Next Article in Journal
GRiM-Net: A Two-Stage Cross-View Visual Localization Framework for UAVs
Next Article in Special Issue
Phase Unwrapping in Seconds: A Spectral ADMM Algorithm for Large-Scale InSAR
Previous Article in Journal
Sun-Induced Chlorophyll Fluorescence (SIF) Enhances Soil Respiration Estimation in Desertified Mining Areas
Previous Article in Special Issue
Multi-Temporal InSAR and Machine Learning for Geohazard Monitoring: A Systematic Review with Emphasis on Noise Mitigation and Model Transferability
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Remote Sensing
Volume 18
Issue 10
10.3390/rs18101476
remotesensing-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Applicability and Feasibility of InSAR-Based Mining Subsidence Monitoring Under Overburden Isolated Grouting Backfill Mining Conditions

by
Zhengpei Zhou
1,
Yufen Niu
1,*,
Zhong Lu
2,
Xuhai Yang
3,
Zhaojiang Zhang
1,
Ziheng Ju
4 and
Jinqi Zhao
2
1
School of Mining and Geomatics Engineering, Hebei University of Engineering, Handan 056038, China
2
School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
3
Moonbristar (Chengdu) Technology Co., Ltd., Chengdu 610000, China
4
School of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2026, 18(10), 1476; https://doi.org/10.3390/rs18101476
Submission received: 18 March 2026 / Revised: 22 April 2026 / Accepted: 22 April 2026 / Published: 8 May 2026
(This article belongs to the Special Issue Advances in Geological Hazard Characterization and Assessment: Merging Remote Sensing with Direct Surveys)
Browse Figures
Versions Notes

Highlights

What are the main findings?
  • Under comparable surface-cover conditions, increases in deformation magnitude and deformation gradient can lead to significant InSAR coherence decay, and areas of large-gradient deformation exhibit a high degree of spatial correspondence with low-coherence or decorrelated zones.
  • Overburden isolated grouting backfilling technology exhibits significant advantages in controlling surface subsidence and deformation gradients, achieving a surface subsidence reduction of up to 83.3%. This allows the subsidence-affected area to maintain relatively high coherence, thereby enabling InSAR to retrieve high-accuracy and continuous deformation time-series results.
What are the implications of the main findings?
  • In conventional caving-mined areas, when subsidence monitoring is conducted using Sentinel-1 C-band data and a conventional SBAS-InSAR/MintPy processing workflow, the large-magnitude and large-gradient deformation in the central part of the subsidence basin readily causes rapid coherence decay and phase-unwrapping difficulties, thereby limiting the applicability of InSAR for subsidence monitoring and making it difficult to support stable monitoring and risk assessment of surface subsidence and its secondary hazards.
  • Overburden isolated grouting backfilling can effectively control both the magnitude and gradient of surface deformation, thereby making the Sentinel-1 C-band data and SBAS-InSAR processing workflow adopted in this study well suited to surface subsidence monitoring. This can provide useful guidance for green, safe, and efficient mining, as well as for surface subsidence monitoring under the “three-under” conditions.

Abstract

With its high spatiotemporal resolution and wide-area coverage, InSAR technology has become an essential tool for monitoring mining-induced surface subsidence. However, the large-gradient deformation caused by the traditional caving method has hindered this technology’s widespread application in subsidence monitoring. With the increasing adoption of backfill mining techniques, both the magnitude and the gradient of surface deformation have been significantly reduced, creating new opportunities for applying InSAR to subsidence monitoring in mining areas. Nevertheless, current research on InSAR under backfill mining conditions remains relatively limited, particularly with respect to the applicability of time-series InSAR techniques in such settings. In this study, the Wu’an mining area, characterized by the traditional caving method, and the Fengfeng mining area, which employs overburden isolated grouting backfill mining, were selected as representative cases. By integrating Small Baseline Subset InSAR (SBAS-InSAR) time-series deformation results with ground-based leveling measurements, we comparatively analyzed the interferometric coherence characteristics and deformation monitoring performance associated with the two mining methods. We then evaluated the monitoring applicability and engineering feasibility of InSAR under overburden isolated grouting backfill mining conditions. The results indicate that, under similar surface land cover conditions, the gradient of surface deformation exerts a strong influence on interferometric coherence. Comparison with leveling measurements shows that the RMSE in the Fengfeng mining area under overburden isolated grouting backfill mining is at least 68.2% lower than that in the Wu’an mining area under caving mining. Moreover, overburden isolated grouting backfill mining can effectively mitigate mining-induced surface deformation, limiting the maximum subsidence in the Fengfeng mining area to less than 300 mm and to less than 200 mm in village areas. Using Sentinel-1 data and a conventional SBAS-InSAR processing workflow, InSAR demonstrates higher reliability and applicability for surface subsidence monitoring in mining areas under overburden isolated grouting backfilling conditions than in those under caving mining conditions. This study aims to provide a useful reference for mining subsidence monitoring based on Sentinel-1 C-band data and a conventional SBAS-InSAR processing workflow.

