bluuebunny/crossref_metadata_2025 huggingface dataset:
Conformational Structure, Infrared Spectra and Light-Induced Transformations of Thymol Isolated in Noble Gas Cryomatrices
Authors: Lopes Jesus Antόnio Jorge , Nunes Cláudio M. , Reva Igor
Published: June 2022

The conformational space of the natural product thymol (2-isopropyl-5-methylphenol) was investigated using quantum chemical calculations at the B3LYP and MP2 levels, which revealed the existence of four types of conformers differing in the orientation of the isopropyl and hydroxyl groups. Thymol monomers were isolated in noble gas (Ar and Xe) matrices (at 15 K) and characterized by IR spectroscopy. With the support of B3LYP harmonic vibrational calculations, the two most stable trans-OH-conformers, differing in the isopropyl orientation, were identified in the cryomatrices. The two less stable cis-OH conformers were not detected as they shall undergo fast tunneling to the most stable ones. Annealing experiments in a Xe matrix up to 75 K did not lead to any conversion between the two isolated conformers, which is in accordance with the significative energy barrier computed for rotamerization of the bulky isopropyl group (~24 kJ mol−1). Vibrational excitation promoted by broadband or by narrowband irradiation, at the 2ν(OH) frequencies of the isolated conformers, did not lead to any conversion either, which was interpreted in terms of a more efficient energy transfer to the hydroxyl rotamerization (associated with a lower energy barrier and a light H-atom) than to the isopropyl rotamerization coordinate. Broadband UV irradiation experiments (λ > 200 nm) led to a prompt transformation of matrix isolated thymol, with spectroscopic evidence suggesting the formation of isomeric alkyl-substituted cyclohexadienones, Dewar isomers and open-chain conjugated ketenes. The photochemical mechanism interpretation concords with that reported for analogous phenol derivatives.
Novel Elastography-Inspired Approach to Angiographic Visualization in Optical Coherence Tomography
Authors: Zykov Alexey A. , Matveyev Alexander L. , Matveev Lev A. , Shabanov Dmitry V. , Zaitsev Vladimir Y.
Published: June 2022

In this paper, we present a new approach to contrast-agent-free angiographic visualization in optical coherence tomography (OCT). The proposed approach has much in common with imaging of local interframe strains in OCT-based elastography and utilizes the fact that the interframe motion of blood particles leads to discontinuity of strains within the vessel cross section. By this reasoning, we call this approach “elastography-inspired”. Here, we first elucidate the essence and main features of the elastography-inspired approach using numerical simulation of OCT data. The simulations allow one to introduce both moving scatterers imitating blood flow in vessels as well as various masking motions imitating natural motions of living “solid” tissue surrounding the vessels. Second, using real OCT signals, we present comparative results of angiographic processing using the proposed elastography-inspired approach and a realization of OCA based on high-pass filtering of temporal variability of a series of OCT B-scans. The two methods can use the same initial dataset and the high-pass filtering OCA has already been routinely applied in both animal experiments and on patients. The new elastography-inspired method has a similar computational efficiency, and it is intrinsically able to compensate spatially-inhomogeneous masking tissue motions and demonstrates high robustness with respect to motion artefacts. Thus, the new approach looks very promising for enabling wider application of OCA in both laboratory studies on animals and, most importantly, for wider clinical applications on patients.
Improving the Performance of Direct Bonded Five-Junction Solar Cells by Optimization of AlInP Window Layer
Authors: Li Ge , Lu Hongbo , Li Xinyi , Zhang Wei
Published: June 2022

It is well-known that the quantum efficiency (QE) of inverted AlGaInP solar cells is less than that of upright ones, and the mechanism has not been well-explained. In this paper, a Si-doped AlInP window layer, compared with an emitter layer, is revealed to be one more important factor that decreases QE. It is noted that the quality of a heavily Si-doped AlInP window layer would decrease and further deteriorate subsequent active layers. An optimization strategy of a Si-doped AlInP window layer is proposed, which proves effective through time-resolved photoluminescence measurements (TRPL) of double heterojunctions. Inverted 2.1 eV AlGaInP solar cells with an improved AlInP window layer are fabricated. A 60 mV Voc increment is achieved with a remarkable enhancement of the fill factor from 0.789 to 0.827. An enhanced QE of 10% to 20% is achieved at short-wavelength and the peak IQE rises from 83.3% to 88.2%, which presents a nearly identical IQE compared with the upright reference. Further optimization in GaAs homojunction sub-cells is performed by introducing an n-GaInP/p-GaAs heterojunction structure, which decreases the recombination loss in the emitter caused by a poor AlInP window layer. The optimized structure significantly improves the Voc of the inverted GaAs-based T-3J solar cells to 3830 mV, boosting the efficiency of SBT five-junction solar cells to 35.61% under AM0 illumination.
Drift Artifacts Correction for Laboratory Cone-Beam Nanoscale X-ray Computed Tomography by Fitting the Partial Trajectory of Projection Centroid
Authors: Liu Mengnan , Han Yu , Xi Xiaoqi , Zhu Linlin , Liu Chang , Tan Siyu , Chen Jian , Li Lei , Yan Bin
Published: June 2022

