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Application of SAR and Remote Sensing Technology in Earth Observation
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Application of SAR and Remote Sensing Technology in Earth Observation

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2892

Special Issue Editors

Dr. Xing Peng

E-Mail Website
Guest Editor
School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
Interests: TomoSAR; forest vertical structure estimation; forest dynamic change monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Qinghua Xie

E-Mail Website
Guest Editor
School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
Interests: PolSAR; PolInSAR; biophysical parameters inversion over forest and agricultural areas
Special Issues, Collections and Topics in MDPI journals
Dr. Yanan Du

E-Mail Website
Guest Editor
School of Geography and Remote Sensing, Guangzhou University, Guangzhou, China
Interests: MTInSAR; surface deformation monitoring; digital elevation model reconstruction
Dr. Bing Zhang

E-Mail Website
Guest Editor
School of Geomatics, Liaoning Technical University, Fuxin, China
Interests: PolInSAR; forest vertical structure estimation; forest dynamic change monitoring; survering adjustment

Special Issue Information

Dear Colleagues,

Global environmental issues are becoming increasingly severe, and remote sensing technology offers unique advantages, such as wide coverage, short revisit cycles, and low costs. These features make it a promising tool for large-scale earth observation, especially Synthetic Aperture Radar (SAR) technology. The fusion of multi-source remote sensing data can provide valuable information on land cover changes, surface subsidence, forest disturbances, and more. This integrated approach contributes to us generating a better understanding of environmental issues and enhances our ability to address and mitigate their impacts effectively.

This Special Issue focuses on all types of remote sensors designed for earth observation. Contributions addressing, but not limited to, the following topics are welcome to be submitted to this Special Issue:

  • Land Classification and Mapping;
  • Forest Classification and Mapping;
  • Forest Biomass Estimation;
  • Forest Height Estimation;
  • Multi-Source Remote Sensing Data Fusion;
  • Land Cover Change Detection;
  • Crop Classification and Mapping;
  • Crop Height Monitoring;
  • Farmland Soil Moisture Content Inversion.

Dr. Xing Peng
Dr. Qinghua Xie
Dr. Yanan Du
Dr. Bing Zhang
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. Sensors 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 2600 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

  • remote sensing
  • synthetic aperture radar
  • land cover
  • earth observation
  • data fusion

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Published Papers (5 papers)

