bluuebunny/crossref_metadata_2025 huggingface dataset:
Using a Groundwater Adjusted Water Balance Approach and Copulas to Evaluate Spatial Patterns and Dependence Structures in Remote Sensing Derived Evapotranspiration Products
Authors: Soltani Mohsen , Koch Julian , Stisen Simon
Published: February 2021

This study aims to improve the standard water balance evapotranspiration (WB ET) estimate, which is typically used as benchmark data for catchment-scale ET estimation, by accounting for net intercatchment groundwater flow in the ET calculation. Using the modified WB ET approach, we examine errors and shortcomings associated with the long-term annual mean (2002–2014) spatial patterns of three remote-sensing (RS) MODIS-based ET products from MODIS16, PML_V2, and TSEB algorithms at 1 km spatial resolution over Denmark, as a test case for small-scale, energy-limited regions. Our results indicate that the novel approach of adding groundwater net in water balance ET calculation results in a more trustworthy ET spatial pattern. This is especially relevant for smaller catchments where groundwater net can be a significant component of the catchment water balance. Nevertheless, large discrepancies are observed both amongst RS ET datasets and compared to modified water balance ET spatial pattern at the national scale; however, catchment-scale analysis highlights that difference in RS ET and WB ET decreases with increasing catchment size and that 90%, 87%, and 93% of all catchments have ∆ET < ±150 mm/year for MODIS16, PML_V2, and TSEB, respectively. In addition, Copula approach captures a nonlinear structure of the joint relationship with multiple densities amongst the RS/WB ET products, showing a complex dependence structure (correlation); however, among the three RS ET datasets, MODIS16 ET shows a closer spatial pattern to the modified WB ET, as identified by a principal component analysis also. This study will help improve the water balance approach by the addition of groundwater net in the ET estimation and contribute to better understand the true correlations amongst RS/WB ET products especially over energy-limited environments.
Remotely Sensed Seasonal Shoreward Intrusion of the East Australian Current: Implications for Coastal Ocean Dynamics
Authors: Xie Senyang , Huang Zhi , Wang Xiao Hua
Published: February 2021

For decades, the presence of a seasonal intrusion of the East Australian Current (EAC) has been disputed. In this study, with a Topographic Position Index (TPI)-based image processing technique, we use a 26-year satellite Sea Surface Temperature (SST) dataset to quantitatively map the EAC off northern New South Wales (NSW, Australia, 28–32°S and ~154°E). Our mapping products have enabled direct measurement (“distance” and “area”) of the EAC’s shoreward intrusion, and the results show that the EAC intrusion exhibits seasonal cycles, moving closer to the coast in austral summer than in winter. The maximum EAC-to-coast distance usually occurs during winter, ranging from 30 to 40 km. In contrast, the minimum distance usually occurs during summer, ranging from 15 to 25 km. Further spatial analyses indicate that the EAC undergoes a seasonal shift upstream of 29°40′S and seasonal widening downstream. This is the first time that the seasonality of the EAC intrusion has been confirmed by long-term remote-sensing observation. The findings provide new insights into seasonal upwelling and shelf circulation previously observed off the NSW coast.
Long-Term Changes in the Unique and Largest Seagrass Meadows in the Bohai Sea (China) Using Satellite (1974–2019) and Sonar Data: Implication for Conservation and Restoration
Authors: Xu Shaochun , Xu Shuai , Zhou Yi , Yue Shidong , Zhang Xiaomei , Gu Ruiting , Zhang Yu , Qiao Yongliang , Liu Mingjie
Published: February 2021

Seagrass meadows play critical roles in supporting a high level of biodiversity but are continuously threatened by human activities, such as sea reclamation. In this study, we reported on a large seagrass (Zostera marina L.) meadow in Caofeidian shoal harbor in the Bohai Sea of northern China. We evaluated the environmental impact of sea reclamation activities using Landsat imagery (1974–2019) by mapping seagrass meadow distribution changes. ISODATA was adopted for the unsupervised classification and mapping of seagrass beds. The error matrix developed using the in situ data obtained from acoustic surveys for Landsat 8OLI image classification was 87.20% accurate. The maps showed rapidly increasing changes in seagrass meadows as the amount of reclaimed land increased. Some seagrass meadows experienced large-scale changes, and sea reclamation has been suggested as the main factor responsible for habitat loss, which results from physical damage, excessive sedimentation, and increased turbidity caused by reclamation. In addition, habitat degradation may have resulted from three storm surges induced by typhoons in 1992–1998. Fortunately, land reclamation, forming an artificial “longshore bar”, buffers seagrass meadows from wave actions, providing relatively sheltered conditions, which has allowed a large habitat increase since 2012. These were the largest eelgrass meadows (3,217.32 ha), with a peripheral area of ~100 km2, in the Bohai Sea of northern China in 2019. However, the existing largest eelgrass beds in China are threatened by trawling, clam harvesting (especially clam sucking), channel dredging, and culture pond construction. Our work will help coastal managers monitor the environmental impacts of reclamation activities on seagrass meadows on a large spatio-temporal scale and will also provide information for seagrass restoration using artificial “longshore bars”.
Validation of Visually Interpreted Corine Land Cover Classes with Spectral Values of Satellite Images and Machine Learning
Authors: Varga Orsolya Gyöngyi , Kovács Zoltán , Bekő László , Burai Péter , Csatáriné Szabó Zsuzsanna , Holb Imre , Ninsawat Sarawut , Szabó Szilárd
Published: February 2021

