bluuebunny/crossref_metadata_2025 huggingface dataset:
Genetic Dissection of Drought Tolerance of Elite Bread Wheat (Triticum aestivum L.) Genotypes Using Genome Wide Association Study in Morocco
Authors: El Gataa Zakaria , Samir Karima , Tadesse Wuletaw
Published: October 2022

Drought is one of the most important yield-limiting factors in Morocco. Identification and deployment of drought-tolerant wheat varieties are important to cope with the challenge of terminal moisture stress and increase wheat productivity. A panel composed of 200 elite spring bread wheat genotypes was phenotyped for yield and agronomic traits for 2 years (2020 and 2021) in Morocco under rainfed and irrigated environments. The panel was genotyped using 20K SNPs and, after filtration, a total of 15,735 SNP markers were used for a genome-wide association study (GWAS) using a mixed linear model (MLM) to identify marker-trait associations (MTA) and putative genes associated with grain yield and yield-related traits under rainfed and irrigated conditions. Significant differences were observed among the elite genotypes for grain yield and yield-related traits. Grain yield performance ranged from 0.97 to 6.16 t/ha under rainfed conditions at Sidi Al-Aidi station and from 3.31 to 9.38 t/h under irrigated conditions at Sidi Al-Aidi station, while Grain yield at Merchouch station ranged from 2.32 to 6.16 t/h under rainfed condition. A total of 159 MTAs (p < 0.001) and 46 genes were discovered, with 67 MTAs recorded under rainfed conditions and 37 MTAs recorded under irrigated conditions at the Sidi Al-Aidi station, while 55 MTAs were recorded under rainfed conditions at Merchouch station. The marker ‘BobWhite_c2988_493’ on chromosome 2B was significantly correlated with grain yield under rainfed conditions. Under irrigated conditions, the marker ‘AX-94653560’ on chromosome 2D was significantly correlated with grain yield at Sidi Al-Aidi station. The maker ‘RAC875_c17918_321’ located on chromosome 4A, associated with grain yield was linked with the gene TraesCS4A02G322700, which encodes for F-box domain-containing protein. The markers and candidate genes discovered in this study should be further validated for their potential use in marker-assisted selection to generate high-yielding wheat genotypes with drought tolerance.
Genetic Diversity and Population Structure of an Arctic Tertiary Relict Tree Endemic to China (Sassafras tzumu) Revealed by Novel Nuclear Microsatellite (nSSR) Markers
Authors: Wang Shuang , Wang Ying , Zhou Jingbo , Li Pan , Lin Hungwei , Peng Ye , Yu Lipeng , Zhang Yunyan , Wang Zhongsheng
Published: October 2022

Sassafras tzumu (Hemsl.) Hemsl., as an Arctic Tertiary relict woody species, is an ecologically and economically important deciduous tree endemic to southern China. Nonetheless, the genetic resources and backgrounds of S. tzumu are still lacking and remain largely unclear. Here, we predicted 16,215 candidate polymorphic nuclear microsatellite (nSSR) loci from the assembled nucleus databases of six geographic-distant individuals of S. tzumu via CandiSSR. Among these nSSRs, the di- (75.53%) and tri-nucleotide (19.75%) repeats were the most abundant, and 27 new polymorphic SSRs were developed and characterized in 136 individuals from six natural populations of S. tzumu. The majority of the above 27 SSRs (24 loci, 88.89%) presented moderate polymorphism (mean PIC = 0.356), and the transferability of these markers in other Sassafras species was high (85.19%). A moderately low level of genetic diversity and a high variation (FST = 0.286) of six wild populations of S. tzumu were illuminated by 16 selected polymorphic nSSRs, with the average expected heterozygosity (HE) of 0.430 at the species level and HE ranging from 0.195 to 0.387 at the population level. Meanwhile, a bottleneck effect was shown in two populations. Consistent with the results of the principal coordinate analysis (PCoA) and phylogenetic trees, structure analysis optimally divided these six S. tzumu populations into two clusters, and the further strong population subdivision appeared from K = 2 to K = 5, which corresponded to two evolutionarily significant units (ESUs). Moreover, the significant correlation between genetic and geographic distance was tested by the Mantel test (r = 0.742, p = 0.006), clarifying the effect about isolation by distance (IBD), which could be possibly explained by the low gene flow (Nm = 0.625), a relatively high degree of inbreeding (FIS = 0.166), a relatively large distribution, and mountainous barriers. Above all, our research not only enlarged the useful genetic resources for future studies of population genetics, molecular breeding, and germplasm management of S. tzumu and its siblings but also contributed to proposing scientific conservation strategies and schemes for the better preservation of S. tzumu and other Sassafras (Lauraceae) species.
Value Coefficient of Polyethylene Fiber Soil Embankment Slope Based on Response Surface Analysis
Authors: Gong Yafeng , Song Jiaxiang , He Yulong , Ma Guirong
Published: October 2022

