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Remote Sensing
Special Issues
Applications of SAR for Environment Observation Analysis
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Applications of SAR for Environment Observation Analysis

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 2489

Special Issue Editors

Dr. Vahid Akbari

E-Mail Website
Guest Editor
Department of Computing Science and Mathematics, University of Stirling, Stirling FK9 4LA, UK
Interests: earth observation; SAR polarimetry; SAR interferometry; machine learning; multivariate statistical analysis
Dr. Nizar Bouhlel

E-Mail Website
Guest Editor
Institut Agro, Univ Angers1, INRAE, IRHS, SFR QuaSaV, 49000 Angers, France
Interests: multivariate statistics; statistical image processing; statistical signal processing; remote sensing; SAR; SAR polarimetry
Special Issues, Collections and Topics in MDPI journals
Dr. Alireza Tabatabaeenejad

E-Mail Website
Guest Editor
The Aerospace Corporation, El Segundo, CA 90245, USA
Interests: radar; remote sensing; antennas and propagation; applied electromagnetics; scattering; inverse modelling
Prof. Dr. Esra Erten

E-Mail Website
Guest Editor
Geomatic Engineering Department, Civil Engineering Faculty, Istanbul Technical University, 34469 Maslak Istanbul, Turkey
Interests: synthetic aperture radar (SAR); polarimetric SAR; multidimensional SAR; multivariate statistics; uncertainty qualification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent decades, an unprecedented amount of synthetic aperture radar (SAR) data have been gathered by the remote sensing community, boosting the development of an increasing number of applications for the analysis of our environment. This is due to the ability of SAR sensors to operate independently of solar illuminations and penetrate clouds. Compared with traditional optical imaging, SAR imaging provides more details about the surface of the Earth because of the way in which SAR signals interact with particular surfaces. A common feature among all SAR satellites is their more detailed collection of data about the Earth’s surface, which makes SAR imagery a highly valuable tool for scientists and policymakers to better understand our changing environment.

This Special Issue aims to publish the latest research advances in statistical modelling, processing, and analysis of SAR remote sensing data for environmental observation analysis. Articles may cover applications of SAR polarimetry, interferometry, and tomography in 1) land, including forests, agriculture, and wetlands; 2) oceans, including ship detection, pollution monitoring, and parameter retrieval; 3) cryospheres, including snow, sea, and land ice, and iceberg detection; and 4) hazards, including floods, earthquakes, fires, volcanoes, landsides, and subsidence.  Scholars, researchers, and engineers are cordially invited to submit their high-quality research articles and reviews for publication in this Special Issue.

Articles may address, but are not limited to, one of the following topics:

  • Statistical modelling of synthetic aperture radar data for land cover classification, object detection, and change detection;
  • Multitemporal big SAR data for environment and resource monitoring;
  • Multimodal (multi-frequency, multi-sensor, multi-resolution) SAR data processing for observation analysis;
  • Deep learning/machine learning techniques in complex SAR data;
  • SAR for sustainable development goals.

Dr. Vahid Akbari
Dr. Nizar Bouhlel
Dr. Alireza Tabatabaeenejad
Prof. Dr. Esra Erten
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 submissions that pass pre-check are 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. Remote Sensing 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 2700 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

  • synthetic aperture radar
  • earth observation
  • machine (deep) learning
  • image processing
  • statistical signal processing
  • environmental monitoring
  • radar polarimetry
  • radar interferometry
  • multivariate statistical analysis
  • big data analysis

Published Papers (2 papers)

