bluuebunny/crossref_metadata_2025 huggingface dataset:
Molecular Simulation Study on Mechanical Properties of Microcapsule-Based Self-Healing Cementitious Materials
Authors: Wang Xianfeng , Xie Wei , Li Long-yuan , Zhu Jihua , Xing Feng
Published: February 2022

Microcapsule-based self-healing concrete can effectively repair micro-cracks in concrete and improve the strength and durability of concrete structures. In this paper, in order to study the effect of epoxy resin on the cement matrix at a microscopic level, molecular dynamics were used to simulate the mechanical and interfacial properties of microcapsule-based self-healing concrete in which uniaxial tension was carried out along the z-axis. The radial distribution function, interface binding energy, and hydrogen bonding of the composite were investigated. The results show that the epoxy resin/C-S-H composite has the maximum stress strength when TEPA is used as the curing agent. Furthermore, the interface binding energy between epoxy resin and cement matrix increases with increasing strain before the stress reaches its peak value. The cured epoxy resin can enhance both the interfacial adhesion and the ductility of the composite, which can meet the needs of crack repair of microcapsule-based self-healing cementitious materials.
The Pathways to Create Containers for Bacteriophage Delivery
Authors: Musin Egor V. , Kim Aleksandr L. , Dubrovskii Alexey V. , Ariskina Elena V. , Kudryashova Ekaterina B. , Tikhonenko Sergey A.
Published: February 2022

Antimicrobial resistance is a global public health threat. One of the possible ways to solve this problem is phage therapy, but the instability of bacteriophages hinders the development of this approach. A bacteriophage delivery system that stabilizes the phage is one of the possible solutions to this problem. This study is dedicated to exploring methods to create encapsulated forms of bacteriophages for delivery. We studied the effect of proteolytic enzymes on the destruction of the polyelectrolyte microcapsule shell and revealed that protease from Streptomyces griseus was able to destroy the membrane of the microcapsule (dextran sulfate/polyarginine)3 ((DS/PArg)3). In addition, the protease decreased the activity of the bacteriophage in the second hour of incubation, and the phage lost activity after 16 h. It was found that a medium with pH 9.02 did not affect the survival of the bacteriophage or E. coli. The bacteriophages were encapsulated into polyelectrolyte microcapsules (DS/PArg)3. It was established that it is impossible to use microcapsules as a means of delivering bacteriophages since the bacteriophages are inactivated. When bacteriophages were included inside a CaCO3 core, it was demonstrated that the phage retained activity before and after the dissolution of the CaCO3 particle. From the results of this study, we recommend using CaCO3 microparticles as a container for bacteriophage delivery through the acidic stomach barrier.
From Strain Stiffening to Softening—Rheological Characterization of Keratins 8 and 18 Networks Crosslinked via Electron Irradiation
Authors: Elbalasy Iman , Wilharm Nils , Herchenhahn Erik , Konieczny Robert , Mayr Stefan G. , Schnauß Jörg
Published: February 2022

Networks of crosslinked keratin filaments are abundant in epithelial cells and tissues, providing resilience against mechanical forces and ensuring cellular integrity. Although studies of in vitro models of reconstituted keratin networks have revealed important mechanical aspects, the mechanical properties of crosslinked keratin structures remain poorly understood. Here, we exploited the power of electron beam irradiation (EBI) to crosslink in vitro networks of soft epithelial keratins 8 and 18 (k8–k18) filaments with different irradiation doses (30 kGy, 50 kGy, 80 kGy, 100 kGy, and 150 kGy). We combined bulk shear rheology with confocal microscopy to investigate the impact of crosslinking on the mechanical and structural properties of the resultant keratin gels. We found that irradiated keratin gels display higher linear elastic modulus than the unirradiated, entangled networks at all doses tested. However, at the high doses (80 kGy, 100 kGy, and 150 kGy), we observed a remarkable drop in the elastic modulus compared to 50 kGy. Intriguingly, the irradiation drastically changed the behavior for large, nonlinear deformations. While untreated keratin networks displayed a strong strain stiffening, increasing irradiation doses shifted the system to a strain softening behavior. In agreement with the rheological behavior in the linear regime, the confocal microscopy images revealed fully isotropic networks with high percolation in 30 kGy and 50 kGy-treated keratin samples, while irradiation with 100 kGy induced the formation of thick bundles and clusters. Our results demonstrate the impact of permanent crosslinking on k8–k18 mechanics and provide new insights into the potential contribution of intracellular covalent crosslinking to the loss of mechanical resilience in some human keratin diseases. These insights will also provide inspiration for the synthesis of new keratin-based biomaterials.
Soybean Oil-Based Biopolymers Induced by Nonthermal Plasma to Enhance the Dyeing of Para-Aramids with a Cationic Dye
Authors: Metzcar Caleb , Ye Xiaofei Philip , Wang Toni , Doona Christopher J.
Published: February 2022