A self-correction method for the drift artifacts of laboratory cone-beam nanoscale X-ray computed tomography (nano-CT) based on the trajectory of projection centroid (TPC) is proposed. This method does not require additional correction phantoms, simplifying the correction process. The whole TPC is estimated by the partial TPC in the optimal projection set. The projection drift is calculated by the measured TPC and the estimated TPC. The interval search method is used so that the proposed method can adapt to the case of a truncated projection due to drift. The fixed-angle scanning experiment of the Siemens star and the partial derivative analysis of the projection position show the necessity of correcting drift artifacts. Further, the Shepp–Logan phantoms with different drift levels are simulated. The results show that the proposed method can effectively estimate the horizontal and vertical drifts within the projection drift range of ±2 mm (27 pixels) with high accuracy. Experiments were conducted on tomato seed and bamboo stick to validate the feasibility of the proposed method for samples with different textures. The correction effect on different reconstructed slices indicates that the proposed method provides performance superior to the reference scanning method (RSM) and global fitting. In addition, the proposed method requires no extra scanning, which improves the acquisition efficiency, as well as radiation utilization.
An Ultra-Broadband Design of TM-Pass/TE-Stop Polarizer Based on Multistage Bragg Gratings
Authors: Dong Yue , Liu Yu , Xu Yin , Zhang Bo
Published: June 2022

In this paper, a multistage Bragg grating with various kinds of periods is introduced in the design of a reflection-based TM-pass/TE-stop polarizer. The cascade grating sections reflect a wide wavelength range of the TE polarization state. Additionally, on the other hand, the TM polarization state always passes through the waveguide. Such a design facilitates the polarizer working bandwidth, which is defined as the wavelength range with an extinction ratio of greater than 20 dB, and can reach 231 nm using only three grating sections. Meanwhile, the incision loss is always less than 0.42 dB over the working wavelength band. Furthermore, if a slightly higher loss is permitted, the polarizer working bandwidth can be extended to further than 310 nm using five grating sections.
Thermodynamic Analysis of ArxXe1-x Solid Solutions Based on Kirkwood–Buff Theory
Authors: Miyaji Masafumi , Simon Jean-Marc , Krüger Peter
Published: June 2022

Kirkwood–Buff Integral (KBI) theory is an important method for the analysis of the structural and thermodynamic properties of liquid solutions. For solids, the calculation of KBIs has become possible only recently through the finite-volume generalisation of KBI theory, but it has so far only been applied to monoatomic crystals. Here, we show that KBI theory can be applied to solid mixtures and compute the KBIs of a ArxXe1−x solid solution, for 0<x<0.1 and temperature T=84−86 K, from pair distribution functions obtained by Monte Carlo simulation. From the KBIs, the isothermal compressibility, partial molar volumes, and thermodynamic factors are calculated and found to be in good agreement with alternative theoretical methods. The analysis of the KBIs and the partial molar volumes give insight into the structure of the mixture. The KBI of Ar pairs is much larger than that of Xe pairs, which indicates the tendency of Ar impurities to accumulate. The evolution of the partial molar volumes with increasing Ar molar fraction x shows a transition at x≈0.06, which reflects the formation of Ar clusters, precursors of the Ar-rich liquid phase. The calculated thermodynamic factors show that the solid(Xe) phase becomes unstable at x≈0.1, indicating the start of the solid (Xe)–liquid (Ar) equilibrium. The chemical potentials of Ar and Xe are obtained from the thermodynamic factor by integration over lnx, and by fitting the data to the Margules equations, the activity coefficients can be estimated over the whole composition range. The present findings extend the domain of applicability of the KBI solution theory from liquids to solids.
Microalgae Polysaccharides: An Alternative Source for Food Production and Sustainable Agriculture
Authors: Moreira Juliana Botelho , Vaz Bruna da Silva , Cardias Bruna Barcelos , Cruz Camila Gonzales , Almeida Ana Claudia Araujo de , Costa Jorge Alberto Vieira , Morais Michele Greque de
Published: June 2022

Carbohydrates or polysaccharides are the main products derived from photosynthesis and carbon fixation in the Calvin cycle. Compared to other sources, polysaccharides derived from microalgae are safe, biocompatible, biodegradable, stable, and versatile. These polymeric macromolecules present complex biochemical structures according to each microalgal species. In addition, they exhibit emulsifying properties and biological characteristics that include antioxidant, anti-inflammatory, antitumor, and antimicrobial activities. Some microalgal species have a naturally high concentration of carbohydrates. Other species can adapt their metabolism to produce more sugars from changes in temperature and light, carbon source, macro and micronutrient limitations (mainly nitrogen), and saline stress. In addition to growing in adverse conditions, microalgae can use industrial effluents as an alternative source of nutrients. Microalgal polysaccharides are predominantly composed of pentose and hexose monosaccharide subunits with many glycosidic bonds. Microalgae polysaccharides can be structural constituents of the cell wall, energy stores, or protective polysaccharides and cell interaction. The industrial use of microalgae polysaccharides is on the rise. These microorganisms present rheological and biological properties, making them a promising candidate for application in the food industry and agriculture. Thus, microalgae polysaccharides are promising sustainable alternatives for potential applications in several sectors, and the choice of producing microalgal species depends on the required functional activity. In this context, this review article aims to provide an overview of microalgae technology for polysaccharide production, emphasizing its potential in the food, animal feed, and agriculture sector.
Properties of Rubberized Concrete Prepared from Different Cement Types
Authors: Idriss Lamiaa K. , Gamal Yasser Abdal Shafey
Published: June 2022