We analyzed the Corine Land Cover 2018 (CLC2018) dataset to reveal the correspondence between land cover categories of the CLC and the spectral information of Landsat-8, Sentinel-2 and PlanetScope images. Level 1 categories of the CLC2018 were analyzed in a 25 km × 25 km study area in Hungary. Spectral data were summarized by land cover polygons, and the dataset was evaluated with statistical tests. We then performed Linear Discriminant Analysis (LDA) and Random Forest classifications to reveal if CLC L1 level categories were confirmed by spectral values. Wetlands and water bodies were the most likely to be confused with other categories. The least mixture was observed when we applied the median to quantify the pixel variance of CLC polygons. RF outperformed the LDA’s accuracy, and PlanetScope’s data were the most accurate. Analysis of class level accuracies showed that agricultural areas and wetlands had the most issues with misclassification. We proved the representativeness of the results with a repeated randomized test, and only PlanetScope seemed to be ungeneralizable. Results showed that CLC polygons, as basic units of land cover, can ensure 71.1–78.5% OAs for the three satellite sensors; higher geometric resolution resulted in better accuracy. These results justified CLC polygons, in spite of visual interpretation, can hold relevant information about land cover considering the surface reflectance values of satellites. However, using CLC as ground truth data for land cover classifications can be questionable, at least in the L1 nomenclature.
Predicting Skipjack Tuna Fishing Grounds in the Western and Central Pacific Ocean Based on High-Spatial-Temporal-Resolution Satellite Data
Authors: Hsu Tung-Yao , Chang Yi , Lee Ming-An , Wu Ren-Fen , Hsiao Shih-Chun
Published: February 2021

Skipjack tuna are the most abundant commercial species in Taiwan’s pelagic purse seine fisheries. However, the rapidly changing marine environment increases the challenge of locating target fish in the vast ocean. The aim of this study was to identify the potential fishing grounds of skipjack tuna in the Western and Central Pacific Ocean (WCPO). The fishing grounds of skipjack tuna were simulated using the habitat suitability index (HSI) on the basis of global fishing activities and remote sensing data from 2012 to 2015. The selected environmental factors included sea surface temperature and front, sea surface height, sea surface salinity, mixed layer depth, chlorophyll a concentration, and finite-size Lyapunov exponents. The final input factors were selected according to their percentage contribution to the total efforts. Overall, 68.3% of global datasets and 35.7% of Taiwanese logbooks’ fishing spots were recorded within 5 km of suitable habitat in the daily field. Moreover, 94.9% and 79.6% of global and Taiwan data, respectively, were identified within 50 km of suitable habitat. Our results showed that the model performed well in fitting daily forecast and actual fishing position data. Further, results from this study could benefit habitat monitoring and contribute to managing sustainable fisheries for skipjack tuna by providing wide spatial coverage information on habitat variation.
Object Detection in Remote Sensing Images via Multi-Feature Pyramid Network with Receptive Field Block
Authors: Yuan Zhichao , Liu Ziming , Zhu Chunbo , Qi Jing , Zhao Danpei
Published: February 2021

Object detection in optical remote sensing images (ORSIs) remains a difficult task because ORSIs always have some specific characteristics such as scale-differences between classes, numerous instances in one image and complex background texture. To address these problems, we propose a new Multi-Feature Pyramid Network (MFPNet) with Receptive Field Block (RFB) that integrates both local and global features to detect scattered objects and targets with scale-differences in ORSIs. We build a Multi-Feature Pyramid Module (M-FPM) with two cascaded convolution pyramids as the main structure of MFPNet, which handles object detection of different scales very well. RFB is designed to construct local context information, which makes the network more suitable for the objects detection around complex background. Asymmetric convolution kernel is introduced to RFB to improve the ability of feature attraction by adding nonlinear transformation. Then, a two-step detection network is constructed to combine the M-FPM and RFB to obtain more accurate results. Through a comprehensive evaluation of the experimental results on two publicly available remote sensing datasets Levir and DIOR, we demonstrate that our method outperforms state-of-the-art networks for about 1.3% mAP in Levir dataset and 4.1% mAP in DIOR dataset. Experimental results prove the effectiveness of our method in ORSIs of complex environments.
Multitemporal Water Extraction of Dongting Lake and Poyang Lake Based on an Automatic Water Extraction and Dynamic Monitoring Framework
Authors: Li Juanjuan , Wang Chao , Xu Lu , Wu Fan , Zhang Hong , Zhang Bo
Published: February 2021

