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Ecological Remote Sensing: A Challenging Section on Ecological Theory and Remote Sensing Remote Sens. (IF 4.509) Pub Date : 2021-02-25 Duccio Rocchini
Note: In lieu of an abstract, this is an excerpt from the first page. Duccio Rocchini (born in 1975 in Siena, Italy) is Full Professor at Alma Mater Studiorum University of Bologna from December 2019, after having been Associate Professor in Biology and Ecology at the University of Trento, from February 2017. He attained his PhD in 2005 under the supervision of Prof. Alessandro Chiarucci, dealing with
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CF2PN: A Cross-Scale Feature Fusion Pyramid Network Based Remote Sensing Target Detection Remote Sens. (IF 4.509) Pub Date : 2021-02-25 Wei Huang; Guanyi Li; Qiqiang Chen; Ming Ju; Jiantao Qu
In the wake of developments in remote sensing, the application of target detection of remote sensing is of increasing interest. Unfortunately, unlike natural image processing, remote sensing image processing involves dealing with large variations in object size, which poses a great challenge to researchers. Although traditional multi-scale detection networks have been successful in solving problems
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Multisensor and Multiresolution Remote Sensing Image Classification through a Causal Hierarchical Markov Framework and Decision Tree Ensembles Remote Sens. (IF 4.509) Pub Date : 2021-02-25 Martina Pastorino; Alessandro Montaldo; Luca Fronda; Ihsen Hedhli; Gabriele Moser; Sebastiano B. Serpico; Josiane Zerubia
In this paper, a hierarchical probabilistic graphical model is proposed to tackle joint classification of multiresolution and multisensor remote sensing images of the same scene. This problem is crucial in the study of satellite imagery and jointly involves multiresolution and multisensor image fusion. The proposed framework consists of a hierarchical Markov model with a quadtree structure to model
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National Crop Mapping Using Sentinel-1 Time Series: A Knowledge-Based Descriptive Algorithm Remote Sens. (IF 4.509) Pub Date : 2021-02-25 Carole Planque; Richard Lucas; Suvarna Punalekar; Sebastien Chognard; Clive Hurford; Christopher Owers; Claire Horton; Paul Guest; Stephen King; Sion Williams; Peter Bunting
National-level mapping of crop types is important to monitor food security, understand environmental conditions, inform optimal use of the landscape, and contribute to agricultural policy. Countries or economic regions currently and increasingly use satellite sensor data for classifying crops over large areas. However, most methods have been based on machine learning algorithms, with these often requiring
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Real-Time Hyperspectral Data Transmission for UAV-Based Acquisition Platforms Remote Sens. (IF 4.509) Pub Date : 2021-02-25 José M. Melián; Adán Jiménez; María Díaz; Alejandro Morales; Pablo Horstrand; Raúl Guerra; Sebastián López; José F. López
Hyperspectral sensors that are mounted in unmanned aerial vehicles (UAVs) offer many benefits for different remote sensing applications by combining the capacity of acquiring a high amount of information that allows for distinguishing or identifying different materials, and the flexibility of the UAVs for planning different kind of flying missions. However, further developments are still needed to
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Using Satellite Image Fusion to Evaluate the Impact of Land Use Changes on Ecosystem Services and Their Economic Values Remote Sens. (IF 4.509) Pub Date : 2021-02-25 Wang Shuangao; Rajchandar Padmanaban; Aires A. Mbanze; João M. N. Silva; Mohamed Shamsudeen; Pedro Cabral; Felipe S. Campos
Accelerated land use change is a current challenge for environmental management worldwide. Given the urgent need to incorporate economic and ecological goals in landscape planning, cost-effective conservation strategies are required. In this study, we validated the benefit of fusing imagery from multiple sensors to assess the impact of landscape changes on ecosystem services (ES) and their economic
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Periglacial Lake Origin Influences the Likelihood of Lake Drainage in Northern Alaska Remote Sens. (IF 4.509) Pub Date : 2021-02-25 Mark J. Lara; Melissa L. Chipman
Nearly 25% of all lakes on earth are located at high latitudes. These lakes are formed by a combination of thermokarst, glacial, and geological processes. Evidence suggests that the origin of periglacial lake formation may be an important factor controlling the likelihood of lakes to drain. However, geospatial data regarding the spatial distribution of these dominant Arctic and subarctic lakes are
