscores the need for continued efforts to reduce both primary and secondary aerosol production to further enhance regional air quality.
AFMUNet: Attention Feature Fusion Network Based on a U-Shaped Structure for Cloud and Cloud Shadow Detection
Authors: Du Wenjie , Fan Zhiyong , Yan Ying , Yu Rui , Liu Jiazheng
Published: April 2024

Cloud detection technology is crucial in remote sensing image processing. While cloud detection is a mature research field, challenges persist in detecting clouds on reflective surfaces like ice, snow, and sand. Particularly, the detection of cloud shadows remains a significant area of concern within cloud detection technology. To address the above problems, a convolutional self-attention mechanism feature fusion network model based on a U-shaped structure is proposed. The model employs an encoder–decoder structure based on UNet. The encoder performs down-sampling to extract deep features, while the decoder uses up-sampling to reconstruct the feature map. To capture the key features of the image, Channel Spatial Attention Module (CSAM) is introduced in this work. This module incorporates an attention mechanism for adaptive field-of-view adjustments. In the up-sampling process, different channels are selected to obtain rich information. Contextual information is integrated to improve the extraction of edge details. Feature fusion at the same layer between up-sampling and down-sampling is carried out. The Feature Fusion Module (FFM) facilitates the positional distribution of the image on a pixel-by-pixel basis. A clear boundary is distinguished using an innovative loss function. Finally, the experimental results on the dataset GF1_WHU show that the segmentation results of this method are better than the existing methods. Hence, our model is of great significance for practical cloud shadow segmentation.
Occurrence Characteristics of Nighttime Merged EIA Based on NASA GOLD Observations from 2018 to 2023
Authors: Wu Kun , Qian Liying
Published: April 2024

The ionosphere equatorial ionization anomaly (EIA) is usually characterized by two plasma density maxima in the Earth’s equatorial region. Merged EIA (MEIA) is a unique phenomenon in the evolution of the EIA. Currently, the occurrence characteristics of MEIA are still not well understood. In this study, we investigate the occurrence characteristics of nighttime MEIA using NASA Global-scale Observations of the Limb and Disk (GOLD) observations between October 2018 and the end of 2023. We found that the occurrence of nighttime MEIA exhibits solar cycle, seasonal, and local time variations. The occurrence rate of the MEIA is inversely dependent on solar activity. Occurrence of the MEIA maximizes near the equinoxes, with a primary (secondary) low occurrence rate near the June (December) solstice. In addition, occurrences of the MEIA are suppressed during the pre-reversal enhancement (PRE), resulting in relatively fewer events. Furthermore, it was found that the occurrence of the MEIA is not significantly dependent on the strength of geomagnetic activity. As far as we know, this study represents the first instance of utilizing observations from GOLD observations to investigate the characteristics of MEIA occurrences and their correlations with solar activity, season, and local time.
Intermittency of Gravity Wave Potential Energy Generated by Mountains Revealed from COSMIC-2 Observations
Authors: Wei Jiarui , Xu Jiyao , Liu Xiao
Published: April 2024

The intermittency of gravity wave potential energy (GWPE) in the upper troposphere and stratosphere was investigated using the Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2) temperature data over three typical mountains (Tibetan Plateau, Rocky Mountains, and Andes). These typical mountains have high sea level elevations but different land–sea contrast. The probability density function (PDF) of GWPE has the independent variable of GWPE and dependent variable of occurrence probability of GWPE over a region. Our analysis showed that the PDFs of GWPE over these three mountains roughly followed lognormal distributions in all heights and months. But, the key parameters (mean value and standard deviation) of lognormal distribution varied with heights and months. Above each mountain, the two key parameters exhibited similar temporal and spatial distributions. They had the largest values around the tropopause region, smaller values in the lower stratosphere (~20–30 km), and larger values in the upper stratosphere (~35–45 km). The intermittency of GWs is represented as the ratio of the GWPE at 50th percentile to the GWPE at 90th percentile. The weakest intermittency was at ~20–30 km (above the zonal mean winds of zero) over the Tibetan Plateau and Rocky Mountains in all months and over the Andes from November to March, respectively. Generally, the weakest intermittency (~0.4) occurred in the region where the key parameters were the smallest around summer. The key parameters of lognormal distribution were dominated by annual variation over the Andes throughout the height range, 8–50 km. However, the semiannual variations are also significant in the lower stratosphere over the Tibetan Plateau and Rocky Mountains. The seasonal variations in the intermittency were not as obvious as those of the key parameters. The lognormal distributions and the intermittencies derived here provide an observational constraint on the tunable parameters in GW parameterization schemes.
Innovative Decision Fusion for Accurate Crop/Vegetation Classification with Multiple Classifiers and Multisource Remote Sensing Data
Authors: Shuai Shuang , Zhang Zhi , Zhang Tian , Luo Wei , Tan Li , Duan Xiang , Wu Jie
Published: April 2024

