bluuebunny/crossref_metadata_2025 huggingface dataset:
Religion and Public Conflict in the Post-COVID Era: The Case of Protestant Churches in South Korea
Authors: Lee Saehwan , Oh Seil
Published: October 2021

Governments have attempted to contain the COVID-19 outbreak with a variety of regulations, including social distancing, facemask mandates, or limits on gatherings. South Korea was concerned by the “supercluster” case of a sectarian religious organization in February 2020. Since then, some Protestant churches have periodically caused cluster infections showing antagonism against health authorities. First, we traced all 2020 cluster cases and identified their denominational characteristics. We then utilized the 2020 CISJD data and conducted a series of multivariate regressions to answer the research question, “What causes differences among denominations in attitudes toward public disease control and in-person service attendance?” Results indicated that Protestants affiliated with liberal churches were more likely to follow public disease control guidelines and less likely to attend in-person religious services during the COVID-19 pandemic as compared with individuals from other denominations. Protestants affiliated with moderate, conservative, and fundamentalist churches tended to share antagonism toward public disease control, while cherishing in-person community rituals. This research highlights social implications of public conflict in Korea, where many Protestant churches have emphasized the significance of traditional worship services, claiming the constitutional right of religious freedom, while the majority of citizens, religious and non-religious, disagree with such exclusive claims against public safety.
The Workaholism Phenomenon in Portugal: Dimensions and Relations with Workplace Spirituality
Authors: Mónico Lisete S. , Margaça Clara
Published: October 2021

Workaholism phenomenon affects a quarter of the employed world population. The concept has been used to describe hardworking employees, which is not resulting from external requirements. Considering that organizations with well-developed workplace spirituality have employees more committed to achieving self-development, but also to serve the company, the relationship between workaholism and workplace spirituality is not straightforward, remaining unclear. The principal aim of this research is to analyze the workaholism phenomenon, considering patterns of workaholic and non-workaholic workers and their relationships with dimensions of workplace spirituality. The sample is comprised of a heterogeneous group of 306 Portuguese employees, who were surveyed by the Workaholism Battery, five dimensions of Workplace Spirituality, and a socio-demographic questionnaire. Cluster analysis defined three workaholic profiles (24% of the sample), and five non-workaholic profiles. Workplace spirituality dimensions differed according to worker profile and associations with work involvement, work enjoyment, and compulsive work addiction. Enthusiastic addicts and work enthusiasts showed the highest workplace spirituality, contrasting mainly with Reluctant hard worker, Disenchanted workers, and Unengaged workers, but also with work addicts. Workaholism is a complex and multidimensional phenomenon, whose dimensions are distinctly related to workplace spirituality. Workplace spirituality development can promote a more balanced and healthy relationship with work.
Exploitation of Mineral Resources Requires Proper People: Expectations of the World’s Top Mining Companies
Authors: Ruban Dmitry A. , Ermolaev Vladimir A. , van Loon Antonius J. (Tom)
Published: October 2021

Human resources are essential for the mining industry. It is important to understand the requirements of the companies regarding the characteristics of their employees. Job- and career-related web pages of 40 of the world’s top mining companies have been analyzed for the general demands on their employees. These demands are attributed intuitively to seven basic categories (each category includes words with more or less coherent meanings). It is found that the most commonly demanded characteristics are skills, dedication to the work, and personality. Surprisingly, the least attention is paid to social issues. The disclosed expectations are weakly coherent, with the only persistent demand being talent. It also appears that North American and European companies are especially concerned of work dedication, whereas Asian and African companies are primarily concerned of skills. The present study deduces that policy improvements in contemporary mining are necessary—at least, the demands on the employees should be made better in agreement with the actual needs of the mining industry.
Precise Point Positioning with Almost Fully Deployed BDS-3, BDS-2, GPS, GLONASS, Galileo and QZSS Using Precise Products from Different Analysis Centers
Authors: Li Xuanping , Pan Lin
Published: October 2021

The space segment of all the five satellite systems capable of providing precise position services, namely BeiDou Navigation Satellite System (BDS) (including BDS-3 and BDS-2), Global Positioning System (GPS), GLObal NAvigation Satellite System (GLONASS), Galileo and Quasi-Zenith Satellite System (QZSS), has almost been fully deployed at present, and the number of available satellites is approximately 136. Currently, the precise satellite orbit and clock products from the analysis centers European Space Agency (ESA), GeoForschungsZentrum Potsdam (GFZ) and Wuhan University (WHU) can support all five satellite systems. Thus, it is necessary to investigate the positioning performance of a five-system integrated precise point positioning (PPP) (i.e., GRECJ-PPP) using the precise products from different analysis centers under the current constellation status. It should be noted that this study only focuses on the long-term performance of PPP based on daily observations. The static GRECJ-PPP can provide a convergence time of 5.9–6.9/2.6–3.1/6.3–7.1 min and a positioning accuracy of 0.2–0.3/0.2–0.3/1.0–1.1 cm in east/north/up directions, respectively, while the corresponding kinematic statistics are 6.8–8.6/3.3–4.0/7.8–8.1 min and 1.0–1.1/0.8/2.5–2.6 cm in three directions, respectively. For completeness, although the real-time precise products from the analysis center Centre National d’Etudes Spatiales (CNES) do not incorporate QZSS satellites, the performance of real-time PPP with the other four satellite systems (i.e., GREC-PPP) is also analyzed. The real-time GREC-PPP can achieve a static convergence time of 8.7/5.2/11.2 min, a static positioning accuracy of 0.6/0.8/1.3 cm, a kinematic convergence time of 11.5/6.9/13.0 min, and a kinematic positioning accuracy of 1.7/1.6/3.6 cm in the three directions, respectively. For comparison, the results of single-system and dual-system PPP are also provided. In addition, the consistency of the precise products from different analysis centers is characterized.
Agricultural Drought Detection with MODIS Based Vegetation Health Indices in Southeast Germany
Authors: Kloos Simon , Yuan Ye , Castelli Mariapina , Menzel Annette
Published: October 2021