Timely and accurate large-scale water body mapping and dynamic monitoring are of great significance for water resource planning, flood control, and disaster reduction applications. Synthetic aperture radar (SAR) systems have the characteristics of strong operability, wide coverage, and all-weather data availability, and play a key role in large-scale water monitoring applications. However, there are still some challenges in the application of highly efficient, high-precision water extraction and dynamic monitoring methods. In this paper, a framework for the automatic extraction and long-term change monitoring of water bodies is proposed. First, a multitemporal water sample dataset is produced based on the bimodal threshold segmentation method. Second, attention block and pyramid module are introduced into the UNet (encoder-decoder) model to construct a robust water extraction network (PA-UNet). Then, GIS modeling is used for the automatic postprocessing of the water extraction results. Finally, the results are mapped and statistically analyzed. The whole process realizes end-to-end input and output. Sentinel-1 data covering Dongting Lake and Poyang Lake are selected for water extraction and dynamic monitoring analysis from 2017 to 2020, and Sentinel-2 images from a similar time frame are selected for verification. The results show that the proposed framework can realize high-precision (the extraction accuracy is higher than 95%), highly efficient automatic water extraction. Multitemporal monitoring results show that Dongting Lake and Poyang Lake fluctuate most in April, July, and November in 2017, 2019, and 2020, and the change trends of the two lakes are the same.
Evaluation and Hydrological Utility of the GPM IMERG Precipitation Products over the Xinfengjiang River Reservoir Basin, China
Authors: Li Xue , Chen Yangbo , Deng Xincui , Zhang Yueyuan , Chen Lingfang
Published: February 2021

As a supplement to gauge observation data, many satellite observations have been used for hydrology and water resource research. This study aims to analyze the quality of the Integrated Multisatellite Retrieval for Global Precipitation Measurement (GPM IMERG) products and their hydrological utility in the Xinfengjiang River reservoir basin (XRRB), a mountainous region in southern China. The grid-based soil and water assessment tool (SWAT) model was used to construct a hydrological model of the XRRB based on two scenarios. The results showed that on a daily scale, the IMERG final run (FR) product was more accurate than the others, with Pearson’s correlation coefficients (CORR) of 0.61 and 0.71 on the grid accumulation scale and the average scale, respectively, and a relative bias (BIAS) of 0.01. In Scenario I (the SWAT model calibrated by rain gauge data), the IMERG-based simulation showed acceptable hydrologic prediction ability on the daily scale and satisfactory hydrological performance on the monthly scale. In Scenario II (the SWAT model calibrated by the FR), the hydrological performances of the FR on the daily and monthly scales were slightly better than those in Scenario I (the CORR was 0.64 and 0.85, the BIAS was 0.01 and −0.02, and the NSE was 0.43 and 0.84). These results showed the potential of the FR for hydrological modeling in tropical mountain watersheds in areas where information is scarce. This study is useful for hydrological, meteorological, and disaster studies in developing countries or remote areas with sparse or low-quality networks of ground-based observation stations.
Improved Real-Time Natural Hazard Monitoring Using Automated DInSAR Time Series
Authors: Kelevitz Krisztina , Tiampo Kristy F. , Corsa Brianna D.
Published: February 2021

As part of the collaborative GeoSciFramework project, we are establising a monitoring system for the Yellowstone volcanic area that integrates multiple geodetic and seismic data sets into an advanced cyber-infrastructure framework that will enable real-time streaming data analytics and machine learning and allow us to better characterize associated long- and short-term hazards. The goal is to continuously ingest both remote sensing (GNSS, DInSAR) and ground-based (seismic, thermal and gas observations, strainmeter, tiltmeter and gravity measurements) data and query and analyse them in near-real time. In this study, we focus on DInSAR data processing and the effects from using various atmospheric corrections and real-time orbits on the automated processing and results. We find that the atmospheric correction provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) is currently the most optimal for automated DInSAR processing and that the use of real-time orbits is sufficient for the early-warning application in question. We show analysis of atmospheric corrections and using real-time orbits in a test case over the Kilauea volcanic area in Hawaii. Finally, using these findings, we present results of displacement time series in the Yellowstone area between May 2018 and October 2019, which are in good agreement with GNSS data where available. These results will contribute to a baseline model that will be the basis of a future early-warning system that will be continuously updated with new DInSAR data acquisitions.
Comparative Monte Carlo Analysis of Background Estimation Algorithms for Unmanned Aerial Vehicle Detection
Authors: Matczak Grzegorz , Mazurek Przemyslaw
Published: February 2021

Background estimation algorithms are important in UAV (Unmanned Aerial Vehicle) vision tracking systems. Incorrect selection of an algorithm and its parameters leads to false detections that must be filtered by the tracking algorithm of objects, even if there is only one UAV within the visibility range. This paper shows that, with the use of genetic optimization, it is possible to select an algorithm and its parameters automatically. Background estimation algorithms (CNT (CouNT), GMG (Godbehere-Matsukawa-Goldberg), GSOC (Google Summer of Code 2017), MOG (Mixture of Gaussian), KNN (K–Nearest Neighbor–based Background/Foreground Segmentation Algorithm), MOG2 (Mixture of Gaussian version 2), and MEDIAN) and the reference algorithm of thresholding were tested. Monte Carlo studies were carried out showing the advantages of the MOG2 algorithm for UAV detection. An empirical sensitivity analysis was presented that rejected the MEDIAN algorithm.
N-YOLO: A SAR Ship Detection Using Noise-Classifying and Complete-Target Extraction
Authors: Tang Gang , Zhuge Yichao , Claramunt Christophe , Men Shaoyang
Published: February 2021