To overcome the recalcitrance of para-aramid textiles against dyeing, this study demonstrated that increasing the functionalities of soybean oil applied to the surface of para-aramids followed by a nonthermal plasma (NTP) treatment improved the dyeing color strength compared with the use of soybean oil alone, and that dyeing occurred through covalent bonding. Particularly, compared with the pretreatment using soybean oil that obtained the highest color strength of 3.89 (as K/S value determined from spectral analysis of the sample reflectance in the visible range), the present pretreatments with either acrylated epoxidized soybean oil (AESO) or a mixture of acrylic acid and soybean oil (AA/Soy) achieved K/S values higher than nine (>9.00). The NTP treatment, after the AESO or AA/Soy pretreatment, was essential in inducing the formation of a polymerized network on the surface of para-aramids that bonded the dye molecules and generating covalent bonds that anchored the polymerized network to the para-aramids, which is difficult to achieve given the high crystallinity and chemical inertness of para-aramids. As an important economic consideration, the sequential experimentation method demonstrated that a simple mixture of AA/Soy could replace the expensive AESO reagent and render a comparable performance in dyeing para-aramids. Among the auxiliary additives tested with the AESO and AA/Soy pretreatments followed by NPT treatment in this study, Polysorbate 80 as a surfactant negatively affected the dyeing, benzyl alcohol as a swelling agent had minimal effect, and NaCl as an electrolyte showed a positive effect. The dyeing method developed in this study did not compromise the strength of para-aramids.
A Comprehensive Optimization of Ultrasound-Assisted Extraction for Lycopene Recovery from Tomato Waste and Encapsulation by Spray Drying
Authors: Li Junyang , Pettinato Margherita , Casazza Alessandro Alberto , Perego Patrizia
Published: February 2022

This study aimed to extract bioactive compounds from tomato waste through ultrasound-assisted extraction (UAE), using ethanol as solvent. Process optimization was carried out by a central composite design of 33 runs for response surface modelling, simultaneously analyzing the effect of temperature (T), time (t), volume (V), liquid-to-solid ratio (L/S), amplitude (A), the pulser duration (on), and their interaction. The best conditions found by the desirability method (T = 65 °C, t = 20 min, L/S = 72 mL/g, A = 65%, on = 33 s, V = 90 mL) were experimentally verified, leading to the production of an extract with interesting properties (total carotenoids of 1408 ±14 µglycopene equivalents/g, lycopene yield of 1536 ± 53 µg/g, 36.1 ± 0.9 µgtrolox equivalents/g as antiradical power). Due to the instability of lycopene, the extract encapsulation by spray drying was undertaken using inulin and maltodextrins as coating agents. The evaluation of wall material composition provided high product recovery (73%), a high content of encapsulated compared to superficial lycopene (15.3 ± 2.9 and 0.30 ± 0.02 µg/g), and a product with good water solubility. The novelty of this work concerned the simultaneous study of the effect and interdependences of the UAE parameters, and the use of inulin to enhance the properties of microparticles.
The Prediction of Spark-Ignition Engine Performance and Emissions Based on the SVR Algorithm
Authors: Zhang Yu , Wang Qifan , Chen Xiaofei , Yan Yuchao , Yang Ruomiao , Liu Zhentao , Fu Jiahong
Published: February 2022

Engine development needs to reduce costs and time. As the current main development methods, 1D simulation has the limitations of low accuracy, and 3D simulation is a long, time-consuming task. Therefore, this study aims to verify the applicability of the machine learning (ML) method in the prediction of engine efficiency and emission performance. The support vector regression (SVR) algorithm was chosen for this paper. By the selection of kernel functions and hyperparameters sets, the relationship between the operation parameters of a spark-ignition (SI) engine and its economic and emissions characteristics was established. The trained SVR algorithm can predict fuel consumption rate, unburned hydrocarbon (HC), carbon monoxide (CO), and nitrogen oxide (NOx) emissions. The determination coefficient (R2) of experimental measured data and model predictions was close to 1, and the root-mean-squared error (RMSE) is close to zero. Additionally, the SVR model captured the corresponding trend of the engine with the input, though some existed small errors. In conclusion, these results indicated that the SVR model was suitable for the applications studied in this research.
Low-Cost Activated Carbon for Petroleum Products Clean-Up
Authors: Kosheleva Ramonna I. , Kyzas George Z. , Kokkinos Nikolaos C. , Mitropoulos Athanasios C.
Published: February 2022

Petroleum products are hazardous both for humans and nature. Diesel oil is one of the main contaminants of land but also of sea, during its transportation. Currently, there are many different clean-up techniques for petroleum products. One of the most common is adsorption by adsorbent materials. Although adsorption is an eco-friendly and cost-effective approach, it lacks efficiency. The present study investigates the performance of low-cost activated carbon, derived from potato peels and activated under different temperature conditions, from 350 °C to 800 °C. The yield of activated carbon decreases with the increase in the carbonization temperature. However, the sample prepared at 600 °C shows an oil sorption capacity of 72 g/g, which is the highest of all samples. Nitrogen adsorption characterization reveals that this specific sample has the highest specific surface (SSA) area of 1052 m2/g and total a pore volume of 2.959 cm3/g, corresponding to a 94% and 77% increase compared to the sample prepared at 350 °C. Oil sorption kinetics experiments show that, for all samples, the maximum uptake is reached after 1h. Oil uptake was also investigated under realistic conditions by introducing the best performance activated carbon to an oil/seawater system, and the outcome does not show a significant decrease in the oil sorption. The outcomes of this study indicate that low-cost adsorbents from agricultural by-products have strong potential as an oil spill response technique.
Papain Enzyme Assisted Extraction of Virgin Coconut Oil as Candidate In-House Reference Material
Authors: Yulirohyami  , Hidayat Habibi , Wijaya Aprisilia Rizky , Fatimah Is
Published: February 2022