Droughts during the growing season are projected to increase in frequency and severity in Central Europe in the future. Thus, area-wide monitoring of agricultural drought in this region is becoming more and more important. In this context, it is essential to know where and when vegetation growth is primarily water-limited and whether remote sensing-based drought indices can detect agricultural drought in these areas. To answer these questions, we conducted a correlation analysis between the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) within the growing season from 2001 to 2020 in Bavaria (Germany) and investigated the relationship with land cover and altitude. In the second step, we applied the drought indices Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI) to primarily water-limited areas and evaluated them with soil moisture and agricultural yield anomalies. We found that, especially in the summer months (July and August), on agricultural land and grassland and below 800 m, NDVI and LST are negatively correlated and thus, water is the primary limiting factor for vegetation growth here. Within these areas and periods, the TCI and VHI correlate strongly with soil moisture and agricultural yield anomalies, suggesting that both indices have the potential to detect agricultural drought in Bavaria.
Validation of Near-Real-Time NOAA-20 CrIS Outgoing Longwave Radiation with Multi-Satellite Datasets on Broad Timescales
Authors: Wang Tianyuan , Zhou Lihang , Tan Changyi , Divakarla Murty , Pryor Ken , Warner Juying , Wei Zigang , Goldberg Mitch , Nalli Nicholas R.
Published: October 2021

The Outgoing Longwave Radiation (OLR) package was first developed as a stand-alone application, and then integrated into the National Oceanic and Atmospheric Administration (NOAA) Unique Combined Atmospheric Processing System (NUCAPS) hyperspectral sounding retrieval system. An objective of this package is to provide near-real-time OLR products derived from the Cross Track Infrared Sounder (CrIS) onboard the Joint Polar Satellite System (JPSS) satellites. It was initially developed and validated with CrIS onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite, and has been expanded to JPSS-1 (renamed NOAA-20 after launch) datasets that are currently available to the public. In this paper, we provide the results of detailed validation tests with NOAA-20 CrIS for large and wide representative conditions at a global scale. In our validation tests, the observations from Clouds and Earth’s Radiant Energy System (CERES) on Aqua were treated as the absolute reference or “truth”, and those from SNPP CrIS OLR were used as the transfer standard. The tests were performed on a 1°×1° global spatial grid over daily, monthly, and yearly timescales. We find that the CrIS OLR products from NOAA-20 agree exceptionally well with those from Aqua CERES and SNPP CrIS OLR products in all conditions: the daily bias is within ±0.6 Wm−2, and the standard deviation (STD) ranges from 4.88 to 9.1 Wm−2. The bias and the STD of OLR monthly mean are better, within 0.3 and 2.0 Wm−2, respectively. These findings demonstrate the consistency between NOAA-20 and SNPP CrIS OLR up to annual scales, and the robustness of NUCAPS CrIS OLR products.
Inversion for Inhomogeneous Surface Duct without a Base Layer Based on Ocean-Scattered Low-Elevation BDS Signals
Authors: Liu Xiaozhou , Cao Yunhua , Wu Zhensen , Wang Hongguang
Published: October 2021