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Using a Groundwater Adjusted Water Balance Approach and Copulas to Evaluate Spatial Patterns and Dependence Structures in Remote Sensing Derived Evapotranspiration Products Remote Sens. (IF 4.509) Pub Date : 2021-02-25 Mohsen Soltani; Julian Koch; Simon Stisen
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
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Remotely Sensed Seasonal Shoreward Intrusion of the East Australian Current: Implications for Coastal Ocean Dynamics Remote Sens. (IF 4.509) Pub Date : 2021-02-25 Senyang Xie; Zhi Huang; Xiao Hua Wang
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
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Effect of the Illumination Angle on NDVI Data Composed of Mixed Surface Values Obtained over Vertical-Shoot-Positioned Vineyards Remote Sens. (IF 4.509) Pub Date : 2021-02-25 Pedro C. Towers; Carlos Poblete-Echeverría
Accurate quantification of the spatial variation of canopy size is crucial for vineyard management in the context of Precision Viticulture. Biophysical parameters associated with canopy size, such as Leaf Area Index (LAI), can be estimated from Vegetation Indices (VI) such as the Normalized Difference Vegetation Index (NDVI), but in Vertical-Shoot-Positioned (VSP) vineyards, common satellite, or aerial
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Validation of Visually Interpreted Corine Land Cover Classes with Spectral Values of Satellite Images and Machine Learning Remote Sens. (IF 4.509) Pub Date : 2021-02-25 Orsolya Gyöngyi Varga; Zoltán Kovács; László Bekő; Péter Burai; Zsuzsanna Csatáriné Szabó; Imre Holb; Sarawut Ninsawat; Szilárd Szabó
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
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Automated Machine Learning for High-Throughput Image-Based Plant Phenotyping Remote Sens. (IF 4.509) Pub Date : 2021-02-25 Joshua C.O. Koh; German Spangenberg; Surya Kant
Automated machine learning (AutoML) has been heralded as the next wave in artificial intelligence with its promise to deliver high-performance end-to-end machine learning pipelines with minimal effort from the user. However, despite AutoML showing great promise for computer vision tasks, to the best of our knowledge, no study has used AutoML for image-based plant phenotyping. To address this gap in
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Airborne Laser Scanning Point Cloud Classification Using the DGCNN Deep Learning Method Remote Sens. (IF 4.509) Pub Date : 2021-02-25 Elyta Widyaningrum; Qian Bai; Marda K. Fajari; Roderik C. Lindenbergh
Classification of aerial point clouds with high accuracy is significant for many geographical applications, but not trivial as the data are massive and unstructured. In recent years, deep learning for 3D point cloud classification has been actively developed and applied, but notably for indoor scenes. In this study, we implement the point-wise deep learning method Dynamic Graph Convolutional Neural
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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 Restorationc Remote Sens. (IF 4.509) Pub Date : 2021-02-25 Shaochun Xu; Shuai Xu; Yi Zhou; Shidong Yue; Xiaomei Zhang; Ruiting Gu; Yu Zhang; Yongliang Qiao; Mingjie Liu
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
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Predicting Skipjack Tuna Fishing Grounds in The Western and Central Pacific Ocean Based on High-Spatial–Temporal-Resolution Satellite Data Remote Sens. (IF 4.509) Pub Date : 2021-02-25 Tung-Yao Hsu; Yi Chang; Ming-An Lee; Ren-Fen Wu; Shih-Chun Hsiao
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
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New Orthophoto Generation Strategies from UAV and Ground Remote Sensing Platforms for High-Throughput Phenotyping Remote Sens. (IF 4.509) Pub Date : 2021-02-25 Yi-Chun Lin; Tian Zhou; Taojun Wang; Melba Crawford; Ayman Habib
Remote sensing platforms have become an effective data acquisition tool for digital agriculture. Imaging sensors onboard unmanned aerial vehicles (UAVs) and tractors are providing unprecedented high-geometric-resolution data for several crop phenotyping activities (e.g., canopy cover estimation, plant localization, and flowering date identification). Among potential products, orthophotos play an important
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Spatial Representation of GPR Data—Accuracy of Asphalt Layers Thickness Mapping Remote Sens. (IF 4.509) Pub Date : 2021-02-25 Šime Bezina; Ivica Stančerić; Josipa Domitrović; Tatjana Rukavina
