bluuebunny/crossref_metadata_2025 huggingface dataset:
Source Localisation Using Wavefield Correlation-Enhanced Particle Swarm Optimisation
Authors: Rossides George , Hunter Alan , Metcalfe Benjamin
Published: April 2022

Particle swarm optimisation (PSO) is a swarm intelligence algorithm used for controlling robotic swarms in applications such as source localisation. However, conventional PSO algorithms consider only the intensity of the received signal. Wavefield signals, such as propagating underwater acoustic waves, permit the measurement of higher order statistics that can be used to provide additional information about the location of the source and thus improve overall swarm performance. Wavefield correlation techniques that make use of such information are already used in multi-element hydrophone array systems for the localisation of underwater marine sources. Additionally, the simplest model of a multi-element array (a two-element array) is characterised by operational simplicity and low-cost, which matches the ethos of robotic swarms. Thus, in this paper, three novel approaches are introduced that enable PSO to consider the higher order statistics available in wavefield measurements. In simulations, they are shown to outperform the standard intensity-based PSO in terms of robustness to low signal-to-noise ratio (SNR) and convergence speed. The best performing approach, cross-correlation bearing PSO (XB-PSO), is capable of converging to the source from as low as −5 dB initial SNR. The original PSO algorithm only manages to converge at 10 dB and at this SNR, XB-PSO converges 4 times faster.
The Effect of the Degree of Freedom and Weight of the Hand Exoskeleton on Joint Mobility Function
Authors: Priadythama Ilham , Yeoh Wen Liang , Loh Ping Yeap , Muraki Satoshi
Published: April 2022

This study aims to investigate the effects of the degree of freedom (DOF) and weight of the hand exoskeleton (HE) on hand joint mobility function (ease of movement, movement range) in fine hand use activities. A three-digit passive HE prototype was built to fit each of the 12 participants. Two DOF setups (three DOF, two DOF), two digits’ weight levels (70 g, 140 g), and barehand conditions were tested. A productivity task (performed with Standardized-Nine Hole Peg Test) and motion tasks, both performing the tip pinch and tripod pinch, were conducted to measure the task completion time and the range of motion (ROM) of the digit joints, respectively, using a motion capture system. The perceived ease rating was also measured. The results showed that DOF reduction and weight addition caused a significant task completion time increase and rating drop (p < 0.05). Meanwhile, the DOF reduction increased the ROM reduction of the proximal interphalangeal joints; however, the weight addition caused a correction of the ROM reduction of several joints (p < 0.05) at the tripod pinch. In conclusion, wearing an HE reduces hand joint mobility, especially in lower DOF. However, a certain weight addition may improve joint mobility in terms of the fingers’ movement range.
First Assessment of GF3-02 SAR Ocean Wind Retrieval
Authors: Yang Junxin , Han Bing , Zhong Lihua , Yuan Xinzhe , Wang Xiaochen , Hu Yuxin , Ding Chibiao
Published: April 2022

On 23 November 2021, the Gaofen-3-02 (GF3-02) satellite was successfully launched in the Jiuquan Satellite Launch Center of China. The primary payload is C-band Synthetic Aperture Radar (SAR), with a maximum resolution of 1 m, and includes 12 imaging modes such as Spotlight, Strip, and TOPSAR, which will play an essential role in marine environment monitoring. As an important marine environmental parameter, the wind speed accuracy retrieved by GF3-02 SAR directly reflects its performance and effectiveness as an operational product. Therefore, based on the wind data of buoys of the National Data Buoy Center (NDBC), ECMWF reanalysis V5 (ERA5), and HY-2B Scatterometer (SCA), a preliminary accuracy assessment of the wind speed retrieved by GF3-02 SAR is carried out in this paper. The wind speed retrieval accuracy of GF3-02 SAR in the co-polarization (HH+VV) data under different Geophysical Model Functions (GMFs) is discussed by using 478 level-1A Single Look Complex (SLC) ocean products acquired in Quad-Polarization Strip I (QPSI) and produced by the National Satellite Ocean Application Service (NSOAS) from January to March 2022. The results show that the optimal root mean square errors (RMSE) are 1.40 m/s, 1.18 m/s, and 1.24 m/s for the VV polarization and 1.39 m/s, 1.19 m/s, and 1.52 m/s for the HH polarization compared to the NDBC wind speed, the ERA5 wind speed, and the HY-2B SCA wind speed, respectively. The preliminary results show that GF3-02 SAR has good wind speed retrieval ability and can meet the needs of operational products.
An Efficient Backprojection Algorithm Based on Wavenumber-Domain Spectral Splicing for Monostatic and Bistatic SAR Configurations
Authors: Sun Huarui , Sun Zhichao , Chen Tianfu , Miao Yuxuan , Wu Junjie , Yang Jianyu
Published: April 2022