At present, global waste tire generation considerably exceeds consumption. Moreover, waste rubber tires (WRTs) are a cause of concern, as huge volumes are being discarded and buried, thus causing serious environmental pollution. Rubberized waste concrete (RWC) is a type of environmentally friendly construction material. The main challenge encountered when manufacturing rubberized concrete is the low adhesive properties between the cement paste and rubber particles. This paper demonstrates the effects, through experiments, of using waste tire rubber instead of recycled coarse aggregate (RCA) on two types of cement, i.e., sulfate-resistant cement (SRC) and ordinary Portland cement (OPC), where SRC is a specially blended cement designed to improve concrete performance and workability in the most aggressive environments. All tested samples contained 10% silica fume (SF) and 0.2% fly ash (FA), and the substitution of recycled aggregate content with waste rubber tier (WRT) at different percentages of 100%, 75%, and 50% was evaluated. The research investigated the synergistic effect on the workability and mechanical properties of various cement types with different amounts of rubber aggregate. It was found that the sulfate-resistant (SRC) type can increase the compressive strength than OPC with a percentage of 25% with the same content of WRT at concrete mix. Moreover, ductility and cracking behavior are improved, and it appears that it is also possible to make lightweight rubber aggregate concrete with this type of mixture.. Using this type of cement, it is possible to restore satisfactory ductility to the waste tires, thus facilitating a reduction in the formation of potential plastic cracks. Moreover, the indicative compressive strength development for SRC with recycled rubber in concrete positively contributes to a reduction in formed cracks. However, SEM microstructural analyses suggest a higher proportion of C–S–H intermixed with sulfate reaction phases of SRC rubberized mortar than those of OPC; thus, given that crystal growth results in a decreased percentage of air voids rather than decreased internal cracking, it is clearly shown that the average crack width increases in OPC mortar compared with SRC. Finally, t-testing was used as an inferential statistical tool to determine whether there is a sizeable distinction between the properties of the two categories of materials, OPC and SRC, by comparing the mean and standard deviation of the values for compressive and tensile strength.
Adaptive Unsupervised-Shadow-Detection Approach for Remote-Sensing Image Based on Multichannel Features
Authors: He Zhanjun , Zhang Zhizheng , Guo Mingqiang , Wu Liang , Huang Ying
Published: June 2022

Shadow detection is an essential research topic in the remote-sensing domain, as the presence of shadow causes the loss of ground-object information in real areas. It is hard to define specific threshold values for the identification of shadow areas with the existing unsupervised approaches due to the complexity of remote-sensing scenes. In this study, an adaptive unsupervised-shadow-detection method based on multichannel features is proposed, which can adaptively distinguish shadow in different scenes. First, new multichannel features were designed in the hue, saturation, and intensity color space, and the shadow properties of high hue, high saturation, and low intensity were considered to solve the insufficient feature-extraction problem of shadows. Then, a dynamic local adaptive particle swarm optimization was proposed to calculate the segmentation thresholds for shadows in an adaptive manner. Finally, experiments performed on the Aerial Imagery dataset for Shadow Detection (AISD) demonstrated the superior performance of the proposed approach in comparison with traditional unsupervised shadow-detection and state-of-the-art deep-learning methods. The experimental results show that the proposed approach can detect the shadow areas in remote-sensing images more accurately and efficiently, with the F index being 82.70% on the testing images. Thus, the proposed approach has better application potential in scenarios without a large number of labeled samples.
Investigating the Relationship between Air Pollutants and Meteorological Parameters Using Satellite Data over Bangladesh
Authors: Rahman Md Masudur , Shuo Wang , Zhao Weixiong , Xu Xuezhe , Zhang Weijun , Arshad Arfan
Published: June 2022

Understanding of the relationship between air pollutants and meteorological parameters on the regional scale is a prerequisite for setting up air pollution prevention and control strategies; however, there is a lack of methodical investigations, particularly in the context of Bangladesh’s deficiency of information on air pollution. This study represents the first attempt to investigate the relationship between air pollutants (NO2, O3, SO2, and CO) and meteorological parameters over Bangladesh using satellite data (OMI and MOPITT) during the period from 2015 to 2020. Geographically weighted regression (GWR) modelling was utilized to assess the relationship between air pollutants and weather variables. The spatial representation and average values of geographically varying coefficients showed that the column densities of air pollutants were affected by the meteorological parameters. For example, NO2 was positively associated with temperature in most of the studied regions, with an average geographically varying coefficient value of 0.12 Dobson units (DU, 1 DU = 2.687 × 1016 molecules/cm2), indicating that NO2 concentrations increase by 0.12 DU/year with every unit increase in temperature. The sources of NO2 and SO2 in Dhaka were identified through emission inventory analysis, and transportation and industry emissions were the most significant influencing factors for NO2 and SO2, respectively. Temperature and pressure showed a higher degree of relationship with all four air pollutants compared with other parameters. The results and discussion presented in this study can be of benefit for policy makers in developing air pollution control strategies in Bangladesh.
The Classification Method Study of Crops Remote Sensing with Deep Learning, Machine Learning, and Google Earth Engine
Authors: Yao Jinxi , Wu Ji , Xiao Chengzhi , Zhang Zhi , Li Jianzhong
Published: June 2022