Information on pavement layer thickness is very important for determining bearing capacity, estimating remaining life and strengthening planning. Ground-penetrating radar (GPR) is a nondestructive testing (NDT) method used for determining the continuous pavement layer thickness in the travel direction. The data obtained with GPR in one survey line is suitable for the needs of repair and rehabilitation
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Signal Photon Extraction Method for Weak Beam Data of ICESat-2 Using Information Provided by Strong Beam Data in Mountainous Areas Remote Sens. (IF 4.509) Pub Date : 2021-02-25 Zhiyu Zhang; Xinyuan Liu; Yue Ma; Nan Xu; Wenhao Zhang; Song Li
The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) can measure the elevations of the Earth’s surface using a sampling strategy with unprecedented spatial detail. In the daytime of mountainous areas where the signal–noise ratio (SNR) of weak beam data is very low, current algorithms do not always perform well on extracting signal photons from weak beam data (i.e., many signal photons were missed)
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Multitemporal Water Extraction of Dongting Lake and Poyang Lake Based on an Automatic Water Extraction and Dynamic Monitoring Framework Remote Sens. (IF 4.509) Pub Date : 2021-02-25 Juanjuan Li; Chao Wang; Lu Xu; Fan Wu; Hong Zhang; Bo Zhang
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
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Evaluation and Hydrological Utility of the GPM IMERG Precipitation Products over the Xinfengjiang River Reservoir Basin, China Remote Sens. (IF 4.509) Pub Date : 2021-02-25 Xue Li; Yangbo Chen; Xincui Deng; Yueyuan Zhang; Lingfang Chen
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
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Improved Real-Time Natural Hazard Monitoring Using Automated DInSAR Time Series Remote Sens. (IF 4.509) Pub Date : 2021-02-25 Krisztina Kelevitz; Kristy F. Tiampo; Brianna D. Corsa
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
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Subpixel Change Detection Based on Radial Basis Function with Abundance Image Difference Measure for Remote Sensing Images Remote Sens. (IF 4.509) Pub Date : 2021-02-25 Zhenxuan Li; Wenzhong Shi; Yongchao Zhu; Hua Zhang; Ming Hao; Liping Cai
Recently, land cover change detection has become a research focus of remote sensing. To obtain the change information from remote sensing images at fine spatial and temporal resolutions, subpixel change detection is widely studied and applied. In this paper, a new subpixel change detection method based on radial basis function (RBF) for remote sensing images is proposed, in which the abundance image
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Using Saildrones to Validate Arctic Sea-Surface Salinity from the SMAP Satellite and from Ocean Models Remote Sens. (IF 4.509) Pub Date : 2021-02-24 Jorge Vazquez-Cuervo; Chelle Gentemann; Wenqing Tang; Dustin Carroll; Hong Zhang; Dimitris Menemenlis; Jose Gomez-Valdes; Marouan Bouali; Michael Steele
The Arctic Ocean is one of the most important and challenging regions to observe—it experiences the largest changes from climate warming, and at the same time is one of the most difficult to sample because of sea ice and extreme cold temperatures. Two NASA-sponsored deployments of the Saildrone vehicle provided a unique opportunity for validating sea-surface salinity (SSS) derived from three separate
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An Accurate Geocoding Method for GB-SAR Images Based on Solution Space Search and Its Application in Landslide Monitoring Remote Sens. (IF 4.509) Pub Date : 2021-02-24 Jialun Cai; Hongguo Jia; Guoxiang Liu; Bo Zhang; Qiao Liu; Yin Fu; Xiaowen Wang; Rui Zhang
Although ground-based synthetic aperture radar (GB-SAR) interferometry has a very high precision with respect to deformation monitoring, it is difficult to match the fan-shaped grid coordinates with the local topography in the geographical space because of the slant range projection imaging mode of the radar. To accurately identify the deformation target and its position, high-accuracy geocoding of
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A Wavelength-Resolution SAR Change Detection Method Based on Image Stack through Robust Principal Component Analysis Remote Sens. (IF 4.509) Pub Date : 2021-02-24 Lucas P. Ramos; Alexandre B. Campos; Christofer Schwartz; Leonardo T. Duarte; Dimas I. Alves; Mats I. Pettersson; Viet T. Vu; Renato Machado
Recently, it was demonstrated that low-frequency wavelength-resolution synthetic aperture radar (SAR) images could be considered to follow an additive mixing model due to their backscatter characteristics. This simplification allows for the use of source separation methods, such as robust principal component analysis (RPCA) via principal component pursuit (PCP), for detecting changes in those images