This paper introduces a fast backprojection synthetic aperture radar (SAR) imaging algorithm based on wavenumber-domain spectral splicing. The traditional fast backprojection (FBP) algorithm establishes the polar coordinate system with the center of the sub-aperture as the origin. Therefore, the coordinates of the image obtained from each sub-aperture are different. Sub-aperture images must be projected to a uniform coordinate system before they can be coherently superimposed to form the final image, which requires a large amount of calculation. In order to deal with this problem, this paper proposes a novel imaging method, which uses the same polar coordinate system for each sub-aperture. The sub-aperture images are then spliced in the wavenumber-domain, and directly added after upsampling. This method avoids the projection from each sub-aperture to the uniform coordinate system, thus improving the imaging accuracy and efficiency. At the same time, the algorithm is suitable for various configurations, and can achieve good imaging results for bistatic forward-looking SAR and high-speed mobile platform. Finally, simulations are presented to demonstrate the effectiveness of the algorithm.
The Cross-Border Transport of PM2.5 from the Southeast Asian Biomass Burning Emissions and Its Impact on Air Pollution in Yunnan Plateau, Southwest China
Authors: Yang Qingjian , Zhao Tianliang , Tian Zhijie , Kumar Kanike Raghavendra , Chang Jiacheng , Hu Weiyang , Shu Zhuozhi , Hu Jun
Published: April 2022

Southeast Asia is one of the largest biomass burning (BB) regions in the world, and the air pollutants generated by this BB have an important impact on air pollution in southern China. However, the mechanism of the cross-border transport of BB pollutants to neighboring regions is yet to be understood. Based on the MODIS remote sensing products and conventional observation data of meteorology and the environment, the WRF-Chem and FLEXPART-WRF models were used to simulate a typical PM2.5 pollution episode that occurred during 24–26 March 2017 to analyze the mechanism of cross-border transport of BB pollutants over Yunnan Plateau (YP) in southwest China. During this air pollution episode, in conjunction with the flourishing BB activities over the neighboring Indo-China Peninsula (ICP) regions in Southeast Asia, and driven by the southwesterly winds prevailing from the ICP to YP, the cross-border transport of pollutants was observed along the transport pathway with the lifting plateau topography in YP. Based on the proximity to the BB sources in ICP, YP was divided into a source region (SR) and a receptor region (RR) for the cross-border transport, and the negative and positive correlation coefficients (R) between PM2.5 concentrations and wind speeds, respectively, were presented, indicating the different impacts of BB emissions on the two regions. XSBN and Kunming, the representative SR and RR sites in the border and hinterland of YP, respectively, have distinct mechanisms that enhance PM2.5 concentrations of air pollution. The SR site is mainly affected by the ICP BB emissions with local accumulation in the stagnant meteorological conditions, whereas the RR site is dominated by the regional transport of PM2.5 with strong winds and vertical mixing. It was revealed that the large PM2.5 contributions of ICP BB emissions lift from the lower altitudes in SR to the higher altitudes in RR for the regional transport of PM2.5. Moreover, the contributions of regional transport of PM2.5 decrease with the increase in transport distance, reflecting an important role of transport distance between the source–receptor areas in air pollution change.
Numerical Analysis of Putative Rock Glaciers on Mount Sharp, Gale Crater, Mars
Authors: Williams Joshua M. , Scuderi Louis A. , Newsom Horton E.
Published: April 2022

The presence of buried glacial ice and putative extinct rock glaciers in Mars’ equatorial regions has implications for understanding its climate history and sensitivity to changes in insolation and has significant implications for past global redistribution of the water ice cryosphere. We quantify the morphology of rock glacier- “like” features on the northern slopes of Aeolis Mons (known also as Mount Sharp) within Gale crater and use this information to evaluate a possible rock glacier origin for these forms. Detailed morphometric evaluation of cross and long profiles of these lobate features, which exhibit higher slopes at their heads, lower slopes at their distal edge, and a convex upward cross-sectional profile and oversteepened sides, resembles active terrestrial rock glaciers. However, the absence of a chevron wrinkle pattern and sublimation features could indicate extensive aeolian reworking and the lack of deflation could indicate a higher rock to ice mixture. The lack of cratering surfaces relative to the cratered surfaces that they overly could indicate a younger age and are possibly indistinguishable in age from the capping units of Mount Sharp, which may have once been more laterally extensive and may have been the source of these mass wasting forms.
Scalable Artificial Intelligence for Earth Observation Data Using Hopsworks
Authors: Hagos Desta Haileselassie , Kakantousis Theofilos , Sheikholeslami Sina , Wang Tianze , Vlassov Vladimir , Payberah Amir Hossein , Meister Moritz , Andersson Robin , Dowling Jim
Published: April 2022