The extraction and classification of crops is the core issue of agricultural remote sensing. The precise classification of crop types is of great significance to the monitoring and evaluation of crops planting area, growth, and yield. Based on the Google Earth Engine and Google Colab cloud platform, this study takes the typical agricultural oasis area of Xiangride Town, Qinghai Province, as an example. It compares traditional machine learning (random forest, RF), object-oriented classification (object-oriented, OO), and deep neural networks (DNN), which proposes a random forest combined with deep neural network (RF+DNN) classification framework. In this study, the spatial characteristics of band information, vegetation index, and polarization of main crops in the study area were constructed using Sentinel-1 and Sentinel-2 data. The temporal characteristics of crops phenology and growth state were analyzed using the curve curvature method, and the data were screened in time and space. By comparing and analyzing the accuracy of the four classification methods, the advantages of RF+DNN model and its application value in crops classification were illustrated. The results showed that for the crops in the study area during the period of good growth and development, a better crop classification result could be obtained using RF+DNN classification method, whose model accuracy, training, and predict time spent were better than that of using DNN alone. The overall accuracy and Kappa coefficient of classification were 0.98 and 0.97, respectively. It is also higher than the classification accuracy of random forest (OA = 0.87, Kappa = 0.82), object oriented (OA = 0.78, Kappa = 0.70) and deep neural network (OA = 0.93, Kappa = 0.90). The scalable and simple classification method proposed in this paper gives full play to the advantages of cloud platform in data and operation, and the traditional machine learning combined with deep learning can effectively improve the classification accuracy. Timely and accurate extraction of crop types at different spatial and temporal scales is of great significance for crops pattern change, crops yield estimation, and crops safety warning.
Effects of Aerosols on Gross Primary Production from Ecosystems to the Globe
Authors: Shu Yamei , Liu Shuguang , Wang Zhao , Xiao Jingfeng , Shi Yi , Peng Xi , Gao Haiqiang , Wang Yingping , Yuan Wenping , Yan Wende , Ning Ying , Li Qinyuan
Published: June 2022

Aerosols affect the gross primary productivity (GPP) of plants by absorbing and scattering solar radiation. However, it is still an open question whether and to what extent the effects of aerosol on the diffuse fraction (Df) can enhance GPP globally. We quantified the aerosol diffuse fertilization effect (DFE) and incorporated it into a light use efficiency (LUE) model, EC-LUE. The new model is driven by aerosol optical depth (AOD) data and is referred to as AOD-LUE. The eddy correlation variance (EC) of the FLUXNET2015 dataset was used to calibrate and validate the model. The results showed that the newly developed AOD-LUE model improved the performance in simulating GPP across all ecosystem types (R2 from 0.6 to 0.68), with the highest performance for mixed forest (average R2 from 0.71 to 0.77) and evergreen broadleaf forest (average R2 from 0.34 to 0.45). The maximum LUE of diffuse photosynthetic active radiation (PAR) (3.61 g C m−2 MJ−1) was larger than that of direct PAR (1.68 g C m−2 MJ−1) through parameter optimization, indicating that the aerosol DFE seriously affects the estimation of GPP, and the separation of diffuse PAR and direct PAR in the GPP model is necessary. In addition, we used AOD-LUE to quantify the impact of aerosol on GPP. Specifically, aerosols impaired GPP in closed shrub (CSH) by 6.45% but enhanced the GPP of grassland (GRA) and deciduous broadleaf forest (DBF) by 3.19% and 2.63%, respectively. Our study stresses the importance of understanding aerosol-radiation interactions and incorporating aerosol effects into regional and global GPP models.
LiDAR Odometry by Deep Learning-Based Feature Points with Two-Step Pose Estimation
Authors: Liu Tianyi , Wang Yan , Niu Xiaoji , Chang Le , Zhang Tisheng , Liu Jingnan
Published: June 2022

An accurate ego-motion estimation solution is vital for autonomous vehicles. LiDAR is widely adopted in self-driving systems to obtain depth information directly and eliminate the influence of changing illumination in the environment. In LiDAR odometry, the lack of descriptions of feature points as well as the failure of the assumption of uniform motion may cause mismatches or dilution of precision in navigation. In this study, a method to perform LiDAR odometry utilizing a bird’s eye view of LiDAR data combined with a deep learning-based feature point is proposed. Orthographic projection is applied to generate a bird’s eye view image of a 3D point cloud. Thereafter, an R2D2 neural network is employed to extract keypoints and compute their descriptors. Based on those keypoints and descriptors, a two-step matching and pose estimation is designed to keep these feature points tracked over a long distance with a lower mismatch ratio compared to the conventional strategy. In the experiment, the evaluation of the proposed algorithm on the KITTI training dataset demonstrates that the proposed LiDAR odometry can provide more accurate trajectories compared with the handcrafted feature-based SLAM (Simultaneous Localization and Mapping) algorithm. In detail, a comparison of the handcrafted descriptors is demonstrated. The difference between the RANSAC (Random Sample Consensus) algorithm and the two-step pose estimation is also demonstrated experimentally. In addition, the data collected by Velodyne VLP-16 is also evaluated by the proposed solution. The low-drift positioning RMSE (Root Mean Square Error) of 4.70 m from approximately 5 km mileage shown in the result indicates that the proposed algorithm has generalization performance on low-resolution LiDAR.
Comparative Sensitivity of Vegetation Indices Measured via Proximal and Aerial Sensors for Assessing N Status and Predicting Grain Yield in Rice Cropping Systems
Authors: Rehman Telha H. , Lundy Mark E. , Linquist Bruce A.
Published: June 2022