Obtaining accurate and real-time spatial distribution information regarding crops is critical for enabling effective smart agricultural management. In this study, innovative decision fusion strategies, including Enhanced Overall Accuracy Index (E-OAI) voting and the Overall Accuracy Index-based Majority Voting (OAI-MV), were introduced to optimize the use of diverse remote sensing data and various classifiers, thereby improving the accuracy of crop/vegetation identification. These strategies were utilized to integrate crop/vegetation classification outcomes from distinct feature sets (including Gaofen-6 reflectance, Sentinel-2 time series of vegetation indices, Sentinel-2 time series of biophysical variables, Sentinel-1 time series of backscatter coefficients, and their combinations) using distinct classifiers (Random Forests (RFs), Support Vector Machines (SVMs), Maximum Likelihood (ML), and U-Net), taking two grain-producing areas (Site #1 and Site #2) in Haixi Prefecture, Qinghai Province, China, as the research area. The results indicate that employing U-Net on feature-combined sets yielded the highest overall accuracy (OA) of 81.23% and 91.49% for Site #1 and Site #2, respectively, in the single classifier experiments. The E-OAI strategy, compared to the original OAI strategy, boosted the OA by 0.17% to 6.28%. Furthermore, the OAI-MV strategy achieved the highest OA of 86.02% and 95.67% for the respective study sites. This study highlights the distinct strengths of various remote sensing features and classifiers in discerning different crop and vegetation types. Additionally, the proposed OAI-MV and E-OAI strategies effectively harness the benefits of diverse classifiers and multisource remote sensing features, significantly enhancing the accuracy of crop/vegetation classification.
Earlier Spring-Summer Phenology and Higher Photosynthetic Peak Altered the Seasonal Patterns of Vegetation Productivity in Alpine Ecosystems
Authors: Yang Fan , Liu Chao , Chen Qianqian , Lai Jianbin , Liu Tiegang
Published: April 2024

Carbon uptake of vegetation is controlled by phenology and photosynthetic carbon uptake capacity. However, our knowledge of the seasonal responses of vegetation productivity to phenological and physiological changes in alpine ecosystems is still weak. In this study, we quantified the spatio-temporal variations of vegetation phenology and gross primary productivity (GPP) across the source region of the Yellow River (SRYR) by analyzing MODIS-derived vegetation phenology and GPP from 2001 to 2019, and explored how vegetation phenology and maximum carbon uptake capacity (GPPmax) affected seasonal GPP over the region. Our results showed that the SRYR experienced significantly advanced trends (p < 0.05) for both start (SOS) and peak (POS) of the growing season from 2001 to 2019. Spring GPP (GPPspr) had a significantly increasing trend (p < 0.01), and the earlier SOS had obvious positive effects on GPPspr. Summer GPP (GPPsum) was significantly and negatively correlated to POS (p < 0.05). In addition, GPPmax had a significant and positive correlation with GPPsum and GPPann (p < 0.01), respectively. It was found that an earlier spring-summer phenology and higher photosynthetic peak enhanced the photosynthetic efficiency of vegetation in spring and summer and altered the seasonal patterns of vegetation productivity in the SRYR under warming and wetting climates. This study indicated that not only spring and autumn phenology but also summer phenology and maximum carbon uptake capacity should be regarded as crucial indicators regulating the carbon uptake process in alpine ecosystems. This research provides important information about how changes in phenology affect vegetation productivity in alpine ecosystems under global climate warming.
Integrating Artificial Intelligence and UAV-Acquired Multispectral Imagery for the Mapping of Invasive Plant Species in Complex Natural Environments
Authors: Amarasingam Narmilan , Vanegas Fernando , Hele Melissa , Warfield Angus , Gonzalez Felipe
Published: April 2024