This paper introduces the Hopsworks platform to the entire Earth Observation (EO) data community and the Copernicus programme. Hopsworks is a scalable data-intensive open-source Artificial Intelligence (AI) platform that was jointly developed by Logical Clocks and the KTH Royal Institute of Technology for building end-to-end Machine Learning (ML)/Deep Learning (DL) pipelines for EO data. It provides the full stack of services needed to manage the entire life cycle of data in ML. In particular, Hopsworks supports the development of horizontally scalable DL applications in notebooks and the operation of workflows to support those applications, including parallel data processing, model training, and model deployment at scale. To the best of our knowledge, this is the first work that demonstrates the services and features of the Hopsworks platform, which provide users with the means to build scalable end-to-end ML/DL pipelines for EO data, as well as support for the discovery and search for EO metadata. This paper serves as a demonstration and walkthrough of the stages of building a production-level model that includes data ingestion, data preparation, feature extraction, model training, model serving, and monitoring. To this end, we provide a practical example that demonstrates the aforementioned stages with real-world EO data and includes source code that implements the functionality of the platform. We also perform an experimental evaluation of two frameworks built on top of Hopsworks, namely Maggy and AutoAblation. We show that using Maggy for hyperparameter tuning results in roughly half the wall-clock time required to execute the same number of hyperparameter tuning trials using Spark while providing linear scalability as more workers are added. Furthermore, we demonstrate how AutoAblation facilitates the definition of ablation studies and enables the asynchronous parallel execution of ablation trials.
Ecological Impacts of Land Use Change in the Arid Tarim River Basin of China
Authors: Hou Yifeng , Chen Yaning , Ding Jianli , Li Zhi , Li Yupeng , Sun Fan
Published: April 2022

Land use/cover change has become an indispensable part of global eco-environmental change research. The Tarim River Basin is the largest inland river basin in China. It is also one of the most ecologically fragile areas in the country, with greening and desertification processes coexisting. This paper analyzes the evolution of land-use/cover change in the Tarim River Basin over the past 30 years based on remote sensing data. The research also explores the contribution of conversion between different land types to the ecological environment by selecting methods, such as transfer matrix and ecological contribution rate. Results indicate that grassland and barren land are the main land types in the region, accounting for 72.46% and 18.87% of the basin area, respectively. From 1990 to 2019, cropland area increased from 33,585.89 km2 to 52,436.40 km2, an increase of 56.13%, while barren land areas decreased from 781,380.57 km2 to 760,783.29 km2. Most of the land-use conversion was grassland to other land types and other land types to barren land. Since 1990, the conversion of barren land to grassland and cropland in the basin has led to ecological improvement, whereas the conversion of grassland to cropland has caused deterioration, but with a generally improving trend. It is anticipated that, over the next decade, changes in land types will involve increases in grassland and woodland area, decreases in barren land and cropland, and an overall improvement in the ecological environment in the watershed. Since agriculture and animal husbandry are the main industries in the Tarim River Basin and the land-use structure is dominated by cropland and grassland, several key measures should be implemented. These include improving land use, rationalizing the use of water and soil resources, slowing down the expansion of cropland, and alleviating the contradiction between humans and land, with the ultimate aim of achieving sustainable development of the social economy and ecological environment.
A Self-Regulating Multi-Clutter Suppression Framework for Small Aperture HFSWR Systems
Authors: Ji Xiaowei , Yang Qiang , Wang Linwei
Published: April 2022

The problem that this paper is concerned with is High Frequency Surface Wave Radar (HFSWR) detection of desired targets against a complex interference background consisting of sea clutter, ionosphere clutter, Radio Frequency Interference (RFI) and atmospheric noise. Eliminating unwanted echoes and exploring obscured targets contribute to achieving ideal surveillance of sea surface targets. In this paper, a Self-regulating Multi-clutter Suppression Framework (SMSF) has been proposed for small aperture HFSWR. SMSF can remove many types of clutter or RFI; meanwhile, it mines the targets merged into clutter and tracks the travelling path of the ship. In SMSF, a novel Dynamic Threshold Mapping Recognition (DTMR) method is first proposed to reduce the atmospheric noise and recognize each type of unwanted echo; these recognized echoes are fed into the proposed Adaptive Prophase-current Dictionary Learning (APDL) algorithm. To make a comprehensive evaluation, we also designed three novel assessment parameters: Obscured Targets Detection Rate (OTDR), Clutter Purification Rate (CPR) and Erroneous Suppression Rate (ESR). The experiment data collected from a small aperture HFSWR system confirm that SMSF has precise suppression performance over most of the classical algorithms and concurrently reveals the moving targets, and OTDR of SMSF is usually higher than compared methods.
A Simple Deposition Model for Debris Flow Simulation Considering the Erosion–Entrainment–Deposition Process
Authors: Lee Seungjun , An Hyunuk , Kim Minseok , Lim Hyuntaek , Kim Yongseong
Published: April 2022

This study aimed to determine the depositional effect and improve the identification of debris flow risk zones. To accomplish this goal, we developed a two-dimensional debris flow model (Deb2D) based on a hyperbolic conservation form of the mass and the momentum balance equation with consideration of the erosion–entrainment effect as well as the depositional effect. In this model, we implemented a widely-used rheological equation—the Voellmy equation—and a quadtree adaptive grid-based shallow-water equation. This model was applied to two study sites to assess the depositional effect. The impact area, volume of soil loss, maximum velocity, inundated depth, and erosion depth resulting from the debris-flow modeling were compared with the field data. The simulation results with/without deposition were evaluated using the receiver operating characteristic method. The implementation results of the erosion–entrainment model with deposition showed superior accuracy when estimating the damage range and flow time.
Weighted Mean Temperature Modelling Using Regional Radiosonde Observations for the Yangtze River Delta Region in China
Authors: Li Li , Li Yuan , He Qimin , Wang Xiaoming
Published: April 2022