The anomalous propagation conditions, particularly the tropospheric ducts, severely impact the regular operation and performance evaluation of radio devices in the atmospheric boundary layer. Therefore, it is necessary to provide the regional distribution of tropospheric ducts for utilizing or avoiding these abnormal propagation phenomena. As significant uncooperative signal sources, the global navigation satellite systems (GNSS) have been widely applied in the remote sensing of the ocean and atmosphere due to the greater convenience and lower cost. With the completed deployment of the BeiDou Navigation Satellite System (BDS) in 2020, an additional source can be chosen in the relevant studies. Taking the BDS as an example, since the scattered signals from the ocean surface at low satellite elevation angles can be effectively trapped by tropospheric ducts, we propose a method to invert for the regional distribution of tropospheric ducts using the received power of ocean-scattered signals in this paper. Firstly, the propagation model was built to calculate the received power, and a suite of simulations was made in various atmospheric environments. The results suggested that the received power is more sensitive to the surface duct without a base layer. Then, we made a preliminary estimation of the tropospheric ducts on the ocean nearby Qingdao utilizing the Weather Research and Forecasting (WRF) model as well as the echo data measured by a Doppler weather radar. Before the inversion, the actual satellite azimuth and elevation angles should be obtained to evaluate the bistatic scattering coefficients and the received powers of the selected satellite signals. Finally, we presented an inversion example using the proposed method. In absence of the actual measurements, the received powers pre-estimated at different SNRs served as the inputs of the inversion process and the estimated duct parameters were used to verify the validity of the proposed inversion method. For both the received power and modified refractivity profile, the fitness between the values pre-estimated using the estimated duct parameters and calculated by the inverted duct parameters gets better as the elevation angle decreases and the SNR increases. The variation of the fitness between the estimated and inverted values is slightly different for each duct parameter. Moreover, the calculation of inversion errors further explained the above behaviors, including the mean absolute error (MAE) and the root mean square error (RMSE). Despite some certain errors, the inversion results maintain the overall tendencies and most characteristics of the estimated values, thus proving the validity of the inversion method.
Fusion of Multi-Temporal PAZ and Sentinel-1 Data for Crop Classification
Authors: Busquier Mario , Valcarce-Diñeiro Rubén , Lopez-Sanchez Juan M. , Plaza Javier , Sánchez Nilda , Arias-Pérez Benjamín
Published: October 2021

The accurate identification of crops is essential to help environmental sustainability and support agricultural policies. This study presents the use of a Spanish radar mission, PAZ, to classify agricultural areas with a very high spatial resolution. PAZ was recently launched, and it operates at X band, joining the synthetic aperture radar (SAR) constellation along with TerraSAR-X and TanDEM-X satellites. Owing to its novelty and its ability to classify crop areas (both taking individually its time series and blending with the Sentinel-1 series), it has been tested in an agricultural area of the central-western part of Spain during 2020. The random forest algorithm was selected to classify the time series under five alternatives of standalone/fused data. The map accuracy resulting from the PAZ series standalone was acceptable, but it highlighted the need for a denser time-series of data. The overall accuracy provided by eight PAZ images or by eight Sentinel-1 images was below 60%. The fusion of both sets of eight images improved the overall accuracy by more than 10%. In addition, the exploitation of the whole Sentinel-1 series, with many more observations (up to 40 in the same temporal window) improved the results, reaching an overall accuracy around 76%. This overall performance was similar to that obtained by the joint use of all the available images of the two frequency bands (C and X).
The Total Operating Characteristic from Stratified Random Sampling with an Application to Flood Mapping
Authors: Liu Zhen , Pontius Jr Robert Gilmore
Published: October 2021

The Total Operating Characteristic (TOC) measures how the ranks of an index variable distinguish between presence and absence in a binary reference variable. Previous methods to generate the TOC required the reference data to derive from a census or a simple random sample. However, many researchers apply stratified random sampling to collect reference data because stratified random sampling is more efficient than simple random sampling for many applications. Our manuscript derives a new methodology that uses stratified random sampling to generate the TOC. An application to flood mapping illustrates how the TOC compares the abilities of three indices to diagnose water. The TOC shows visually and quantitatively each index’s diagnostic ability relative to baselines. Results show that the Modified Normalized Difference Water Index has the greatest diagnostic ability, while the Normalized Difference Vegetation Index has diagnostic ability greater than the Normalized Difference Water Index at the threshold where the Diagnosed Presence equals the Abundance of water. Some researchers consider only one accuracy metric at only one threshold, whereas the TOC allows visualization of several metrics at all thresholds. The TOC gives more information and clearer interpretation compared to the popular Relative Operating Characteristic. Our software generates the TOC from a census, simple random sample, or stratified random sample. The TOC Curve Generator is free as an executable file at a website that our manuscript gives.
Modelling the Spectral Index to Detect a Baltic-Type Crude Oil Emulsion Dispersed in the Southern Baltic Sea
Authors: Otremba Zbigniew , Piskozub Jacek
Published: October 2021

Information was obtained on the possibility of detecting oil-in-water emulsions located under the sea based on the modelling of the directional distribution of the radiance field above the water surface. The optical sea model used applies to the southern Baltic Sea, while the oil emulsion model is based on the optical properties of crude oil extracted in this region of the sea. The analyses were carried out while taking into account eight wavelengths in the range 412–676 nm, assuming different thicknesses of the layer contaminated with oil. The most favourable combination of two wavelengths (555/412 nm) for the determination of an index related to the polluted sea area compared to the same index for oil-free water (difference index) was identified, the value of which is indicative of the presence of the oil emulsion in water. Changes in the difference index depending on the viewing direction are shown for almost the entire upper hemisphere (zenith angles from 0° to 80°). The observation directions for which the detection of emulsions should be the most effective are shown.
Applicability of Data Acquisition Characteristics to the Identification of Local Artefacts in Global Digital Elevation Models: Comparison of the Copernicus and TanDEM-X DEMs
Authors: Marešová Jana , Gdulová Kateřina , Pracná Petra , Moravec David , Gábor Lukáš , Prošek Jiří , Barták Vojtěch , Moudrý Vítězslav
Published: October 2021

