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Advances on Radar Scattering of Terrain and Applications

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

Deadline for manuscript submissions: closed (30 January 2023) | Viewed by 11291

Special Issue Editors

Laboratoire d’Informatique Signal et Image de la Côte d’Opale (LISIC), Université du Littoral Côte d’Opale (ULCO), Maison de la Recherche Blaise Pascal BP 719, 62228 Calais CEDEX, France
Interests: signal processing; information fusion; GNSS; radar; GNSS-reflectometry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Radar scattering of terrain has been an extensive research subject for its wide applications. For example, in microwave remote sensing of terrain, it has been a common practice to retrieve, by analyzing the sensitivity of the scattering behavior and mechanisms, the geophysical parameters of interest from scattering and emission measurements. However, radar scattering is a complex process, and many research works deal with the fine characterization of the reflecting surface, which is in general statistically anisotropic and electromagnetically inhomogeneous. One critical application is estimating the soil moisture, which plays a vital role in governing water and energy cycles, predicting flood and drought events, and understanding the Earth’s climate change. Remote sensing of soil moisture has been investigated using ground-based, airborne, and spaceborne radiometers, and radars. In this regard, active and passive sensors, including radiometers and monostatic and bistatic radars, have been used. This Special Issue focuses on radar scattering from rough surface modeling, simulation, and measurement, emphasizing techniques for soil moisture remote sensing. In this context, application or methodological contributions to this Special Issue may include but are not limited to:

  • Radar scattering from random media—modeling, simulation, and measurements;
  • High-resolution radar measurements;
  • Active–passive radar observations, e.g., GNSS-R;
  • Statistical-based and AI-based geophysical parameter estimation;
  • Radar applications to vegetative medium;
  • Radar application to water body observations.

Prof. Dr. Kun-Shan Chen
Prof. Dr. Serge Reboul
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

  • Radar scattering
  • Random media
  • Rough surface
  • Soil moisture
  • GNSS-R
  • Geophysical parameter estimation

Published Papers (6 papers)