The utilization of polymers can strengthen soil, but at a high price. In this study, value coefficients were proposed to evaluate the cost-effectiveness of fiber-reinforced roadbeds, and the effects of embankment-slope-influencing factors on the value coefficients were analyzed by response surface methodology. Ultrahigh-molecular-weight polyethylene fiber (UPEF) was used as the reinforcement material for soil. First, the shear strength parameters of fiber soil with different fiber diameters were obtained from the direct shear tests to set the parameters of the finite element models. Second, three factors, namely filling height, slope angle, and fiber diameter, were selected as input parameters based on the Box–Behnken Design (BBD) experimental design method, and their effects on the value coefficient of the fiber soil embankment slope were investigated. Finally, the design parameters at the maximum value coefficient of the fiber soil embankment slope were determined based on the results of the response surface analysis. The results indicated that the addition of UPEF could effectively improve the cohesion of the soil; the interaction between the filling height and fiber diameter is most obvious. The optimization of design parameters based on the value coefficient of the fiber soil slope is a slope-engineering design method considering comprehensive benefits.
Hot-Melt and Pressure-Sensitive Adhesives Based on Styrene-Isoprene-Styrene Triblock Copolymer, Asphaltene/Resin Blend and Naphthenic Oil
Authors: Ilyin Sergey O. , Melekhina Viktoria Y. , Kostyuk Anna V. , Smirnova Nina M.
Published: October 2022

Asphaltene/resin blend (ARB) extracted from heavy crude oil was used to modify poly(styrene-block-isoprene-block-styrene) (SIS) to make it an adhesive. There were prepared double and triple mixtures containing 10–60% SIS, 10–40% ARB, and 10–50% naphthenic oil used as an additional plasticizer. The viscoelasticity of the mixtures at 25 °C and 120 °C was studied, their flow curves were obtained, and the temperature dependences of the loss tangent and the components of the complex modulus were measured. In addition, the mixtures were used as hot-melt adhesives (HMAs) and pressure-sensitive adhesives (PSAs) in the shear, peel, and pull-off tests of the adhesive bonds that they formed with steel. Both naphthenic oil and ARB act as plasticizers for SIS and make it sticky. However, only the combined use of ARB and the oil allows for achieving the best set of adhesive properties of the SIS-based mixture. High-quality HMA requires low oil content (optimal SIS/ARB/oil ratio is 50/40/10, pull-off adhesion strength (τt) of 1990 kPa), whereas a lot of the oil is needed to give SIS characteristics of a PSA (SIS/ARB/oil is 20/40/40, τt of 100 kPa). At the same time, the resulting PSA can be used as a hot-melt pressure-sensitive adhesive (HMPSA) that has many times lower viscosity than HMA (13.9 Pa·s versus 2640 Pa·s at 120 °C and 1 s−1) but provides a less strong adhesive bond (τt of 960 kPa).
The Optimization and Evaluation of Flibanserin Fast-Dissolving Oral Films
Authors: Alghaith Adel F. , Mahrous Gamal M. , Shazly Gamal A. , Zidan Diaa Eldin Z. , Alhamed Abdullah S. , Alqinyah Mohammed , Almutairi Mohammed M. , Syed Saeed A.
Published: October 2022

Flibanserin (FLB) is a drug used for female hypotensive sexual desire disorder approved by the FDA in August 2015. FLB exhibits extensive hepatic first-pass metabolism and low aqueous solubility, hence poor oral bioavailability. In this study, beta hydroxypropyl cyclodextrin-FLB inclusion complexes were incorporated into orally fast dissolving films. This dosage form was expected to improve FLB aqueous solubility, which would give fast onset of action and decrease presystemic metabolism, hence improving oral bioavailability. The inclusion complex at a ratio of 1:1 was prepared by the kneading method. Differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR), and powder X-ray diffractometry (XRD) were used to confirm complex formation. The Box–Behnken design (15 different formulae of FLB fast-dissolving oral films (FLBFDOFs) were utilized for the optimization of the prepared films. The Expert Design 11 program was utilized to examine the effects of three selected factors, polymer concentration (X1), plasticizer concentration (X2), and disintegrant concentration (X3) on four responses: disintegration time (DT), initial dissolution rate (IDR), dissolution efficiency (DE), and film quality (QF). Numerical optimization was performed by minimizing disintegration time (Y1), while maximizing the initial drug dissolution rate (Y2), dissolution efficiency (Y3), and the quality factor (Y4). The statistical analysis showed that X1 has a significant positive effect on the disintegration time and a significant negative effect on IDR. While X2 and X3 produced a nonsignificant negative effect on IDR. Dissolution efficiency was maximized at the middle concentration of both X2 and X3. The best film quality was observed at the middle concentration of both X1 and X2. In addition, increasing X3 leads to an improvement in film quality. The optimized film cast from an aqueous solution contains hydroxypropyl cellulose (2%) as a hydrophilic film-forming agent and propylene glycol (0.8%) as a plasticizer and cross povidone (0.2%) as a disintegrant. The prepared film released 98% of FLB after 10 min and showed good physical and mechanical properties. The optimized formula showed a disintegration time of 30 s, IDR of 16.6% per minute, DE15 of 77.7%, and QF of 90%. This dosage form is expected to partially avoid the pre-systemic metabolism with a fast onset of action, hence improving its bioavailability that favors an advantage over conventional dosage forms.
Influence of Parameters and Performance Evaluation of 3D-Printed Tungsten Mixed Filament Shields
Authors: Yoon Myeong Seong , Jang Hui Min , Kwon Kyung Tae
Published: October 2022

