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Modeling, Processing and Analysis of Microwave Remote Sensing Data

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

Deadline for manuscript submissions: 26 June 2024 | Viewed by 8799

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Dr. Pasquale Imperatore

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National Research Council of Italy (CNR), Institute for Electromagnetic Sensing of the Environment (IREA), Naples, Italy
Interests: SAR processing; SAR interferometry; SAR calibration; SAR modeling; electromagnetic scattering; random layered media; parallel algorithms; high performance computing
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Dr. Gerardo Di Martino

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Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Napoli, Italy
Interests: electromagnetic propagation; electromagnetic modeling; microwave remote sensing and electromagnetics; SAR signal processing and simulation; information retrieval from SAR data
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Dr. Alessio Di Simone

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Guest Editor

Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Napoli, Italy
Interests: microwave remote sensing and electromagnetics; SAR and GNSS-R signal processing and simulation; information retrieval from SAR and GNSS-R data; radio-wave propagation; electromagnetic scattering in natural and urban environments
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Dr. Giampaolo Ferraioli

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Guest Editor

Department of Science and Technology, University of Naples “Parthenope”, Centro Direzionale Isola C4, 80143 Naples, Italy
Interests: synthetic aperture radar (SAR); SAR interferometry; changing detection; despeckling; denoising; edge detection; SAR tomography
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Dr. Hugo Carreno-Luengo

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Guest Editor

NASA CYGNSS Mission, Climate and Space Sciences and Engineering Department, University of Michigan, Ann Arbor, MI 48109, USA
Interests: GNSS-reflectometry; microwave radiometry; bistatic scattering; SmallSats; planetary sciences; water cycle; carbon cycle
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Microwave remote sensing, including active (SAR, scatterometers, altimeters, etc.) and passive (e.g., GNSS-R, radiometers) systems, is a mature technology boosting a seamless monitoring and mapping of the Earth’s resources and human activities at various spatial and temporal scales. With respect to other competing Earth observation technologies, microwave remote sensing systems offer imaging performance insensitive to daylight and weather conditions.

Due to the complex electromagnetic mechanisms at the base of microwave remote sensing imagery formation, a reliable interpretation of microwave remote sensing data as well as their meaningful exploitation in quantitative information retrieval procedures is still a challenging problem in a wide variety of applications, such as soil moisture retrieval, snow, ice and sea surface analysis, biomass estimation, and change detection. This calls for both an accurate electromagnetic modeling of the relationships between measured data and the parameters of interest, as well as the development of proper processing methods, including techniques for data calibration, synthesis, simulation, and analysis. Microwave multisensor data fusion techniques are of interest for this Special Issue as well.

This Special Issue aims to highlight the latest research advances in the modeling, processing, and analysis of microwave remote sensing data, including active and passive systems, with a special focus on multisensor, multifrequency, multipolarization, and multiresolution imagery. We invite scholars, researchers, and engineers to submit their high-quality manuscripts for publication in this Special Issue and solicit original contributions in the form of research articles or review articles.

We particularly welcome contributions focused on, but not limited to, one of the following topics:

Microwave remote sensing;
Bistatic and multistatic microwave remote sensing systems;
Electromagnetic scattering and propagation modeling;
Compressive sensing;
Information retrieval from microwave remotely sensed data;
Calibration and validation activities;
Data simulation and value-added products synthesis;
Statistical modeling of microwave remote sensing data;
Global navigation satellite systems reflectometry (GNSS-R) and signals of opportunity (SoOp) applications;
Multitemporal and polarimetric SAR processing, including multibaseline interferometric and tomographic processing;
Multidimensional (multisensor, multimodal, multifrequency, multipolarization, multiresolution, multitemporal) data processing;
Multisource and multiscale data fusion;
Machine and deep learning methods;
Spaceborne, airborne, ground-based, UAV-based remote sensing platforms;
New microwave remote sensing missions and campaigns;
High-performance computing (HPC) for large-scale Earth observation applications.

Dr. Pasquale Imperatore
Dr. Gerardo Di Martino
Dr. Alessio Di Simone
Dr. Giampaolo Ferraioli
Dr. Hugo Carreno-Luengo
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

microwave remote sensing
synthetic aperture radar (SAR)
passive bistatic radar
global navigation satellite systems—reflectometry
data processing
data synthesis
data fusion
modeling

Published Papers (8 papers)