This study concerns papain enzyme assisted synthesis of virgin coconut oil (VCO) as a candidate in-house reference material. The study was conducted to obtain optimization of the VCO preparation with green processes as the standardized product which high stability and homogeneity. The method is expected to produce the candidate of in-house reference material to ensure the standards and quality of the VCO product. Based on the results of this study, the preparation of VCO was carried out using the papain. An optimum yield of 24.30%. was achieved under the following conditions: enzyme to coconut milk with a mass ratio of 0.6 g/L, under 500 mL water/g of coconut powder, at the temperature of 70 °C by five stages of extraction. The physicochemical properties as well as organoleptic feature of VCO which consist water content, peroxide number, free fatty acids, and iodine numbers are fit with the standard. The parameters exhibited the homogeneity and stability which be able recommended as candidate in-house reference material and have potentially as antibacterial agent. Antibacterial activity test showed that VCO has potential against Escherichia coli, Staphylococcus aureus, Propionibacterium acnes and Pseudomonas aeruginosa as shown by the inhibition zone in the testing.
First Detection of Methanogens in Orthopedic Prosthesis Infection: A Four-Case Founding Series
Authors: Djemai Kenza , Gouriet Frédérique , Argenson Jean-Noël , Seng Piseth , Stein Andreas , Drancourt Michel
Published: February 2022

Orthopedic prosthesis infection must be medically managed after appropriate microbiological documentation. While bacteria and fungi are acknowledged to be causative opportunistic pathogens in this situation, the potential role of methanogens in orthopedic prosthesis infections is still unknown. In a retrospective study, a total of 100 joint and bone samples collected from 25 patients were screened by specific PCR assays for the detection of methanogens. PCR-positive samples were observed by autofluorescence, electron microscopy and tentatively cultured under specific culture conditions. Methanogens were detected by quantitative PCR in 4/100 samples, in the presence of negative controls. Sequencing identified Methanobrevibacter oralis in two cases, Methanobrevibacter smithii in one case and Methanobrevibacter wolinii in one case. Microscopic methods confirmed molecular findings and bacterial culture yielded two strains of Staphylococcus aureus, one strain of Staphylococcus epidermidis and one strain of Proteus mirabilis. These unprecedented data highlight the presence of methanogens in joint and bone samples of patients also diagnosed with bacterial orthopedic prosthesis infection, questioning the role of methanogens as additional opportunistic co-pathogens in this situation.
Heterogeneity in Religious Commitment and Its Predictors
Authors: Hohenschue Oliver , Riegel Ulrich , Zimmermann Mirjam
Published: February 2022

Nowadays, people show various forms of religious commitment. To better understand this heterogeneity, it is necessary to analyze the factors that predict that commitment. For this purpose, the paper raises the question of whether and how such commitment is predicted by (a) the character of the religious institution to which the individual belongs; (b) individual characteristics, such as intelligence and attitude toward religion; and (c) social environment, such as one’s religious upbringing in the family home. According to logistic regression analysis, it is predominantly religious belonging and religious socialization in the family home that predict religious commitment. Religious socialization is by far the most effective predictor when the difference between marginal and slight religious commitment is regarded. The predictive power of religious belonging depends on the particular religious tradition, as being Muslim has a bigger effect on religious commitment than being Protestant or Catholic. Individual characteristics, however, have no impact on how an individual engages with religion.
The Wavelet Analysis: The Case of Non-Performing Loans in China
Authors: Di Febo Elisa , Angelini Eliana
Published: February 2022

China has accelerated its banking sector reform in recent years, paying particular attention to non-performing loans (NPLs). The paper’s scope is to analyse the relationship between NPLs and macroeconomic variables in China using quarterly data from 2008/Q1 to 2021/Q1 applying wavelet analysis, which allows the study to scan both short- and long-term causal relationships and connections. The analysis produces interesting results. The GDP does not appear to be as important and as much of a driving force in the dynamics of NPLs as in other emerging countries. On the other hand, inflation shows a highly dynamic dependence on NPLs as it varies over time; however, the most interesting data is the relationship between NPLs and economic policy uncertainty. In the short term, the variables are in phase. In the long term, an increase of EPU has a reduction effect on NPLs, indicating that it affects commercial bank loan sizes by reducing enterprise demand for and bank supply of credit resources.
The Black Desert Drone Survey: New Perspectives on an Ancient Landscape
Authors: Hill Austin Chad , Rowan Yorke M.
Published: February 2022