Currently, protective clothing used in clinical field is the most representative example of efforts to reduce radiation exposure to radiation workers. However, lead is classified as a substance harmful to the human body that can cause lead poisoning. Therefore, research on the development of lead-free radiation shielding bodies is being conducted. In this study, the shielding body was manufactured by changing the size, layer, and height of the nozzle, using a 90.7% pure tungsten filament, a 3D printer material, and we compared its performance with existing protection tools. Our findings revealed that the shielding rate of the mixed tungsten filament was higher than that of the existing protective tools, confirming its potency to replace lead as the most protective material in clinical field.
Effect of Biomass as Nucleating Agents on Crystallization Behavior of Polylactic Acid
Authors: Shi Kang , Liu Guoshuai , Sun Hui , Yang Biao , Weng Yunxuan
Published: October 2022

Polylactic acid (PLA) is one of the most productive biodegradable materials. Its bio-based source makes it truly carbon neutral. However, PLA is hard to crystallize as indicated by a low crystallization rate and a low crystallinity under conventional processing conditions, which limits its wider application. One of the most effective ways to enhance the crystallization ability of PLA is to add nucleating agents. In the context of increasing global environmental awareness and the decreasing reserves of traditional petroleum-based materials, biomass nucleating agents, compared with commonly used petroleum-based nucleating agents, have received widespread attention in recent years due to their abundance, biodegradability and renewability. This paper summarizes the research progress on biomass nucleating agents for regulating the crystallization behavior of polylactic acid. Examples of biomass nucleating agents include cellulose, hemicellulose, lignin, amino acid, cyclodextrins, starch, wood flour and natural plant fiber. Such green components from biomass for PLA are believed to be a promising solution for the development of a wholly green PLA-based system or composites.
Controlled Structure of Polyester/Hydroxyapatite Microparticles Fabricated via Pickering Emulsion Approach
Authors: Minaev Nikita V. , Minaeva Svetlana A. , Sherstneva Anastasia A. , Chernenok Tatiana V. , Sedova Yulia K. , Minaeva Ekaterina D. , Yusupov Vladimir I. , Akopova Tatiana A. , Timashev Peter S. , Demina Tatiana S.
Published: October 2022

Biodegradable polyester/hydroxyapatite microparticles are widely proposed as microcarriers for drug/cell delivery or scaffolds for bone tissue regeneration. The current research implements the surfactant-free approach for the fabrication of polyester-based microparticles filled with hydroxyapatite nanoparticles (nHA) via the oil/water Pickering emulsion solvent evaporation technique for the first time, to the best of our knowledge. The process of polyester microparticle fabrication using nHA for the oil/water interface stabilization was studied as a function of phase used for nHA addition, which allows the preparation of a range of microparticles either filled with nHA or having it as a shell over the polymeric core. The effect of processing conditions (polymer nature, polymer/nHA ratio, ultrasound treatment) on particles’ total yield, size distribution, surface and volume morphology, and chemical structure was analyzed using SEM, EDX, Raman spectroscopy, and mapping. Addition of nHA either within the aqueous or oil phase allowed the effective stabilization of the oil/water interface without additional molecular surfactants, giving rise to hybrid microparticles in which total yield, size distribution, and surface morphology depended on all studied processing conditions. Preliminary ultrasound treatment of any phase before the emulsification process led to a complex effect but did not affect the homogeneity of nHA distribution within the polymeric core of the hybrid microparticles.
Predicting Material Properties of Additively Manufactured Acrylonitrile Butadiene Styrene via a Multiscale Analysis Process
Authors: Nguyen Phan Quoc Khang , Zohdi Nima , Kamlade Patrick , Yang Richard (Chunhui)
Published: October 2022

Additive manufacturing (AM) has inherent mechanical strength inconsistencies when the build orientation changes. To address this issue, theoretical models, including analytical and numerical models, can be developed to predict the material properties of additively manufactured materials. This study develops a systematic finite element (FE)-based multiscale numerical model and simulation process for the polymer acrylonitrile butadiene styrene (ABS). ABS samples are fabricated using fused deposition modelling (FDM) to determine the material properties and mechanical behaviours. For macroscale analysis, good agreement between the numerical and experimental tensile strength of transverse samples proved that the FE model is applicable for applying a reverse engineering method in simulating the uniaxial tension of samples. The FE modelling method shows its capability to consider infill density effects. For mesoscale analysis, two methods are developed. The first method is a representative volume element (RVE)-based numerical model for all longitudinal samples. The second method is analytical and based on the rule of mixtures (ROM). Modified rule of mixtures (MROM) models are also developed, which demonstrate an improvement compared to the original ROM models. The research outcomes of this study can facilitate the AM process of parts in various engineering fields.
Processes of Electrospun Polyvinylidene Fluoride-Based Nanofibers, Their Piezoelectric Properties, and Several Fantastic Applications
Authors: Bai Yubin , Liu Yanan , Lv He , Shi Hongpu , Zhou Wen , Liu Yang , Yu Deng-Guang
Published: October 2022