High-resolution images provided by synthetic aperture radar (SAR) play an increasingly important role in the field of ship detection. Numerous algorithms have been so far proposed and relative competitive results have been achieved in detecting different targets. However, ship detection using SAR images is still challenging because these images are still affected by different degrees of noise while inshore ships are affected by shore image contrasts. To solve these problems, this paper introduces a ship detection method called N-YOLO, which based on You Only Look Once (YOLO). The N-YOLO includes a noise level classifier (NLC), a SAR target potential area extraction module (STPAE) and a YOLOv5-based detection module. First, NLC derives and classifies the noise level of SAR images. Secondly, the STPAE module is composed by a CA-CFAR and expansion operation, which is used to extract the complete region of potential targets. Thirdly, the YOLOv5-based detection module combines the potential target area with the original image to get a new image. To evaluate the effectiveness of the N-YOLO, experiments are conducted using a reference GaoFen-3 dataset. The detection results show that competitive performance has been achieved by N-YOLO in comparison with several CNN-based algorithms.
Assessing within-Field Corn and Soybean Yield Variability from WorldView-3, Planet, Sentinel-2, and Landsat 8 Satellite Imagery
Authors: Skakun Sergii , Kalecinski Natacha I. , Brown Meredith G. L. , Johnson David M. , Vermote Eric F. , Roger Jean-Claude , Franch Belen
Published: February 2021

Crop yield monitoring is an important component in agricultural assessment. Multi-spectral remote sensing instruments onboard space-borne platforms such as Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) have shown to be useful for efficiently generating timely and synoptic information on the yield status of crops across regional levels. However, the coarse spatial resolution data inherent to these sensors provides little utility at the management level. Recent satellite imagery collection advances toward finer spatial resolution (down to 1 m) alongside increased observational cadence (near daily) implies information on crops obtainable at field and within-field scales to support farming needs is now possible. To test this premise, we focus on assessing the efficiency of multiple satellite sensors, namely WorldView-3, Planet/Dove-Classic, Sentinel-2, and Landsat 8 (through Harmonized Landsat Sentinel-2 (HLS)), and investigate their spatial, spectral (surface reflectance (SR) and vegetation indices (VIs)), and temporal characteristics to estimate corn and soybean yields at sub-field scales within study sites in the US state of Iowa. Precision yield data as referenced to combine harvesters’ GPS systems were used for validation. We show that imagery spatial resolution of 3 m is critical to explaining 100% of the within-field yield variability for corn and soybean. Our simulation results show that moving to coarser resolution data of 10 m, 20 m, and 30 m reduced the explained variability to 86%, 72%, and 59%, respectively. We show that the most important spectral bands explaining yield variability were green (0.560 μm), red-edge (0.726 μm), and near-infrared (NIR − 0.865 μm). Furthermore, the high temporal frequency of Planet and a combination of Sentinel-2/Landsat 8 (HLS) data allowed for optimal date selection for yield map generation. Overall, we observed mixed performance of satellite-derived models with the coefficient of determination (R2) varying from 0.21 to 0.88 (averaging 0.56) for the 30 m HLS and from 0.09 to 0.77 (averaging 0.30) for 3 m Planet. R2 was lower for fields with higher yields, suggesting saturation of the satellite-collected reflectance features in those cases. Therefore, other biophysical variables, such as soil moisture and evapotranspiration, at similar fine spatial resolutions are likely needed alongside the optical imagery to fully explain the yields.
Reservoir-Induced Land Deformation: Case Study from the Grand Ethiopian Renaissance Dam
Authors: Chen Yu , Ahmed Mohamed , Tangdamrongsub Natthachet , Murgulet Dorina
Published: February 2021

The Nile River stretches from south to north throughout the Nile River Basin (NRB) in Northeast Africa. Ethiopia, where the Blue Nile originates, has begun the construction of the Grand Ethiopian Renaissance Dam (GERD), which will be used to generate electricity. However, the impact of the GERD on land deformation caused by significant water relocation has not been rigorously considered in the scientific research. In this study, we develop a novel approach for predicting large-scale land deformation induced by the construction of the GERD reservoir. We also investigate the limitations of using the Gravity Recovery and Climate Experiment Follow On (GRACE-FO) mission to detect GERD-induced land deformation. We simulated three land deformation scenarios related to filling the expected reservoir volume, 70 km3, using 5-, 10-, and 15-year filling scenarios. The results indicated: (i) trends in downward vertical displacement estimated at −17.79 ± 0.02, −8.90 ± 0.09, and −5.94 ± 0.05 mm/year, for the 5-, 10-, and 15-year filling scenarios, respectively; (ii) the western (eastern) parts of the GERD reservoir are estimated to move toward the reservoir’s center by +0.98 ± 0.01 (−0.98 ± 0.01), +0.48 ± 0.00 (−0.48 ± 0.00), and +0.33 ± 0.00 (−0.33 ± 0.00) mm/year, under the 5-, 10- and 15-year filling strategies, respectively; (iii) the northern part of the GERD reservoir is moving southward by +1.28 ± 0.02, +0.64 ± 0.01, and +0.43 ± 0.00 mm/year, while the southern part is moving northward by −3.75 ± 0.04, −1.87 ± 0.02, and −1.25 ± 0.01 mm/year, during the three examined scenarios, respectively; and (iv) the GRACE-FO mission can only detect 15% of the large-scale land deformation produced by the GERD reservoir. Methods and results demonstrated in this study provide insights into possible impacts of reservoir impoundment on land surface deformation, which can be adopted into the GERD project or similar future dam construction plans.
The Influence of ENSO and MJO on Drought in Different Ecological Geographic Regions in China
Authors: Zhou Lei , Wang Siyu , Du Mingyi , Chen Qiang , He Congcong , Zhang Jun , Zhu Yinuo , Gong Yiting
Published: February 2021