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Research

17 pages, 6074 KiB  
Article
Modeling and Analysis of Microwave Emission from Multiscale Soil Surfaces Using AIEM Model
by Ying Yang, Kun-Shan Chen and Rui Jiang
Remote Sens. 2022, 14(22), 5899; https://doi.org/10.3390/rs14225899 - 21 Nov 2022
Cited by 1 | Viewed by 1151
Abstract
Natural rough surfaces have inherent multiscale roughness. This article presents the modeling and analysis of microwave emission from a multiscale soil surface. Unlike the linear superposition of different correlation functions with various correlation lengths, we applied the frequency modulation concept to characterize the [...] Read more.
Natural rough surfaces have inherent multiscale roughness. This article presents the modeling and analysis of microwave emission from a multiscale soil surface. Unlike the linear superposition of different correlation functions with various correlation lengths, we applied the frequency modulation concept to characterize the multiscale roughness, in which the modulation does not destroy the surface’s curvature but only modifies it. The multiscale effect on emission under different observation geometries and surface parameters was examined using an AIEM model. The paper provides new insights into the dependence of polarized emissivity on multiscale roughness: V-polarized emissivity is much less sensitive to multiscale roughness across the moisture content from dry to wet (5–30%). The H-polarized is sensitive to multiscale roughness, especially at higher moisture content. The predicted emissivity will have considerable uncertainty, even for the same baseline correlation length, without accounting for the multiscale roughness effect. V-polarized emissivity is less sensitive to the multiscale effect than H-polarized and the higher modulation ratio indicates larger emissivity. The higher modulation ratio indicates larger emissivity. Multiscale roughness weakens the polarization difference, particularly in higher moisture conditions. In addition, ignoring the multiscale effect leads to underestimated emissivity to a certain extent, particularly at the larger RMS height region. Finally, when accounting for multiscale roughness, model predictions of emission from a soil surface are in good agreement with two independently measured data sets. Full article
(This article belongs to the Special Issue Advances on Radar Scattering of Terrain and Applications)
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14 pages, 5102 KiB  
Article
Bistatic Radar Scattering from Non-Gaussian Height Distributed Rough Surfaces
by Ying Yang, Kun-Shan Chen and Suyun Wang
Remote Sens. 2022, 14(18), 4457; https://doi.org/10.3390/rs14184457 - 07 Sep 2022
Cited by 1 | Viewed by 1305
Abstract
In modeling a rough surface, it is common to assume a Gaussian height distribution. This hypothesis cannot describe an eventual asymmetry between crests and troughs of natural surfaces. We analyzed the bistatic scattering from a rough surface with non-Gaussian height distributions using the [...] Read more.
In modeling a rough surface, it is common to assume a Gaussian height distribution. This hypothesis cannot describe an eventual asymmetry between crests and troughs of natural surfaces. We analyzed the bistatic scattering from a rough surface with non-Gaussian height distributions using the Kirchhoff scattering theory. Two extreme cases of Gamma-distributed surfaces were compared in particular: exponential and Gaussian distributions. The bistatic angular dependence was examined under various root mean square (RMS) heights and power spectrum densities. Contribution sources to the coherent and incoherent scattering components were singled out relating to the surface height distribution. For an exponential height surface, the coherent scattering strengthens even as the surface becomes rough. The non-Gaussian effect on the incoherent scattering is connected with surface power spectrum density. The height distribution impacts differ in the different regions of the bistatic scattering plane and thus complicate the differentiation of the scattering patterns due to height distribution. Full article
(This article belongs to the Special Issue Advances on Radar Scattering of Terrain and Applications)
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47 pages, 6656 KiB  
Article
Theory of Microwave Remote Sensing of Vegetation Effects, SoOp and Rough Soil Surface Backscattering
by Leung Tsang, Tien-Hao Liao, Ruoxing Gao, Haokui Xu, Weihui Gu and Jiyue Zhu
Remote Sens. 2022, 14(15), 3640; https://doi.org/10.3390/rs14153640 - 29 Jul 2022
Cited by 14 | Viewed by 2092
Abstract
In this paper, we provide updates on our recent work on the theory of microwave remote sensing for applications in remote sensing of soil moisture and snow water equivalent (SWE). The three topics are the following. (i) For the effects of forests and [...] Read more.
In this paper, we provide updates on our recent work on the theory of microwave remote sensing for applications in remote sensing of soil moisture and snow water equivalent (SWE). The three topics are the following. (i) For the effects of forests and vegetation, we developed the hybrid method of NMM3D full-wave simulations over the vegetation field and forest canopies. In the hybrid method, we combined the use of commercial off-the-shelf software and wave multiple scattering theory (W-MST). The results showed much larger transmission than classical radiative transfer theory. (ii) In signals of opportunity at L-band and P-band, which are radar bistatic scattering in the vicinity of the specular direction, we developed the Analytical Kirchhoff solution (AKS) and Numerical Kirchhoff approach (NKA) in the calculations of coherent waves and incoherent waves. We also took into account of the effects of topographical elevations and slopes which have strong influences. (iii) In rough surface radar backscattering, we used the volume integral equation approach for NMM3D full-wave simulations for soil surfaces with kh up to 15. The simulations were calculated for the X-band and Ku-band and the results showed saturation effects. The simulation results can be applied to microwave remote sensing of SWE at these two frequencies. Full article
(This article belongs to the Special Issue Advances on Radar Scattering of Terrain and Applications)
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22 pages, 18475 KiB  
Article
Airborne GNSS Reflectometry for Water Body Detection
by Hamza Issa, Georges Stienne, Serge Reboul, Mohamad Raad and Ghaleb Faour
Remote Sens. 2022, 14(1), 163; https://doi.org/10.3390/rs14010163 - 31 Dec 2021
Cited by 4 | Viewed by 2476
Abstract