This paper presents the results of a large scale, drone-based aerial survey in northeastern Jordan. Drones have rapidly become one of the most cost-effective and efficient tools for collecting high-resolution landscape data, fitting between larger-scale, lower-resolution satellite data collection and the significantly more limited traditional terrestrial survey approaches. Drones are particularly effective in areas where anthropogenic features are visible on the surface but are too small to identify with commonly and economically available satellite data. Using imagery from fixed-wing and rotary-wing aircraft, along with photogrammetric processing, we surveyed an extensive archaeological landscape spanning 32 km2 at the site of Wadi al-Qattafi in the eastern badia region of Jordan, the largest archaeological drone survey, to date, in Jordan. The resulting data allowed us to map a wide range of anthropogenic features, including hunting traps, domestic structures, and tombs, as well as modern alterations to the landscape including road construction and looting pits. We documented thousands of previously unrecorded and largely unknown prehistoric structures, providing an improved understanding of major shifts in the prehistoric use of this landscape.
Trajectory Recovery and Terrain Reconstruction Based on Descent Images under Dual-Restrained Conditions: Tianwen-1
Authors: Qi Chen , Liu Shaochuang , Xu Yaming , Xu Aigong , Zhang Jianli , Ma Youqing , Li Minglei , Xu Xinchao , Yang Huan , Yan Yongzhe
Published: February 2022

Tianwen-1 is the first Mars probe launched by China and the first mission in the world to successfully complete the three steps of exploration (orbiting, landing, and roving) at the one time. Based on the unverifiable descent images which cover the full range of the landing area, trajectory recovery and fine terrain reconstruction are important parts of the planetary exploration process. In this paper, a novel trajectory recovery and terrain reconstruction (TR-TR) algorithm employing descent images is proposed for the dual-restrained conditions: restraints of the flat terrain resulting in an unstable solution of the descent trajectory and of the parabolic descent trajectory causing low accuracy of terrain reconstruction, respectively. A landing simulation experiment on a landing field with Mars-like landform was carried out to test the robustness and feasibility of the algorithm. The experiment result showed that the horizontal error of the recovered trajectory didn’t exceed 0.397 m, and the elevation error of the reconstructed terrain was no more than 0.462 m. The algorithm successfully recovered the descent trajectory and generated high-resolution terrain products using in-orbit data of Tianwen-1, which provided effective support for the mission planning of the Zhurong rover. The analysis of the results indicated that the descent trajectory has parabolic properties. In addition, the reconstructed terrain contains abundant information and the vertical root mean square error (RMSE) of ground control points is smaller than 1.612 m. Terrain accuracy obtained by in-orbit data is lower than that obtained by field experiment. The work in this paper has made important contributions to the surveying and mapping of Tianwen-1 and has great application value.
Evaluation of Agricultural Bare Soil Properties Retrieval from Landsat 8, Sentinel-2 and PRISMA Satellite Data
Authors: Mzid Nada , Castaldi Fabio , Tolomio Massimo , Pascucci Simone , Casa Raffaele , Pignatti Stefano
Published: February 2022

The PRISMA satellite is equipped with an advanced hyperspectral Earth observation technology capable of improving the accuracy of quantitative estimation of bio-geophysical variables in various Earth Science Applications and in particular for soil science. The purpose of this research was to evaluate the ability of the PRISMA hyperspectral imager to estimate topsoil properties (i.e., organic carbon, clay, sand, silt), in comparison with current satellite multispectral sensors. To investigate this expectation, a test was carried out using topsoil data collected in Italy following two approaches. Firstly, PRISMA, Sentinel-2 and Landsat 8 spectral simulated datasets were obtained from the spectral resampling of a laboratory soil library. Subsequently, bare soil reflectance data were obtained from two experimental areas in Italy, using real satellites images, at dates close to each other. The estimation models of soil properties were calibrated employing both Partial Least Square Regression and Cubist Regression algorithms. The results of the study revealed that the best accuracies in retrieving topsoil properties were obtained by PRISMA data, using both laboratory and real datasets. Indeed, the resampled spectra of the hyperspectral imager provided the best Ratio of Performance to Inter-Quartile distance (RPIQ) for clay (4.87), sand (3.80), and organic carbon (2.59) estimation, for the spectral soil library datasets. For the bare soil reflectance obtained from real satellite imagery, a higher level of prediction accuracy was obtained from PRISMA data, with RPIQ ± SE values of 2.32 ± 0.07 for clay, 3.85 ± 0.19 for silt, and 3.51 ± 0.16 for soil organic carbon. The results for the PRISMA hyperspectral satellite imagery with the Cubist Regression provided the best performance in the prediction of silt, sand, clay and SOC. The same variables were better estimated using PLSR models in the case of the resampled hyperspectral data. The statistical accuracy in the retrieval of SOC from real and resampled PRISMA data revealed the potential of the actual hyperspectral satellite. The results supported the expected good ability of the PRISMA imager to estimate topsoil properties.
Regulated Ecosystem Services Trade-Offs: Synergy Research and Driver Identification in the Vegetation Restoration Area of the Middle Stream of the Yellow River
Authors: Wang Ge , Yue Depeng , Niu Teng , Yu Qiang
Published: February 2022