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Fusion of GF and MODIS Data for Regional-Scale Grassland Community Classification with EVI2 Time-Series and Phenological Features Remote Sens. (IF 4.509) Pub Date : 2021-02-24 Zhenjiang Wu; Jiahua Zhang; Fan Deng; Sha Zhang; Da Zhang; Lan Xun; Tehseen Javed; Guizhen Liu; Dan Liu; Mengfei Ji
Satellite-borne multispectral data are suitable for regional-scale grassland community classification owing to comprehensive coverage. However, the spectral similarity of different communities makes it challenging to distinguish them based on a single multispectral data. To address this issue, we proposed a support vector machine (SVM)–based method integrating multispectral data, two-band enhanced
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Vegetation Trends, Drought Severity and Land Use-Land Cover Change during the Growing Season in Semi-Arid Contexts Remote Sens. (IF 4.509) Pub Date : 2021-02-24 Felicia O. Akinyemi
Drought severity and impact assessments are necessary to effectively monitor droughts in semi-arid contexts. However, little is known about the influence land use-land cover (LULC) has—in terms of the differences in annual sizes and configurations—on drought effects. Coupling remote sensing and Geographic Information System techniques, drought evolution was assessed and mapped. During the growing season
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A Regional Zenith Tropospheric Delay (ZTD) Model Based on GPT3 and ANN Remote Sens. (IF 4.509) Pub Date : 2021-02-24 Fei Yang; Jiming Guo; Chaoyang Zhang; Yitao Li; Jun Li
The delays of radio signals transmitted by global navigation satellite system (GNSS) satellites and induced by neutral atmosphere, which are usually represented by zenith tropospheric delay (ZTD), are required as critical information both for GNSS positioning and navigation and GNSS meteorology. Establishing a stable and reliable ZTD model is one of the interests in GNSS research. In this study, we
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Locating and Dating Land Cover Change Events in the Renosterveld, a Critically Endangered Shrubland Ecosystem Remote Sens. (IF 4.509) Pub Date : 2021-02-24 Glenn R. Moncrieff
Land cover change is the leading cause of global biodiversity decline. New satellite platforms allow for monitoring of habitats in increasingly fine detail, but most applications have been limited to forested ecosystems. I demonstrate the potential for detailed mapping and accurate dating of land cover change events in a highly biodiverse, Critically Endangered, shrubland ecosystem—the Renosterveld
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Modeling and Simulation of Very High Spatial Resolution UXOs and Landmines in a Hyperspectral Scene for UAV Survey Remote Sens. (IF 4.509) Pub Date : 2021-02-24 Milan Bajić, Jr.; Milan Bajić
This paper presents methods for the modeling and simulation of explosive target placement in terrain spectral images (i.e., real hyperspectral 90-channel VNIR data), considering unexploded ordnances, landmines, and improvised explosive devices. The models used for landmine detection operate at sub-pixel levels. The presented research uses very fine spatial resolutions, 0.945 × 0.945 mm for targets
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Mars3DNet: CNN-Based High-Resolution 3D Reconstruction of the Martian Surface from Single Images Remote Sens. (IF 4.509) Pub Date : 2021-02-24 Zeyu Chen; Bo Wu; Wai Chung Liu
Three-dimensional (3D) surface models, e.g., digital elevation models (DEMs), are important for planetary exploration missions and scientific research. Current DEMs of the Martian surface are mainly generated by laser altimetry or photogrammetry, which have respective limitations. Laser altimetry cannot produce high-resolution DEMs; photogrammetry requires stereo images, but high-resolution stereo
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Normalized Difference Vegetation Index Temporal Responses to Temperature and Precipitation in Arid Rangelands Remote Sens. (IF 4.509) Pub Date : 2021-02-24 Ernesto Sanz; Antonio Saa-Requejo; Carlos H. Díaz-Ambrona; Margarita Ruiz-Ramos; Alfredo Rodríguez; Eva Iglesias; Paloma Esteve; Bárbara Soriano; Ana M. Tarquis
Rangeland degradation caused by increasing misuses remains a global concern. Rangelands have a remarkable spatiotemporal heterogeneity, making them suitable to be monitored with remote sensing. Among the remotely sensed vegetation indices, Normalized Difference Vegetation Index (NDVI) is most used in ecology and agriculture. In this paper, we research the relationship of NDVI with temperature, precipitation