Precipitable water vapor can be estimated from the Global Navigation Satellite System (GNSS) signal’s zenith wet delay (ZWD) by multiplying a conversion factor, which is a function of weighted mean temperature (Tm) over the GNSS station. Obtaining Tm is an important step in GNSS precipitable water vapor (PWV) conversion. In this study, aiming at the problem that Tm is affected by space and time, observations from seven radiosonde stations in the Yangtze River Delta region of China during 2015−2016 were used to establish both linear and nonlinear multifactor regional Tm model (RTM). Compared with the Bevis model, the results showed that the bias of yearly one-factor RTM, two-factor RTM and three-factor RTM was reduced by 0.55 K, 0.68 K and 0.69 K, respectively. Meanwhile, the RMSE of yearly one-factor, two-factor and three-factor RTM was reduced by 0.56 K, 0.80 K and 0.83 K, respectively. Compared with the yearly three-factor linear RTM, the mean bias and RMSE of the linear seasonal three-factor RTMs decreased by 0.06 K and 0.10 K, respectively. The precision of nonlinear seasonal three-factor RTMs is comparable to linear seasonal three-factor RTMs, but the expressions of the linear RTMs are easier to use. Therefore, linear seasonal three-factor RTMs are more suitable for calculating Tm and are recommended to use for PWV conversion in the Yangtze River Delta region.
Estimation of Daily and Instantaneous Near-Surface Air Temperature from MODIS Data Using Machine Learning Methods in the Jingjinji Area of China
Authors: Wang Chunling , Bi Xu , Luan Qingzu , Li Zhanqing
Published: April 2022

Meteorologically observed air temperature (Ta) is limited due to low density and uneven distribution that leads to uncertain accuracy. Therefore, remote sensing data have been widely used to estimate near-surface Ta on various temporal scales due to their spatially continuous characteristics. However, few studies have focused on instantaneous Ta when satellites overpass. This study aims to produce both daily and instantaneous Ta datasets at 1 km resolution for the Jingjinji area, China during 2018–2019, using machine learning methods based on remote sensing data, dense meteorological observation station data, and auxiliary data (such as elevation and normalized difference vegetation index). Newly released Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 surface Downward Shortwave Radiation (DSR) was introduced to improve the accuracy of Ta estimation. Five machine learning algorithms were implemented and compared so that the optimal one could be selected. The random forest (RF) algorithm outperformed the others (such as decision tree, feedforward neural network, generalized linear model) and RF obtained the highest accuracy in model validation with a daily root mean square error (RMSE) of 1.29 °C, mean absolute error (MAE) of 0.94 °C, daytime instantaneous RMSE of 1.88 °C, MAE of 1.35 °C, nighttime instantaneous RMSE of 2.47 °C, and MAE of 1.83 °C. The corresponding R2 was 0.99 for daily average, 0.98 for daytime instantaneous, and 0.95 for nighttime instantaneous. Analysis showed that land surface temperature (LST) was the most important factor contributing to model accuracy, followed by solar declination and DSR, which implied that DSR should be prioritized when estimating Ta. Particularly, these results outperformed most models presented in previous studies. These findings suggested that RF could be used to estimate daily instantaneous Ta at unprecedented accuracy and temporal scale with proper training and very dense station data. The estimated dataset could be very useful for local climate and ecology studies, as well as for nature resources exploration.
Response of Ecohydrological Variables to Meteorological Drought under Climate Change
Authors: Zhang Yuan , Fu Bojie , Feng Xiaoming , Pan Naiqing
Published: April 2022

Drought is the most widespread climatic extreme that has negative impacts on ecohydrology. Studies have shown that drought can cause certain degrees of disturbances to different ecohydrological variables, but the duration and severity thresholds of drought that are sufficient to cause changes in ecohydrological variables remain largely unknown. At the same time, we should not ignore the dynamic variation of drought’s effect on ecohydrological variables under the condition of climate change. Here, we derived the thresholds of several ecohydrological variables in response to drought in a historical period (1982–2015), including evapotranspiration (ET), soil moisture (SM), the vapor pressure deficit (VPD) and the normalized difference vegetation index (NDVI), and we projected the occurrence probability’s change trend of drought events that cause changes in ecohydrological variables under future climate change. The results show that the impact of drought on ecohydrological variables is not dependent on drought indicators. ET and NDVI were expected to decrease in most parts of the world due to increases in radiation (RAD) and temperature (TEMP) and decreases in precipitation (PRE) during drought periods. SM decreased in most regions of the world (93.47%) during the drought period, while VPD increased in 85.41% of the globe. The response thresholds for different ecohydrological variables to drought in the same area did not differ significantly (especially for ET, SM and VPD). When a drought lasted for 8 to 15 months and the corresponding drought severity reached 10 to 15 (the inverse of the cumulative values of the drought index when the drought occurs), the drought caused changes in the ecohydrological variables in most regions of the world. Compared with arid and semiarid regions, ecohydrological variables are more sensitive to drought in humid and semihumid regions (p < 0.05), and high-intensity human activities in different climatic conditions increased significantly the severity of drought processes. Between 2071 and 2100, more than half of the world’s ecohydrological variables are expected to be more susceptible to drought disturbances (regions with shorter return periods of drought events that cause significant changes in ET, SM, VPD and NDVI account for 60.1%, 64.4%, 59.6% and 54.5% of the global land area, respectively).
Accuracy and Precision of Stem Cross-Section Modeling in 3D Point Clouds from TLS and Caliper Measurements for Basal Area Estimation
Authors: Witzmann Sarah , Matitz Laura , Gollob Christoph , Ritter Tim , Kraßnitzer Ralf , Tockner Andreas , Stampfer Karl , Nothdurft Arne
Published: April 2022