In arid and semi-arid regions, vegetation restoration will have a significant impact on ecosystem services (ESs). Accurate assessment of the relationship and driving mechanism between ESs will play an important role in the implementation of subsequent vegetation restoration projects and ecosystem management. The purpose of this study is to evaluate and identify the relationship between ESs, and explore the impact and driving mechanism of ecological restoration on the relationship between ESs. Taking the middle reaches of the Yellow River as the study area, this study analyzed three ESs including: Net Primary Productivity (NPP), soil conservation (SC), and water yield (WY), in the vegetation restoration area (VRA) in the middle reaches of the Yellow River for 20 years (2000–2010 and 2010–2020 years(a)). Taking the ecological restoration unit (ERU) as the evaluation unit, we evaluated the impact of three vegetation restoration models on ESs. Using geo-detectors to identify the economic, social and natural drivers that affect the relationship between ESS. The results showed the following: (1) Different vegetation restoration models will lead to significant differences in the restoration rate for ESs. They will lead to an increase in the recovery rate of NPP and SC. The first vegetation restoration mode and the third planting restoration mode will reduce the restoration rate for the WY. (2) The three vegetation restoration models will enhance the synergistic relationship between NPP and SC. They will weaken the trade-off relationship between NPP and WY, SC and WY. (3) Temperature, precipitation, and NDVI will affect the changes in ESs in VRAs. The ESs trade-off-synergy relationship will be affected by precipitation, NDVI, and GDP. This study showed that, with the implementation of vegetation restoration projects, ESs in time and space and the complex heterogeneity of ESs will affect ecosystem management. The results of this study will be helpful for the implementation of subsequent vegetation restoration projects and provide scientific advice for ecosystem management.
Implementing Spatio-Temporal 3D-Convolution Neural Networks and UAV Time Series Imagery to Better Predict Lodging Damage in Sorghum
Authors: Varela Sebastian , Pederson Taylor L. , Leakey Andrew D. B.
Published: February 2022

Unmanned aerial vehicle (UAV)-based remote sensing is gaining momentum in a variety of agricultural and environmental applications. Very-high-resolution remote sensing image sets collected repeatedly throughout a crop growing season are becoming increasingly common. Analytical methods able to learn from both spatial and time dimensions of the data may allow for an improved estimation of crop traits, as well as the effects of genetics and the environment on these traits. Multispectral and geometric time series imagery was collected by UAV on 11 dates, along with ground-truth data, in a field trial of 866 genetically diverse biomass sorghum accessions. We compared the performance of Convolution Neural Network (CNN) architectures that used image data from single dates (two spatial dimensions, 2D) versus multiple dates (two spatial dimensions + temporal dimension, 3D) to estimate lodging detection and severity. Lodging was detected with 3D-CNN analysis of time series imagery with 0.88 accuracy, 0.92 Precision, and 0.83 Recall. This outperformed the best 2D-CNN on a single date with 0.85 accuracy, 0.84 Precision, and 0.76 Recall. The variation in lodging severity was estimated by the best 3D-CNN analysis with 9.4% mean absolute error (MAE), 11.9% root mean square error (RMSE), and goodness-of-fit (R2) of 0.76. This was a significant improvement over the best 2D-CNN analysis with 11.84% MAE, 14.91% RMSE, and 0.63 R2. The success of the improved 3D-CNN analysis approach depended on the inclusion of “before and after” data, i.e., images collected on dates before and after the lodging event. The integration of geometric and spectral features with 3D-CNN architecture was also key to the improved assessment of lodging severity, which is an important and difficult-to-assess phenomenon in bioenergy feedstocks such as biomass sorghum. This demonstrates that spatio-temporal CNN architectures based on UAV time series imagery have significant potential to enhance plant phenotyping capabilities in crop breeding and Precision agriculture applications.
Modelling of Greek Lakes Water Quality Using Earth Observation in the Framework of the Water Framework Directive (WFD)
Authors: Markogianni Vassiliki , Kalivas Dionissios , Petropoulos George P. , Dimitriou Elias
Published: February 2022