1. Introduction

To date, coal remains China’s most secure and reliable primary energy source, meaning that it substantially contributes to national energy security and sustained economic development [1,2]. However, high-intensity and large-scale coal extraction, while generating economic benefits, inevitably induces a range of geohazards, such as surface subsidence, building damage, and landslides, which pose serious threats to surface infrastructure and the local ecological environment in mining areas [3,4]. Therefore, to safeguard such infrastructure and protect the lives and property of local residents, it is critical to establish efficient and high-precision time-series monitoring of surface subsidence in mining areas.
Traditional approaches to monitoring such surface subsidence primarily rely on ground-based surveys, such as leveling, total-station measurements, and GNSS measurements, which yield only a limited number of discrete monitoring points for point- and profile-based subsidence analyses [5,6]. However, these methods are constrained by harsh field conditions, low efficiency, and insufficient spatial coverage and thus cannot meet the demand for wide-area, high-spatiotemporal-resolution dynamic subsidence monitoring in mining areas [7,8]. With the continuous advancement of synthetic aperture radar (SAR) satellite technology, InSAR has provided a new avenue for surface deformation monitoring, owing to its all-weather, day-and-night imaging capability, wide coverage, and high accuracy [9,10]. Since 1996, when Carnec et al. [11] first applied differential InSAR (D-InSAR) to subsidence monitoring in mining areas, numerous studies have investigated mining subsidence using InSAR techniques. To address the limitations of D-InSAR, such as temporal/spatial decorrelation and atmospheric delays, researchers have proposed a suite of time-series InSAR methods, including Persistent Scatterer InSAR (PS-InSAR) [12], SBAS-InSAR [13], and Distributed Scatterer InSAR (DS-InSAR) [14], which have been successfully applied in mining regions worldwide [15,16,17]. Nevertheless, current research on and applications of InSAR primarily focus on subsidence monitoring in mining areas employing the caving method. Surface subsidence induced by this mining method is typically characterized by high subsidence rates, large cumulative deformation, and steep deformation gradients, such that the relative displacement between adjacent pixels may exceed half a wavelength [18,19]. This, in turn, causes rapid coherence decay or even complete decorrelation in the central part of the subsidence basin and leads to phase unwrapping failure, making it difficult for InSAR to effectively capture large-gradient deformation in mining areas [20,21,22]. Although pixel offset tracking (POT) has been explored to capture such deformation [23,24,25,26] its accuracy is fundamentally constrained by the spatial resolution of SAR imagery, which remains insufficient for the precision required in mining subsidence monitoring. Consequently, the broader application of InSAR for such monitoring has been hindered to some extent.
Since waste rock was first utilized for stope backfilling at the North Lyell Mine in Australia in 1915 [27], various backfill mining technologies have been extensively investigated and applied in both metal and coal mines worldwide [28,29]. In recent years, with the increasing intensity and extent of mining activities, environmentally friendly backfill mining has been widely promoted and applied in China to improve the recovery of coal resources under the “three-under” conditions (beneath buildings, water bodies, and railways) and to support green, safe, and sustainable mining [30,31,32,33]. This approach offers significant advantages for controlling surface subsidence and mitigating the adverse impacts of mining on surface structures and the ecological environment. It has become a key technological pathway toward green, safe, and efficient mining [34,35,36]. Sun et al. [37] demonstrated, in engineering practice, that paste backfilling can significantly reduce mining-induced ground deformation, keeping building displacements and strains within the Grade I damage threshold. Wang et al. [38] used the probability integral method (PIM) to predict cumulative surface subsidence under non-backfill conditions and, in comparison with those under ultra-high-water material backfilling, found that the affected area and the maximum cumulative subsidence decreased by 22% and 61.5%, respectively. Xuan et al. [39] proposed an overburden isolated grouting backfilling (OIGB) technology, which, as validated by field tests, markedly increased grout take and reduced surface subsidence, achieving a subsidence-reduction rate of up to 89%. Owing to its high coal recovery, low backfilling cost, and minimal interference between extraction and backfilling operations [40], the OIGB method has been widely adopted for subsidence mitigation, coal extraction beneath buildings, and solid-waste reduction across various mining scenarios [41]. However, subsidence monitoring under backfill mining conditions still predominantly relies on ground-based techniques [42]. Although some studies have attempted to employ D-InSAR for this task [43,44], the relevant literature remains limited, particularly regarding the applicability of time-series InSAR techniques under backfill mining conditions.
Building on this, we employ SBAS-InSAR to retrieve time-series surface deformation for representative mines in the Wu’an mining area (characterized by the caving method) and the Fengfeng mining area (characterized by OIGB). The SBAS-InSAR results are validated against ground-based leveling measurements. On this basis, we compare the interferometric coherence characteristics and deformation monitoring performance associated with the two mining methods. Furthermore, by integrating the leveling measurements, we characterize the spatiotemporal evolution of surface subsidence in the Fengfeng mining area under OIGB conditions, thereby evaluating the applicability of InSAR for deformation monitoring and the engineering feasibility of its wider deployment under such conditions.