Several global digital elevation models (DEMs) have been developed in the last two decades. The most recent addition to the family of global DEMs is the TanDEM-X DEM. The original version of the TanDEM-X DEM is, however, a nonedited product (i.e., it contains local artefacts such as voids, spikes, and holes). Therefore, subsequent identification of local artefacts and their editing is necessary. In this study, we evaluated the accuracy of the original TanDEM-X DEM and its improved edited version, the Copernicus DEM, in three major European mountain ranges (the Alps, the Carpathians, and the Pyrenees) using a digital surface model derived from airborne laser scanning data as a reference. In addition, to evaluate the applicability of data acquisition characteristics (coverage map, consistency mask, and height error map) and terrain characteristics (slope, aspect, altitude) to the localization of problematic sites, we modeled their associations with the TanDEM-X DEM error. We revealed local occurrences of large errors in the TanDEM-X DEM that were typically found on steep ridges or in canyons, which were largely corrected in the Copernicus DEM. The editing procedure used for the Copernicus DEM construction was evidently successful as the RMSE for the TanDEM-X and Copernicus DEMs at the 90 m resolution improved from 45 m to 12 m, from 16 m to 6 m, and from 24 m to 9 m for the Alps, the Pyrenees, and the Carpathians, respectively. The Copernicus DEM at the 30 m resolution performed similarly well. The boosted regression trees showed that acquisition characteristics provided as auxiliary data are useful for locating problematic sites and explained 28–50% of deviance of the absolute vertical error. The absolute vertical error was strongly related to the height error map. Finally, up to 26% of cells in the Copernicus DEM were filled using DEMs from different time periods and, hence, users performing multitemporal analysis or requiring data from a specific time period in the mountain environment should be wary when using TanDEM-X and its derivations. We suggest that when filling problematic sites using alternative DEMs, more attention should be paid to the period of their collection to minimize the temporal displacement in the final products.
Optimizing Matching Area for Underwater Gravity-Aided Inertial Navigation Based on the Convolution Slop Parameter-Support Vector Machine Combined Method
Authors: Wang Shuoqi , Zheng Wei , Li Zhaowei
Published: October 2021

This paper focuses on the selection of matching areas in the gravity-aided inertial navigation system. Firstly, the Sobel operator was used in convolution of the gravity anomaly map to obtain the feature map. The convolution slope parameters were constructed by combining the feature map and the gravity anomaly map. The characteristic parameters, such as the difference between convolution rows and columns, convolution variance of the feature map, the pooling difference, and range of the gravity anomaly map, were combined. Based on the support vector machine algorithm, the convolution slope parameter-support vector machine combined method is proposed. Second, we selected the appropriate training sample set and set parameters to verify. The results show that compared with the pre-calibration results, the classification accuracy of the test set is more than 92%, which proves that the convolution slope parameter-support vector machine combined method can effectively distinguish between the suitable and the unsuitable area. Thirdly, we applied this method to another region. The navigation experiment was performed in the split-matching area. The average positioning error was better than 100 m, and the correct rate was more than 90%. The results show that sailing in the selected area can accurately match the trajectory and reduce the positioning error.
An Endmember Bundle Extraction Method Based on Multiscale Sampling to Address Spectral Variability for Hyperspectral Unmixing
Authors: Ye Chuanlong , Liu Shanwei , Xu Mingming , Du Bo , Wan Jianhua , Sheng Hui
Published: October 2021

With the improvement of spatial resolution of hyperspectral remote sensing images, the influence of spectral variability is gradually appearing in hyperspectral unmixing. The shortcomings of endmember extraction methods using a single spectrum to represent one type of material are revealed. To address spectral variability for hyperspectral unmixing, a multiscale resampling endmember bundle extraction (MSREBE) method is proposed in this paper. There are four steps in the proposed endmember bundle extraction method: (1) boundary detection; (2) sub-images in multiscale generation; (3) endmember extraction from each sub-image; (4) stepwise most similar collection (SMSC) clustering. The SMSC clustering method is aimed at solving the problem in determining which endmember bundle the extracted endmembers belong to. Experiments carried on both a simulated dataset and real hyperspectral datasets show that the endmembers extracted by the proposed method are superior to those extracted by the compared methods, and the optimal results in abundance estimation are maintained.
Drone SAR Image Compression Based on Block Adaptive Compressive Sensing
Authors: Choi Jihoon , Lee Wookyung
Published: October 2021