The proliferation of invasive plant species poses a significant ecological threat, necessitating effective mapping strategies for control and conservation efforts. Existing studies employing unmanned aerial vehicles (UAVs) and multispectral (MS) sensors in complex natural environments have predominantly relied on classical machine learning (ML) models for mapping plant species in natural environments. However, a critical gap exists in the literature regarding the use of deep learning (DL) techniques that integrate MS data and vegetation indices (VIs) with different feature extraction techniques to map invasive species in complex natural environments. This research addresses this gap by focusing on mapping the distribution of the Broad-leaved pepper (BLP) along the coastal strip in the Sunshine Coast region of Southern Queensland in Australia. The methodology employs a dual approach, utilising classical ML models including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM) in conjunction with the U-Net DL model. This comparative analysis allows for an in-depth evaluation of the performance and effectiveness of both classical ML and advanced DL techniques in mapping the distribution of BLP along the coastal strip. Results indicate that the DL U-Net model outperforms classical ML models, achieving a precision of 83%, recall of 81%, and F1–score of 82% for BLP classification during training and validation. The DL U-Net model attains a precision of 86%, recall of 76%, and F1–score of 81% for BLP classification, along with an Intersection over Union (IoU) of 68% on the separate test dataset not used for training. These findings contribute valuable insights to environmental conservation efforts, emphasising the significance of integrating MS data with DL techniques for the accurate mapping of invasive plant species.
Synergy between Short-Range Lidar and In Situ Instruments for Determining the Atmospheric Boundary Layer Lidar Ratio
Authors: Bedoya-Velásquez Andres Esteban , Ceolato Romain , Titos Gloria , Bravo-Aranda Juan Antonio , Casans Andrea , Patrón Diego , Fernández-Carvelo Sol , Guerrero-Rascado Juan Luis , Alados-Arboledas Lucas
Published: April 2024

Short-range elastic backscatter lidar (SR-EBL) systems are remote sensing instruments for studying low atmospheric boundary layer processes. This work presents a field campaign oriented to filling the gap between the near-surface aerosol processes regarding aerosol radiative properties and connecting them with the atmospheric boundary layer (ABL), centering attention on the residual layer and the ABL transition periods. A Colibri Aerosol Lidar (CAL) instrument, based on the short-range lidar with high spatio-temporal resolution, was used for the first time in the ACTRIS AGORA facility (Andalusian Global Observatory of the Atmosphere) in Granada (Spain). This study showed the possibility of combining lidar and in situ measurements in the lowermost 150 m. The results address, on the one hand, the characterization of the short-range lidar for developing a method to find the calibration constant of the system and to correct the incomplete overlap to further data exploitation. On the other hand, relevant radiative properties such as the temporal series of the aerosol lidar ratio and extinction coefficient were quantified. The campaign was divided in three different periods based on the vehicular emission peak in the early mornings, namely, before, during, and after the emission peak. For before and after the emission peak data classification, aerosol properties presented closer values; however, large variability was obtained after the emission peak reaching the maximum values of extinction and a lidar ratio up to 51.5 ± 11.9 (Mm)−1 and 36.0 ± 10.5 sr, respectively. During the emission peaks, the values reached for extinction and lidar ratio were up to 136.8 ± 26.5 (Mm)−1 and 119.0 ± 22.7 sr, respectively.
Spatial Distribution and Differentiation Analysis of Coastal Aquaculture in China Based on Remote Sensing Monitoring
Authors: Meng Dan , Yang Xiaomei , Wang Zhihua , Liu Yueming , Zhang Junyao , Liu Xiaoliang , Liu Bin
Published: April 2024