Reflectance-based vegetation indices can be valuable for assessing crop nitrogen (N) status and predicting grain yield. While proximal sensors have been widely studied in agriculture, there is increasing interest in utilizing aerial sensors. Given that few studies have compared aerial and proximal sensors, the objective of this study was to quantitatively compare the sensitivity of aerially sensed Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red-Edge Index (NDRE) and proximally sensed NDVI for assessing total N uptake at panicle initiation (PI-NUP) and predicting grain yield in rice. Nitrogen response trials were established over a 3-year period (10 site-years) at various locations throughout the Sacramento Valley rice growing region of California. At PI, a multispectral unmanned aircraft system (UAS) was used to measure NDVIUAS and NDREUAS (average ground sampling distance: 3.7 cm pixel−1), and a proximal GreenSeeker (GS) sensor was used to record NDVIGS. To enable direct comparisons across the different indices on an equivalent numeric scale, each index was normalized by calculating the Sufficiency-Index (SI) relative to a non-N-limiting plot. Kernel density distributions indicated that NDVIUAS had a narrower range of values that were poorly differentiated compared to NDVIGS and NDREUAS. The critical PI-NUP where yields did not increase with higher PI-NUP averaged 109 kg N ha−1 (±4 kg N ha−1). The relationship between SI and PI-NUP for the NDVIUAS saturated lower than this critical PI-NUP (96 kg N ha−1), whereas NDVIGS and NDREUAS saturated at 111 and 130 kg N ha−1, respectively. This indicates that NDVIUAS was less suitable for making N management decisions at this crop stage than NDVIGS and NDREUAS. Linear mixed effects models were developed to evaluate how well each SI measured at PI was able to predict grain yield. The NDVIUAS was least sensitive to variation in yields as reflected by having the highest slope (2.4 Mg ha−1 per 0.1 SI). In contrast, the slopes for NDVIGS and NDREUAS were 0.9 and 1.1 Mg ha−1 per 0.1 SI, respectively, indicating greater sensitivity to yields. Altogether, these results indicate that the ability of vegetation indices to inform crop management decisions depends on the index and the measurement platform used. Both NDVIGS and NDREUAS produced measurements sensitive enough to inform N fertilizer management in this system, whereas NDVIUAS was more limited.
Spatial Sustainable Development Assessment Using Fusing Multisource Data from the Perspective of Production-Living-Ecological Space Division: A Case of Greater Bay Area, China
Authors: Gao Ku , Yang Xiaomei , Wang Zhihua , Zhang Huifang , Huang Chong , Zeng Xiaowei
Published: June 2022

United Nations Sustainable Development Goal SDG11.3.1—the ratio of land consumption rate (LCR) to population growth rate (PGR) (LCRPGR)—aims to measure the efficiency and sustainability of urban land use. In recent years, SDG11.3.1 has been widely used in sustainable urban development research. However, previous studies have focused on the urban core area, while the sustainable development status of the urban peripheral areas (suburban and rural areas) that contribute significantly to the ecological environment has been neglected. To this end, relying on land use/cover change (LUCC) data obtained from high-resolution remote sensing satellite images rather than the single impervious surface data used in traditional research, according to the multiple functions of the land use type, the city is divided into three types of space: production, living, and ecological spaces. Research from the perspective of multi-scale coordination is of great significance for gaining a comprehensive understanding of the sustainable development status of urban space. Taking the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China as an example, in this paper, LUCC remote sensing data and comprehensive population and gross domestic product (GDP) data are used. From the multi-functional production-living-ecological space perspective, based on the original land use efficiency indicator, the ratio of land consumption rate (LCR) to economic growth rate (EGR) (LCREGR) is introduced and the analytic hierarchy process (AHP) is used to comprehensively evaluate the sustainable development level (SDL) of the space between 2000–2010 and 2010–2020 on the urban agglomeration and prefecture-level city scales. The results show that (1) the level of and changes in the spatial sustainable development are significantly different at different scales; (2) the division of the production-living-ecological spaces can guide cities to optimize different types of spaces in the future. This paper proposes a new evaluation method for spatial sustainable development, which provides a useful reference for any country or region in the world.
An Integrated Algorithm for Extracting Terrain Feature-Point Clusters Based on DEM Data
Authors: Hu Jinlong , Luo Mingliang , Bai Leichao , Duan Jinliang , Yu Bing
Published: June 2022