The utilization of terrestrial laser scanning (TLS) data for forest inventory purposes has increasingly gained recognition in the past two decades. Volume estimates from TLS data are usually derived from the integral of cross-section area estimates along the stem axis. The purpose of this study was to compare the performance of circle, ellipse, and spline fits applied to cross-section area modeling, and to evaluate the influence of different modeling parameters on the cross-section area estimation. For this purpose, 20 trees were scanned with FARO Focus3D X330 and afterward felled to collect stem disks at different heights. The contours of the disks were digitized under in vitro laboratory conditions to provide reference data for the evaluation of the in situ TLS-based cross-section modeling. The results showed that the spline model fit achieved the most precise and accurate estimate of the cross-section area when compared to the reference cross-section area (RMSD (Root Mean Square Deviation) and bias of only 3.66% and 0.17%, respectively) and was able to exactly represent the shape of the stem disk (ratio between intersection and union of modeled and reference cross-section area of 88.69%). In comparison, contour fits with ellipses and circles yielded higher RMSD (5.28% and 10.08%, respectively) and bias (1.96% and 3.27%, respectively). The circle fit proved to be especially robust with respect to varying parameter settings, but provided exact estimates only for regular-shaped stem disks, such as those from the upper parts of the stem. Spline-based models of the cross-section at breast height were further used to examine the influence of caliper orientation on the volume estimation. Simulated caliper measures of the DBH showed an RMSD of 3.99% and a bias of 1.73% when compared to the reference DBH, which was calculated via the reference cross-section area, resulting in biased estimates of basal area and volume. DBH estimates obtained by simulated cross-calipering showed statistically significant deviations from the reference. The findings cast doubt on the customary utilization of manually calipered diameters as reference data when evaluating the accuracy of TLS data, as TLS-based estimates have reached an accuracy level surpassing traditional caliper measures.
Laboratory Heat Flux Estimates of Seawater Foam for Low Wind Speeds
Authors: Chickadel C. Chris , Branch Ruth , Asher William E. , Jessup Andrew T.
Published: April 2022

Laboratory experiments were conducted to measure the heat flux from seafoam continuously generated in natural seawater. Using a control volume technique, heat flux was calculated from foam and foam-free surfaces as a function of ambient humidity (ranged from 40% to 78%), air–water temperature difference (ranged from −9 °C to 0 °C), and wind speed (variable up to 3 m s−1). Water-surface skin temperature was imaged with a calibrated thermal infrared camera, and near-surface temperature profiles in the air, water, and foam were recorded. Net heat flux from foam surfaces increased with increasing wind speed and was shown to be up to four times greater than a foam-free surface. The fraction of the total heat flux due to the latent heat flux was observed for foam to be 0.75, with this value being relatively constant with wind speed. In contrast, for a foam-free surface the fraction of the total heat flux due to the latent heat flux decreased at higher wind speeds. Temperature profiles through foam are linear and have larger gradients, which increased with wind speed, while foam free surfaces show the expected logarithmic profile and show no variation with temperature. The radiometric surface temperatures show that foam is cooler and more variable than a foam-free surface, and bubble-resolving thermal images show that radiometrically transparent bubble caps and burst bubbles reveal warm foam below the cool surface layer, contributing to the enhanced variability.
Approaches for Joint Retrieval of Wind Speed and Significant Wave Height and Further Improvement for Tiangong-2 Interferometric Imaging Radar Altimeter
Authors: Li Guo , Zhang Yunhua , Dong Xiao
Published: April 2022

The interferometric imaging radar altimeter (InIRA) adopts a short baseline along with small incidence angles to acquire interferometric signals from the sea surface with high accuracy, thus the wide-swath sea surface height (SSH) and backscattering coefficient (σ0) can be obtained simultaneously. This work presents an approach to jointly retrieve the wind speed and significant wave height (SWH) for the Chinese Tiangong-2 interferometric imaging radar altimeter (TG2-InIRA). This approach utilizes a multilayer perceptron (MLP) joint retrieval model based on σ0 and SSH data. By comparing with the European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data, the root mean square errors (RMSEs) of the retrieved wind speed and the SWH are 1.27 m/s and 0.36 m, respectively. Based on the retrieved SWH, two enhanced wind speed retrieval models are developed for high sea states and low sea states, respectively. The results show that the RMSE of the retrieved wind speed is 1.12 m/s when the SWHs < 4 m; the RMSE is 0.73 m/s when the SWHs ≥ 4 m. Similarly, two enhanced SWH retrieval models for relatively larger and relatively smaller wind speed regions are developed based on the retrieved wind speed with corresponding RMSEs of 0.19 m and 0.16 m, respectively. The comparison between the retrieved results and the buoy data shows that they are highly consistent. The results show that the additional information of SWH can be used to improve the accuracy of wind speed retrieval at small incidence angles, and also the additional information of wind speed can be used to improve the SWH retrieval. The stronger the correlation between wind speed and SWH, the greater the improvement of the retrieved results. The proposed method can achieve joint retrieval of wind speed and SWH accurately, which complements the existing wind speed and SWH retrieval methods for InIRA.
A Camera-Based Method for Collecting Rapid Vegetation Data to Support Remote-Sensing Studies of Shrubland Biodiversity
Authors: Questad Erin J. , Antill Marlee , Liu Nanfeng , Stavros E. Natasha , Townsend Philip A. , Bonfield Susan , Schimel David
Published: April 2022