2. Research Areas and Data Introduction

2.1. Overview of Research Areas

To comprehensively assess differences in the applicability of InSAR under caving and backfill mining conditions, two typical mining areas with comparable geological settings were selected in Handan City, Hebei Province. The locations of the study areas are shown in Figure 1.
The first study area is located in southern Wu’an City, on the eastern piedmont of the Taihang Mountains. It is bounded by Yongnian District (Handan) to the east, Anyang (Henan Province) to the south, She County (Handan) to the west, and Shahe (Xingtai) to the north. Wu’an is one of China’s 58 key coal-producing counties and one of the country’s four major iron-ore bases [45]. The region is rich in mineral resources, with more than ten mineral types have been identified, including coal, iron, marble, and bauxite, with proven reserves of approximately 560 Mt of iron ore, 1.42 Gt of coal, and 120 Mt of gypsum. The area’s topography is complex and generally decreases in elevation from south to north, comprising mountainous areas, low hills, and basins. This study focuses on working face 132153, with a strike length of 840 m, a dip length of 75 m, and a maximum elevation difference of 12 m. The coal seam has an average thickness of 5.0 m, a dip angle of 21–25°, and an average burial depth of 550 m. The working face advances from north to south. Mining commenced in February 2018 and ceased in June 2019, adopting the caving method.
The second study area lies in the Fengfeng Mining District of Handan, on the eastern piedmont of Jiushan, a branch of the Taihang Mountains. It is bounded by Hanshan District (Handan) to the east, Cixian to the south, Wu’an to the west, and Shahe (Xingtai) to the north. Fengfeng is an important coal-producing base within the North China coal-bearing basin. The study area contains proven coal reserves of approximately 3.5 Gt, with stable seam occurrence and good coal quality. The terrain generally descends in elevation from west to east, transitioning from low mountains and hills in the west to plains in the east. The selected working face has a strike length of 790 m, a dip length of 340 m, an average seam thickness of 2.5 m, an average seam dip angle of 13°, and an average burial depth of 650 m. Because a village is located immediately west of the working face, OIGB was employed to control surface subsidence and ensure the structural safety of village buildings. Fly ash slurry was used as the primary backfilling material. Mining commenced in February 2023 and was completed in May 2024.