Terrain feature points, such as the peaks and saddles, are the basic framework of surface topography and its undulations, which significantly affect the spatial distribution of surface topography. In the past, terrain feature points were extracted separately for each type, while the internal connections between the terrain feature points were ignored. Therefore, this work proposes an integrated algorithm for extracting terrain feature-point clusters, including the peaks, saddles and runoff nodes, based on the DEM data. This method includes two main processes: positive terrain-constrained ridgeline extraction and terrain feature-point cluster extraction. Firstly, a threshold determination method of flow accumulation in the hydrological analysis is proposed by combining morphological characteristics with runoff simulation, and the ridgelines are extracted based on this threshold. Subsequently, the peaks and their control areas are extracted by space segmentation. Meanwhile, the saddles and runoff nodes are obtained by spatial intersection. Finally, the integrated terrain feature-point clusters are obtained by merging the three extracted terrain feature points. This method was experimented with in the six typical sample areas in Shaanxi Province and verified its results by contour lines and optical images. It shows that the spatial positions of the extracted terrain feature clusters are accurate, and the coupling relationships are great. Finally, the experiments show that the statistical attributes of point clusters and their spatial distribution trends have an obvious correlation with geomorphic types and geomorphic zoning, which can provide an important reference for geomorphic zoning and mapping.
Intercomparison of Resampling Algorithms for Advanced Technology Microwave Sounder (ATMS)
Authors: Xie Yuchen , Weng Fuzhong
Published: June 2022

The observations from satellite microwave-sounding instruments have been widely used in weather and climate studies. Since the data resolution varies with frequency and satellite viewing angle, it is normally required that the measurements at each frequency be resampled to obtain a uniform resolution prior to various applications. In this study, the ATOVS and AVHRR pre-processing package (AAPP) Fourier transform algorithm is modified for ATMS data and the results are compared with those derived from Backus–Gilbert inversion (BGI) and the original AAPP. From the simulated and observed ATMS data, we demonstrated the new algorithm has better results in terms of imaging quality and noise suppression, compared with BGI and AAPP. In general, our modified AAPP algorithm reduces the error by at least about 0.5 K in ATMS channels 2 and 6 and at all the viewing angles.
The Identification of Impact Craters from GRAIL-Acquired Gravity Data by U-Net Architecture
Authors: Chen Zhaoxi , Chen Zidan
Published: June 2022

The identification of impact craters on the Moon and other planetary bodies is of great significance to studying and constraining the dynamical process and evolution of the Solar System. Traditionally, this has been performed through the visual examination of images. Due to the effect of overburden, some structural features cannot be effectively identified from optical images, resulting in limitations in the scope, efficiency and accuracy of identification. In this paper, we investigate the viability of convolutional neural networks (CNNs) to perform the detection of impact craters from GRAIL-acquired gravity data. The ideal values of each hyperparameter in U-net architecture are determined after dozens of iterations of model training, testing and evaluation. The final model was evaluated by the Loss function with the low value of 0.04, indicating that the predicted output of the model reached a relatively high fitting degree with the prior labelled output. The comparative results with different methods show that the proposed method has a clear detection of the target features, with an accuracy of more than 80%. In addition, the detection results of the whole image account for 83% of the number of manually delineated gravity anomalies. The proposed method can still achieve the same quality for the identification of the gravity anomalies caused by impact craters under the condition that the resolution of GRAIL gravity data are not superior. Our results demonstrate that the U-net architecture can be a very effective tool for the rapid and automatic identification of impact craters from gravity map on the Moon, as well as other Solar System bodies.
Mapping Spatiotemporal Changes in Forest Type and Aboveground Biomass from Landsat Long-Term Time-Series Analysis—A Case Study from Yaoluoping National Nature Reserve, Anhui Province of Eastern China
Authors: Yang Boxiang , Zhang Yali , Mao Xupeng , Lv Yingying , Shi Fang , Li Mingshi
Published: June 2022

A natural reserve’s forest is an important base for promoting natural education, scientific research, biodiversity conservation and carbon accounting. Dynamic monitoring of the forest type and forest aboveground biomass (AGB) in a nature reserve is an important foundation for assessing the forest succession stage and trend. Based on the Landsat images covering the National Nature Reserve of Yaoluoping in Anhui province spanning from 1987 to 2020, a total of 42 Landsat scenes, the forest cover product set was first developed by using the well-established vegetation change tracker (VCT) model. On this basis, a new vegetation index, NDVI_DR, which considers the phenological characteristics of different forest types, was proposed to distinguish coniferous forest from broad-leaved forest. Next, multiple modeling factors, including remote sensing spectral signatures, vegetation indices, textural measures derived from gray level co-occurrence matrix and wavelet analysis and topographic attributes, were compiled to model the AGB in 2011 by forest type separately by using the stochastic gradient boosting (SGB) algorithm. Then, using the 2011 Landsat image as the base, all the Landsat images in the other years involved in the modelling were relatively normalized by using the weighted invariant pixels (WIP) method, followed by an extrapolation of the 2011 AGB model to other years to create a time-series of AGB. The results showed that the overall accuracy of the VCT-based forest classification products was over 90%. The annual forest type classifications derived from NDVI_DR thresholding gained an overall accuracy above 92%, with a kappa coefficient above 0.8. The 2011 forest-type-dependent SGB-based AGB estimation model achieved an independent validation R2 at 0.63 and an RMSE at 11.18 t/ha for broad-leaved forest, and 0.61 and 14.26 t/ha for coniferous forest. The mapped time-series of AGB showed a gradual increasing trend over the past three decades. The driving factors responsible for the observed forest cover and AGB changes were analyzed to provide references for reasonable protection and development. The proposed methodology is a reliable tool for evaluating the management status, which can be extended to other similar regions.
Ocean Wave Inversion Based on Hybrid Along- and Cross-Track Interferometry
Authors: Sun Daozhong , Wang Yunhua , Xu Zhichao , Zhang Yanmin , Zhang Yubin , Meng Junmin , Sun Hanwei , Yang Lei
Published: June 2022