The decline in biodiversity in Mediterranean-type ecosystems (MTEs) and other shrublands underscores the importance of understanding the trends in species loss through consistent vegetation mapping over broad spatial and temporal ranges, which is increasingly accomplished with optical remote sensing (imaging spectroscopy). Airborne missions planned by the National Aeronautics and Space Administration (NASA) and other groups (e.g., US National Ecological Observatory Network, NEON) are essential for improving high-quality maps of vegetation and plant species. These surveys require robust and efficient ground calibration/validation data; however, barriers to ground-data collection exist, such as steep terrain, which is a common feature of Mediterranean-type ecosystems. We developed and tested a method for rapidly collecting ground-truth data for shrubland plant communities across steep topographic gradients in southern California. Our method utilizes semi-aerial photos taken with a high-resolution digital camera mounted on a telescoping pole to capture groundcover, and a point-intercept image-classification program (Photogrid) that allows efficient sub-sampling of field images to derive vegetation percent-cover estimates while reducing human bias. Here, we assessed the quality of data collection using the image-based method compared to a traditional point-intercept ground survey and performed time trials to compare the efficiency of various survey efforts. The results showed no significant difference in estimates of percent cover and Simpson’s diversity derived from the point-intercept and those derived using the image-based method; however, there was lower correspondence in estimates of species richness and evenness. The image-based method was overall more efficient than the point-intercept surveys, reducing the total survey time by 13 to 46 min per plot depending on sampling effort. The difference in survey time between the two methods became increasingly greater when the vegetation height was above 1 m. Due to the high correspondence between estimates of species percent cover derived from the image-based compared to the point-intercept method, we recommend this type of survey for the verification of remote-sensing datasets featuring percent cover of individual species or closely related plant groups, for use in classifying UAS imagery, and especially for use in MTEs that have steep, rugged terrain and other situations such as tall, dense-growing shrubs where traditional field methods are dangerous or burdensome.
Fourfold Bounce Scattering-Based Reconstruction of Building Backs Using Airborne Array TomoSAR Point Clouds
Authors: Li Xiaowan , Zhang Fubo , Liang Xingdong , Li Yanlei , Guo Qichang , Wan Yangliang , Bu Xiangxi , Liu Yunlong
Published: April 2022

Building reconstruction using high-resolution tomographic synthetic aperture radar (TomoSAR) point clouds has been very attractive in numerous applications, such as urban planning and dynamic city modeling. However, for side-looking TomoSAR, it is a challenge to reconstruct the obscured backs of buildings using traditional single-bounce scattering-based methods. It comes to our attention that the higher-order scattering points in airborne array TomoSAR point clouds may provide rich information on the backs of buildings. In this paper, the fourfold bounce (FB) scattering model of combined buildings in airborne array TomoSAR is derived, which not only explains the cause of FB scattering but also gives the distribution pattern of FB scattering points. Furthermore, a novel FB scattering-based method for the reconstruction of building backs is proposed. First, a two-step geometric constraint is used to detect the candidate FB scattering points. Subsequently, the FB scattering points are further detected by seed point selection and density estimation in the radar coordinate system. Finally, the backs of buildings can be reconstructed using the footprint inverted from the FB scattering points and the height information of the illuminated facades. To verify the FB scattering model and the effectiveness of the proposed method, the results from the simulated point clouds and the real airborne array TomoSAR point clouds are presented. Compared with the traditional roof point-based methods, the outstanding advantage of the proposed method is that it allows for the high-precision reconstruction of building backs, even in the case of poor roof points. Moreover, this paper may provide a novel perspective for the three-dimensional (3D) reconstruction of dense urban areas.
Editorial for the Special Issue: “3D Virtual Reconstruction for Cultural Heritage”
Authors: Gonizzi Barsanti Sara
Published: April 2022

The use of 3D modelling, computer-aided design (CAD), augmented reality (AR) and virtual reality (VR) for the acquisition and virtual reconstruction of Cultural Heritage is of great importance in the analysis, study, documentation and dissemination of the past [...]
Feedback Refined Local-Global Network for Super-Resolution of Hyperspectral Imagery
Authors: Tang Zhenjie , Xu Qing , Wu Pengfei , Shi Zhenwei , Pan Bin
Published: April 2022