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17 pages, 10714 KiB

Open AccessArticle

Characterization of River Width Measurement Capability by Space Borne GNSS-Reflectometry

by April Warnock, Christopher S. Ruf and Arie L. Knoll

Remote Sens. 2024, 16(8), 1446; https://doi.org/10.3390/rs16081446 - 19 Apr 2024

Viewed by 266

Abstract

In recent years, Global Navigation Satellite System reflectometry (GNSS-R) has been explored as a methodology for inland water body characterization. However, thorough characterization of the sensitivity and behavior of the GNSS-R signal to inland water bodies is still needed to progress this area of research. In this paper, we characterize the uncertainty associated with Cyclone Global Navigation Satellite System (CYGNSS) measurements on the determination of river width. The characterization study uses simulated data from a forward model that accurately simulates CYGNSS observations of mixed water/land scenes. The accuracy of the forward model is demonstrated by comparisons to actual observations of known water body shapes made at particular measurement geometries. Simulated CYGNSS data are generated over a range of synthetic scenes modeling a straight river subreach, and the results are analyzed to determine a predictive relationship between the peak SNR measured over the river subreaches and the river widths. An uncertainty analysis conducted using this predictive relationship indicates that, for simplistic river scenes, the SNR over the river is predictive of the river width to within +/−5 m. The presence of clutter (surrounding water bodies) within ~500 m of a river causes perturbations in the SNR measured over the river, which can render the river width retrievals unreliable. The results of this study indicate that, for isolated, straight rivers, GNSS-R data are able to measure river widths as narrow as 160 m with ~3% error. Full article

(This article belongs to the Special Issue Modeling, Processing and Analysis of Microwave Remote Sensing Data)

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29 pages, 9945 KiB

Open AccessArticle

Forward Modeling of Robust Scattering Centers from Dynamic Ships on Time-Varying Sea Surfaces for Remote Sensing Target-Recognition Applications

by Rumeng Chen, Mengbo Hua and Siyuan He

Remote Sens. 2024, 16(5), 860; https://doi.org/10.3390/rs16050860 - 29 Feb 2024

Viewed by 773

Abstract

This paper presents a forward modeling method for the scattering center (SC) model of dynamic ships on time-varying sea surfaces, tailored for remote sensing and target-recognition applications. Grounded in ship hydrodynamics, the methodology delineates ship movements amidst fluctuating waves, harnessing computer graphics to integrate ship–sea geometries across diverse temporal instances. Utilizing the four-path model, the composite scattering effects are segregated into distinct ship and sea contributions, along with their mutual interactions. Augmented by high-frequency electromagnetic principles, the paper quantifies and deduces SC parameters, culminating in a 3-D parameterized SC model for complex maritime targets. Unlike conventional inverse methods, this approach employs a “cause-to-effect” forward strategy, establishing clear links between SCs and local geometries, enhancing the model’s physical clarity. Using the fishing ship as a case, this research compared the normalized similarity index and position-matching rate between the reconstructed synthetic aperture radar (SAR) image and the simulated SAR image. The results indicate that all computed results exceeded 90%. Furthermore, a comparison was conducted between the reconstructed radar cross-sections (RCS) obtained by expanding the model within a large angular range and the simulated results. The root mean square error between the two was less than 3 dB, affirming the accuracy and effectiveness of the proposed model. Additionally, the research examines the variations in SCs during the six-degrees-of-freedom motions, providing a detailed quantitative analysis of their temporal trends in amplitude and position. In summary, this investigation furnishes an efficient and economical framework for rapid radar characterization in dynamic, variable marine environments, fostering advancements in remote sensing and maritime target identification. Full article

(This article belongs to the Special Issue Modeling, Processing and Analysis of Microwave Remote Sensing Data)

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26 pages, 45161 KiB

Open AccessArticle

Polarimetric Synthetic Aperture Radar Image Classification Based on Double-Channel Convolution Network and Edge-Preserving Markov Random Field

by Junfei Shi, Mengmeng Nie, Shanshan Ji, Cheng Shi, Hongying Liu and Haiyan Jin

Remote Sens. 2023, 15(23), 5458; https://doi.org/10.3390/rs15235458 - 22 Nov 2023

Viewed by 917

Abstract

Deep learning methods have gained significant popularity in the field of polarimetric synthetic aperture radar (PolSAR) image classification. These methods aim to extract high-level semantic features from the original PolSAR data to learn the polarimetric information. However, using only original data, these methods cannot learn multiple scattering features and complex structures for extremely heterogeneous terrain objects. In addition, deep learning methods always cause edge confusion due to the high-level features. To overcome these limitations, we propose a novel approach that combines a new double-channel convolutional neural network (CNN) with an edge-preserving Markov random field (MRF) model for PolSAR image classification, abbreviated to “DCCNN-MRF”. Firstly, a double-channel convolution network (DCCNN) is developed to combine complex matrix data and multiple scattering features. The DCCNN consists of two subnetworks: a Wishart-based complex matrix network and a multi-feature network. The Wishart-based complex matrix network focuses on learning the statistical characteristics and channel correlation, and the multi-feature network is designed to learn high-level semantic features well. Then, a unified network framework is designed to fuse two kinds of weighted features in order to enhance advantageous features and reduce redundant ones. Finally, an edge-preserving MRF model is integrated with the DCCNN network. In the MRF model, a sketch map-based edge energy function is designed by defining an adaptive weighted neighborhood for edge pixels. Experiments were conducted on four real PolSAR datasets with different sensors and bands. The experimental results demonstrate the effectiveness of the proposed DCCNN-MRF method. Full article