Mastering the spatial and temporal differences of ENSO (EI Niño-Southern Oscillation) and MJO (Madden–Julian Oscillation) and their influence on drought is very important for accurately monitoring and forecasting drought. In this study, spatiotemporal characteristics and variability of the impact of ENSO and MJO on drought were analyzed from the perspectives of meteorological drought and agricultural drought through temporal and spatial correlation analyses of China’s 48 eco-geographical regions. The results show a strong correlation between drought and ENSO and MJO in general. The spatial correlation coefficients are different, and the response of extreme events varies in different regions. The influence of ENSO and MJO on agricultural drought is higher than that on meteorological drought. ENSO and MJO have a considerable influence on agricultural drought in regions such as the Qinghai-Tibet Plateau and Xinjiang, with the highest correlation coefficient of 0.72. A significant influence of ENSO and MJO on meteorological drought was found in the Jiangnan region with the highest correlation coefficient of 0.40. In addition, agricultural drought shows a significant time lag in response to ENSO events. When the lag time is six months, the time series presents the highest correlation coefficient with the mean value of the correlation coefficient reaching 0.38 and the maximum value reaching 0.75. This research is of great significance for understanding the spatiotemporal correlation between climate patterns and drought on a large regional scale and it provides further insights into the teleconnection mechanisms of drought.
Spatiotemporal Investigations of Multi-Sensor Air Pollution Data over Bangladesh during COVID-19 Lockdown
Authors: Qiu Zhongfeng , Ali Md. Arfan , Nichol Janet E. , Bilal Muhammad , Tiwari Pravash , Habtemicheal Birhanu Asmerom , Almazroui Mansour , Mondal Sanjit Kumar , Mazhar Usman , Wang Yu , Sarker Sajib , Mustafa Farhan , Rahman Muhammad Ashfaqur
Published: February 2021

This study investigates spatiotemporal changes in air pollution (particulate as well as gases) during the COVID-19 lockdown period over major cities of Bangladesh. The study investigated the aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites, PM2.5 and PM10 from Copernicus Atmosphere Monitoring Service (CAMS), and NO2 and O3 from TROPOMI-5P, from March to June 2019–2020. Additionally, aerosol subtypes from the Cloud-Aerosol Lidar and Infrared Pathfinder (CALIPSO) were used to explore the aerosol types. The strict lockdown (26 March–30 May 2020) led to a significant reduction in AOD (up to 47%) in all major cities, while the partial lockdown (June 2020) led to increased and decreased AOD over the study area. Significant reductions in PM2.5 (37–77%) and PM10 (33–70%) were also observed throughout the country during the strict lockdown and partial lockdown. The NO2 levels decreased by 3–25% in March 2020 in the cities of Rajshahi, Chattogram, Sylhet, Khulna, Barisal, and Mymensingh, in April by 3–43% in Dhaka, Chattogram, Khulna, Barisal, Bhola, and Mymensingh, and May by 12–42% in Rajshahi, Sylhet, Mymensingh, and Rangpur. During the partial lockdown in June, NO2 decreased (9–35%) in Dhaka, Chattogram, Sylhet, Khulna, Barisal, and Rangpur compared to 2019. On the other hand, increases were observed in ozone (O3) levels, with an average increase of 3–12% throughout the country during the strict lockdown and only a slight reduction of 1–3% in O3 during the partial lockdown. In terms of aerosol types, CALIPSO observed high levels of polluted dust followed by dust, smoke, polluted continental, and clean marine-type aerosols over the country in 2019, but all types were decreased during the lockdown. The study concludes that the strict lockdown measures were able to significantly improve air quality conditions over Bangladesh due to the shutdown of industries, vehicles, and movement of people.
Contemporaneous Monitoring of the Whole Dynamic Earth System from Space, Part I: System Simulation Study Using GEO and Molniya Orbits
Authors: Cullingworth Christopher , Muller Jan-Peter
Published: February 2021

Despite the wealth of data produced by previous and current Earth Observation platforms feeding climate models, weather forecasts, disaster monitoring services and countless other applications, the public still lacks the ability to access a live, true colour, global view of our planet, and nudge them towards a realisation of its fragility. The ideas behind commercialization of Earth photography from space has long been dominated by the analytical value of the imagery. What specific knowledge and actionable intelligence can be garnered from these evermore frequent revisits of the planet’s surface? How can I find a market for this analysis? However, what is rarely considered is what is the educational value of the imagery? As students and children become more aware of our several decades of advance in viewing our current planetary state, we should find mechanisms which serve their curiosity, helping to satisfy our children’s simple quest to explore and learn more about what they are seeing. The following study describes the reasons why current GEO and LEO observation platforms are inadequate to provide truly global RGB coverage on an update time-scale of 5-min and proposes an alternative, low-cost, GEO + Molniya 3U CubeSat constellation to perform such an application.
Deep Neural Networks for Road Sign Detection and Embedded Modeling Using Oblique Aerial Images
Authors: Mao Zhu , Zhang Fan , Huang Xianfeng , Jia Xiangyang , Gong Yiping , Zou Qin
Published: February 2021