Given the great importance of lakes in Earth’s environment and human life, continuous water quality (WQ) monitoring within the frame of the Water Framework Directive (WFD) is the most crucial aspect for lake management. In this study, Earth Observation (EO) data from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) sensors have been combined with co-orbital in situ measurements from 50 lakes located in Greece with the main objective of delivering robust WQ assessment models. Correlation analysis among in situ co-orbital WQ data (Chlorophylla, Secchi depths, Total phosphorus-TP-) contributed to distinguishing their inter-relationships and improving the WQ models’ accuracy. Subsequently, stepwise multiple regression analysis (MLR) of the available TP and Secchi depth datasets was implemented to explore the potential to establish optimal quantitative models regardless of lake characteristics. Then, further MLR analysis concerning whether the lakes are natural or artificial was conducted with the basic aim of generating different remote sensing derived models for different types of lakes, while their combination was further utilized to assess their trophic status. Correlation matrix results showed a high and positive relationship between TP and Chlorophyll-a (0.85), whereas high negative relationships were found between Secchi depth with TP (−0.84) and Chlorophyll-a (−0.83). MLRs among Landsat data and Secchi depths resulted in 3 optimal models concerning the assessment of Secchi depth of all lakes (Secchigeneral; R = 0.78; RMSE = 0.24 m), natural (Secchinatural; R = 0.95; RMSE = 0.14 m) and artificial (Secchiartificial; R = 0.62; RMSE = 0.1 m), with reliable accuracy. Study findings showed that TP-related MLR analyses failed to deliver a statistically acceptable model for the reservoirs; nevertheless, they delivered a robust TPgeneral (R = 0.71; RMSE = 1.41 mg/L) and TPnatural model (R = 0.93; RMSE = 1.43 mg/L). Subsequently, trophic status classification was conducted herein, calculating Carlson’s Trophic State Index (TSI) initially throughout all lakes and then oriented toward natural-only and artificial-only lakes. Those three types of TSI (general, natural, artificial) were calculated based on previously published satellite-derived Chlorophyll-a (Chl-a) assessment models and the hereby specially designed WQ models (Secchi depth, TP). The higher deviation of satellite-derived TSI values in relation to in situ ones was detected in reservoirs and shallower lakes (mean depth < 5 m), indicating noticeable divergences among natural and artificial lakes. All in all, the study findings provide important support toward the perpetual WQ monitoring and trophic status prediction of Greek lakes and, by extension, their sustainable management, particularly in cases when ground truth data is limited.
The Forest Change Footprint of the Upper Indus Valley, from 1990 to 2020
Authors: Yan Xinrong , Wang Juanle
Published: February 2022

The upper Indus Valley is the most important and vulnerable water tower in the South Asian subcontinent, which provides a vital water supply for 230 million people in the basin. Forests play an important role in water conservation in this region, and the security of upstream forests forms the foundation downstream water and food security. However, a big challenge is to effectively monitor the dynamics of the forest in this region. Thus, we used the LandTrendr spectral-temporal segmentation algorithm combined with 8203 scenes of multi-source remote sensing data to study the forest change footprint in the upper Indus Valley. The overall accuracy of LandTrendr extraction for forest disturbance and recovery was 86.01%, and the Kappa coefficient was 0.73. The results showed the following: (1) From 1990 to 2020, the area of forest recovery was 1.01% more than that of disturbance, 70% of disturbance occurred between 1990 and 2001, and 60% of recovery occurred between 1999 and 2012. (2) Although the overall trend of forest disturbance and recovery was balanced, there were significant differences in forest management status among the different regions. Nepal has the highest forest stability, India has the largest area of forest disturbance, and Pakistan and China have the largest areas of forest recovery. (3) India’s Himachal Pradesh and Jammu and Kashmir are the two provinces with the largest disturbed areas, primarily due to grazing, fires, and commercial tree planting. Pakistan’s North-West Frontier, Azad Kashmir, and China’s Tibet Ali region were major contributors to the recovery, which was driven by afforestation policies in both countries. This study provides an important data base and monitoring method for planning land and forest use in Indus Valley countries, protecting fragile environments, and promoting policies for the Sustainable Development Goals.
From SMOS Soil Moisture to 3-hour Precipitation Estimates at 0.1° Resolution in Africa
Authors: Pellarin Thierry , Zoppis Alexandre , Román-Cascón Carlos , Kerr Yann H. , Rodriguez-Fernandez Nemesio , Panthou Geremy , Philippon Nathalie , Cohard Jean-Martial
Published: February 2022

Several recent studies have shown that knowledge of the spatiotemporal dynamics of soil moisture intrinsically contains information on precipitation. In this study, we show how SMOS measurements can be used to generate a near-real-time precipitation product with a spatial resolution of 0.1° and a temporal resolution of 3 h. The principle consists of assimilating the SMOS data into a model that simulates the evolution of soil moisture, which is forced by a satellite precipitation product. The assimilation of SMOS soil moisture leads to an adjustment of the satellite precipitation rates. Using data from more than 200 rain gauges set up in Africa between 2010 and 2021, we show that the PrISM algorithm (for Precipitation Inferred from Soil Moisture) almost systematically improves the initial precipitation product. One of the original features of this study is that we used the IMERG-Early satellite precipitation product, which has a finer spatial resolution (0.1°) than SMOS (~0.25°). Despite this, the methodology reduces both the RMSE and bias of IMERG-Early. The RMSE is reduced from 8.0 to 6.3 mm/day, and the absolute bias is reduced from 0.81 to 0.63 mm/day on average over the 200 rain gauges. PrISM performs even slightly better on average than IMERG-Final in terms of RMSE (6.8 mm/day for IMERG-Final) but better scores are obtained by IMERG-Final in terms of absolute bias (0.35 mm/day), which utilizes a network of field measurements to correct the biases of the IMERG-Early product with a 2.5-month delay. Therefore, the use of SMOS soil moisture measurements for Africa can be an advantageous alternative to the use of gauge measurements for debiasing rainfall satellite products in real time.
Video Satellite Imagery Super-Resolution via Model-Based Deep Neural Networks
Authors: He Zhi , Li Xiaofang , Qu Rongning
Published: February 2022