Powered by advanced deep-learning technology, multi-spectral image super-resolution methods based on convolutional neural networks have recently achieved great progress. However, the single hyperspectral image super-resolution remains a challenging problem due to the high-dimensional and complex spectral characteristics of hyperspectral data, which make it difficult for general 2D convolutional neural networks to simultaneously capture spatial and spectral prior information. To deal with this issue, we propose a novel Feedback Refined Local-Global Network (FRLGN) for the super-resolution of hyperspectral image. To be specific, we develop a new Feedback Structure and a Local-Global Spectral block to alleviate the difficulty in spatial and spectral feature extraction. The Feedback Structure can transfer the high-level information to guide the generation process of low-level features, which is achieved by a recurrent structure with finite unfoldings. Furthermore, in order to effectively use the high-level information passed back, a Local-Global Spectral block is constructed to handle the feedback connections. The Local-Global Spectral block utilizes the feedback high-level information to correct the low-level feature from local spectral bands and generates powerful high-level representations among global spectral bands. By incorporating the Feedback Structure and Local-Global Spectral block, the FRLGN can fully exploit spatial-spectral correlations among spectral bands and gradually reconstruct high-resolution hyperspectral images. Experimental results indicate that FRLGN presents advantages on three public hyperspectral datasets.
Characteristics of Precipitation and Floods during Typhoons in Guangdong Province
Authors: Yan Yan , Wang Guihua , Wu Huan , Gu Guojun , Nanding Nergui
Published: April 2022

The spatial and temporal characteristics of precipitation and floods during typhoons in Guangdong province were examined by using TRMM TMPA 3B42 precipitation data and the Dominant River Routing Integrated with VIC Environment (DRIVE) model outputs for the period 1998–2019. The evaluations based on gauge-measured and model-simulated streamflow show the reliability of the DRIVE model. The typhoon tracks are divided into five categories for those that landed on or influenced Guangdong province. Generally, the spatial distribution of precipitation and floods differ for different typhoon tracks. Precipitation has a similar spatial distribution to flood duration (FD) but is substantially different from flood intensity (FI). The average precipitation over Guangdong province usually reaches its peak at the landing time of typhoons, while the average FD and FI reach their peaks several hours later than precipitation peak. The lagged correlations between precipitation and FD/FI are hence always higher than their simultaneous correlations.
Large-Scale Detection of the Tableland Areas and Erosion-Vulnerable Hotspots on the Chinese Loess Plateau
Authors: Liu Kai , Na Jiaming , Fan Chenyu , Huang Ying , Ding Hu , Wang Zhe , Tang Guoan , Song Chunqiao
Published: April 2022

Tableland areas, featured by flat and broad landforms, provide precious land resources for agricultural production and human settlements over the Chinese Loess Plateau (CLP). However, severe gully erosion triggered by extreme rainfall and intense human activities makes tableland areas shrink continuously. Preventing the loss of tableland areas is of real urgency, in which generating its accurate distribution map is the critical prerequisite. However, a plateau-scale inventory of tableland areas is still lacking across the Loess Plateau. This study proposed a large-scale approach for tableland area mapping. The Sentinel-2 imagery was used for the initial delineation based on object-based image analysis and random forest model. Subsequently, the drainage networks extracted from AW3D30 DEM were applied for correcting commission and omission errors based on the law that rivers and streams rarely appear on the tableland areas. The automatic mapping approach performs well, with the overall accuracies over 90% in all four investigated subregions. After the strict quality control by manual inspection, a high-quality inventory of tableland areas at 10 m resolution was generated, demonstrating that the tableland areas occupied 9507.31 km2 across the CLP. Cultivated land is the dominant land-use type on the tableland areas, yet multi-temporal observations indicated that it has decreased by approximately 500 km2 during the year of 2000 to 2020. In contrast, forest and artificial surfaces increased by 57.53% and 73.10%, respectively. Additionally, we detected 455 vulnerable hotspots of the tableland with a width of less than 300 m. Particular attention should be paid to these areas to prevent the potential split of a large tableland, accompanied by damage on roads and buildings. This plateau-scale tableland inventory and erosion-vulnerable hotspots are expected to support the environmental protection policymaking for sustainable development in the CLP region severely threatened by soil erosion and land degradation.
Global Mapping of Soil Water Characteristics Parameters— Fusing Curated Data with Machine Learning and Environmental Covariates
Authors: Gupta Surya , Papritz Andreas , Lehmann Peter , Hengl Tomislav , Bonetti Sara , Or Dani
Published: April 2022