Since the third scientific and technological revolution, electronic information technology has developed rapidly, and piezoelectric materials that can convert mechanical energy into electrical energy have become a research hotspot. Among them, piezoelectric polymers are widely used in various fields such as water treatment, biomedicine, and flexible sensors due to their good flexibility and weak toxicity. However, compared with ceramic piezoelectric materials, the piezoelectric properties of polymers are poor, so it is very important to improve the piezoelectric properties of polymers. Electrospinning technology can improve the piezoelectric properties of piezoelectric polymers by adjusting electrospinning parameters to control the piezoelectrically active phase transition of polymers. In addition, the prepared nanofibrous membrane is also a good substrate for supporting piezoelectric functional particles, which can also effectively improve the piezoelectric properties of polymers by doping particles. This paper reviews the piezoelectric properties of various electrospun piezoelectric polymer membranes, especially polyvinylidene fluoride (PVDF)-based electrospun nanofibrous membranes (NFs). Additionally, this paper introduces the various methods for increasing piezoelectric properties from the perspective of structure and species. Finally, the applications of NFs in the fields of biology, energy, and photocatalysis are discussed, and the future research directions and development are prospected.
Improved Electrical Signal of Non-Poled 3D Printed Zinc Oxide-Polyvinylidene Fluoride Nanocomposites
Authors: Joshi Sharmad , Gazmin Enrique , Glover Jayden , Weeks Nathan , Khan Fazeel , Iacono Scott , Corti Giancarlo
Published: October 2022

Polyvinylidene fluoride (PVDF) presents highly useful piezo and pyro electric properties but they are predicated upon the processing methods and the ensuing volume fraction of the β-phase. Production of PVDF with higher β-phase content for additive manufacturing (AM) is particularly desirable because it can enable the creation of custom parts with enhanced properties. Necessary steps from compounding to the testing of a 3D printed piezo sensitive sensor are presented in this paper. AM process variables and the influence of zinc oxide (ZnO) nanofiller on crystallinity, viscosity, and electromechanical properties of PVDF, have been explored. Fourier-transform infrared spectroscopy (FTIR) measurements confirm that a high cooling rate (HCR) of 30 °C min−1 promotes the conversion of the α-into the β-phase, reaching a maximum of 80% conversion with 7.5–12.5% ZnO content. These processing conditions increase the elastic modulus up to 40%, while maintaining the ultimate strength, ≈46 MPa. Furthermore, HCR 10% ZnO-PVDF produces four times higher volts per Newton when compared to low cooling rate, 5 °C min−1, pristine PVDF. A piezoelectric biomedical sensor application has been presented using HCR and ZnO nanofiller. This technique also reduces the need for post-poling which can reduce manufacturing time and cost.
Predictive Commutation Failure Suppression Strategy for High Voltage Direct Current System Considering Harmonic Components of Commutation Voltage
Authors: Liu Xiaolin , Cao Zeyu , Gao Bingtuan , Zhou Zhuan , Wang Xingang , Zhang Feng
Published: October 2022

The commutation failure of high voltage direct current (HVDC) systems could lead to unstable operation of the alternating current/direct current (AC/DC) hybrid power grid. The commutation voltage distortion caused by harmonics is a considerable but unclear factor of commutation failure. According to the control switching process of HVDC systems, the commutation voltage-time area method is employed to analyze and reveal the influence mechanism of harmonic components of commutation voltage on first and subsequent commutation failures. Considering the distortion characteristics of AC voltage, a predictive commutation failure suppression strategy considering multiple harmonics of commutation voltage is proposed. In this strategy, the new extinction angle and the zero-crossing offset angle after voltage distortion are calculated considering the harmonic components so as to obtain the compensation margin of the lag trigger angle by combining the correction margin with the voltage change rate. Moreover, the tuning method of parameters of extinction angle and voltage prediction variables are provided. Finally, a case study based on CIGRE standard HVDC system is performed and analyzed by using power systems computer-aided design (PSCAD) software. Compared with the International Council on Large Electric systems (CIGRE) standard test model and traditional commutation failure prevention (CFPREV) control model, the results verify that the proposed strategy can effectively reduce the risk of first and subsequent commutation failures and improve the sensitivity of CFPREV control.
Introduction to the Special Issue “Practical Theology Amid Environmental Crises”
Authors: McCarroll Pamela R. , Kim-Cragg HyeRan
Published: October 2022

By most accounts, the world we live in has entered the epoch of the Anthropocene, the period in which human activity has irrevocably altered the geology, biosphere and climate of the planet [...]
Environmental Assessment of Wastewater Treatment and Reuse for Irrigation: A Mini-Review of LCA Studies
Authors: Mehmeti Andi , Canaj Kledja
Published: October 2022

This paper provides an overview of existing LCA literature analyzing the environmental impacts of wastewater treatment and reuses, with irrigation as a process or scenario. Fifty-nine (n = 59) papers published between 2010 and 2022 were reviewed to provide insights into the methodological choices (goals, geographical scope, functional units, system boundaries, life cycle impact assessment (LCIA) procedures). The results show that LCA research has steadily increased in the last six years. The LCAs are case-study specific, apply a process perspective, and are primarily conducted by European authors. The LCAs are mainly midpoint-oriented with global warming, acidification and eutrophication potential as the most common impact categories reported. Volumetric-based functional units are the most widely applied. The most commonly used LCIA models were ReCiPe and CML, with Ecoinvent as the most commonly used database and SimaPro as the primary LCA software tool. Despite the fact that these methods cover a wide range of midpoint impact categories, nearly half of the studies focused on a few life cycle impact category indicators. In many studies, the LCA scope is frequently narrowed, and the assessment does not look at the cradle-to-grave system boundary but rather at cradle-to-gate or gate-to-gate system boundaries. Regardless of technology or other system boundary assumptions, the design of environmentally efficient wastewater reuse schemes is primarily determined by the type of energy supplied to the product’s life cycle. Our findings highlight that more holistic studies that take into account the expansion of system boundaries and the use of a broad set of environmental impact categories, supported by uncertainty and/or sensitivity analysis, are required. The overview presented in this paper serves as groundwork for future LCA studies in the field of irrigation with treated wastewater.
Sim-to-Real Deep Reinforcement Learning for Safe End-to-End Planning of Aerial Robots
Authors: Ugurlu Halil Ibrahim , Pham Xuan Huy , Kayacan Erdal
Published: October 2022

