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Advances in Spaceborne SAR—Technology and Applications (Second Edition)
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Advances in Spaceborne SAR—Technology and Applications (Second Edition)

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 4198

Special Issue Editors

Prof. Dr. Bingnan Wang

E-Mail Website
Guest Editor
National Key Laboratory of Microwave Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
Interests: imaging and applications multi-dimensional SAR; SAR imaging and processing; inteferometric SAR and synthetic aperture ladar
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yongsheng Zhou

E-Mail Website
Guest Editor
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
Interests: target detection and recognition; radar calibration; digital signal processing; and image processing
Prof. Dr. Bing Han

E-Mail Website
Guest Editor
Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
Interests: advanced spaceborne SAR signal processing; remote sensing information extraction
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Massimiliano Pieraccini

E-Mail Website
Guest Editor
Department of Information Engineering, University of Florence, Via Santa Marta 3, 50139 Firenze, Italy
Interests: radar imaging; synthetic aperture radar; electromagnetics; RF engineering; antennas and propagation; remote sensing; telecommunications engineering; radar signal processing; SAR interferometry; electrical & electronics engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, several synthetic aperture radar (SAR) systems have been proposed and developed. In addition to conventional high-performance spaceborne SAR systems, low-cost SAR systems, of small size and lightweight, mounted on aircrafts at low altitude, including UAVs and drones as well as small-to-medium satellites at high altitude, provide all-weather and high-resolution imaging capability. Those new small and low-cost SAR systems can dramatically reduce an observation period by deploying several sensors and consequently offer a new era of SAR applications. A high temporal resolution with a competitive performance encourages remote sensing community to develop many innovative applications for Earth monitoring and management as well as for surveillance.

This Special Issue focuses on current and upcoming developments in the international SAR constellation that promote synergies among the different SAR missions for interdisciplinary scientific and societal applications. Recent progress in new SAR technology and missions would bring perfect opportunities for introducing their potentials to the remote sensing community. With the ongoing progress of high-performance SAR in UAVs, aircrafts, and spaceborne missions, SAR constellation has become an effective tool for imaging and quantifying surface properties and dynamics. Along with SAR technological innovation, applications of SAR are rapidly diversified with increasing data availability and short temporal resolution, leading the development of a wide variety of new and novel application methods and products useful for the public interest, as well as scientific research. This Special Issue encourages submissions of studies about innovative SAR applications for land, ocean, and polar regions, as well as the synergistic use of multiple sensors at multiple scales. We invite all scientists, engineers, and government decision-makers devoted to various activities around SAR technology and applications. The following summary of topics provides the guidelines for paper submission, but all relevant topics and papers are welcome in this Special Issue.

  • Current and upcoming SAR missions: introducing state of the art and performance of current and planned and being developed SAR systems and missions.
  • Micro-SAR sensor and applications: concept and technology of micro-SAR antenna and systems mounted on UAV platforms, airplanes, and small satellites for cost-effective applications such as surveillance, monitoring natural and anthropogenic disasters, surface water management, etc.
  • Multi-sensor and multi-scale data analysis and processing: data processing and application methods to fully exploit the enhanced capability of various current and upcoming SAR systems. Synergistic use of multiple sensors to enhance the spatial, temporal, and polarimetric scales.
  • Methodological progress in SAR application methods: innovative methods and applications utilizing machine learning, SAR interferometry and polarimetry, quantified applications and physical model inversion, hybrid methods and data merging, etc.
  • Applications of SAR to land, oceans, cryosphere, and other fields: qualitative and quantitative applications and results obtained by SAR and/or the synergetic use of optical and microwave remote sensing, which make a significant contribution to both scientific understanding, forecasting, and consequently to societal benefits in various respects.