In this paper, an adaptive block compressive sensing (BCS) method is proposed for compression of synthetic aperture radar (SAR) images. The proposed method enhances the compression efficiency by dividing the magnitude of the entire SAR image into multiple blocks and subsampling individual blocks with different compression ratios depending on the sparsity of coefficients in the discrete wavelet transform domain. Especially, a new algorithm is devised that selects the best block measurement matrix from a predetermined codebook to reduce the side information about measurement matrices transferred from the remote sensing node to the ground station. Through some modification of the iterative thresholding algorithm, a new clustered BCS recovery method is proposed that classifies the blocks into multiple clusters according to the compression ratio and iteratively reconstructs the SAR image from the received compressed data. Since the blocks in the same cluster are concurrently reconstructed using the same measurement matrix, the proposed structure mitigates the increase in computational complexity when adopting multiple measurement matrices. Using existing SAR images and experimental data obtained by self-made drone SAR and vehicular SAR systems, it is shown that the proposed scheme provides a good tradeoff between the peak signal-to-noise ratio and the computational load compared to conventional BCS-based compression techniques.
An Advanced Photogrammetric Solution to Measure Apples
Authors: Grilli Eleonora , Battisti Roberto , Remondino Fabio
Published: October 2021

This work presents an advanced photogrammetric pipeline for inspecting apple trees in the field, automatically detecting fruits from videos and quantifying their size and number. The proposed approach is intended to facilitate and accelerate farmers’ and agronomists’ fieldwork, making apple measurements more objective and giving a more extended collection of apples measured in the field while also estimating harvesting/apple-picking dates. In order to do this rapidly and automatically, we propose a pipeline that uses smartphone-based videos and combines photogrammetry, deep learning and geometric algorithms. Synthetic, laboratory and on-field experiments demonstrate the accuracy of the results and the potential of the proposed method. Acquired data, labelled images, code and network weights, are available at 3DOM-FBK GitHub account.
InsulatorGAN: A Transmission Line Insulator Detection Model Using Multi-Granularity Conditional Generative Adversarial Nets for UAV Inspection
Authors: Chen Wenxiang , Li Yingna , Zhao Zhengang
Published: October 2021

Insulator detection is one of the most significant issues in high-voltage transmission line inspection using unmanned aerial vehicles (UAVs) and has attracted attention from researchers all over the world. The state-of-the-art models in object detection perform well in insulator detection, but the precision is limited by the scale of the dataset and parameters. Recently, the Generative Adversarial Network (GAN) was found to offer excellent image generation. Therefore, we propose a novel model called InsulatorGAN based on using conditional GANs to detect insulators in transmission lines. However, due to the fixed categories in datasets such as ImageNet and Pascal VOC, the generated insulator images are of a low resolution and are not sufficiently realistic. To solve these problems, we established an insulator dataset called InsuGenSet for model training. InsulatorGAN can generate high-resolution, realistic-looking insulator-detection images that can be used for data expansion. Moreover, InsulatorGAN can be easily adapted to other power equipment inspection tasks and scenarios using one generator and multiple discriminators. To give the generated images richer details, we also introduced a penalty mechanism based on a Monte Carlo search in InsulatorGAN. In addition, we proposed a multi-scale discriminator structure based on a multi-task learning mechanism to improve the quality of the generated images. Finally, experiments on the InsuGenSet and CPLID datasets demonstrated that our model outperforms existing state-of-the-art models by advancing both the resolution and quality of the generated images as well as the position of the detection box in the images.
Characteristics of the Annual Maximum and Minimum Water Temperatures in Tonle Sap Lake, Cambodia from 2000 to 2019
Authors: Fujihara Yoichi , Okuyama Koki , Hoshikawa Keisuke , Fujii Hideto , Nakamura Takashi , Lun Sambo , Siev Sokly
Published: October 2021

The Tonle Sap Lake contains unique hydrological environments and ecosystems. Although water temperature is an important consideration in lake management, information on the water temperature of the lake is limited. Thus, we investigated the characteristics of the daytime water temperatures of the Tonle Sap Lake from 2000 to 2019 using MOD11A1, a Moderate Resolution Imaging Spectroradiometers (MODIS) product. Moreover, the relationship between water temperature fluctuations and hydrological–meteorological conditions was analyzed. The maximum and minimum daytime water temperatures were recorded in May (30.7 °C) and January (24.6 °C), respectively, each a month after the maximum and minimum air temperatures were recorded. The annual maximum, average, and minimum water levels showed a downward trend (−0.14, −0.08, and −0.01 m/y, respectively). The annual maximum water temperature increased at a rate of 0.17 °C/decade, whereas the annual minimum water temperature decreased at a rate of 0.91 °C/decade. The annual maximum daytime water temperature had a strong negative correlation with water level change (flood pulse) and a weaker correlation with air temperature. The annual minimum daytime water temperature presented the strongest positive correlation with water level change (flood pulse) in the previous year. However, there was no correlation between the annual minimum daytime water temperature and air temperature. These results indicate that water temperature in the Tonle Sap Lake is mainly affected by water level fluctuations, which are mainly driven by flood pulses.
Comparison of UAS-Based Structure-from-Motion and LiDAR for Structural Characterization of Short Broadacre Crops
Authors: Zhang Fei , Hassanzadeh Amirhossein , Kikkert Julie , Pethybridge Sarah Jane , van Aardt Jan
Published: October 2021