In this study, a novel end-to-end path planning algorithm based on deep reinforcement learning is proposed for aerial robots deployed in dense environments. The learning agent finds an obstacle-free way around the provided rough, global path by only depending on the observations from a forward-facing depth camera. A novel deep reinforcement learning framework is proposed to train the end-to-end policy with the capability of safely avoiding obstacles. The Webots open-source robot simulator is utilized for training the policy, introducing highly randomized environmental configurations for better generalization. The training is performed without dynamics calculations through randomized position updates to minimize the amount of data processed. The trained policy is first comprehensively evaluated in simulations involving physical dynamics and software-in-the-loop flight control. The proposed method is proven to have a 38% and 50% higher success rate compared to both deep reinforcement learning-based and artificial potential field-based baselines, respectively. The generalization capability of the method is verified in simulation-to-real transfer without further training. Real-time experiments are conducted with several trials in two different scenarios, showing a 50% higher success rate of the proposed method compared to the deep reinforcement learning-based baseline.
Weekly Small Uncrewed Aerial System Surveys, Structure from Motion, and Empirical Orthogonal Function Analyses Reveal Unique Modes of Sediment Exchange Generated by Seasonal and Episodic Phenomena: Waikīkī, Hawaiʻi
Authors: McDonald Kristian K. , Fletcher Charles H. , Anderson Tiffany R. , Habel Shellie
Published: October 2022

Small uncrewed aerial systems (sUASs) provide an efficient way to reveal processes controlling the morphology of sandy shorelines so that they can be more effectively managed. One of Hawaiʻi’s most popular tourist destinations, Waikīkī’s Royal Hawaiian Beach, features patterns of sediment transport driven by trade-wind activity, seasonal wave conditions, tropical storm activity, and other phenomena that make it an effective laboratory for the study of beach morphology. To evaluate the efficacy of using consumer-grade sUASs to monitor subaerial sand volume and processes that drive beach morphodynamics, we conducted weekly aerial and ground surveys from which high-resolution point clouds, digital elevation models, and orthomosaics were generated through structure from motion (SfM) photogrammetry. Our period of observation (April to November 2018) bracketed the Central Pacific hurricane season and the season of elevated southerly swell. Both phenomena are known to significantly influence sediment transport in the study area. Using empirical orthogonal function (EOF) analysis, we described combinations of single and dual littoral cell behavior generated by both longshore sediment transport and abrupt episodic fluctuations in cross-shore transport. While past studies have investigated morphological change at this location, this unique single and dual cell behavior within the greater littoral system had not been previously revealed. This study demonstrates that sUASs are capable of capturing high-resolution spatial and temporal topographic data that allow for detailed evaluation of both seasonal processes and abrupt perturbations of beach systems. These processes drive significant changes in beach area, volume, and overall beach morphology and their understanding critical to effective management in an era of sea level rise-driven change. The employed methodology was designed to be highly efficient and universally applicable to sandy shorelines whilst also being relatively inexpensive and instrumentation readily available, allowing for a more comprehensive understanding of these unique coastal environments.
JPSS-1 VIIRS Prelaunch Reflective Solar Band Testing and Performance
Authors: Moyer David , Angal Amit , Oudrari Hassan , Haas Evan , Ji Qiang , De Luccia Frank , Xiong Xiaoxiong
Published: October 2022

The Visible Infrared Imaging Radiometer Suite (VIIRS) instruments on board both the Suomi National Polar-orbiting Partnership (S-NPP) and the first Joint Polar Satellite System (JPSS-1) spacecraft provides calibrated reflectance, radiance, and brightness temperature products for weather and climate applications. It has 22 bands with resolutions of 375 and 750 m for imaging and moderate bands, respectively, on 4 focal planes covering a spectral range of 400–12,490 nm. The bands are stratified into reflective solar bands (RSBs), thermal emissive bands (TEBs), and the Day/Night Band (DNB). VIIRS has three on-board calibration sectors: the solar diffuser (SD), on-board calibrator blackbody (OBCBB), and space view (SV). The on-board calibrator targets are used to track on-orbit degradation and background offset drift. Extensive prelaunch radiometric testing of the RSB, TEB, and DNB detector’s radiometric sensitivity and noise was performed for both S-NPP and JPSS-1 VIIRS. The combination of prelaunch testing and on-orbit calibrators is used to produce calibrated sensor data record (SDR) reflectance, radiance, and brightness temperatures for use in environmental data record (EDR) products. This paper will discuss the prelaunch radiometric calibration activities for the RSBs only and includes the dynamic range, calibration coefficients, detector noise, and radiometric uncertainties for JPSS-1 VIIRS.
Crop Classification and Representative Crop Rotation Identifying Using Statistical Features of Time-Series Sentinel-1 GRD Data
Authors: Zhou Xin , Wang Jinfei , He Yongjun , Shan Bo
Published: October 2022

