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Special Issue "Remote Sensor Based Geoscience Applications"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: 30 September 2020.

Special Issue Editors

Dr. Alessandro Bonforte
Website
Guest Editor
Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Catania – Osservatorio Etneo. Piazza Roma, 2 – 95125 Catania, Italy
Interests: ground deformation; volcano geodesy; volcano-tectonics; volcanology; active tectonics
Special Issues and Collections in MDPI journals
Dr. Jan Kropáček
Website
Guest Editor
Department of Applied Geoinformatics and Spatial Planning Czech University of Life Sciences Prague, Kamycka 129, 16500 Prague, Czechia
Interests: cryosphere change; monitoring of lakes; mapping of snow; assessment of geohazards; optical remote sensing; satellite altimetry; historical remote sensing data; reconnaissance satellites
Dr. Francesco Zucca
Website
Guest Editor
Earth & Env. Sciences Dept.-University of Pavia, 27100 Pavia Italy
Interests: change analysis; multi temporal; hyperspectral; UAV; SAR; InSAR; landslides; virtual outcrops; soil moisture; 3D
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The main objective of this Special Issue is to collect papers (both original research articles and review papers) to give insights on new geosciences applications of remote sensors and to understand the state-of-the-art of the very broad spectrum of applications and sensors used for the Geosciences.

The planetary emergencies (e.g., climate change, extreme events) and the risks they pose to populations, infrastructures, and ecosystems require increasingly advanced knowledge as well as the advanced collection and exploitation of data and information to face hazards.

Progress in remote sensing technology since the mid-2000s has drastically increased the availability of data. Technological evolution related to new sensors, platforms, and of course new data processing approaches that are increasingly powerful, rapid, and more accessible, has laid the foundations for a revolution that was unimaginable just a few years ago.

This Special Issue would like to represent a complete and up-to-date perspective of how the development of new sensors, new networks, and the integration of heterogeneous measurement systems and new processing technologies may contribute to improve knowledge and open up new horizons and challenges facing geoscientists.

Dr. Alessandro Bonforte
Dr. Jan Kropáček
Dr. Francesco Zucca
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • natural hazards 
  • Earth observation
  • geodynamics 
  • climate 
  • ecosystem 
  • monitoring 
  • remote sensing
  • satellite 
  • UAV

Published Papers (4 papers)