The use of small unmanned aerial system (UAS)-based structure-from-motion (SfM; photogrammetry) and LiDAR point clouds has been widely discussed in the remote sensing community. Here, we compared multiple aspects of the SfM and the LiDAR point clouds, collected concurrently in five UAS flights experimental fields of a short crop (snap bean), in order to explore how well the SfM approach performs compared with LiDAR for crop phenotyping. The main methods include calculating the cloud-to-mesh distance (C2M) maps between the preprocessed point clouds, as well as computing a multiscale model-to-model cloud comparison (M3C2) distance maps between the derived digital elevation models (DEMs) and crop height models (CHMs). We also evaluated the crop height and the row width from the CHMs and compared them with field measurements for one of the data sets. Both SfM and LiDAR point clouds achieved an average RMSE of ~0.02 m for crop height and an average RMSE of ~0.05 m for row width. The qualitative and quantitative analyses provided proof that the SfM approach is comparable to LiDAR under the same UAS flight settings. However, its altimetric accuracy largely relied on the number and distribution of the ground control points.
Maize Yield Prediction at an Early Developmental Stage Using Multispectral Images and Genotype Data for Preliminary Hybrid Selection
Authors: Danilevicz Monica F. , Bayer Philipp E. , Boussaid Farid , Bennamoun Mohammed , Edwards David
Published: October 2021

Assessing crop production in the field often requires breeders to wait until the end of the season to collect yield-related measurements, limiting the pace of the breeding cycle. Early prediction of crop performance can reduce this constraint by allowing breeders more time to focus on the highest-performing varieties. Here, we present a multimodal deep learning model for predicting the performance of maize (Zea mays) at an early developmental stage, offering the potential to accelerate crop breeding. We employed multispectral images and eight vegetation indices, collected by an uncrewed aerial vehicle approximately 60 days after sowing, over three consecutive growing cycles (2017, 2018 and 2019). The multimodal deep learning approach was used to integrate field management and genotype information with the multispectral data, providing context to the conditions that the plants experienced during the trial. Model performance was assessed using holdout data, in which the model accurately predicted the yield (RMSE 1.07 t/ha, a relative RMSE of 7.60% of 16 t/ha, and R2 score 0.73) and identified the majority of high-yielding varieties, outperforming previously published models for early yield prediction. The inclusion of vegetation indices was important for model performance, with a normalized difference vegetation index and green with normalized difference vegetation index contributing the most to model performance. The model provides a decision support tool, identifying promising lines early in the field trial.
Climate Effects on Vertical Forest Phenology of Fagus sylvatica L., Sensed by Sentinel-2, Time Lapse Camera, and Visual Ground Observations
Authors: Uphus Lars , Lüpke Marvin , Yuan Ye , Benjamin Caryl , Englmeier Jana , Fricke Ute , Ganuza Cristina , Schwindl Michael , Uhler Johannes , Menzel Annette
Published: October 2021

Contemporary climate change leads to earlier spring phenological events in Europe. In forests, in which overstory strongly regulates the microclimate beneath, it is not clear if further change equally shifts the timing of leaf unfolding for the over- and understory of main deciduous forest species, such as Fagus sylvatica L. (European beech). Furthermore, it is not known yet how this vertical phenological (mis)match—the phenological difference between overstory and understory—affects the remotely sensed satellite signal. To investigate this, we disentangled the start of season (SOS) of overstory F.sylvatica foliage from understory F. sylvatica foliage in forests, within nine quadrants of 5.8 × 5.8 km, stratified over a temperature gradient of 2.5 °C in Bavaria, southeast Germany, in the spring seasons of 2019 and 2020 using time lapse cameras and visual ground observations. We explained SOS dates and vertical phenological (mis)match by canopy temperature and compared these to Sentinel-2 derived SOS in response to canopy temperature. We found that overstory SOS advanced with higher mean April canopy temperature (visual ground observations: −2.86 days per °C; cameras: −2.57 days per °C). However, understory SOS was not significantly affected by canopy temperature. This led to an increase of vertical phenological mismatch with increased canopy temperature (visual ground observations: +3.90 days per °C; cameras: +2.52 days per °C). These results matched Sentinel-2-derived SOS responses, as pixels of higher canopy height advanced more by increased canopy temperature than pixels of lower canopy height. The results may indicate that, with further climate change, spring phenology of F. sylvatica overstory will advance more than F. sylvatica understory, leading to increased vertical phenological mismatch in temperate deciduous forests. This may have major ecological effects, but also methodological consequences for the field of remote sensing, as what the signal senses highly depends on the pixel mean canopy height and the vertical (mis)match.
SSSGAN: Satellite Style and Structure Generative Adversarial Networks
Authors: Marín Javier , Escalera Sergio
Published: October 2021