Compared with a monoculture planting mode, the practice of crop rotations improves fertilizer efficiency and increases crop yield. Large-scale crop rotation monitoring relies on the results of crop classification using remote sensing technology. However, the limited crop classification accuracy cannot satisfy the accurate identification of crop rotation patterns. In this paper, a crop classification and rotation mapping scheme combining the random forest (RF) algorithm and new statistical features extracted from time-series ground range direction (GRD) Sentinel-1 images. First, the synthetic aperture radar (SAR) time-series stacks are established, including VH, VV, and VH/VV channels. Then, new statistical features named the objected generalized gamma distribution (OGΓD) features are introduced to compare with other object-based features for each polarization. The results showed that the OGΓD σVH achieved 96.66% of the overall accuracy (OA) and 95.34% of the Kappa, improving around 4% and 6% compared with the object-based backscatter in VH polarization, respectively. Finally, annual crop-type maps for five consecutive years (2017–2021) are generated using the OGΓD σVH and the RF. By analyzing the five-year crop sequences, the soybean-corn (corn-soybean) is the most representative rotation in the study region, and the soybean-corn-soybean-corn-soybean (together with corn-soybean-corn-soybean-corn) has the highest count with 100 occurrences (25.20% of the total area). This study offers new insights into crop rotation monitoring, giving the basic data for government food planning decision-making.
Impact of Atmospheric Correction Methods Parametrization on Soil Organic Carbon Estimation Based on Hyperion Hyperspectral Data
Authors: Mruthyunjaya Prajwal , Shetty Amba , Umesh Pruthviraj , Gomez Cécile
Published: October 2022

Visible Near infrared and Shortwave Infrared (VNIR/SWIR, 400–2500 nm) remote sensing data is becoming a tool for topsoil properties mapping, bringing spatial information for environmental modeling and land use management. These topsoil properties estimates are based on regression models, linking a key topsoil property to VNIR/SWIR reflectance data. Therefore, the regression model’s performances depend on the quality of both topsoil property analysis (measured on laboratory over-ground soil samples) and Bottom-of-Atmosphere (BOA) VNIR/SWIR reflectance which are retrieved from Top-Of-Atmosphere radiance using atmospheric correction (AC) methods. This paper examines the sensitivity of soil organic carbon (SOC) estimation to BOA images depending on two parameters used in AC methods: aerosol optical depth (AOD) in the FLAASH (Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes) method and water vapor (WV) in the ATCOR (ATmospheric CORrection) method. This work was based on Earth Observing-1 Hyperion Hyperspectral data acquired over a cultivated area in Australia in 2006. Hyperion radiance data were converted to BOA reflectance using seven values of AOD (from 0.2 to 1.4) and six values of WV (from 0.4 to 5 cm), in FLAASH and ATCOR, respectively. Then a Partial Least Squares regression (PLSR) model was built from each Hyperion BOA data to estimate SOC over bare soil pixels. This study demonstrated that the PLSR models were insensitive to the AOD variation used in the FLAASH method, with R2cv and RMSEcv of 0.79 and 0.4%, respectively. The PLSR models were slightly sensitive to the WV variation used in the ATCOR method, with R2cv ranging from 0.72 to 0.79 and RMSEcv ranging from 0.41 to 0.47. Regardless of the AOD values, the PLSR model based on the best parametrization of the ATCOR model provided similar SOC prediction accuracy to PLSR models using the FLAASH method. Variation in AOD using the FLAASH method did not impact the identification of bare soil pixels coverage which corresponded to 82.35% of the study area, while a variation in WV using the ATCOR method provided a variation of bare soil pixels coverage from 75.04 to 84.04%. Therefore, this work recommends (1) the use of the FLAASH AC method to provide BOA reflectance values from Earth Observing-1 Hyperion Hyperspectral data before SOC mapping or (2) a careful selection of the WV parameter when using ATCOR.
From Video to Hyperspectral: Hyperspectral Image-Level Feature Extraction with Transfer Learning
Authors: Sun Yifan , Liu Bing , Yu Xuchu , Yu Anzhu , Gao Kuiliang , Ding Lei
Published: October 2022

Hyperspectral image classification methods based on deep learning have led to remarkable achievements in recent years. However, these methods with outstanding performance are also accompanied by problems such as excessive dependence on the number of samples, poor model generalization, and time-consuming training. Additionally, the previous patch-level feature extraction methods have some limitations, for instance, non-local information is difficult to model, etc. To solve these problems, this paper proposes an image-level feature extraction method with transfer learning. Firstly, we look at a hyperspectral image with hundreds of contiguous spectral bands from a sequential image perspective. We attempt to extract the global spectral variation information between adjacent spectral bands by using the optical flow estimation method. Secondly, we propose an innovative data adaptation strategy to bridge the gap between hyperspectral and video data, and transfer the optical flow estimation network pre-trained with video data to the hyperspectral feature extraction task for the first time. Thirdly, we utilize the traditional classifier to achieve classification. Simultaneously, a vote strategy combined with features at different scales is proposed to improve the classification accuracy further. Extensive, well-designed experiments on four scenes of public hyperspectral images demonstrate that the proposed method (Spe-TL) can obtain results that are competitive with advanced deep learning methods under various sample conditions, with better time effectiveness to adapt to new target tasks. Moreover, it can produce more detailed classification maps that subtly reflect the authentic distribution of ground objects in the original image.
Indoor–Outdoor Point Cloud Alignment Using Semantic–Geometric Descriptor
Authors: Yang Yusheng , Fang Guorun , Miao Zhonghua , Xie Yangmin
Published: October 2022

