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Active and Passive Remote Sensing of Oceans and Environment (APRSOE or APRS)

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

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 13431

Special Issue Editor


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Guest Editor
Lab-STICC, UMR CNRS 6285, ENSTA Bretagne, 29806 Brest, France
Interests: computer science; engineering; observation; propagation; wave scattering; scattering in random media; monostatic and bistatic scattering; electromagnetic radar cross section; sea clutter; active and passive sensors (Radar, Lidar, Optics, GNSS); radar applications; data assimilation (n-D); sea surface and environment; extraction of parameters from the observed scene: imagery and target parameter estimation; direct and inverse problems; remote sensing of the ocean and the environment
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Special Issue Information

Dear Colleagues,

Observation and perception systems play an increasingly important role in the control, detection, localization, and monitoring of objects present in a natural environment (or only in the characterization and extraction of information from that environment). For example, the sea surface (or under the sea surface) is a complex environment and, in a coastal area, many practices can be distinguished. In this Special Issue, we provide an inventory of the progress made in exploiting active or passive sensors in data collection and exploitation in remote sensing of the oceans and the natural environment. This could include four important factors in decision-making, including the extraction of parameters from the observed area. The first factor concerns the utility of different sensors (depending on the different characteristics and limits of each, but also according to the intended application), as well as their use for the observation and perception of different scenes (depending on the geometry observation, multisource data, temporal data, multiscale data or multiphysical data). The second factor concerns the modeling, the fine characterization, and the specificities of the observed scene (sea, coastal zones, heterogeneous zones of the sea, and other zones of the environment). As for the third factor, it includes taking into account the physical aspects of the observed area. Finally, the fourth factor concerns the problem of inversion, including the innovative processing of available signals/images and/or those collected by sensors, and the extraction, from airborne or satellite images, of the relevant parameters (salinity and temperature of ocean surface water, wind speed and direction, currents, pollutants, moisture content, etc.) of the observed scene.

In this Special Issue, we provide an overview of technological and scientific advances in remote sensing by passive or active sensors (such as radar, lidar, optical, and GNSS sensors) in oceans, coastal areas, and the environment. In particular, it will be important to present new advances in the control of the dynamic nature of the oceans (in deep waters), but also in the relatively heterogeneous nature of the coasts, particularly in the context of climate change. In the estimation of parameters and characteristics of the observed surface, this Special Issue is also interested in different applications integrating physical and hydrodynamic phenomena. Authors are encouraged to submit contributions on the exploitation of satellite or airborne images or other signals of interest in the context of environmental characterization problems, including data fusion techniques support for decision, and artificial intelligence for different applications related to the environment. This could include issues such as automatic target recognition (ATR) and changes and/or modifications in an environment such as maritime environments.

Dr. Ali Khenchaf
Guest Editor

Manuscript Submission Information

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Keywords

  • Active and passive sensors
  • GNSS, radar and lidar
  • New sensors/platforms
  • Heterogeneous environment (maritime, terrestrial, etc.)
  • Monostatic, bistatic, multistatic configurations
  • Electromagnetic/physical/hydrodynamic modeling
  • EM scattering models/methods, clutter
  • Direct and inverse problems
  • Airborne, satellite and GNSS data
  • Corrections and data preparation of radar/lidar/optical images
  • Remote sensing of oceans and environment
  • Data fusion and help for decision-making
  • Development, exploitation and use of artificial intelligence (AI)

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Published Papers (3 papers)