This work presents Satellite Style and Structure Generative Adversarial Network (SSGAN), a generative model of high resolution satellite imagery to support image segmentation. Based on spatially adaptive denormalization modules (SPADE) that modulate the activations with respect to segmentation map structure, in addition to global descriptor vectors that capture the semantic information in a vector with respect to Open Street Maps (OSM) classes, this model is able to produce consistent aerial imagery. By decoupling the generation of aerial images into a structure map and a carefully defined style vector, we were able to improve the realism and geodiversity of the synthesis with respect to the state-of-the-art baseline. Therefore, the proposed model allows us to control the generation not only with respect to the desired structure, but also with respect to a geographic area.
Comparison of Total Column and Surface Mixing Ratio of Carbon Monoxide Derived from the TROPOMI/Sentinel-5 Precursor with In-Situ Measurements from Extensive Ground-Based Network over South Korea
Authors: Jeong Ukkyo , Hong Hyunkee
Published: October 2021

Atmospheric carbon monoxide (CO) significantly impacts climate change and human health, and has become the focus of increased air quality and climate research. Since 2018, the Troposphere Monitoring Instrument (TROPOMI) has provided total column amounts of CO (CTROPOMI) with a high spatial resolution to monitor atmospheric CO. This study compared and assessed the accuracy of CTROPOMI measurements using surface in-situ measurements (SKME) obtained from an extensive ground-based network over South Korea, where CO level is persistently affected by both local emissions and trans-boundary transport. Our analysis reveals that the TROPOMI effectively detected major emission sources of CO over South Korea and efficiently complemented the spatial coverage of the ground-based network. In general, the correlations between CTROPOMI and SKME were lower than those for NO2 reported in a previous study, and this discrepancy was partly attributed to the lower spatiotemporal variability. Moreover, vertical CO profiles were sampled from the ECMWF CAMS reanalysis data (EAC4) to convert CTROPOMI to surface mixing ratios (STROPOMI). STROPOMI showed a significant underestimation compared with SKME by approximately 40%, with a moderate correlation of approximately 0.51. The low biases of STROPOMI were more significant during the winter season, which was mainly attributed to the underestimation of the EAC4 CO at the surface. This study can contribute to the assessment of satellite and model data for monitoring surface air quality and greenhouse gas emissions.
Evaluating Calibration and Spectral Variable Selection Methods for Predicting Three Soil Nutrients Using Vis-NIR Spectroscopy
Authors: Guo Peng , Li Ting , Gao Han , Chen Xiuwan , Cui Yifeng , Huang Yanru
Published: October 2021

Soil nutrients, including soil available potassium (SAK), soil available phosphorous (SAP), and soil organic matter (SOM), play an important role in farmland soil productivity, food security, and agricultural management. Spectroscopic analysis has proven to be a rapid, nondestructive, and effective technique for predicting soil properties in general and potassium, phosphorous, and organic matter in particular. However, the successful estimation of soil nutrient content by visible and near-infrared (Vis-NIR) reflectance spectroscopy depends on proper calibration methods (including preprocessing transformation methods and multivariate methods for regression analysis) and the selection of appropriate variable selection techniques. In this study, raw spectrum and 13 preprocessing transformations combined with 2 variable selection methods (competitive adaptive reweighted sampling (CARS) and the successive projections algorithm (SPA)) and 2 regression algorithms (support vector machine (SVM) and partial least squares regression (PLSR)), for a total of 56 calibration methods, were investigated for modeling and predicting the above three soil nutrients using hyperspectral Vis-NIR data (400–2450 nm). The results show that first-order derivatives based on logarithmic and inverse transformations (FD-LGRs) can provide better predictions of soil available potassium and phosphorous, and the best form of soil organic matter transformation is SG+MSC. CARS was superior to the SPA in selecting effective variables, and the PLSR model outperformed the SVM models. The best estimation accuracies (R2, RMSE) for soil available potassium, phosphorous, and organic matter were 0.7532, 32.3090 mg/kg; 0.7440, 6.6910 mg/kg; and 0.9009, 3.2103 g/kg, respectively, and their corresponding calibration methods were (FD-LGR)/SPA/PLSR, (FD-LGR)/SPA/PLSR, and SG+MSC/CARS/SVM, respectively. Overall, for the prediction of the soil nutrient content, organic matter was superior to available phosphorous, followed by available potassium. It was concluded that the application of hyperspectral images (Vis-NIR data) was an efficient method for mapping and monitoring soil nutrients at the regional scale, thus contributing to the development of precision agriculture.
Photometric Observations of Aerosol Optical Properties and Emission Flux Rates of Stromboli Volcano Plume during the PEACETIME Campaign
Authors: Sellitto Pasquale , Salerno Giuseppe , Doussin Jean-François , Triquet Sylvain , Dulac François , Desboeufs Karine
Published: October 2021

The characterisation of aerosol emissions from volcanoes is a crucial step towards the assessment of their importance for regional air quality and regional-to-global climate. In this paper we present, for the first time, the characterisation of aerosol emissions of the Stromboli volcano, in terms of their optical properties and emission flux rates, carried out during the PEACETIME oceanographic campaign. Using sun-photometric observations realised during a near-ideal full plume crossing, a plume-isolated aerosol optical depth of 0.07–0.08 in the shorter-wavelength visible range, decreasing to about 0.02 in the near infrared range, was found. An Ångström exponent of 1.40 ± 0.40 was also derived. This value may suggest the dominant presence of sulphate aerosols with a minor presence of ash. During the crossing, two separate plume sections were identified, one possibly slightly affected by ash coming from a mild explosion, and the other more likely composed of pure sulphate aerosols. Exploiting the full crossing scan of the plume, an aerosol emission flux rate of 9–13 kg/s was estimated. This value was 50% larger than for typical passively degassing volcanoes, thus pointing to the importance of mild explosions for aerosol emissions in the atmosphere.
A Universal Fuzzy Logic Optical Water Type Scheme for the Global Oceans
Authors: Jia Tianxia , Zhang Yonglin , Dong Rencai
Published: October 2021