Video satellite imagery has become a hot research topic in Earth observation due to its ability to capture dynamic information. However, its high temporal resolution comes at the expense of spatial resolution. In recent years, deep learning (DL) based super-resolution (SR) methods have played an essential role to improve the spatial resolution of video satellite images. Instead of fully considering the degradation process, most existing DL-based methods attempt to learn the relationship between low-resolution (LR) satellite video frames and their corresponding high-resolution (HR) ones. In this paper, we propose model-based deep neural networks for video satellite imagery SR (VSSR). The VSSR is composed of three main modules: degradation estimation module, intermediate image generation module, and multi-frame feature fusion module. First, the blur kernel and noise level of LR video frames are flexibly estimated by the degradation estimation module. Second, an intermediate image generation module is proposed to iteratively solve two optimal subproblems and the outputs of this module are intermediate SR frames. Third, a three-dimensional (3D) feature fusion subnetwork is leveraged to fuse the features from multiple video frames. Different from previous video satellite SR methods, the proposed VSSR is a multi-frame-based method that can merge the advantages of both learning-based and model-based methods. Experiments on real-world Jilin-1 and OVS-1 video satellite images have been conducted and the SR results demonstrate that the proposed VSSR achieves superior visual effects and quantitative performance compared with the state-of-the-art methods.
A GRU and AKF-Based Hybrid Algorithm for Improving INS/GNSS Navigation Accuracy during GNSS Outage
Authors: Tang Yanan , Jiang Jinguang , Liu Jianghua , Yan Peihui , Tao Yifeng , Liu Jingnan
Published: February 2022

The integrated navigation system consisting of an inertial navigation system (INS) and Global Navigation Satellite System (GNSS) provides continuous high-accuracy positioning whereas the navigation accuracy during a GNSS outage inevitably degrades owing to INS error divergence. To reduce such degradation, a gated recurrent unit (GRU) and adaptive Kalman filter (AKF)-based hybrid algorithm is proposed. The GRU network, which has advantages of high accuracy and efficiency, is constructed to predict the position variations during GNSS outage. Furthermore, this paper takes the GRU-predicted error accumulation into consideration, and introduces AKF as a supplementary methodology to improve the navigation performance. The proposed hybrid algorithm is trained and tested by practical road datasets and compared with four algorithms, including the standard KF, Multi-Layer Perceptron (MLP)-aided KF, Long Short Time Memory (LSTM) aided KF, and GRU-aided KF. Periods of 180 and 120 s GNSS outage are employed to test the performance of the proposed algorithm in different time scales. The comparison result between the standard KF and neural network-aided KF indicates that the neural network is an effective methodology for bridging GNSS outages. The performance comparison between three kinds of neural networks demonstrate that both recurrent neural networks surpass the MLP in prediction position variation, and the GRU transcends the LSTM in prediction accuracy and training efficiency. Furthermore, it is concluded that the adaptive estimation theory is an effective complement to neural network-aided navigation, as the GRU-aided AKF reduced the horizontal error of GRU-aided KF by 31.71% and 16.12% after 180 and 120 s of GNSS outage, respectively.
Missing Data Recovery via Deep Networks for Limited Ground Penetrating Radar Measurements
Authors: Kumlu Deniz , Tas Kubra , Erer Isin
Published: February 2022

Missing data problem frequently occurs during data acquisition in ground-penetrating radar (GPR) and recovery of the missing entries prior to any processing is vital in GPR imaging. Existing missing data recovery methods are based on low-rank matrix completion or the recently proposed deep generative networks. However, the former approaches suffer from producing satisfying results under severe missing data cases and the latter require a large amount of data for training. This study proposes two methods based on deep networks for the missing data recovery. The first method uses pyramid-context encoder network (PEN-Net) architecture which consists of three parts: attention transfer network, guided Pyramid-context encoder, and a multi-scale decoder. Although the method needs training, it requires considerably less data compared to the existing U-Net based method. The second method, deep image prior (DIP), is a regularization based data recovery method which uses an untrained network as a prior. This method does not need any training, network weights are initialized randomly and updated during the iterations to minimize the cost function. Different experiments are reported for both pixel and column-wise missing cases in simulated and real data. The simulated data results show that the proposed methods have a noticeably better performance than conventional methods for the challenging pixel-wise case around 17–27% and moderate level column-wise missing case around 15%. Besides, they can also deal with extreme column-wise missing data cases where the conventional methods fail completely. Real data results further verify the superiority of the proposed methods.
A Combined Approach for Retrieving Bathymetry from Aerial Stereo RGB Imagery
Authors: Wang Jiali , Chen Ming , Zhu Weidong , Hu Liting , Wang Yasong
Published: February 2022