The hybrid interferometric synthetic aperture radar system is a combination of an along-track configuration and cross-track configuration. Based on linear ocean wave theory, an ocean wave inversion algorithm for a hybrid interferometric synthetic aperture radar system is proposed in this work. Using the interferometric synthetic aperture radar images acquired by the TerraSAR-X and TanDEM-X satellites and the interferometric synthetic aperture radar images acquired by an airborne interferometric radar altimeter with a certain degree of squint, the profile of ocean waves and the corresponding orbital velocities were retrieved by combining the new inversion algorithm with the cross-spectra. Meanwhile, key parameters of ocean waves, such as the significant wave height, significant wave orbital velocity, propagation direction, and wavelength of the dominant waves, were also extracted from the ocean wave spectra retrieved in this study. In order to evaluate the reliability of the new inversion algorithm, the retrieved significant wave heights were compared with those provided by the European Centre for Medium-Range Weather Forecasts and measured by a Global Navigation Satellite System buoy. The results showed that for the ocean waves retrieved from the spaceborne hybrid interferometric synthetic aperture radar images, the differences between the retrieved significant wave heights of the four subareas selected in this paper and those provided by European Centre for Medium-Range Weather Forecasts were approximately 0.01, –0.17, –0.55, and –0.37 m, respectively, and for the ocean waves retrieved from the airborne interferometric radar altimeter images, the differences between the retrieved significant wave heights corresponding to the M920 and M3120 images used in this paper and those measured by the Global Navigation Satellite System buoy were approximately –0.05 and –0.09, respectively. Therefore, the method proposed in this work could retrieve the ocean wave spectra well when the velocity bunching had a small influence; however, as the nonlinear influence of the velocity bunching increased, the difference between the significant wave heights retrieved using this method and provided by the European Centre for Medium-Range Weather Forecasts also increased.
Comparison of Machine Learning-Based Snow Depth Estimates and Development of a New Operational Retrieval Algorithm over China
Authors: Yang Jianwei , Jiang Lingmei , Pan Jinmei , Shi Jiancheng , Wu Shengli , Wang Jian , Pan Fangbo
Published: June 2022

Snow depth estimation with passive microwave (PM) remote sensing is challenged by spatial variations in the Earth’s surface, e.g., snow metamorphism, land cover types, and topography. Thus, traditional static snow depth retrieval algorithms cannot capture snow thickness well. In this study, we present a new operational retrieval algorithm, hereafter referred to as the pixel-based method (0.25° × 0.25° grid-level), to provide more accurate and nearly real-time snow depth estimates. First, the reference snow depth was retrieved using a previously proposed model in which a microwave snow emission model was coupled with a machine learning (ML) approach. In this process, an effective grain size (effGS) value was optimized by utilizing the snow microwave emission model, and then the nonlinear relationship between snow depth and multiple predictive variables, e.g., effGS, longitude, elevation, and brightness temperature (Tb) gradients, was established with the ML technique to retrieve reference snow depth data. To select a robust and well-performing ML approach, we compared the performance of widely used support vector regression (SVR), artificial neural network (ANN) and random forest (RF) algorithms over China. The results show that the three ML models performed similarly in snow depth estimation, which was attributed to the inclusion of effGS in the training samples. In this study, the RF model was used to retrieve the snow depth reference dataset due to its slightly stronger robustness according to our comparison of results. Second, the pixel-based algorithm was built based on the retrieved reference snow depth dataset and satellite Tb observations (18.7 GHz and 36.5 GHz) from Advanced Microwave Scanning Radiometer 2 (AMSR2) during the 2012–2020 period. For the pixel-based algorithm, the fitting coefficients were achieved dynamically pixel by pixel, making it superior to the traditional static methods. Third, the built pixel-based algorithm was verified using ground-based observations and was compared to the AMSR2, GlobSnow-v3.0, and ERA5-land products during the 2012–2020 period. The pixel-based algorithm exhibited an overall unbiased root mean square error (unRMSE) and R2 of 5.8 cm and 0.65, respectively, outperforming GlobSnow-v3.0, with unRMSE and R2 values of 9.2 cm and 0.22, AMSR2, with unRMSE and R2 values of 18.5 cm and 0.13, and ERA5-land, with unRMSE and R2 values of 10.5 cm and 0.33, respectively. However, the pixel-based algorithm estimates were still challenged by the complex terrain, e.g., the unRMSE was up to 17.4 cm near the Tien Shan Mountains. The proposed pixel-based algorithm in this study is a simple and operational method that can retrieve accurate snow depths based solely on spaceborne PM data in comparatively flat areas.
Real-Time Synchronous 3-D Detection of Air Pollution and Wind Using a Solo Coherent Doppler Wind Lidar
Authors: Yuan Jinlong , Wu Yunbin , Shu Zhifeng , Su Lian , Tang Dawei , Yang Yuanjian , Dong Jingjing , Yu Saifen , Zhang Zhen , Xia Haiyun
Published: June 2022