This article is dedicated to the study of airborne GNSS-R signal processing techniques for water body detection and edge localization using a low-altitude airborne carrier with high rate reflectivity measurements. A GNSS-R setup on-board a carrier with reduced size and weight was developed [...] Read more.
This article is dedicated to the study of airborne GNSS-R signal processing techniques for water body detection and edge localization using a low-altitude airborne carrier with high rate reflectivity measurements. A GNSS-R setup on-board a carrier with reduced size and weight was developed for this application. We develop a radar technique for automatic GNSS signal segmentation in order to differentiate in-land water body surfaces based on the reflectivity measurements associated to different areas of reflection. Such measurements are derived from the GNSS signal amplitudes. We adapt a transitional model to characterize the changes in the measurements of the reflected GNSS signals from one area to another. We propose an on-line/off-line change detection algorithm for GNSS signal segmentation. A real flight experimentation took place in the context of this work obtaining reflections from different surfaces and landforms. We show, using the airborne GNSS measurements obtained, that the proposed radar technique detects in-land water body surfaces along the flight trajectory with high temporal (50 Hz ) and spatial resolution (order of 10 to 100 m2). We also show that we can localize the edges of the detected water body surfaces at meter accuracy. Full article
(This article belongs to the Special Issue Advances on Radar Scattering of Terrain and Applications)
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18 pages, 2789 KiB  
Article
Entropy Metrics of Radar Signatures of Sea Surface Scattering for Distinguishing Targets
by Rui Jiang, Li-Na Li, Qiang Sun, Si-Zhang Hong, Jian-Jie Gao and Xin-Hui Xu
Remote Sens. 2021, 13(19), 3950; https://doi.org/10.3390/rs13193950 - 02 Oct 2021
Cited by 1 | Viewed by 1528
Abstract
This paper analyzes sea clutter by a random series without assuming the scattering being independent. We quantitated the complexity of sea clutter by applying multiscale sample entropy. We found that above certain wave heights or wind speeds, and for HH or VV polarization, [...] Read more.
This paper analyzes sea clutter by a random series without assuming the scattering being independent. We quantitated the complexity of sea clutter by applying multiscale sample entropy. We found that above certain wave heights or wind speeds, and for HH or VV polarization, the target can be distinguished from sea clutter by regarding (i) the sample entropy at large scale factors or (ii) the complexity index (CI) as entropy metrics. This is because the backscattering amplitudes of range bins with the primary target were found equipped with the lowest sample entropy at large scale factors or the lowest CI compared to that of range bins with sea clutter only. To further cover low-to-moderate sea states, we constructed a polarized complexity index (PCI) based on the polarization signatures of the multiscale sample entropy of sea clutter. We demonstrated that the PCI is yet another alternative entropy metric and can achieve a superb performance on distinguishing targets within 1993’s IPIX radar data sets. In each data set, the range bins with the primary target turned to have the lowest PCI compared to that of range bins with sea clutter alone. Moreover, in our experiment using 1993’s IPIX radar data sets, the PCIs of range bins with sea clutter only were almost the same and stable in each data set, further suggesting that the proposed PCI metric can be applied in the presence of no or multiple targets through proper fitting curves. Full article
(This article belongs to the Special Issue Advances on Radar Scattering of Terrain and Applications)
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19 pages, 2369 KiB  
Article
Global Sensitivity Analysis of a Water Cloud Model toward Soil Moisture Retrieval over Vegetated Agricultural Fields
by Chunfeng Ma, Shuguo Wang, Zebin Zhao and Hanqing Ma
Remote Sens. 2021, 13(19), 3889; https://doi.org/10.3390/rs13193889 - 28 Sep 2021
Cited by 7 | Viewed by 1530
Abstract
The release of high-spatiotemporal-resolution Sentinel-1 Synthetic Aperture Radar (SAR) data to the public has provided an unprecedented opportunity to map soil moisture at watershed and agricultural field scales. However, the existing retrieval algorithms fail to derive soil moisture with expected accuracy. Insufficient understanding [...] Read more.
The release of high-spatiotemporal-resolution Sentinel-1 Synthetic Aperture Radar (SAR) data to the public has provided an unprecedented opportunity to map soil moisture at watershed and agricultural field scales. However, the existing retrieval algorithms fail to derive soil moisture with expected accuracy. Insufficient understanding of the effects of soil and vegetation parameters on the backscatters is an important reason for this failure. To this end, we present a Sensitivity Analysis (SA) to quantify the effects of parameters on the dual-polarized backscatters of Sentinel-1 based on a Water Cloud Model (WCM) and multiple global SA methods. The identification of the incidence angle and polarization of Sentinel-1 and the description scheme of vegetation parameters (A, B and α) in WCM are especially emphasized in this analysis towards an optimal estimation of parameters. Multiple SA methods derive identical parameter importance ranks, indicating that a highly reasonable and reliable SA is performed. Comparison between two existing vegetation description schemes shows that the scheme using Vegetation Water Content (VWC) outperforms the scheme combing particle moisture content and VWC. Surface roughness, soil moisture, VWC, and B, are most sensitive on the backscatters. Variation of parameter sensitivity indices with incidence angle at different polarizations indicates that VV- and VH- polarized backscatters at small incidence angles are the optimal options for soil moisture and surface roughness estimation, respectively, while VV-polarized backscatter at larger incidence angles is well-suited for VWC and B estimation and HH-polarized backscatter is well suited for roughness estimation. This analysis improves the understanding of the effects of vegetated surface parameters on multi-angle and multi-polarized backscatters of Sentinel-1 SAR, informing improvement in SAR-based soil moisture retrieval. Full article
(This article belongs to the Special Issue Advances on Radar Scattering of Terrain and Applications)
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