Multiple datasets related to pond and marine aquaculture have been published using diverse remote sensing technologies, yet a comprehensive dataset detailing spatial distribution on both land and sea sides is lacking. Firstly, a meticulous comparison of datasets which we selected related to aquaculture ponds and marine, ensuring consistency in trends. Subsequently, the datasets published by our team were edited and integrated to illustrate aquaculture activities on both sides of China’s coastal zone. Finally, a spatial differentiation of coastal aquaculture in major provinces was analyzed. This analysis also utilizes the types of coastline and statistical data, guiding coordinated resource management efforts. The results unveil a distinctive spatial distribution pattern, concentrating aquaculture in the northern regions—Bohai Sea, Jiangsu, Fujian, and Pearl River coasts in Guangdong. The provinces rich in aquaculture resources, such as Shandong, Guangdong, and Liaoning, exhibit extensive coastlines. However, remote sensing monitoring suggests an underestimation of Liaoning’s marine aquaculture compared to statistical yearbook data. Furthermore, southern provinces like Guangdong and Fujian exhibit significantly higher aquaculture output than Liaoning. Zhejiang leads in fishing output. The paper outlines the future development direction of coastal aquaculture, emphasizing a strategic, integrated land–sea approach for sustainable development.
Assessment of the Added Value of the GOCE GPS Data on the GRACE Monthly Gravity Field Solutions
Authors: Guo Xiang , Lian Yidu , Sun Yu , Zhou Hao , Luo Zhicai
Published: April 2024

The time-varying gravity field models derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission suffer from pronounced longitudinal stripe errors in the spatial domain. A potential way to mitigate such errors is to combine GRACE data with observations from other sources. In this study, we investigate the impacts on GRACE monthly gravity field solutions of incorporating the GPS data collected by the Gravity Field and Steady-State Ocean Circulation Explorer (GOCE) mission. To that end, we produce GRACE/GOCE combined monthly gravity field solutions through combination on the normal equation level and compare them with the GRACE-only solutions, for which we have considered the state-of-the-art ITSG-Grace2018 solutions. Analysis in the spectral domain reveals that the combined solutions have a notably lower noise level beyond degree 30, with cumulative errors up to degree 96 being reduced by 31%. A comparison of the formal errors reveals that the addition of GOCE GPS data mainly improves (near-) sectorial coefficients and resonant orders, which cannot be well determined by GRACE alone. In the spatial domain, we also observe a significant reduction by at least 30% in the noise of recovered mass changes after incorporating the GOCE GPS data. Furthermore, the signal-to-noise ratios of mass changes over 180 large river basins were improved by 8–20% (dependent on the applied Gaussian filter radius). These results demonstrate that the GOCE GPS data can augment the GRACE monthly gravity field solutions and support a future GOCE-type mission for tracking more accurate time-varying gravity fields.
Landslide Hazard Assessment for Wanzhou Considering the Correlation of Rainfall and Surface Deformation
Authors: She Xiangjie , Li Deying , Yang Shuo , Xie Xiaoxu , Sun Yiqing , Zhao Wenjie
Published: April 2024

The landslide hazard assessment plays a crucial role in landslide risk mitigation and land use planning. The result of landslide hazard assessment corrected by surface deformation, obtained through time-series InSAR, has usually proven to have good application capabilities. However, the issue lies in the uncertainty of InSAR results, where some deformations cannot be calculated, and some are not true deformations. This uncertainty of InSAR results will lead to errors in landslide hazard assessment. Here, we attempt to evaluate landslide hazards by considering combined rainfall and surface deformation. The main objective of this research was to mitigate the impact of bias and explore the accurate landslide hazard assessment method. A total of 201 landslides and 11 geo-environment factors were utilized for landslide susceptibility assessment by support vector machine (SVM) model in Wanzhou District, Three Gorges Reservoir Area (TGRA). The preliminary hazard is obtained by analyzing the statistical data of landslides and rainfall. Based on the SAR image data of Sentinel-1A satellites from September 2019 to October 2021, the SBAS-InSAR method was used to analyze surface deformation. The correlation between surface deformation and rainfall was analyzed, and the deformation factor variables were applied to landslide hazard assessment. The research results demonstrate that the error caused by the uncertainty of InSAR results can be effectively avoided by analyzing the relationship between rainfall and surface deformation. Our results can effectively adjust and correct the hazard results and eliminate the errors in the general hazard assessment. Our proposed method can be used to assess the landslide hazard in more detail and provide a reference for fine risk management and control.