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Research

Open AccessArticle
Improving Radar Rainfall Estimations with Scaled Raindrop Size Spectra in Mei-Yu Frontal Rainstorms
Sensors 2020, 20(18), 5257; https://doi.org/10.3390/s20185257 - 14 Sep 2020
Abstract
Hydrological calibration of raw weather radar rainfall estimation relies on in situ rainfall measurements. Raindrop size distribution (DSD) was collected during three typical Mei-Yu rainstorms in July 2014 using three particle size velocity (Parsivel) DSD sensors along the Mei-Yu front in Nanjing, Chuzhou, [...] Read more.
Hydrological calibration of raw weather radar rainfall estimation relies on in situ rainfall measurements. Raindrop size distribution (DSD) was collected during three typical Mei-Yu rainstorms in July 2014 using three particle size velocity (Parsivel) DSD sensors along the Mei-Yu front in Nanjing, Chuzhou, and the western Pacific, respectively. To improve the radar precipitation estimation in different parts of the Mei-Yu front, a scaling method was adopted to formulate the DSD model and further derive the ZR relations. The results suggest a distinct variation of DSDs in different parts of the Mei-Yu front. Compared with statistical radar Z–ARb relations obtained by mathematical fitting techniques, the use of a DSD model fitting based on a scaling law formulation theoretically shows a significant improvement in both stratiform (33.9%) and convective (2.8%) rainfall estimations of the Mei-Yu frontal system, which indicates that using a scaling law can better reflect the DSD variations in different parts of the Mei-Yu front. Polarimetric radar has indisputable advantages with multiparameter detection ability. Several dual-polarization radar estimators are also established by DSD sensor data, and the R(ZH, ZDR) estimator is proven to be more accurate than traditional Z–R relations in Mei-Yu frontal rainfall, with potential applications for operational C-band polarimetric radar. Full article
(This article belongs to the Special Issue Remote Sensor Based Geoscience Applications)
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Open AccessArticle
ANN-Based Estimation of Low-Latitude Monthly Ocean Latent Heat Flux by Ensemble Satellite and Reanalysis Products
Sensors 2020, 20(17), 4773; https://doi.org/10.3390/s20174773 - 24 Aug 2020
Abstract
Ocean latent heat flux (LHF) is an essential variable for air–sea interactions, which establishes the link between energy balance, water and carbon cycle. The low-latitude ocean is the main heat source of the global ocean and has a great influence on [...] Read more.
Ocean latent heat flux (LHF) is an essential variable for air–sea interactions, which establishes the link between energy balance, water and carbon cycle. The low-latitude ocean is the main heat source of the global ocean and has a great influence on global climate change and energy transmission. Thus, an accuracy estimation of high-resolution ocean LHF over low-latitude area is vital to the understanding of energy and water cycle, and it remains a challenge. To reduce the uncertainties of individual LHF products over low-latitude areas, four machine learning (ML) methods (Artificial Neutral Network (ANN), Random forest (RF), Bayesian Ridge regression and Random Sample Consensus (RANSAC) regression) were applied to estimate low-latitude monthly ocean LHF by using two satellite products (JOFURO-3 and GSSTF-3) and two reanalysis products (MERRA-2 and ERA-I). We validated the estimated ocean LHF using 115 widely distributed buoy sites from three buoy site arrays (TAO, PIRATA and RAMA). The validation results demonstrate that the performance of LHF estimations derived from the ML methods (including ANN, RF, BR and RANSAC) were significantly better than individual LHF products, indicated by R2 increasing by 3.7–46.4%. Among them, the LHF estimation using the ANN method increased the R2 of the four-individual ocean LHF products (ranging from 0.56 to 0.79) to 0.88 and decreased the RMSE (ranging from 19.1 to 37.5) to 11 W m−2. Compared to three other ML methods (RF, BR and RANSAC), ANN method exhibited the best performance according to the validation results. The results of relative uncertainty analysis using the triangle cornered hat (TCH) method show that the ensemble LHF product using ML methods has lower relative uncertainty than individual LHF product in most area. The ANN was employed to implement the mapping of annual average ocean LHF over low-latitude at a spatial resolution of 0.25° during 2003–2007. The ocean LHF fusion products estimated from ANN methods were 10–30 W m−2 lower than those of the four original ocean products (MERRA-2, JOFURO-3, ERA-I and GSSTF-3) and were more similar to observations. Full article
(This article belongs to the Special Issue Remote Sensor Based Geoscience Applications)
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Open AccessArticle
Detecting Matching Blunders of Multi-Source Remote Sensing Images via Graph Theory
Sensors 2020, 20(13), 3712; https://doi.org/10.3390/s20133712 - 02 Jul 2020
Abstract
Large radiometric and geometric distortion in multi-source images leads to fewer matching points with high matching blunder ratios, and global geometric relationship models between multi-sensor images are inexplicit. Thus, traditional matching blunder detection methods cannot work effectively. To address this problem, we propose [...] Read more.
Large radiometric and geometric distortion in multi-source images leads to fewer matching points with high matching blunder ratios, and global geometric relationship models between multi-sensor images are inexplicit. Thus, traditional matching blunder detection methods cannot work effectively. To address this problem, we propose two matching blunder detection methods based on graph theory. The proposed methods can build statistically significant clusters in the case of few matching points with high matching blunder ratios, and use local geometric similarity constraints to detect matching blunders when the global geometric relationship is not explicit. The first method (named the complete graph-based method) uses clusters constructed by matched triangles in complete graphs to encode the local geometric similarity of images, and it can detect matching blunders effectively without considering the global geometric relationship. The second method uses the triangular irregular network (TIN) graph to approximate a complete graph to reduce to computational complexity of the first method. We name this the TIN graph-based method. Experiments show that the two graph-based methods outperform the classical random sample consensus (RANSAC)-based method in recognition rate, false rate, number of remaining matching point pairs, dispersion, positional accuracy in simulated and real data (image pairs from Gaofen1, near infrared ray of Gaofen1, Gaofen2, panchromatic Landsat, Ziyuan3, Jilin1and unmanned aerial vehicle). Notably, in most cases, the mean false rates of RANSAC, the complete graph-based method and the TIN graph-based method in simulated data experiments are 0.50, 0.26 and 0.14, respectively. In addition, the mean positional accuracy (RMSE measured in units of pixels) of the three methods is 2.6, 1.4 and 1.5 in real data experiments, respectively. Furthermore, when matching blunder ratio is no higher than 50%, the computation time of the TIN graph-based method is nearly equal to that of the RANSAC-based method, and roughly 2 to 40 times less than that of the complete graph-based method. Full article
(This article belongs to the Special Issue Remote Sensor Based Geoscience Applications)
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Open AccessArticle
Multi-Scale Feature Integrated Attention-Based Rotation Network for Object Detection in VHR Aerial Images
Sensors 2020, 20(6), 1686; https://doi.org/10.3390/s20061686 - 18 Mar 2020
Cited by 3
Abstract
Accurate and robust detection of multi-class objects in very high resolution (VHR) aerial images has been playing a significant role in many real-world applications. The traditional detection methods have made remarkable progresses with horizontal bounding boxes (HBBs) due to CNNs. However, HBB detection [...] Read more.
Accurate and robust detection of multi-class objects in very high resolution (VHR) aerial images has been playing a significant role in many real-world applications. The traditional detection methods have made remarkable progresses with horizontal bounding boxes (HBBs) due to CNNs. However, HBB detection methods still exhibit limitations including the missed detection and the redundant detection regions, especially for densely-distributed and strip-like objects. Besides, large scale variations and diverse background also bring in many challenges. Aiming to address these problems, an effective region-based object detection framework named Multi-scale Feature Integration Attention Rotation Network (MFIAR-Net) is proposed for aerial images with oriented bounding boxes (OBBs), which promotes the integration of the inherent multi-scale pyramid features to generate a discriminative feature map. Meanwhile, the double-path feature attention network supervised by the mask information of ground truth is introduced to guide the network to focus on object regions and suppress the irrelevant noise. To boost the rotation regression and classification performance, we present a robust Rotation Detection Network, which can generate efficient OBB representation. Extensive experiments and comprehensive evaluations on two publicly available datasets demonstrate the effectiveness of the proposed framework. Full article
(This article belongs to the Special Issue Remote Sensor Based Geoscience Applications)
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