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Research

28 pages, 19592 KiB  
Article
Evaluating the Uncertainty in Coherence-Change-Detection-Based Maps of Torrential Sediment Transport in Arid Environments
by Joan Botey i Bassols, Carmen Bedia, María Cuevas-González, Sonia Valdivielso, Michele Crosetto and Enric Vázquez-Suñé
Remote Sens. 2023, 15(20), 4964; https://doi.org/10.3390/rs15204964 - 14 Oct 2023
Viewed by 843
Abstract
InSAR coherence-change detection (CCD) is a promising remote sensing technique that is able to map areas affected by torrential sediment transport triggered by flash floods in arid environments. CCD maps the changes in the interferometric coherence between synthetic aperture radar images (InSAR coherence), [...] Read more.
InSAR coherence-change detection (CCD) is a promising remote sensing technique that is able to map areas affected by torrential sediment transport triggered by flash floods in arid environments. CCD maps the changes in the interferometric coherence between synthetic aperture radar images (InSAR coherence), a parameter that measures the stability of the radar signal between two different SAR images, i.e., data acquisitions. In arid environments, such changes are mainly due to changes in the surface. However, the residual effect of other factors on the InSAR coherence cannot be completely excluded. Therefore, CCD-based maps contain the uncertainty of whether the detected changes are actual changes in the observed surface or just errors related to those residual effects. Thus, in this paper, the results of four CCD mapping methods, with different degrees of complexity and sensitivity to the different factors affecting the InSAR coherence, are compared in order to evaluate the existence of the errors and their importance. The obtained CCD maps are also compared with changes in satellite optical images and a field campaign. The results lead to the conclusion that CCD maps are reliable in the identification of the zones affected by sediment transport, although the precision in the delimitation of the affected area remains an open issue. However, highly rugged relief areas still require a thorough analysis of the results in order to discard the geometric effects related to the perpendicular baseline. Full article
(This article belongs to the Special Issue Applications of SAR for Environment Observation Analysis)
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26 pages, 12615 KiB  
Article
Sentinel-1 Time Series for Predicting Growing Stock Volume of Boreal Forest: Multitemporal Analysis and Feature Selection
by Shaojia Ge, Erkki Tomppo, Yrjö Rauste, Ronald E. McRoberts, Jaan Praks, Hong Gu, Weimin Su and Oleg Antropov
Remote Sens. 2023, 15(14), 3489; https://doi.org/10.3390/rs15143489 - 11 Jul 2023
Cited by 1 | Viewed by 1053
Abstract
Copernicus Sentinel-1 images are widely used for forest mapping and predicting forest growing stock volume (GSV) due to their accessibility. However, certain important aspects related to the use of Sentinel-1 time series have not been thoroughly explored in the literature. These include the [...] Read more.
Copernicus Sentinel-1 images are widely used for forest mapping and predicting forest growing stock volume (GSV) due to their accessibility. However, certain important aspects related to the use of Sentinel-1 time series have not been thoroughly explored in the literature. These include the impact of image time series length on prediction accuracy, the optimal feature selection approaches, and the best prediction methods. In this study, we conduct an in-depth exploration of the potential of long time series of Sentinel-1 SAR data to predict forest GSV and evaluate the temporal dynamics of the predictions using extensive reference data. Our boreal coniferous forests study site is located near the Hyytiälä forest station in central Finland and covers an area of 2500 km2 with nearly 17,000 stands. We considered several prediction approaches and fine-tuned them to predict GSV in various evaluation scenarios. Our analyses used 96 Sentinel-1 images acquired over three years. Different approaches for aggregating SAR images and choosing feature (predictor) variables were evaluated. Our results demonstrate a considerable decrease in the root mean squared errors (RMSEs) of GSV predictions as the number of images increases. While prediction accuracy using individual Sentinel-1 images varied from 85 to 91 m3/ha RMSE, prediction accuracy with combined images decreased to 75.6 m3/ha. Feature extraction and dimension reduction techniques facilitated the achievement of near-optimal prediction accuracy using only 8–10 images. Examined methods included radiometric contrast, mutual information, improved k-Nearest Neighbors, random forests selection, Lasso, and Wrapper approaches. Lasso was the most optimal, with RMSE reaching 77.1 m3/ha. Finally, we found that using assemblages of eight consecutive images resulted in the greatest accuracy in predicting GSV when initial acquisitions started between September and January. Full article
(This article belongs to the Special Issue Applications of SAR for Environment Observation Analysis)
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