Prof. Dr. Bingnan Wang
Prof. Dr. Yongsheng Zhou
Prof. Dr. Bing Han
Prof. Dr. Massimiliano Pieraccini
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

  • new innovative SAR system
  • missions and sensors
  • micro-SAR sensor and applications
  • data analysis and methods
  • machine learning and/or new applications
  • land applications
  • ocean and cryosphere applications
  • other applications

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Published Papers (5 papers)

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25 pages, 9142 KiB  
Article
Restricted Label-Based Self-Supervised Learning Using SAR and Multispectral Imagery for Local Climate Zone Classification
by Amjad Nawaz, Wei Yang, Hongcheng Zeng, Yamin Wang and Jie Chen
Remote Sens. 2025, 17(8), 1335; https://doi.org/10.3390/rs17081335 - 8 Apr 2025
Viewed by 330
Abstract
Deep learning techniques have garnered significant attention in remote sensing scene classification. However, obtaining a large volume of labeled data for supervised learning (SL) remains challenging. Additionally, SL methods frequently struggle with limited generalization ability. To address these limitations, self-supervised multi-mode representation learning [...] Read more.
Deep learning techniques have garnered significant attention in remote sensing scene classification. However, obtaining a large volume of labeled data for supervised learning (SL) remains challenging. Additionally, SL methods frequently struggle with limited generalization ability. To address these limitations, self-supervised multi-mode representation learning (SSMMRL) is introduced for local climate zone classification (LCZC). Unlike conventional supervised learning methods, SSMMRL utilizes a novel encoder architecture that exclusively processes augmented positive samples (PSs), eliminating the need for negative samples. An attention-guided fusion mechanism is integrated, using positive samples as a form of regularization. The novel encoder captures informative representations from the unannotated So2Sat-LCZ42 dataset, which are then leveraged to enhance performance in a challenging few-shot classification task with limited labeled samples. Co-registered Synthetic Aperture Radar (SAR) and Multispectral (MS) images are used for evaluation and training. This approach enables the model to exploit extensive unlabeled data, enhancing performance on downstream tasks. Experimental evaluations on the So2Sat-LCZ42 benchmark dataset show the efficacy of the SSMMRL method. Our method for LCZC outperforms state-of-the-art (SOTA) approaches. Full article
(This article belongs to the Special Issue Advances in Spaceborne SAR—Technology and Applications (Second Edition))
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21 pages, 12585 KiB  
Article
Research on Frequency-Modulated Continuous Wave Inverse Synthetic Aperture Ladar Imaging Based on Digital Delay
by Ruihua Shi, Gen Sun, Yinshen Wang, Wei Li, Maosheng Xiang and Juanying Zhao
Remote Sens. 2025, 17(5), 751; https://doi.org/10.3390/rs17050751 - 21 Feb 2025
Viewed by 377
Abstract
Inverse synthetic aperture ladar (ISAL) systems combine laser coherent detection technology with inverse synthetic aperture imaging methods, offering advantages such as compact size, long detection range, and high resolution. The traditional optical delay line technique is widely used in frequency-modulated continuous wave (FMCW) [...] Read more.
Inverse synthetic aperture ladar (ISAL) systems combine laser coherent detection technology with inverse synthetic aperture imaging methods, offering advantages such as compact size, long detection range, and high resolution. The traditional optical delay line technique is widely used in frequency-modulated continuous wave (FMCW) ISAL imaging systems, but its flexibility is limited, posing challenges for high-precision signal processing. Additionally, frequency modulation errors, atmospheric disturbances, and other errors inevitably affect image quality. Therefore, this paper proposes a signal processing method based on digital delay for FMCW ISAL, aiming to achieve the high-resolution imaging of targets across several kilometers. Firstly, the paper introduces the FMCW ISAL system. By introducing digital delay technology, it enables the flexible and real-time adjustment of reference signal delay. Next, to address the frequency offset issue caused by the introduction of digital delay technology, a preprocessing