Hydrological and climatic modeling of near-surface water and energy fluxes is critically dependent on the availability of soil hydraulic parameters. Key among these parameters is the soil water characteristic curve (SWCC), a function relating soil water content (θ) to matric potential (ψ). The direct measurement of SWCC is laborious, hence, reported values of SWCC are spatially sparse and usually have only a small number of data pairs (θ, ψ) per sample. Pedotransfer function (PTF) models have been used to correlate SWCC with basic soil properties, but evidence suggests that SWCC is also shaped by vegetation-promoted soil structure and climate-modified clay minerals. To capture these effects in their spatial context, a machine learning framework (denoted as Covariate-based GeoTransfer Functions, CoGTFs) was trained using (a) a novel and comprehensive global dataset of SWCC parameters and (b) global maps of environmental covariates and soil properties at 1 km spatial resolution. Two CoGTF models were developed: one model (CoGTF-1) was based on predicted soil covariates because measured soil data are not generally available, and the other (CoGTF-2) used measured soil properties to model SWCC parameters. The spatial cross-validation of CoGTF-1 resulted, for the predicted van Genuchten SWCC parameters, in concordance correlation coefficients (CCC) of 0.321–0.565. To validate the resulting global maps of SWCC parameters and to compare the CoGTF framework to two pedotransfer functions from the literature, the predicted water contents at 0.1 m, 3.3 m, and 150 m matric potential were evaluated. The accuracy metrics for CoGTF were considerably better than PTF-based maps.
An Adaptive Focal Loss Function Based on Transfer Learning for Few-Shot Radar Signal Intra-Pulse Modulation Classification
Authors: Jing Zehuan , Li Peng , Wu Bin , Yuan Shibo , Chen Yingchao
Published: April 2022

To solve the difficulty associated with radar signal classification in the case of few-shot signals, we propose an adaptive focus loss algorithm based on transfer learning. Firstly, we trained a one-dimensional convolutional neural network (CNN) with radar signals of three intra-pulse modulation types in the source domain, which were effortlessly obtained and had sufficient samples. Then, we transferred the knowledge obtained by the convolutional layer to nine types of few-shot complex intra-pulse modulation classification tasks in the target domain. We propose an adaptive focal loss function based on the focal loss function, which can estimate the parameters based on the ratio of hard samples to easy samples in the data set. Compared with other existing algorithms, our proposed algorithm makes good use of transfer learning to transfer the acquired prior knowledge to new domains, allowing the CNN model to converge quickly and achieve good recognition performance in case of insufficient samples. The improvement based on the focal loss function allows the model to focus on the hard samples while estimating the focusing parameter adaptively instead of tediously repeating experiments. The experimental results show that the proposed algorithm had the best recognition rate at different sample sizes with an average recognition rate improvement of 4.8%, and the average recognition rate was better than 90% for different signal-to-noise ratios (SNRs). In addition, upon comparing the training processes of different models, the proposed method could converge with the least number of generations and the shortest time under the same experimental conditions.
Hyperspectral Image Classification Based on Spectral Multiscale Convolutional Neural Network
Authors: Shi Cuiping , Sun Jingwei , Wang Liguo
Published: April 2022

In recent years, convolutional neural networks (CNNs) have been widely used for hyperspectral image classification, which show good performance. Compared with using sufficient training samples for classification, the classification accuracy of hyperspectral images is easily affected by a small number of samples. Moreover, although CNNs can effectively classify hyperspectral images, due to the rich spatial and spectral information of hyperspectral images, the efficiency of feature extraction still needs to be further improved. In order to solve these problems, a spatial–spectral attention fusion network using four branch multiscale block (FBMB) to extract spectral features and 3D-Softpool to extract spatial features is proposed. The network consists of three main parts. These three parts are connected in turn to fully extract the features of hyperspectral images. In the first part, four different branches are used to fully extract spectral features. The convolution kernel size of each branch is different. Spectral attention block is adopted behind each branch. In the second part, the spectral features are reused through dense connection blocks, and then the spectral attention module is utilized to refine the extracted spectral features. In the third part, it mainly extracts spatial features. The DenseNet module and spatial attention block jointly extract spatial features. The spatial features are fused with the previously extracted spectral features. Experiments are carried out on four commonly used hyperspectral data sets. The experimental results show that the proposed method has better classification performance than some existing classification methods when using a small number of training samples.
Discussion on InSAR Identification Effectivity of Potential Landslides and Factors That Influence the Effectivity
Authors: Liang Jingtao , Dong Jihong , Zhang Su , Zhao Cong , Liu Bin , Yang Lei , Yan Shengwu , Ma Xiaobo
Published: April 2022

The southwest mountainous area of China is one of the areas with the most landslides in the world. In this paper, we used Ya’an City and Garzê Tibetan Autonomous Prefecture in Sichuan Province as the research areas to explore the identification application effects of large-area potential landslides using synthetic aperture radar (SAR) data with different wavelength types (Sentinel-1, ALOS-2), different processing methods (SBAS-InSAR, Stacking-InSAR), and different geological environmental conditions. The results show the following: (1) The effect of identifying landslides with different slope directions is largely affected by the satellite orbit direction; when we identify landslide hazards across a large area, the joint monitoring mode of ascending and descending orbit data is required. (2) The period of monitoring affects the identification effect of potential landslides when landslide identification is carried out in southwestern China; the InSAR monitoring period is recommended to be more than 2 years. (3) In different geological environmental regions, SBAS technology and Stacking technology have their own advantages; Stacking technology identifies more potential landslides, and SBAS technology identifies potential landslides with higher accuracy; (4) the degree of vegetation coverage has a great impact on the landslide identification effect of different SAR data sources. In low-density vegetation coverage areas, the landslide identification result using Sentinel-1 data seems to be better than the result using ALOS-2 data. In high-density vegetation coverage areas, the landslide identification result using ALOS-2 data is better than that using Sentinel-1 data.