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Comparison of a Smartfin with an Infrared Sea Surface Temperature Radiometer in the Atlantic Ocean Remote Sens. (IF 4.509) Pub Date : 2021-02-24 Robert J. W. Brewin; Werenfrid Wimmer; Philip J. Bresnahan; Tyler Cyronak; Andreas J. Andersson; Giorgio Dall’Olmo
The accuracy and precision of satellite sea surface temperature (SST) products in nearshore coastal waters are not well known, owing to a lack of in-situ data available for validation. It has been suggested that recreational watersports enthusiasts, who immerse themselves in nearshore coastal waters, be used as a platform to improve sampling and fill this gap. One tool that has been used worldwide
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Using Geophysics to Characterize a Prehistoric Burial Mound in Romania Remote Sens. (IF 4.509) Pub Date : 2021-02-24 Alexandru Hegyi; Dragoș Diaconescu; Petru Urdea; Apostolos Sarris; Michał Pisz; Alexandru Onaca
A geophysical investigation was carried across the M3 burial mound from Silvașu de Jos —Dealu Țapului, a tumuli necropolis in western Romania, where the presence of the Yamnaya people was certified archaeologically. For characterizing the inner structure of the mound, two conventional geophysical methods have been used: a geomagnetic survey and electrical resistivity tomography (ERT). The results allowed
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Can Nighttime Satellite Imagery Inform Our Understanding of Education Inequality? Remote Sens. (IF 4.509) Pub Date : 2021-02-24 Bingxin Qi; Xuantong Wang; Paul Sutton
Education is a human right, and equal access to education is important for achieving sustainable development. Measuring socioeconomic development, especially the changes to education inequality, can help educators, practitioners, and policymakers with decision- and policy-making. This article presents an approach that combines population distribution, human settlements, and nighttime light (NTL) data
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SEVIRI Aerosol Optical Depth Validation Using AERONET and Intercomparison with MODIS in Central and Eastern Europe Remote Sens. (IF 4.509) Pub Date : 2021-02-24 Ajtai N.; Mereuta A.; Stefanie H.; Radovici A.; Botezan C.; Zawadzka-Manko O.; Stachlewska I. S.; Stebel K.; Zehner C.
This paper presents the validation results of Aerosol Optical Depth (AOD) retrieved from the Spinning Enhanced Visible Infrared Radiometer (SEVIRI) data using the near-real-time algorithm further developed in the frame of the Satellite-based Monitoring Initiative for Regional Air quality (SAMIRA) project. The SEVIRI AOD was compared against multiple data sources: six stations of the Aerosol Robotic
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Unsupervised Machine Learning on Domes in the Lunar Gardner Region: Implications for Dome Classification and Local Magmatic Activities on the Moon Remote Sens. (IF 4.509) Pub Date : 2021-02-24 Yuchao Chen; Qian Huang; Jiannan Zhao; Xiangyun Hu
Lunar volcanic domes are essential windows into the local magmatic activities on the Moon. Classification of domes is a useful way to figure out the relationship between dome appearances and formation processes. Previous studies of dome classification were manually or semi-automatically carried out either qualitatively or quantitively. We applied an unsupervised machine-learning method to domes that
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Uncertainty Assessment of the Vertically-Resolved Cloud Amount for Joint CloudSat–CALIPSO Radar–Lidar Observations Remote Sens. (IF 4.509) Pub Date : 2021-02-23 Andrzej Z. Kotarba; Mateusz Solecki
The joint CloudSat–Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) climatology remains the only dataset that provides a global, vertically-resolved cloud amount statistic. However, data are affected by uncertainty that is the result of a combination of infrequent sampling, and a very narrow, pencil-like swath. This study provides the first global assessment of these uncertainties
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Deep Learning-Based Semantic Segmentation of Urban Features in Satellite Images: A Review and Meta-Analysis Remote Sens. (IF 4.509) Pub Date : 2021-02-23 Bipul Neupane; Teerayut Horanont; Jagannath Aryal
Availability of very high-resolution remote sensing images and advancement of deep learning methods have shifted the paradigm of image classification from pixel-based and object-based methods to deep learning-based semantic segmentation. This shift demands a structured analysis and revision of the current status on the research domain of deep learning-based semantic segmentation. The focus of this