(This article belongs to the Special Issue Modeling, Processing and Analysis of Microwave Remote Sensing Data)

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24 pages, 9484 KiB

Open AccessArticle

Neural Network-Based Wind Measurements in Rainy Conditions Using the HY-2A Scatterometer

by Jing Wang, Xuetong Xie, Ruru Deng, Mingsen Lin and Xiankun Yang

Remote Sens. 2023, 15(17), 4357; https://doi.org/10.3390/rs15174357 - 04 Sep 2023

Viewed by 772

Abstract

Wind measurement using spaceborne scatterometers has been used for various scientific and operational purposes. However, the major problem of such measurements is contamination by rain. To improve the wind measurement using the HY-2A scatterometer under rainy conditions, a neural network-based model was established in this study. The model is almost autonomous in that it only needs the backscatter coefficient measurement data and the observation geometry information from the HY-2A scatterometer itself. The model can distinguish between rain-contaminated wind pixels and rain-free wind pixels and significantly improve the accuracy of wind speed measurements using HY-2A scatterometer alone. TAO data and linearly calibrated ECMWF data were used in the study to validate the neural network-inverted wind speed. Under no rain conditions, the RMS of the neural network-inverted wind speed and TAO wind speed was 1.06 m/s, with a deviation of −0.21 m/s, which is a small difference from the standard method inverted wind speed. Under rain conditions, the RMS and deviation were 1.94 m/s and 0.66 m/s, respectively, which were better than the statistical results of the conventional maximum likelihood estimation method. The validated results using linearly calibrated data also indicate that the neural network-inverted wind speed is closer to the validation data under rain conditions. Full article

(This article belongs to the Special Issue Modeling, Processing and Analysis of Microwave Remote Sensing Data)

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18 pages, 6704 KiB

Open AccessArticle

Enabling High-Resolution Micro-Vibration Detection Using Ground-Based Synthetic Aperture Radar: A Case Study for Pipeline Monitoring

by Benyamin Hosseiny, Jalal Amini, Hossein Aghababaei and Giampaolo Ferraioli

Remote Sens. 2023, 15(16), 3981; https://doi.org/10.3390/rs15163981 - 11 Aug 2023

Cited by 1 | Viewed by 1267

Abstract

The wellbeing of pipelines is influenced by a range of factors, such as internal and external pressures, as well as deterioration over time due to issues like erosion and corrosion. It is thus essential to establish a reliable monitoring system that can precisely examine pipeline behavior over time in order to prevent potential damages. To this end, pipelines are inspected based on internal and external approaches. Radar, as a non-contact sensing system, can be a suitable choice for external pipeline inspection. Radar is capable of the transmission and receiving of thousands of signals in a second, which reconstructs the displacement signal and is used for a vibration analysis. Synthetic aperture radar (SAR) imaging adds cross-range resolution to radar signals. However, a data acquisition rate of longer than several seconds makes it unsuitable for sub-second vibration monitoring. This study aims to address this limitation by presenting a method for high-resolution vibration monitoring using ground-based SAR (GBSAR) signals. To this end, a signal processing method by modifying the radar’s signal model is presented, which allows for estimating scattering targets’ vibration parameters and angle of arrival with high resolution. The proposed method is validated with numerical simulation and a real case study comprising water pipelines. Moreover, various analyses are presented for the in-depth evaluation of the method’s performance in different situations. The results indicate that the proposed method can be effective in detecting pipeline vibration frequencies with micro-scale amplitudes while providing high spatial resolution for generating accurate vibration maps of pipelines. Also, the comparison with the radar observations shows a high degree of agreement between the frequency responses with the maximum error of 0.25 Hz in some rare instances. Full article

(This article belongs to the Special Issue Modeling, Processing and Analysis of Microwave Remote Sensing Data)

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32 pages, 9228 KiB

Open AccessArticle

Fast SAR Image Simulation Based on Echo Matrix Cell Algorithm Including Multiple Scattering

by Ke Wu, Guowang Jin, Xin Xiong, Hongmin Zhang and Limei Wang

Remote Sens. 2023, 15(14), 3637; https://doi.org/10.3390/rs15143637 - 21 Jul 2023