Aligning indoor and outdoor point clouds is a challenging problem since the overlapping area is usually limited, thus resulting in a lack of correspondence features. The windows and doors can be observed from both sides and are usually utilized as shared features to make connections between indoor and outdoor models. However, the registration performance using the geometric features of windows and doors is limited due to the considerable number of extracted features and the mismatch of similar features. This paper proposed an indoor/outdoor alignment framework with a semantic feature matching method to solve the problem. After identifying the 3D window and door instances from the point clouds, a novel semantic–geometric descriptor (SGD) is proposed to describe the semantic information and the spatial distribution pattern of the instances. The best object match is identified with an improved Hungarian algorithm using indoor and outdoor SGDs. The matching method is effective even when the numbers of objects are not equal in the indoor and outdoor models, which is robust to measurement occlusions and feature outliers. The experimental results conducted in the collected dataset and the public dataset demonstrated that the proposed method could identify accurate object matches under complicated conditions, and the alignment accuracy reached the centimeter level.
Biases Analysis and Calibration of ICESat-2/ATLAS Data Based on Crossover Adjustment Method
Authors: Wang Tao , Fang Yong , Zhang Shuangcheng , Cao Bincai , Wang Zhenlei
Published: October 2022

The new-generation photon-counting laser altimeter aboard the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has acquired unprecedented high-density laser data on the global surface. The continuous analysis and calibration of potential systematic biases in laser data are important for generating highly accurate data products. Current studies mainly calibrate the absolute systematic bias of laser altimeters based on external reference data. There are few studies that focus on the analysis and calibration of relative systematic biases in long-term laser data. This paper explores a method for systematic biases analysis and calibration of ICESat-2 laser data based on track crossovers for the first time. In the experiment, the simulated data and ICESat-2 data were used to verify the algorithm. The results show that, during the three-year period in orbit, the standard deviation (STD) and bias of the crossover differences of the ICESat-2 terrain data were 0.82 m and −0.03 m, respectively. The simulation validation well demonstrate that the crossover adjustment can calibrate the relative bias between different beams. For ICESat-2 data, the STD of the estimated systematic bias after crossover adjustment was 0.09 m, and the mean absolute error (MAE) was 0.07 m. Compared with airborne lidar data, the bias and root mean square error (RMSE) of the ICESat-2 data remained basically unchanged after adjustment, i.e., −0.04 m and 0.38 m, respectively. This shows that the current ICESat-2 data products possess excellent internal and external accuracy. This study shows the potential of crossover for evaluating and calibrating the accuracy of spaceborne photon-counting laser altimeter data products, in terms of providing a technical approach to generate global/regional high-accuracy point cloud data with consistent accuracy.
Time-Lagged Ensemble Quantitative Precipitation Forecasts for Three Landfalling Typhoons in the Philippines Using the CReSS Model, Part II: Verification Using Global Precipitation Measurement Retrievals
Authors: Wang Chung-Chieh , Tsai Chien-Hung , Jou Ben Jong-Dao , David Shirley J. , Pura Alvin G. , Lee Dong-In , Tsuboki Kazuhisa , Lee Ji-Sun
Published: October 2022

In this study, high-resolution quantitative precipitation forecasts (QPFs) in lagged runs with a cloud-resolving model are evaluated for three typhoons in the Philippines: Mangkhut (2018), Koppu (2015), and Melor (2015), hitting northern Luzon, central Luzon, and the middle section of the Philippine archipelago, respectively. In Part I of this study, the QPFs were verified using 56 gauge observations on land over the Philippines. Here, in Part II, they are verified against the Global Precipitation Measurement (GPM) satellite estimates (also covering nearby oceans), using categorical scores in the same way. For each typhoon, rainfall valid at a selected 24 h period and the whole event (48 or 72 h) is examined. For 24 h rainfall inside the short range (lead time ≤ 72 h), good QPFs (with a threat score of ≥0.2) were produced for Koppu at 200 mm by almost all runs, and at 100 mm by all runs for Mangkhut, but only 22% of the runs for Melor. At longer lead times, good QPFs at 100 mm were also produced by all runs for Koppu, half of the runs for Mangkhut, and only 1 out of 16 runs for Melor. For whole events (48 or 72 h), the QPFs were similarly the best for Koppu, followed by Mangkhut, and least ideal for Melor. The quality of the GPM data during the three typhoons was found to be generally good and suitable for QPF verification, and the results were more stable and, thus, more reliable for the assessment of bias. However, the threat scores using the GPM dropped lower at high thresholds, and the results could become different from those obtained against the gauges (Part I), suggesting a much higher skill. Thus, verification using rain gauges is still needed toward high thresholds, especially over mountain regions where satellite estimates tend to exhibit larger errors.
Glacier Motion Monitoring Using a Novel Deep Matching Network with SAR Intensity Images
Authors: Shen Huifang , Zhou Shudong , Fang Li , Yang Jian
Published: October 2022