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Research

24 pages, 17849 KB  
Article
Land Subsidence in the Loess Plateau: SBAS-InSAR Analysis of Yan’an New District During 2017–2022
by Yang Hong, Peng Chen, Yibin Yao, Liangcai Qiu, Hang Liu, Chengchang Zhu and Jierui Lu
Sensors 2025, 25(20), 6298; https://doi.org/10.3390/s25206298 - 11 Oct 2025
Viewed by 397
Abstract
Located on the Loess Plateau, the Yan’an New District (YND) has experienced significant geological instability due to large-scale mountain excavation and city construction (MECC). This study applied the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to 66 ascending Sentinel-1A SAR images [...] Read more.
Located on the Loess Plateau, the Yan’an New District (YND) has experienced significant geological instability due to large-scale mountain excavation and city construction (MECC). This study applied the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to 66 ascending Sentinel-1A SAR images acquired between January 2017 and May 2022 to investigate ground deformation patterns and influencing factors. Results show that the maximum subsidence rate reached −86 mm/year, with a maximum cumulative deformation of 400 mm. Groundwater storage was identified as the key natural driver, exhibiting a significant positive correlation (r = 0.4–0.8) with cumulative deformation with a two-month lag. Fill thickness emerged as the dominant anthropogenic factor, controlling the duration of soil consolidation and thus the deformation rate. Regulating groundwater extraction and improving recharge can effectively reduce subsidence risks. These findings provide scientific guidance for geological hazard early warning and urban planning in YND. Full article
(This article belongs to the Special Issue Application of SAR and Remote Sensing Technology in Earth Observation)
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18 pages, 31572 KB  
Article
Polarimetric Time-Series InSAR for Surface Deformation Monitoring in Mining Area Using Dual-Polarization Data
by Xingjun Ju, Sihua Gao and Yongfeng Li
Sensors 2025, 25(19), 5968; https://doi.org/10.3390/s25195968 - 25 Sep 2025
Viewed by 474
Abstract
Timely and reliable surface deformation monitoring is critical for hazard prevention and resource management in mining areas. However, traditional Time-Series Interferometric (TSI) Synthetic Aperture Radar techniques often suffer from low coherent point density in mining environments, limiting their effectiveness. To overcome this limitation, [...] Read more.
Timely and reliable surface deformation monitoring is critical for hazard prevention and resource management in mining areas. However, traditional Time-Series Interferometric (TSI) Synthetic Aperture Radar techniques often suffer from low coherent point density in mining environments, limiting their effectiveness. To overcome this limitation, we propose an adaptive Polarimetric TSI (PolTSI) method that exploits dual-polarization Sentinel-1 data to achieve more reliable deformation monitoring in complex mining terrains. The method employs a dual-strategy optimization: amplitude dispersion–based optimization for Permanent Scatterer (PS) pixels and minimum mean square error (MMSE)-based polarimetric filtering followed by coherence maximization for Distributed Scatterer (DS) pixels. Experimental results from an open-pit mining area demonstrate that the proposed approach significantly improves phase quality and spatial coverage. In particular, the number of coherent monitoring points increased from 31,183 with conventional TSI to 465,328 using the proposed approach, corresponding to a 1392% improvement. This substantial enhancement confirms the method’s robustness in extracting deformation signals from low-coherence, heterogeneous mining surfaces. As one of the few studies to apply Polarimetric InSAR (Pol-InSAR) in active mining regions, our work demonstrates the underexplored potential of dual-pol SAR data for improving both the spatial density and reliability of time-series deformation mapping. The results provide a solid technical foundation for large-scale, high-precision surface monitoring in complex mining environments. Full article
(This article belongs to the Special Issue Application of SAR and Remote Sensing Technology in Earth Observation)
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16 pages, 4406 KB  
Article
Integration of Physical Features and Machine Learning: CSF-RF Framework for Optimizing Ground Point Filtering in Vegetated Regions
by Sisi Zhang, Chenyao Qu, Zhimin Wu and Wei Wang
Sensors 2025, 25(19), 5950; https://doi.org/10.3390/s25195950 - 24 Sep 2025
Viewed by 400
Abstract
Complex terrain conditions and dense vegetation cover in a vegetation area present significant challenges for point cloud data processing and the accurate extraction of ground points. This work integrates the physical characteristics between ground and non-ground points from the traditional Cloth Simulation Filter [...] Read more.