method for unified frequency shift correction is proposed to ensure signal consistency. Then, a set of internal calibration signal datasets is generated based on digital delay technology. Following this, a frequency modulation error iteration estimation method based on gradient descent is introduced. Without the need for target echo signals, the method accurately estimates the frequency modulation phase errors of both the transmitted and reference signals using only the internal calibration signals. Finally, this paper effectively decomposes the motion of the target, derives the echo model for the FMCW ISAL system, and constructs compensation functions to eliminate the intra-pulse Doppler shift and the residual video phase (RVP). Additionally, the Phase Gradient Autofocus (PGA) algorithm is used after two-dimensional imaging to eliminate the impact of atmospheric disturbances. We conducted two sets of experiments on point targets and surface targets to verify the effectiveness of error compensation in improving imaging quality. The results show that the two-dimensional resolution of point targets was optimized to 3 cm (range) × 0.6 cm (azimuth), while the resolution and entropy of the surface targets were both improved by 0.1. These results demonstrate that the proposed method effectively enhances target imaging quality and provides a new technical approach for high-precision signal processing in FMCW ISAL imaging. Full article
(This article belongs to the Special Issue Advances in Spaceborne SAR—Technology and Applications (Second Edition))
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23 pages, 5693 KiB  
Article
Sea Surface Wind Speed Retrieval Using Gaofen-3-02 SAR Full Polarization Data
by Kuo Zhang, Yuxin Hu, Junxin Yang and Xiaochen Wang
Remote Sens. 2025, 17(4), 591; https://doi.org/10.3390/rs17040591 - 9 Feb 2025
Viewed by 579
Abstract
The primary payload onboard the Gaofen-3-02 (GF3-02) satellite is a C-band Synthetic Aperture Radar (SAR) capable of achieving a maximum resolution of 1 m. This instrument is critical to monitor the marine environment, particularly for tracking sea surface wind speeds, an important marine [...] Read more.
The primary payload onboard the Gaofen-3-02 (GF3-02) satellite is a C-band Synthetic Aperture Radar (SAR) capable of achieving a maximum resolution of 1 m. This instrument is critical to monitor the marine environment, particularly for tracking sea surface wind speeds, an important marine environmental parameter. In this study, we utilized 192 sets of GF3-02 SAR data, acquired in Quad-Polarization Strip I (QPSI) mode in March 2022, to retrieve sea surface wind speeds. Prior to wind speed retrieval for vertical-vertical (VV) polarization, radiometric calibration accuracy was analyzed, yielding good performance. The results showed a bias and root mean square errors (RMSEs) of 0.02 m/s and 1.36 m/s, respectively, when compared to the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis V5 (ERA5) data. For horizontal–horizontal (HH) polarization, two types of polarization ratio (PR) models were introduced based on the GF3-02 SAR data. Combining these refitted PR models with CMOD5.N, the results for HH polarization exhibited a bias of −0.18 m/s and an RMSE of 1.25 m/s in comparison to the ERA5 data. Regarding vertical–horizontal (VH) polarization, two linear models based on both measured normalized radar cross sections (NRCSs) and denoised NRCSs were developed. The findings indicate that denoising significantly enhances the accuracy of wind speed measurements for VH polarization when dealing with low wind speeds. When compared against buoy data, the wind speed retrieval results demonstrated a bias of 0.23 m/s and an RMSE of 1.77 m/s. Finally, a comparative analysis of the above retrieval results across all three polarizations was conducted to further understand their respective performances. Full article
(This article belongs to the Special Issue Advances in Spaceborne SAR—Technology and Applications (Second Edition))
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25 pages, 24547 KiB  
Article
A Radio Frequency Interference Screening Framework—From Quick-Look Detection Using Statistics-Assisted Network to Raw Echo Tracing
by Jiayuan Shen, Bing Han, Yang Li, Zongxu Pan, Di Yin, Yugang Feng and Guangzuo Li
Remote Sens. 2024, 16(22), 4195; https://doi.org/10.3390/rs16224195 - 11 Nov 2024
Cited by 1 | Viewed by 1031
Abstract