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Mapping Woody Volume of Mediterranean Forests by Using SAR and Machine Learning: A Case Study in Central Italy Remote Sens. (IF 4.509) Pub Date : 2021-02-23 Emanuele Santi; Marta Chiesi; Giacomo Fontanelli; Alessandro Lapini; Simonetta Paloscia; Simone Pettinato; Giuliano Ramat; Leonardo Santurri
In this paper, multi-frequency synthetic aperture radar (SAR) data at L- and C-bands (ALOS PALSAR and Envisat/ASAR) were used to estimate forest biomass in Tuscany, in Central Italy. The ground measurements of woody volume (WV, in m3/ha), which can be considered as a proxy of forest biomass, were retrieved from the Italian National Forest Inventory (NFI). After a preliminary investigation to assess
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Beamforming of LOFAR Radio-Telescope for Passive Radiolocation Purposes Remote Sens. (IF 4.509) Pub Date : 2021-02-23 Aleksander Droszcz; Konrad Jędrzejewski; Julia Kłos; Krzysztof Kulpa; Mariusz Pożoga
This paper presents the results of investigations on the beamforming of a low-frequency radio-telescope LOFAR which can be used as a receiver in passive coherent location (PCL) radars for aerial and space object detection and tracking. The use of a LOFAR radio-telescope for the passive tracking of space objects can be a highly cost-effective solution due to the fact that most of the necessary equipment
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Intercomparison of Global Sea Surface Salinity from Multiple Datasets over 2011–2018 Remote Sens. (IF 4.509) Pub Date : 2021-02-23 Hao Liu; Zexun Wei
The variability in sea surface salinity (SSS) on different time scales plays an important role in associated oceanic or climate processes. In this study, we compare the SSS on sub-annual, annual, and interannual time scales among ten datasets, including in situ-based and satellite-based SSS products over 2011–2018. Furthermore, the dominant mode on different time scales is compared using the empirical
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Rangeland Fractional Components Across the Western United States from 1985 to 2018 Remote Sens. (IF 4.509) Pub Date : 2021-02-23 Matthew Rigge; Collin Homer; Hua Shi; Debra K. Meyer; Brett Bunde; Brian Granneman; Kory Postma; Patrick Danielson; Adam Case; George Xian
Monitoring temporal dynamics of rangelands to detect and understand change in vegetation cover and composition provides a wealth of information to improve management and sustainability. Remote sensing allows the evaluation of both abrupt and gradual rangeland change at unprecedented spatial and temporal extents. Here, we describe the production of the National Land Cover Database (NLCD) Back in Time
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Multi-Feature Fusion for Weak Target Detection on Sea-Surface Based on FAR Controllable Deep Forest Model Remote Sens. (IF 4.509) Pub Date : 2021-02-23 Jiahuan Zhang; Hongjun Song
Target detection on the sea-surface has always been a high-profile problem, and the detection of weak targets is one of the most difficult problems and the key issue under this problem. Traditional techniques, such as imaging, cannot effectively detect these types of targets, so researchers choose to start by mining the characteristics of the received echoes and other aspects for target detection.
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InSAR Monitoring of Landslide Activity in Dominica Remote Sens. (IF 4.509) Pub Date : 2021-02-23 Mary-Anne Fobert; Vern Singhroy; John G. Spray
Dominica is a geologically young, volcanic island in the eastern Caribbean. Due to its rugged terrain, substantial rainfall, and distinct soil characteristics, it is highly vulnerable to landslides. The dominant triggers of these landslides are hurricanes, tropical storms, and heavy prolonged rainfall events. These events frequently lead to loss of life and the need for a growing portion of the island’s
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Landsat and Sentinel-2 Based Burned Area Mapping Tools in Google Earth Engine Remote Sens. (IF 4.509) Pub Date : 2021-02-23 Ekhi Roteta; Aitor Bastarrika; Magí Franquesa; Emilio Chuvieco
Four burned area tools were implemented in Google Earth Engine (GEE), to obtain regular processes related to burned area (BA) mapping, using medium spatial resolution sensors (Landsat and Sentinel-2). The four tools are (i) the BA Cartography tool for supervised burned area over the user-selected extent and period, (ii) two tools implementing a BA stratified random sampling to select the scenes and
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Upscaling Northern Peatland CO2 Fluxes Using Satellite Remote Sensing Data Remote Sens. (IF 4.509) Pub Date : 2021-02-23 Sofia Junttila; Julia Kelly; Natascha Kljun; Mika Aurela; Leif Klemedtsson; Annalea Lohila; Mats B. Nilsson; Janne Rinne; Eeva-Stiina Tuittila; Patrik Vestin; Per Weslien; Lars Eklundh