Oblique photogrammetry-based three-dimensional (3D) urban models are widely used for smart cities. In 3D urban models, road signs are small but provide valuable information for navigation. However, due to the problems of sliced shape features, blurred texture and high incline angles, road signs cannot be fully reconstructed in oblique photogrammetry, even with state-of-the-art algorithms. The poor reconstruction of road signs commonly leads to less informative guidance and unsatisfactory visual appearance. In this paper, we present a pipeline for embedding road sign models based on deep convolutional neural networks (CNNs). First, we present an end-to-end balanced-learning framework for small object detection that takes advantage of the region-based CNN and a data synthesis strategy. Second, under the geometric constraints placed by the bounding boxes, we use the scale-invariant feature transform (SIFT) to extract the corresponding points on the road signs. Third, we obtain the coarse location of a single road sign by triangulating the corresponding points and refine the location via outlier removal. Least-squares fitting is then applied to the refined point cloud to fit a plane for orientation prediction. Finally, we replace the road signs with computer-aided design models in the 3D urban scene with the predicted location and orientation. The experimental results show that the proposed method achieves a high mAP in road sign detection and produces visually plausible embedded results, which demonstrates its effectiveness for road sign modeling in oblique photogrammetry-based 3D scene reconstruction.
Evaluation of Tropospheric Moisture Characteristics Among COSMIC-2, ERA5 and MERRA-2 in the Tropics and Subtropics
Authors: Johnston Benjamin R. , Randel William J. , Sjoberg Jeremiah P.
Published: February 2021

Global navigation satellite system (GNSS) radio occultation (RO) receivers onboard the recently-launched COSMIC-2 (C2) satellite constellation provide an unprecedented number of high vertical resolution moisture profiles throughout the tropical and subtropical atmosphere. In this study, the distribution and variability of water vapor was investigated using specific humidity retrievals from C2 observations and compared to collocated ERA5 and MERRA-2 reanalysis profiles within 40°N to 40°S from September to December 2019, which is prior to the assimilation of C2 in the reanalyses. Negative C2 moisture biases are evident within the boundary layer, so we focused on levels above the boundary layer in this study. Overall, C2 specific humidity shows excellent agreement with that of ERA5 and has larger differences with that of MERRA-2. In the tropical mid-troposphere, C2 shows positive biases compared to ERA5 (6–12%) and larger negative biases with MERRA-2 (15–30%). Strong correlations are observed between C2 and reanalysis specific humidity in the subtropics (>0.8) whereas correlations are slightly weaker in the deep tropics, especially for MERRA-2. Profile pairs with large moisture differences often occur in areas with sharp moisture gradients, highlighting the importance of measurement resolution. Locations which demonstrated weaker humidity correlations in active convection regions show that ERA5 has a negative specific humidity bias at 3 km in higher moisture environments, whereas MERRA-2 displays a large positive bias at 7 km. However, additional explanations for profile pairs with large moisture differences remain unclear and require further study.
Sea Surface Salinity Estimation and Spatial-Temporal Heterogeneity Analysis in the Gulf of Mexico
Authors: Fu Zhiyi , Wu Fangfang , Zhang Zhengliang , Hu Linshu , Zhang Feng , Hu Bifeng , Du Zhenhong , Shi Zhou , Liu Renyi
Published: February 2021

As an important parameter to characterize physical and biogeochemical processes, sea surface salinity (SSS) has received extensive attention. Cubist is a data mining model, which can be well-suited to estimate and analyze SSS in the Gulf of Mexico (GOM) because it can reflect the SSS internal heterogeneity in the GOM—overall circular distribution, and the seasonality related to temperature and river discharge changes. Using remote sensing reflectance (Rrs) at 412, 443, 488 (490), 555, and 667 (670) nm and sea surface temperature (SST), a cubist model was developed to estimate SSS with high accuracy with the overall performance demonstrates a root mean square error (RMSE) of 0.27 psu and correlation coefficient of 0.97 of R2. The model divides the GOM area according to model rules into four sub-regions, which include estuary, nearshore, and open sea, reflecting the gradient distribution of SSS. The division of sub-regions and seasonal changes can be explained by the distribution of water bodies, river discharges, and local wind forces since it is obvious that the estuary region reaches the largest low-value area and spreads eastward with the monsoon in the spring when the river flow increases to the highest value. While the east to west wind in the non-summer monsoon period guides the plume westward, and the lowest river discharge in winter corresponds to the smallest low value area. After comparison with other statistical models, the cubist model showed satisfactory results in independent verification of cruise data, proving the estimation capability under different geographical conditions (such as estuaries and open seas) and seasons. Therefore, considering high accuracy and heterogeneity mining, the cubist-based model is an ideal method for coastal SSS estimation and spatial-temporal heterogeneity analysis, and can provide ideas for model construction for coastal areas with similar geographic environments.
Uncertainty and Sensitivity Analysis of a Remote-Sensing-Based Penman–Monteith Model to Meteorological and Land Surface Input Variables
Authors: Majozi Nobuhle , Mannaerts Chris , Ramoelo Abel , Mathieu Renaud , Verhoef Wouter
Published: February 2021