Synthetic Aperture Radar technology is highly convenient for monitoring the glacier surface motion in unfavorable areas due to its advantages of being independent of time and weather conditions. A novel glacier motion monitoring method based on the deep matching network (DMN) is proposed in this paper. The network learns the relationship between the glacier SAR image patch-pairs and the corresponding matching labels in an end-to-end manner. Unlike conventional methods that utilize shallow feature tracking, the DMN performs a similarity measurement of deep features, which comprises feature extraction and a metric network. Feature extraction adopts the framework of a Siamese neural network to improve the training efficiency and dense connection blocks to increase the feature utilization. In addition, a self-sample learning method is introduced to generate training samples with matching labels. The experiments are performed on simulated SAR images and real SAR intensity images of the Taku Glacier and the Yanong Glacier, respectively. The results confirm the superiority of the DMN presented in the paper over other methods, even in case of strong noise. Furthermore, a quantitative 2D velocity field of real glaciers is obtained to provide reliable support for high-precision, long-term and large-scale automatic glacier motion monitoring.
An Effective Design Formula for Single-Layer Printed Spiral Coils with the Maximum Quality Factor (Q-Factor) in the Megahertz Frequency Range
Authors: Park Young-Jin , Kim Ji-Eun , Lee Su-Hyeong , Cho Kyung-Hwan
Published: October 2022

This paper presents a design formula for a printed spiral coil to ensure the maximum quality factor (Q-factor). The formula is composed of a pattern’s metal thickness, single pattern width, total pattern width, and turn number, and is effective in the megahertz (MHz) frequency range. During the formula’s design, the resistance, self-inductance, and Q-factor are calculated according to the ratio of each pattern’s width and total pattern width and the turn number for different metal thicknesses, frequencies, and total pattern widths using a volume filament model (VFM). With a given turn number and metal thickness, the optimal ratio of individual and total pattern widths can be determined to ensure the maximum Q-factor. To verify the formula, some optimal coils were fabricated, and the calculations and measurements were shown to have good agreement. Furthermore, the optimized coils were shown to have higher coupling efficiency than the coils without optimal dimensions.
Daily Estimation of Global Solar Irradiation and Temperatures Using Artificial Neural Networks through the Virtual Weather Station Concept in Castilla and León, Spain
Authors: Diez Francisco J. , Boukharta Ouiam F. , Navas-Gracia Luis M. , Chico-Santamarta Leticia , Martínez-Rodríguez Andrés , Correa-Guimaraes Adriana
Published: October 2022

In this article, the interpolation of daily data of global solar irradiation, and the maximum, average, and minimum temperatures were measured. These measurements were carried out in the agrometeorological stations belonging to the Agro-climatic Information System for Irrigation (SIAR, in Spanish) of the Region of Castilla and León, in Spain, through the concept of Virtual Weather Station (VWS), which is implemented with Artificial Neural Networks (ANNs). This is serving to estimate data in every point of the territory, according to their geographic coordinates (i.e., longitude and latitude). The ANNs of the Multilayer Feed-Forward Perceptron (MLP) used are daily trained, along with data recorded in 53 agro-meteorological stations, and where the validation of the results is conducted in the station of Tordesillas (Valladolid). The ANN models for daily interpolation were tested with one, two, three, and four neurons in the hidden layer, over a period of 15 days (from 1 to 15 June 2020), with a root mean square error (RMSE, MJ/m2) of 1.23, 1.38, 1.31, and 1.04, respectively, regarding the daily global solar irradiation. The interpolation of ambient temperature also performed well when applying the VWS concept, with an RMSE (°C) of 0.68 for the maximum temperature with an ANN of four hidden neurons, 0.58 for the average temperature with three hidden neurons, and 0.83 for the minimum temperature with four hidden neurons.
Inversion of Soil Organic Matter Content Based on Improved Convolutional Neural Network
Authors: Ma Li , Zhao Lei , Cao Liying , Li Dongming , Chen Guifen , Han Ye
Published: October 2022

Soil organic matter (SOM) is an important source of nutrients required during crop growth and is an important component of cultivated soil. In this paper, we studied the possibility of using deep learning methods to establish a multi-feature model to predict SOM content. Moreover, using Nong’an County of Changchun City as the study area, Sentinel-2A remote sensing images were taken as the data source to construct the dataset by using field sampling and image processing. The LeNet-5 convolutional neural network model was chosen as the deep learning model, which was improved based on the basic model. The evaluation metrics were selected as the root mean square error (RMSE) and the coefficient of determination R2. Through comparison, the R2 of the improved model was found to be higher than that of the linear regression method, Support Vector Machines (SVM) (RMSE = 2.471, R2 = 0.4035), and Random Forest (RF) (RMSE = 2.577, R2 = 0.4913). The result shows that: (1) It is feasible to use the multispectral data extracted from remote sensing images for soil organic matter content inversion based on the deep learning model with a minimum RMSE of 2.979 and with the R2 reaching 0.89. (2) The choice of features has an impact on the prediction of the model to a certain extent. After ranking the importance of features, selecting the appropriate number of features for inversion provides better results than full feature inversion, and the computational speed is improved.