Peatlands play an important role in the global carbon cycle as they contain a large soil carbon stock. However, current climate change could potentially shift peatlands from being carbon sinks to carbon sources. Remote sensing methods provide an opportunity to monitor carbon dioxide (CO2) exchange in peatland ecosystems at large scales under these changing conditions. In this study, we developed empirical
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Fusion of Airborne LiDAR Point Clouds and Aerial Images for Heterogeneous Land-Use Urban Mapping Remote Sens. (IF 4.509) Pub Date : 2021-02-23 Yasmine Megahed; Ahmed Shaker; Wai Yeung Yan
The World Health Organization has reported that the number of worldwide urban residents is expected to reach 70% of the total world population by 2050. In the face of challenges brought about by the demographic transition, there is an urgent need to improve the accuracy of urban land-use mappings to more efficiently inform about urban planning processes. Decision-makers rely on accurate urban mappings
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TNNG: Total Nuclear Norms of Gradients for Hyperspectral Image Prior Remote Sens. (IF 4.509) Pub Date : 2021-02-23 Ryota Yuzuriha; Ryuji Kurihara; Ryo Matsuoka; Masahiro Okuda
We introduce a novel regularization function for hyperspectral image (HSI), which is based on the nuclear norms of gradient images. Unlike conventional low-rank priors, we achieve a gradient-based low-rank approximation by minimizing the sum of nuclear norms associated with rotated planes in the gradient of a HSI. Our method explicitly and simultaneously exploits the correlation in the spectral domain
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Multiscale Weighted Adjacent Superpixel-based Composite Kernel for Hyperspectral Image Classification Remote Sens. (IF 4.509) Pub Date : 2021-02-23 Yaokang Zhang; Yunjie Chen
This paper presents a composite kernel method (MWASCK) based on multiscale weighted adjacent superpixels (ASs) to classify hyperspectral image (HSI). The MWASCK adequately exploits spatial-spectral features of weighted adjacent superpixels to guarantee that more accurate spectral features can be extracted. Firstly, we use a superpixel segmentation algorithm to divide HSI into multiple superpixels.
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Improving the Selection of Vegetation Index Characteristic Wavelengths by Using the PROSPECT Model for Leaf Water Content Estimation Remote Sens. (IF 4.509) Pub Date : 2021-02-23 Jian Yang; Yangyang Zhang; Lin Du; Xiuguo Liu; Shuo Shi; Biwu Chen
Equivalent water thickness (EWT) is a major indicator for indirect monitoring of leaf water content in remote sensing. Many vegetation indices (VIs) have been proposed to estimate EWT based on passive or active reflectance spectra. However, the selection of the characteristics wavelengths of VIs is mainly based on statistical analysis for specific vegetation species. In this study, a characteristic
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Diurnal Cycle of Passive Microwave Brightness Temperatures over Land at a Global Scale Remote Sens. (IF 4.509) Pub Date : 2021-02-23 Zahra Sharifnezhad; Hamid Norouzi; Satya Prakash; Reginald Blake; Reza Khanbilvardi
Satellite-borne passive microwave radiometers provide brightness temperature (TB) measurements in a large spectral range which includes a number of frequency channels and generally two polarizations: horizontal and vertical. These TBs are widely used to retrieve several atmospheric and surface variables and parameters such as precipitation, soil moisture, water vapor, air temperature profile, and land
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Interdecadal Changes in Aerosol Optical Depth Over Pakistan Based on the MERRA-2 Reanalysis Data During 1980–2018 Remote Sens. (IF 4.509) Pub Date : 2021-02-23 Rehana Khan; Kanike Raghavendra Kumar; Tianliang Zhao; Waheed Ullah; Gerrit de Leeuw
The spatiotemporal evolution and trends in aerosol optical depth (AOD) over environmentally distinct regions in Pakistan are investigated for the period 1980–2018. The AOD data for this period was obtained from the Modern-era retrospective analysis for research and applications, version 2 (MERRA-2) reanalysis atmospheric products, together with the Moderate-resolution imaging spectroradiometer (MODIS)
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High-Accuracy Real-time Kinematic Positioning with Multiple Rover Receivers Sharing Common Clock Remote Sens. (IF 4.509) Pub Date : 2021-02-23 Lin Zhao; Jiachang Jiang; Liang Li; Chun Jia; Jianhua Cheng