Viewed by 923

Abstract

We present a novel fast synthetic aperture radar (SAR) image simulation method based on the echo matrix cell algorithm including multiple scattering. To improve the efficiency of SAR image simulation while ensuring the fidelity of the simulated results, we first discretized the target facets set in the SAR beams footprint into lattice targets using the range-Doppler (RD) imaging geometry model and provided the basis for simulating electromagnetic wave transmission. Based on the simulation of electromagnetic waves transmission, we used the ray tracing algorithm to calculate the multiple backscattering field including multi-polarimetric information and various material properties. Then, based on the echo matrix cell algorithm, we set the echo matrix cell as the subfield of the target backscattering field and designed the CUDA kernel function to implement a computation parallelization for SAR echo generation. All the echo matrix cells are traversed in parallel to obtain the total backscattering field of the target, reproducing the time-varying characteristic of the backscatter coefficient for each lattice target within the synthetic aperture time. The echo signal is processed using the RD imaging algorithm to obtain the simulated SAR image. Finally, we select some targets including aircraft carrier and airplane models for simulation tests. The computation efficiency is improved over 170-fold by comparing the computations of the proposed method and CPU single-thread. We also performed some qualitative and quantitative evaluations on the fidelity of the simulated SAR images. The experimental results verify the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Modeling, Processing and Analysis of Microwave Remote Sensing Data)

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14 pages, 2749 KiB

Open AccessArticle

High-Resolution Humidity Observations Based on Commercial Microwave Links (CML) Data—Case of Tel Aviv Metropolitan Area

by Yoav Rubin, Shira Sohn and Pinhas Alpert

Remote Sens. 2023, 15(2), 345; https://doi.org/10.3390/rs15020345 - 06 Jan 2023

Cited by 2 | Viewed by 1320

Abstract

The humidity in the atmosphere plays a crucial role in a wide range of atmospheric processes determined by the water-vapor concentration in the air. The accuracy of weather forecasts is largely dictated by the humidity field measured at low atmospheric levels. At the near-surface level, the absolute humidity variations can be large due to the variability of land cover (LC). Cities are one of the primary LCs which have a substantial impact on the humidity field. Large urban areas are expanding, causing a significant change in the near-surface humidity field. Current measurement tools, however, do not satisfactorily assess the cities’ effects on the humidity field. This paper presents an innovative method for high-resolution humidity measurements based on the cellular network. Here, the humidity field around Tel Aviv was retrieved from the cellular network during the summer of 2017. The results show a well-noticed impact of the city and other LC types on the humidity field over the Tel Aviv metropolitan area. The method presented here can offer an improved description of the humidity field at the city-canopy level and therefore provide a better assessment of the urban/LC effects on the environment, atmospheric modeling, and particularly on clouds/rain development. Full article

(This article belongs to the Special Issue Modeling, Processing and Analysis of Microwave Remote Sensing Data)

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16 pages, 11776 KiB

Open AccessTechnical Note

Biomass Estimation with GNSS Reflectometry Using a Deep Learning Retrieval Model

by Georgios Pilikos, Maria Paola Clarizia and Nicolas Floury

Remote Sens. 2024, 16(7), 1125; https://doi.org/10.3390/rs16071125 - 22 Mar 2024

Viewed by 718

Abstract

GNSS Reflectometry (GNSS-R) is an emerging technique for the remote sensing of the environment. Traditional GNSS-R bio-geophysical parameter retrieval algorithms and deep learning models utilize observables derived from only the peak power of the delay-Doppler maps (DDMs), discarding the rest. This reduces the data available, which potentially hinders estimation accuracy. In addition, reflections from water bodies dominate the signal amplitude, and using only the peak power in those areas is challenging. Motivated by all the above, we propose a novel deep learning retrieval model for biomass estimation that uses the full DDM of surface reflectivity. Experiments using CYGNSS data have illustrated the improvements achieved when using the full DDM of surface reflectivity. Our proposed model was able to estimate biomass, trained using the ESA Climate Change Initiative (CCI) biomass map, outperforming the model that used peak reflectivity. Global and regional analysis is provided along with an illustration of how biomass estimation is achieved when using the full DDM around water bodies. GNSS-R could become an efficient method for biomass monitoring with fast revisit times. However, an elaborate calibration is necessary for the retrieval models, to associate GNSS-R data with bio-geophysical parameters on the ground. To achieve this, further developments with improved training data are required, as well as work using in situ validation data. Nevertheless, using GNSS-R and deep learning retrieval models has the potential to enable fast and persistent biomass monitoring and help us better understand our changing climate. Full article

(This article belongs to the Special Issue Modeling, Processing and Analysis of Microwave Remote Sensing Data)

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