Synthetic aperture radar (SAR) is often affected by other high-power electromagnetic devices during ground observation, which causes unintentional radio frequency interference (RFI) with the acquired echo, bringing adverse effects into data processing and image interpretation. When faced with the task of screening massive [...] Read more.
Synthetic aperture radar (SAR) is often affected by other high-power electromagnetic devices during ground observation, which causes unintentional radio frequency interference (RFI) with the acquired echo, bringing adverse effects into data processing and image interpretation. When faced with the task of screening massive SAR data, there is an urgent need for the global perception and detection of interference. The existing RFI detection method usually only uses a single type of data for detection, ignoring the information association between the data at all levels of the real SAR product, resulting in some computational redundancy. Meanwhile, current deep learning-based algorithms are often unable to locate the range of RFI coverage in the azimuth direction. Therefore, a novel RFI processing framework from quick-looks to single-look complex (SLC) data and then to raw echo is proposed. We take the data of Sentinel-1 terrain observation with progressive scan (TOPS) mode as an example. By combining the statistics-assisted network with the sliding-window algorithm and the error-tolerant training strategy, it is possible to accurately detect and locate RFI in the quick looks of an SLC product. Then, through the analysis of the TOPSAR imaging principle, the position of the RFI in the SLC image is preliminarily confirmed. The possible distribution of the RFI in the corresponding raw echo is further inferred, which is one of the first attempts to use spaceborne SAR data to elucidate the RFI location mapping relationship between image data and raw echo. Compared with directly detecting all of the SLC data, the time for the proposed framework to determine the RFI distribution in the SLC data can be shortened by 53.526%. All the research in this paper is conducted on Sentinel-1 real data, which verify the feasibility and effectiveness of the proposed framework for radio frequency signals monitoring in advanced spaceborne SAR systems. Full article
(This article belongs to the Special Issue Advances in Spaceborne SAR—Technology and Applications (Second Edition))
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12 pages, 2699 KiB  
Technical Note
Accuracy Assessment of a Digital Elevation Model Constructed Using the KOMPSAT-5 Dataset
by Je-Yun Lee, Sang-Hoon Hong, Kwang-Jae Lee and Joong-Sun Won
Remote Sens. 2025, 17(5), 826; https://doi.org/10.3390/rs17050826 - 27 Feb 2025
Viewed by 489
Abstract
The Interferometric Synthetic Aperture Radar (InSAR) has significantly advanced in its usage for analyzing surface information such as displacement or elevation. In this study, we evaluated a digital elevation model (DEM) constructed using X-band KOMPSAT-5 interferometric datasets provided by the Korea Aerospace Research [...] Read more.
The Interferometric Synthetic Aperture Radar (InSAR) has significantly advanced in its usage for analyzing surface information such as displacement or elevation. In this study, we evaluated a digital elevation model (DEM) constructed using X-band KOMPSAT-5 interferometric datasets provided by the Korea Aerospace Research Institute (KARI). The 28-day revisit cycle of KOMPSAT-5 poses challenges in maintaining interferometric correlation. To address this, four KOMPSAT-5 images were employed in a multi-baseline interferometric approach to mitigate temporal decorrelation effects. Despite the slightly longer temporal baselines, the analysis revealed sufficient coherence (>0.8) in three interferograms. The height of ambiguity ranged from 59 to 74 m, which is a moderate height of sensitivity to extract topography over the study area of San Francisco in the USA. Unfortunately, only ascending acquisition mode datasets were available for this study. The derived DEM was validated against three reference datasets: Copernicus GLO-30 DEM, ICESat-2, and GEDI altimetry. A high coefficient of determination (R2 > 0.9) demonstrates the feasibility of the interferometric application of KOMPSAT-5. Full article
(This article belongs to the Special Issue Advances in Spaceborne SAR—Technology and Applications (Second Edition))
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