Since the traditional real-time kinematic positioning method is limited by the reduced satellite visibility from the deprived navigational environments, we, therefore, propose an improved RTK method with multiple rover receivers sharing a common clock. The proposed method can enhance observational redundancy by blending the observations from each rover receiver together so that the model strength will
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The SARSense Campaign: Air- and Space-Borne C- and L-Band SAR for the Analysis of Soil and Plant Parameters in Agriculture Remote Sens. (IF 4.509) Pub Date : 2021-02-23 David Mengen; Carsten Montzka; Thomas Jagdhuber; Anke Fluhrer; Cosimo Brogi; Stephani Baum; Dirk Schüttemeyer; Bagher Bayat; Heye Bogena; Alex Coccia; Gerard Masalias; Verena Trinkel; Jannis Jakobi; François Jonard; Yueling Ma; Francesco Mattia; Davide Palmisano; Uwe Rascher; Giuseppe Satalino; Maike Schumacher; Christian Koyama; Marius Schmidt; Harry Vereecken
With the upcoming L-band Synthetic Aperture Radar (SAR) satellite mission Radar Observing System for Europe L-band SAR (ROSE-L) and its integration into existing C-band satellite missions such as Sentinel-1, multi-frequency SAR observations with high temporal and spatial resolution will become available. The SARSense campaign was conducted between June and August 2019 to investigate the potential for
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The Impact of Shale Oil and Gas Development on Rangelands in the Permian Basin Region: An Assessment Using High-Resolution Remote Sensing Data Remote Sens. (IF 4.509) Pub Date : 2021-02-23 Haoying Wang
The environmental impact of shale energy development is a growing concern in the US and worldwide. Although the topic is well-studied in general, shale development’s impact on drylands has received much less attention in the literature. This study focuses on the effect of shale development on land cover in the Permian Basin region—a unique arid/semi-arid landscape experiencing an unprecedented intensity
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Assessing Near Real-Time Satellite Precipitation Products for Flood Simulations at Sub-Daily Scales in a Sparsely Gauged Watershed in Peruvian Andes Remote Sens. (IF 4.509) Pub Date : 2021-02-23 Harold Llauca; Waldo Lavado-Casimiro; Karen León; Juan Jimenez; Kevin Traverso; Pedro Rau
This study investigates the applicability of Satellite Precipitation Products (SPPs) in near real-time for the simulation of sub-daily runoff in the Vilcanota River basin, located in the southeastern Andes of Peru. The data from rain gauge stations are used to evaluate the quality of Integrated Multi-satellite Retrievals for GPM–Early (IMERG-E), Global Satellite Mapping of Precipitation–Near Real-Time
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Hyperspectral Image Destriping and Denoising Using Stripe and Spectral Low-Rank Matrix Recovery and Global Spatial-Spectral Total Variation Remote Sens. (IF 4.509) Pub Date : 2021-02-23 Fang Yang; Xin Chen; Li Chai
Hyperspectral image (HSI) is easily corrupted by different kinds of noise, such as stripes, dead pixels, impulse noise, Gaussian noise, etc. Due to less consideration of the structural specificity of stripes, many existing HSI denoising methods cannot effectively remove the heavy stripes in mixed noise. In this paper, we classify the noise on HSI into three types: sparse noise, stripe noise, and Gaussian
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Tracking the Evolution of Riverbed Morphology on the Basis of UAV Photogrammetry Remote Sens. (IF 4.509) Pub Date : 2021-02-23 Teresa Gracchi; Guglielmo Rossi; Carlo Tacconi Stefanelli; Luca Tanteri; Rolando Pozzani; Sandro Moretti
Unmanned aerial vehicle (UAV) photogrammetry has recently become a widespread technique to investigate and monitor the evolution of different types of natural processes. Fluvial geomorphology is one of such fields of application where UAV potentially assumes a key role, since it allows for overcoming the intrinsic limits of satellite and airborne-based optical imagery on one side, and in situ traditional
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Factors Influencing the Accuracy of Shallow Snow Depth Measured Using UAV-Based Photogrammetry Remote Sens. (IF 4.509) Pub Date : 2021-02-23 Sangku Lee; Jeongha Park; Eunsoo Choi; Dongkyun Kim
Factors influencing the accuracy of UAV-photogrammetry-based snow depth distribution maps were investigated. First, UAV-based surveys were performed on the 0.04 km2 snow-covered study site in South Korea for 37 times over the period of 13 days under 16 prescribed conditions composed of various photographing times, flight altitudes, and photograph overlap ratios. Then, multi-temporal Digital Surface
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