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15 pages, 35895 KiB  
Article
Electromagnetic Scattering Model for Far Wakes of Ship with Wind Waves on Sea Surface
by Letian Wang, Min Zhang and Jiong Liu
Remote Sens. 2021, 13(21), 4417; https://doi.org/10.3390/rs13214417 - 3 Nov 2021
Cited by 3 | Viewed by 2266
Abstract
A comprehensive electromagnetic scattering model for ship wakes on the sea surface is proposed to study the synthetic aperture radar (SAR) imagery for ship wakes. Our model considers a coupling of various wave systems, including Kelvin wake, turbulent wake, and the ocean ambient [...] Read more.
A comprehensive electromagnetic scattering model for ship wakes on the sea surface is proposed to study the synthetic aperture radar (SAR) imagery for ship wakes. Our model considers a coupling of various wave systems, including Kelvin wake, turbulent wake, and the ocean ambient waves induced by the local wind. The fluid–structure coupling between the ship and the water surface is considered using the Reynolds–averaged Navier–Stokes (RANS) equation, and the wave–current effect between the ship wake and wind waves is considered using the wave modulation model. The scattering model can better describe the interaction of the ship wakes on sea surface and illustrates well the features of the ship wakes with local wind waves in SAR images. Full article
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25 pages, 13869 KiB  
Article
Improved Filtering of ICESat-2 Lidar Data for Nearshore Bathymetry Estimation Using Sentinel-2 Imagery
by Congshuang Xie, Peng Chen, Delu Pan, Chunyi Zhong and Zhenhua Zhang
Remote Sens. 2021, 13(21), 4303; https://doi.org/10.3390/rs13214303 - 26 Oct 2021
Cited by 42 | Viewed by 4255
Abstract
The accurate estimation of nearshore bathymetry is necessary for multiple aspects of coastal research and practices. The traditional shipborne single-beam/multi-beam echo sounders and Airborne Lidar bathymetry (ALB) have a high cost, are inefficient, and have sparse coverage. The Satellite-derived bathymetry (SDB) method has [...] Read more.
The accurate estimation of nearshore bathymetry is necessary for multiple aspects of coastal research and practices. The traditional shipborne single-beam/multi-beam echo sounders and Airborne Lidar bathymetry (ALB) have a high cost, are inefficient, and have sparse coverage. The Satellite-derived bathymetry (SDB) method has been proven to be a promising tool in obtaining bathymetric data in shallow water. However, current empirical SDB methods for multispectral imagery data usually rely on in situ depths as control points, severely limiting their spatial application. This study proposed a satellite-derived bathymetry method without requiring a priori in situ data by merging active and passive remote sensing (SDB-AP). It realizes rapid bathymetric mapping with only satellite remotely sensed data, which greatly extends the spatial coverage and temporal scale. First, seafloor photons were detected from the ICESat-2 raw photons based on an improved adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, which could calculate the optimal detection parameters for seafloor photons by adaptive iteration. Then, the bathymetry of the detected seafloor photons was corrected because of the refraction that occurs at the air–water interface. Afterward, the outlier photons were removed by an outlier-removal algorithm to improve the retrieval accuracy. Subsequently, the high spatial resolution (0.7 m) ICESat-2 derived bathymetry data were gridded to match the Sentinel-2 data with a lower spatial resolution (10 m). All of the ICESate-2 gridded data were randomly separated into two parts: 80% were employed to train the empirical bathymetric model, and the remaining 20% were used to quantify the inversion accuracy. Finally, after merging the ICESat-2 data and Sentinel-2 multispectral images, the bathymetric maps over St. Thomas of the United States Virgin Islands, Acklins Island in the Bahamas, and Huaguang Reef in the South China Sea were produced. The ICESat-2-derived results were compared against in situ data over the St. Thomas area. The results showed that the estimated bathymetry reached excellent inversion accuracy and the corresponding RMSE was 0.68 m. In addition, the RMSEs between the SDB-AP estimated depths and the ICESat-2 bathymetry results of St. Thomas, Acklins Island, and Huaguang Reef were 0.96 m, 0.91 m, and 0.94 m, respectively. Overall, the above results indicate that the SDB-AP method is effective and feasible for different shallow water regions. It has great potential for large-scale and long-term nearshore bathymetry in the future. Full article
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13 pages, 5025 KiB  
Letter
Sea Surface Wind Speed Retrieval from the First Chinese GNSS-R Mission: Technique and Preliminary Results
by Cheng Jing, Xinliang Niu, Chongdi Duan, Feng Lu, Guodong Di and Xiaofeng Yang
Remote Sens. 2019, 11(24), 3013; https://doi.org/10.3390/rs11243013 - 14 Dec 2019
Cited by 110 | Viewed by 5593
Abstract
Launched on 5 June 2019, the BuFeng-1 A/B twin satellites were part of the first Chinese global navigation satellite system reflectometry (GNSS-R) satellite mission. In this letter, a brief introduction of the BF-1 mission and its preliminary results of sea surface wind retrieval [...] Read more.
Launched on 5 June 2019, the BuFeng-1 A/B twin satellites were part of the first Chinese global navigation satellite system reflectometry (GNSS-R) satellite mission. In this letter, a brief introduction of the BF-1 mission and its preliminary results of sea surface wind retrieval are presented. Empirical fully developed sea (FDS) geophysical model functions (GMFs) relating the normalized bistatic radar cross-section to the sea surface wind speed are proposed for the BF-1 GNSS-R instruments. The FDS GMFs are derived from the collocated BF-1 observations, the European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data, and the advanced scatterometer (ASCAT) satellite observations. The preliminary tests reveal that the root-mean-square error (RMSE) between the derived wind speed and the reanalysis is 2.63 m/s for wind speeds in the range of 0.5–40.5 m/s. Further comparisons with the ASCAT observations and mooring buoys show that the RMSEs are 2.04 m/s and 1.77 m/s, respectively, at low-to-moderate wind speeds. This study demonstrates the effectiveness of BF-1 and provides a basis for the future GMF development of the BF-1 A/B mission. Full article
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