The classification of natural waters is a way to generalize and systematize ocean color science. However, there is no consensus on an optimal water classification template in many contexts. In this study, we conducted an unsupervised classification of the PACE (Plankton, Aerosols, Cloud, and Ocean Ecosystem) synthetic hyperspectral data set, divided the global ocean waters into 15 classes, then obtained a set of fuzzy logic optical water type schemes (abbreviated as the U-OWT in this study) that were tailored for several multispectral satellite sensors, including SeaWiFS, MERIS, MODIS, OLI, VIIRS, MSI, and OLCI. The consistency analysis showed that the performance of U-OWT on different satellite sensors was comparable, and the sensitivity analysis demonstrated the U-OWT could resist a certain degree of input disturbance on remote sensing reflectance. Compared to existing ocean-aimed optical water type schemes, the U-OWT can distinguish more mesotrophic and eutrophic water classes. Furthermore, the U-OWT was highly compatible with other water classification taxonomies, including the trophic state index, the multivariate absorption combinations, and the Forel-Ule Scale, which indirectly demonstrated the potential for global applicability of the U-OWT. This finding was also helpful for the further conversion and unification of different water type taxonomies. As the fundamental basis, the U-OWT can be applied to many oceanic fields that need to be explored in the future. To promote the reproducibility of this study, an IDL®-based standalone U-OWT calculation tool is freely distributed.
Improved Single-Frequency Kinematic Orbit Determination Strategy of Small LEO Satellite with the Sun-Pointing Attitude Mode
Authors: Fu Wenju , Wang Lei , Chen Ruizhi , Zhou Haitao , Li Tao , Han Yi
Published: October 2021

Kinematic orbit determination (KOD) of low earth orbit (LEO) satellites only using single-frequency global navigation satellite system (GNSS) data is a suitable solution for space applications demanding meter-level orbit precision. For some small LEO satellites with the sun-pointing attitude mode, the rotation of the GNSS antenna radiation pattern changes the observation noise characteristics. Since the rotation angle information of the antenna plane may not be available for most low-cost missions, the true elevation cannot be computed and a general elevation-dependent weighting model remains invalid for the onboard GNSS observations. Furthermore, the low-stability GNSS receiver clock oscillator of the LEO satellite at high speeds makes single-frequency cycle slip detection ineffective and difficult since the clock steering events occur frequently. In this study, we investigated the improved KOD strategy to improve the performance of orbit solution using single-frequency GPS and BeiDou navigation satellite system (BDS) observations collected from the Luojia-1A satellite. The weighting model based on exponential function and signal strength is proposed according to the analysis of satellite attitude impact, and a joint single-frequency detection algorithm of receiver clock jump and cycle slip is investigated as well. Based on the GPS/BDS-combined KOD results, it is demonstrated that the clock jump and cycle slip can be properly detected and observations can be effectively utilized with the proposed weighting model considering satellite attitude, which significantly improves the availability and accuracy of orbit solution. The number of available epochs is increased by 12.9% benefitting from this strategy. The orbital root mean square (RMS) precision improvements in the radial, along-track, and cross-track directions are 22.1%, 16.4%, and 6.5%, respectively. Combining BDS observations also contributes to orbit precision improvement, which reaches up to 28.8%.
Phytoplankton Bloom Changes under Extreme Geophysical Conditions in the Northern Bering Sea and the Southern Chukchi Sea
Authors: Park Jinku , Lee Sungjae , Jo Young-Heon , Kim Hyun-Cheol
Published: October 2021

The northern Bering Sea and the southern Chukchi Sea are undergoing rapid regional biophysical changes in connection with the recent extreme climate change in the Arctic. The ice concentration in 2018 was the lowest since observations began in the 1970s, due to the unusually warm southerly wind in winter, which continued in 2019. We analyzed the characteristics of spring phytoplankton biomass distribution under the extreme environmental conditions in 2018 and 2019. Our results show that higher phytoplankton biomass during late spring compared to the 18-year average was observed in the Bering Sea in both years. Their spatial distribution is closely related to the open water extent following winter-onset sea ice retreat in association with dramatic atmospheric conditions. However, despite a similar level of shortwave heat flux, the 2019 springtime biomass in the Chukchi Sea was lower than that in 2018, and was delayed to summer. We confirmed that this difference in bloom timing in the Chukchi Sea was due to changes in seawater properties, determined by a combination of northward oceanic heat flux modulation by the disturbance from more extensive sea ice in winter and higher surface net shortwave heat flux than usual.