Complex terrain conditions and dense vegetation cover in a vegetation area present significant challenges for point cloud data processing and the accurate extraction of ground points. This work integrates the physical characteristics between ground and non-ground points from the traditional Cloth Simulation Filter (CSF) algorithm and the strong learning capability of the machine learning Random Forest (RF) framework, developing the CSF-RF fusion algorithm for filtering ground points in vegetated areas, which can improve the accuracy of point cloud filtering in complex terrain environments. Both type I and type II errors do not exceed 0.05%, and the total error is maintained within 0.03%. Particularly in areas with dense vegetation and severe terrain undulations, the advantages are evident: the CSF-RF algorithm achieves a total error of only 0.19%, representing a 79.6% relative reduction compared with the 0.93% error of the CSF algorithm, while also reducing cases of ground point omission. Thus, it can be seen that the CSF-RF algorithm can effectively reduce vegetation interference and exhibits good stability, providing effective technical support for the accurate extraction of Digital Elevation Models (DEMs) in vegetated areas. Full article
(This article belongs to the Special Issue Application of SAR and Remote Sensing Technology in Earth Observation)
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23 pages, 2620 KB  
Article
An Efficient SAR Raw Signal Simulator Accounting for Large Trajectory Deviation
by Shaoqi Dai, Haiyan Zhang, Cheng Wang, Zhongwei Lin, Yi Zhang and Jinhe Ran
Sensors 2025, 25(14), 4260; https://doi.org/10.3390/s25144260 - 9 Jul 2025
Cited by 1 | Viewed by 502
Abstract
A synthetic aperture radar (SAR) raw signal simulator is useful for supporting algorithm innovation, system scheme verification, etc. Trajectory deviation is a realistic factor that should be considered in a SAR raw signal simulator and is very important for applications such as motion [...] Read more.
A synthetic aperture radar (SAR) raw signal simulator is useful for supporting algorithm innovation, system scheme verification, etc. Trajectory deviation is a realistic factor that should be considered in a SAR raw signal simulator and is very important for applications such as motion composition and image formation for a SAR with nonlinear trajectory. However, existing efficient simulators become deteriorated and even invalid when the magnitude of trajectory deviation increases. Therefore, we designed an efficient SAR raw signal simulator that accounts for large trajectory deviation. Based on spatial spectrum analysis of the SAR raw signal, it is disclosed and verified that the 2D spatial frequency spectrum of the SAR raw signal is an arc of a circle at a fixed transmitted signal frequency. Based on this finding, the proposed method calculates the SAR raw signal by curvilinear integral in the 2D frequency domain. Compared with existing methods, it can precisely simulate the SAR raw signal in the case that the deviation radius is much larger. Moreover, taking advantage of the fast Fourier transform (FFT), the computational complexity of this method is much less than the time-domain ones. Furthermore, this method is applicable for multiple SAR acquisition modes and diverse waveforms and compatible with radar antenna beam width, squint angle, radar signal bandwidth, and trajectory fluctuation. Experimental results show its outstanding performance for simulating the raw signal of SAR with large trajectory deviation. Full article
(This article belongs to the Special Issue Application of SAR and Remote Sensing Technology in Earth Observation)
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14 pages, 16915 KB  
Article
Performance Assessment of Change Detection Based on Robust PCA for Wavelength Resolution SAR Images Using Nonidentical Flight Passes
by Lucas P. Ramos, Viet T. Vu, Mats I. Pettersson, Patrik Dammert, Leonardo T. Duarte and Renato Machado
Sensors 2025, 25(8), 2506; https://doi.org/10.3390/s25082506 - 16 Apr 2025
Viewed by 701
Abstract
One of the main challenges in Synthetic Aperture Radar (SAR) change detection involves using SAR images from different flight passes. Depending on the flight pass, objects have different specular reflections since the radar cross-sections of these objects can be totally different between passes. [...] Read more.
One of the main challenges in Synthetic Aperture Radar (SAR) change detection involves using SAR images from different flight passes. Depending on the flight pass, objects have different specular reflections since the radar cross-sections of these objects can be totally different between passes. Then, it is common knowledge that the flight passes must be close to identical for conventional SAR change detection. Wavelength-resolution SAR refers to a SAR system with a spatial resolution approximately equal to the wavelength. This high relative resolution helps to stabilize the ground clutter in the SAR images. Consequently, the restricted requirement about identical flight passes for SAR change detection can be relaxed, and SAR change detection becomes possible with nonidentical passes. This paper shows that robust principal component analysis (RPCA) is efficient for change detection even using wavelength-resolution SAR images acquired with very different flight passes. It presents several SAR change detection experimental results using flight pass differences up to 95°. For slightly different passes, e.g., 5°, our method reached a false alarm rate (FAR) of approximately one false alarm per square kilometer for a probability of detection (PD) above 90%. In a particular setting, it achieves a PD of 97.5% for a FAR of 0.917 false alarms per square kilometer, even using SAR images acquired with nonidentical passes. Full article
(This article belongs to the Special Issue Application of SAR and Remote Sensing Technology in Earth Observation)
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