This study analysed the uncertainty and sensitivity of core and intermediate input variables of a remote-sensing-data-based Penman–Monteith (PM-Mu) evapotranspiration (ET) model. We derived absolute and relative uncertainties of core measured meteorological and remote-sensing-based atmospheric and land surface input variables and parameters of the PM-Mu model. Uncertainties of important intermediate data components (i.e., net radiation and aerodynamic and surface resistances) were also assessed. To estimate the instrument measurement uncertainties of the in situ meteorological input variables, we used the reported accuracies of the manufacturers. Observational accuracies of the remote sensing input variables (land surface temperature (LST), land surface emissivity (εs), leaf area index (LAI), land surface albedo (α)) were derived from peer-reviewed satellite sensor validation reports to compute their uncertainties. The input uncertainties were propagated to the final model’s evapotranspiration estimation uncertainty. Our analysis indicated relatively high uncertainties associated with relative humidity (RH), and hence all the intermediate variables associated with RH, like vapour pressure deficit (VPD) and the surface and aerodynamic resistances. This is in contrast to other studies, which reported LAI uncertainty as the most influential. The semi-arid conditions and seasonality of the regional South African climate and high temporal frequency of the variations in VPD, air and land surface temperatures could explain the uncertainties observed in this study. The results also showed the ET algorithm to be most sensitive to the air-land surface temperature difference. An accurate assessment of those in situ and remotely sensed variables is required to achieve reliable evapotranspiration model estimates in these generally dry regions and climates. A significant advantage of the remote-sensing-based ET method remains its full area coverage in contrast to classic-point (station)-based ET estimates.
Sentinel-1 Big Data Processing with P-SBAS InSAR in the Geohazards Exploitation Platform: An Experiment on Coastal Land Subsidence and Landslides in Italy
Authors: Cigna Francesca , Tapete Deodato
Published: February 2021

The growing volume of synthetic aperture radar (SAR) imagery acquired by satellite constellations creates novel opportunities and opens new challenges for interferometric SAR (InSAR) applications to observe Earth’s surface processes and geohazards. In this paper, the Parallel Small BAseline Subset (P-SBAS) advanced InSAR processing chain running on the Geohazards Exploitation Platform (GEP) is trialed to process two unprecedentedly big stacks of Copernicus Sentinel-1 C-band SAR images acquired in 2014–2020 over a coastal study area in southern Italy, including 296 and 283 scenes in ascending and descending mode, respectively. Each stack was processed in the GEP in less than 3 days, from input SAR data retrieval via repositories, up to generation of the output P-SBAS datasets of coherent targets and their displacement histories. Use-cases of long-term monitoring of land subsidence at the Capo Colonna promontory (up −2.3 cm/year vertical and −1.0 cm/year east–west rate), slow-moving landslides and erosion landforms, and deformation at modern coastal protection infrastructure in the city of Crotone are used to: (i) showcase the type and precision of deformation products outputting from P-SBAS processing of big data, and the derivable key information to support value-adding and geological interpretation; and (ii) discuss potential and challenges of big data processing using cloud/grid infrastructure.
The Potential Impact of Assimilating Synthetic Microwave Radiances Onboard a Future Geostationary Satellite on the Prediction of Typhoon Lekima Using the WRF Model
Authors: Wang Yuanbing , He Jieying , Chen Yaodeng , Min Jinzhong
Published: February 2021

Geostationary meteorological satellites can provide continuous observations of high-impact weather events with a high temporal and spatial resolution. Sounding the atmosphere using a microwave instrument onboard a geostationary satellite has aroused great study interests for years, as it would increase the observational efficiency as well as provide a new perspective in the microwave spectrum to the measuring capability for the current observational system. In this study, the capability of assimilating future geostationary microwave sounder (GEOMS) radiances was developed in the Weather Research and Forecasting (WRF) model’s data assimilation (WRFDA) system. To investigate if these frequently updated and widely distributed microwave radiances would be beneficial for typhoon prediction, observational system simulation experiments (OSSEs) using synthetic microwave radiances were conducted using the mesoscale numerical model WRF and the advanced hybrid ensemble–variational data assimilation method for the Lekima typhoon that occurred in early August 2019. The results show that general positive forecast impacts were achieved in the OSSEs due to the assimilation of GEOMS radiances: errors of analyses and forecasts in terms of wind, humidity, and temperature were both reduced after assimilating GEOMS radiances when verified against ERA-5 data. The track and intensity predictions of Lekima were also improved before 68 h compared to the best track data in this study. In addition, rainfall forecast improvements were also found due to the assimilation impact of GEOMS radiances. In general, microwave observations from geostationary satellites provide the possibility of frequently assimilating wide-ranging microwave information into a regional model in a finer resolution, which can potentially help improve numerical weather prediction (NWP).
Industrial Energy Assessment Training Effectiveness Evaluation: An Eye-Tracking Study
Authors: Ghanbari Laleh , Wang Chao , Jeon Hyun Woo
Published: February 2021