The monitoring and tracking of urban air pollution is a challenging environmental issue. The approach of synchronous 3-D detection of wind and pollution using a solo coherent Doppler wind lidar (CDWL) is developed and demonstrated. The 3-D distribution of pollutant is depicted by the backscatter coefficient based on signal intensity of CDWL. Then, a high-resolution wind field is derived to track the local air pollution source with its diffusion and to analyze transboundary air pollution episodes. The approach is experimentally implemented in a chemical industry park. Smoke plumes caused by point source pollutions are captured well using plan position indicator (PPI) scanning with low elevation. A typical source of pollution is located, combining the trajectory of the smoke plume and the horizontal wind vector. In addition, transboundary air pollution caused by the transport of dust storms is detected in a vertical profile scanning pattern, which is consistent with the results of national monitoring stations and backward trajectory models. Our present work provides a significant 3-D detection approach to air pollution monitoring with its sources, paths, and heights by using a solo-CDWL system.
Registration of Multisensor Images through a Conditional Generative Adversarial Network and a Correlation-Type Similarity Measure
Authors: Maggiolo Luca , Solarna David , Moser Gabriele , Serpico Sebastiano Bruno
Published: June 2022

The automatic registration of multisensor remote sensing images is a highly challenging task due to the inherently different physical, statistical, and textural characteristics of the input data. Information-theoretic measures are often used to favor comparing local intensity distributions in the images. In this paper, a novel method based on the combination of a deep learning architecture and a correlation-type area-based functional is proposed for the registration of a multisensor pair of images, including an optical image and a synthetic aperture radar (SAR) image. The method makes use of a conditional generative adversarial network (cGAN) in order to address image-to-image translation across the optical and SAR data sources. Then, once the optical and SAR data are brought to a common domain, an area-based ℓ2 similarity measure is used together with the COBYLA constrained maximization algorithm for registration purposes. While correlation-type functionals are usually ineffective in the application to multisensor registration, exploiting the image-to-image translation capabilities of cGAN architectures allows moving the complexity of the comparison to the domain adaptation step, thus enabling the use of a simple ℓ2 similarity measure, favoring high computational efficiency, and opening the possibility to process a large amount of data at runtime. Experiments with multispectral and panchromatic optical data combined with SAR images suggest the effectiveness of this strategy and the capability of the proposed method to achieve more accurate registration as compared to state-of-the-art approaches.
Measuring Floating Thick Seep Oil from the Coal Oil Point Marine Hydrocarbon Seep Field by Quantitative Thermal Oil Slick Remote Sensing
Authors: Leifer Ira , Melton Christopher , Daniel William J. , Tratt David M. , Johnson Patrick D. , Buckland Kerry N. , Kim Jae Deok , Marston Charlotte
Published: June 2022

Remote sensing techniques offer significant potential for generating accurate thick oil slick maps critical for marine oil spill response. However, field validation and methodology assessment challenges remain. Here, we report on an approach to leveraging oil emissions from the Coal Oil Point (COP) natural marine hydrocarbon seepage offshore of southern California, where prolific oil seepage produces thick oil slicks stretching many kilometers. Specifically, we demonstrate and validate a remote sensing approach as part of the Seep Assessment Study (SAS). Thick oil is sufficient for effective mitigation strategies and is set at 0.15 mm. The brightness temperature of thick oil, TBO, is warmer than oil-free seawater, TBW, allowing segregation of oil from seawater. High spatial-resolution airborne thermal and visible slick imagery were acquired as part of the SAS; including along-slick “streamer” surveys and cross-slick calibration surveys. Several cross-slick survey-imaged short oil slick segments that were collected by a customized harbor oil skimmer; termed “collects”. The brightness temperature contrast, ΔTB (TBO − TBW), for oil pixels (based on a semi-supervised classification of oil pixels) and oil thickness, h, from collected oil for each collect provided the empirical calibration of ΔTB(h). The TB probability distributions provided TBO and TBW, whereas a spatial model of TBW provided ΔTB for the streamer analysis. Complicating TBW was the fact that streamers were located at current shears where two water masses intersect, leading to a TB discontinuity at the slick. This current shear arose from a persistent eddy down current of the COP that provides critical steering of oil slicks from the Coal Oil Point. The total floating thick oil in a streamer observed on 23 May and a streamer observed on 25 May 2016 was estimated at 311 (2.3 bbl) and 2671 kg (20 bbl) with mean linear floating oil 0.14 and 2.4 kg m−1 with uncertainties by Monte Carlo simulations of 25% and 7%, respectively. Based on typical currents, the average of these two streamers corresponds to 265 g s−1 (~200 bbl day−1) in a range of 60–340 bbl day−1, with significant short-term temporal variability that suggests slug flow for the seep oil emissions. Given that there are typically four or five streamers, these data are consistent with field emissions that are higher than the literature estimates.