Shallow water bathymetry is critical in understanding and managing marine ecosystems. Bathymetric inversion models using airborne/satellite multispectral data are an efficient way to retrieve shallow bathymetry due to the affordable cost of airborne/satellite images and less field work required. With the increasing availability and popularity of unmanned aerial vehicle (UAV) imagery, this paper explores a new approach to obtain bathymetry using UAV visual-band (RGB) images. A combined approach is therefore proposed for retrieving bathymetry from aerial stereo RGB imagery, which is the combination of a new stereo triangulation method (an improved projection image based two-medium stereo triangulation method) and spectral inversion models. In general, the inversion models require some bathymetry reference points, which are not always feasible in many scenarios, and the proposed approach employs a new stereo triangulation method to obtain reliable bathymetric points, which act as the reference points of the inversion models. Using various numbers of triangulation points as the reference points together with a Geographical Weighted Regression (GWR) model, a series of experiments were conducted using UAV RGB images of a small island, and the results were validated against LiDAR points. The promising results indicate that the proposed approach is an efficient technique for shallow water bathymetry retrieval, and together with UAV platforms, it could be deployed easily to conduct a broad range of applications within marine environments.
Understanding Haze: Modeling Size-Resolved Mineral Aerosol from Satellite Remote Sensing
Authors: Sanwlani Nivedita , Das Reshmi
Published: February 2022

Mineral dust aerosols are composed of a complex mixture of silicates, carbonates, oxides, and sulfates. The minerals’ chemical composition and size distribution are vital parameters to evaluate dust environmental impacts. However, the quantification of minerals remains a challenge due to the sparse in situ measurements of dust samples. Here we derive the size-resolved mineralogical composition of airborne dust aerosols from MODIS (Terra and Aqua) satellite-acquired optical measurements and compare it with chemically analyzed elemental (Al, Fe, Ca, Mg) concentrations of aerosols for PM2.5 and PM10 from Chonburi, Chiang Rai, and Bangkok in Thailand, and from Singapore. MODIS-derived mineral retrievals exhibited high correlations with elemental concentrations with R2 ≥ 0.84 for PM2.5 and ≥0.96 for PM10. High mineral dust activity was detected in the vicinity of biomass-burning areas with gypsum and calcite exhibiting tracer characteristics of combustion. The spatiotemporal pattern of the MODIS-derived minerals matched with Ozone Monitoring Instrument (OMI)-derived dust, sulfates, and carbonaceous aerosols, indicating the model’s consistency. Variation in aerosol loading by ±90% led to deviation in the mineral concentration by <10%. An uncertainty of 6.4% between AERONET-measured and MODIS-derived AOD corresponds to a < ± 2% uncertainty in MODIS-derived mineral concentration, demonstrating the robustness of the model.
Bamboo Forest Mapping in China Using the Dense Landsat 8 Image Archive and Google Earth Engine
Authors: Qi Shuhua , Song Bin , Liu Chong , Gong Peng , Luo Jin , Zhang Meinan , Xiong Tianwei
Published: February 2022

It is of great significance to understand the extent and distribution of bamboo for its valuable ecological services and economic benefits. However, it is challenging to map bamboo using remote sensing images over a large area because of the similarity between bamboo and other vegetation types, the availability of clear optical images, huge workload of image processing, and sample collection. In this study, we use the Landsat 8 times series images archive to map bamboo forests in China via the Google Earth engine. Several spectral indices were calculated and used as classification features, including the normalized difference vegetation index (NDVI), the normalized difference moisture index (NDMI) and textural features of the gray-level co-occurrence matrix (GLCM). We found that the bamboo forest covered an area of 709.92 × 104 hectares, with the provinces of Fujian, Jiangxi, and Zhejiang containing the largest area concentrations. The bamboo forest map was accurate and reliable with an average producer’s accuracy of 89.97%, user’s accuracy of 78.45% and kappa coefficient of 0.7789. In addition, bamboo was mainly distributed in forests with an elevation of 300–1200 m above sea level, average annual precipitation of 1200–1500 mm and average day land surface temperature of 19–25 °C. The NDMI is particularly useful in differentiating bamboo from other vegetation because of the clear difference in canopy moisture content, whilst NDVI and elevation are also helpful to improve the bamboo classification accuracy. The bamboo forest map will be helpful for bamboo forest industry planning and could be used for evaluating the ecological service of the bamboo forest.
Simulation and Analysis of Electromagnetic Scattering from Anisotropic Plasma-Coated Electrically Large and Complex Targets
Authors: Rao Zhenmin , Zhu Guoqiang , He Siyuan , Li Chao , Yang Zewang , Liu Jian
Published: February 2022

An efficient physical optics (PO) calculation method is proposed for the electromagnetic (EM) scattering of electrically large targets coated with magnetized plasma characterized by asymmetric tensor dielectric parameters. The outer surface of the arbitrarily shaped target is discretized into triangular elements. According to the principle of tangent plane approximation and by using the plane wave spectrum expansion method, the scattered field from one triangular element is derived as a double integral in the spectral domain. To obtain the solution in the spatial domain, the saddle point method is used to asymptotically calculate the integral. Then, the equivalent surface currents (ESCs) are constructed by calculating the surface field at the outer surface of the planar model, from which the PO solution is derived by using the Stratton–Chu integral. Moreover, to interpret the field propagation process in the plasma layer quantitatively, the total scattered field of the coated planar model is decomposed into the superposition of different mode field components. It is observed that the scattered fields demonstrate an inherent cross-polarization phenomenon due to the nonreciprocal constitutive relation of the plasma, which is a distinct feature and is different from the general anisotropic medium whose dielectric parameters can be diagonalized. The effectiveness of the proposed method is verified by numerical results. Furthermore, the proposed algorithm consumes less calculation time and memory as compared to commercial full solvers.