It is essential to understand the effectiveness of any training program so it can be improved accordingly. Various studies have applied standard metrics for the evaluation of visual behavior to recognize the areas of interest that attract individuals’ attention as there is a high correlation between attentional behavior and where one is focusing on. However, through reviewing the literature, we believe that studies that applied eye-tracking technologies for training purposes are still limited, especially in the industrial energy assessment training field. In this paper, the effectiveness of industrial energy assessment training was quantitatively evaluated by measuring the attentional allocation of trainees using eye-tracking technology. Moreover, this study identifies the areas that require more focus based on evaluating the performance of subjects after receiving the training. Additionally, this research was conducted in a controlled environment to remove the distractions that may be caused by environmental factors to only concentrate on variables that influence the learning behavior of subjects. The experiment results showed that after receiving the training, the subjects’ performance in energy assessment was significantly improved in two areas: production, and recycling and waste management, and the designed training program enhanced the knowledge of participants in identifying energy-saving opportunities to the knowledge level of experienced participants.
Fear Recognition for Women Using a Reduced Set of Physiological Signals
Authors: Miranda Jose A. , F. Canabal Manuel , Gutiérrez-Martín Laura , Lanza-Gutierrez Jose M. , Portela-García Marta , López-Ongil Celia
Published: February 2021

Emotion recognition is benefitting from the latest research into physiological monitoring and wireless communications, among other remarkable achievements. These technologies can indeed provide solutions to protect vulnerable people in scenarios such as personal assaults, the abuse of children or the elderly, gender violence or sexual aggression. Cyberphysical systems using smart sensors, artificial intelligence and wearable and inconspicuous devices can serve as bodyguards to detect these risky situations (through fear-related emotion detection) and automatically trigger a protection protocol. As expected, these systems should be trained and customized for each user to ensure the best possible performance, which undoubtedly requires a gender perspective. This paper presents a specialized fear recognition system for women based on a reduced set of physiological signals. The architecture proposed is characterized by the usage of three physiological sensors, lightweight binary classification and the conjunction of linear (temporal and frequency) and non-linear features. Moreover, a binary fear mapping strategy between dimensional and discrete emotional information based on emotional self-report data is implemented to avoid emotional bias. The architecture is evaluated using a public multi-modal physiological dataset with two approaches (subject-dependent and subject-independent models) focusing on the female participants. As a result, the proposal outperforms the state-of-the-art in fear recognition, achieving a recognition rate of up to 96.33% for the subject-dependent model.
An Adaptive Weight Learning-Based Multitask Deep Network for Continuous Blood Pressure Estimation Using Electrocardiogram Signals
Authors: Fan Xiaomao , Wang Hailiang , Zhao Yang , Li Ye , Tsui Kwok Leung
Published: February 2021

Estimating blood pressure via combination analysis with electrocardiogram and photoplethysmography signals has attracted growing interest in continuous monitoring patients’ health conditions. However, most wearable/portal monitoring devices generally acquire only one kind of physiological signals due to the consideration of energy cost, device weight and size, etc. In this study, a novel adaptive weight learning-based multitask deep learning framework based on single lead electrocardiogram signals is proposed for continuous blood pressure estimation. Specifically, the proposed method utilizes a 2-layer bidirectional long short-term memory network as the sharing layer, followed by three identical architectures of 2-layer fully connected networks for task-specific blood pressure estimation. To learn the importance of task-specific losses automatically, an adaptive weight learning scheme based on the trend of validation loss is proposed. Extensive experiment results on Physionet Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) II waveform database demonstrate that the proposed method using electrocardiogram signals obtains estimating performance of 0.12±10.83 mmHg, 0.13±5.90 mmHg, and 0.08±6.47 mmHg for systolic blood pressure, diastolic blood pressure, and mean arterial pressure, respectively. It can meet the requirements of the British Hypertension Society standard and US Association of Advancement of Medical Instrumentation standard with a considerable margin. Combined with a wearable/portal electrocardiogram device, the proposed model can be deployed to a healthcare system to provide a long-term continuous blood pressure monitoring service, which would help to reduce the incidence of malignant complications to hypertension.
Research on a Gas Concentration Prediction Algorithm Based on Stacking
Authors: Xu Yonghui , Meng Ruotong , Zhao Xi
Published: February 2021

Machine learning algorithms play an important role in the detection of toxic, flammable and explosive gases, and they are extremely important for the study of mixed gas classification and concentration prediction methods. To solve the problem of low prediction accuracy of gas concentration regression prediction algorithms, a gas concentration prediction algorithm based on a stacking model is proposed in the current research. In this paper, the stochastic forest, extreme random regression tree and gradient boosting decision tree (GBDT) regression algorithms are selected as the base learning devices and use the stacking algorithm to take the output of each base learning device as input to train a new model to produce a final output. Through the stacking model, the grid search algorithm is studied to automatically optimize the parameters so that the performance of the entire system can reach the optimal parameters. Through experimental simulation, the gas concentration prediction algorithm based on stacking model has better prediction effect than other integrated frame algorithms and the accuracy of mixed gas concentration prediction is improved.