2.2. Data

This study combined Sentinel-1A SAR imagery, the ALOS World 3D 30 m (AW3D30) digital elevation model (DEM), and ground-based leveling measurements. The SAR data were used to generate interferometric coherence maps and to retrieve time-series surface deformation for the two mining areas. The AW3D30 DEM was employed to simulate and remove the topographic phase, and the leveling measurements were subsequently used to validate the accuracy of the InSAR time-series deformation.
Sentinel-1A, launched by the European Space Agency (ESA) on 3 April 2014, is an Earth-observation satellite equipped with a C-band SAR sensor, providing open access data and a 12-day repeat cycle. In this study, VV-polarized Sentinel-1A SAR data for both study areas were obtained from the ASF DAAC. For Wu’an, the temporal coverage spanned from 1 February 2018 to 22 April 2021; for Fengfeng, it spanned from 11 February 2023 to 8 November 2024. The AW3D30 DEM is a global 30 m product generated by the Japan Aerospace Exploration Agency (JAXA) from ALOS PRISM stereo imagery acquired during 2006–2011. In addition, surface subsidence monitoring points were deployed along both the strike and dip directions of the working faces in the two mining areas (Figure 1). Specifically, 48 monitoring points were established in the Wu’an mining area, including 26 along strike and 22 along dip, whereas 12 monitoring points were established in the Fengfeng mining area, with 6 along strike and 6 along dip. Subsidence measurements at all monitoring points were conducted using fourth-order leveling. The principal datasets and acquisition parameters for the Wu’an and Fengfeng sites are summarized in Table 1.

3. Methodology

In this study, interferogram generation and preprocessing were performed using the GAMMA 2019 software developed by GAMMA Remote Sensing (Gümligen, Switzerland), in conjunction with the MintPy time-series InSAR analysis tool. SBAS-InSAR processing was conducted for the Wu’an mining area under caving mining conditions and the Fengfeng mining area under OIGB conditions, yielding interferometric coherence maps and surface deformation time series for the two mining methods. The InSAR results were validated against ground-based leveling measurements to assess accuracy. On this basis, we compared the coherence characteristics and deformation monitoring performance of the two mining methods to evaluate the monitoring applicability and limitations of InSAR under different mining regimes. Furthermore, by integrating the leveling data, we characterized the spatiotemporal evolution of surface subsidence in the Fengfeng mining area under OIGB conditions, thereby providing an assessment of InSAR’s applicability and the engineering feasibility of its broader deployment under such conditions. The overall processing workflow is illustrated in Figure 2.

3.1. Time-Series Deformation Inversion

3.1.1. Interferometric Pair Selection

In this study, Sentinel-1A data for the Wu’an and Fengfeng mining areas were processed using GAMMA software. The multilook factors in the range and azimuth directions were set to 4 and 1, respectively, and identical spatiotemporal baseline criteria were used to construct interferometric pairs. To ensure adequate connectivity of the interferometric network, the spatial baseline threshold was moderately relaxed to ±120 m and the temporal baseline threshold was extended to 36 days, thereby generating the interferometric pair combinations shown in Figure 3. It should be noted that, during subsequent MintPy processing, a coherence threshold is selected on a pixel-by-pixel basis to determine whether each interferometric pair is retained for a given pixel. Subsequently, SBAS-InSAR time-series processing was performed in MintPy to obtain intermediate coherence products and the final surface deformation monitoring results.

3.1.2. Time-Series Inversion Using MintPy

MintPy is an open-source Python 3 package for InSAR time-series deformation analysis that supports co-registered and phase-unwrapped interferometric products generated by mainstream SAR processing platforms, including ISCE, ARIA, FRInGE, HyP3, GMTSAR, SNAP, and GAMMA. In this study, interferometric products derived from Sentinel-1A data were processed using GAMMA software. Each interferogram was adaptively filtered using a 32 × 32 window, and phase unwrapping was performed using the minimum cost flow method with an unwrapping threshold of 0.4. The resulting interferometric products were then used as input to MintPy. MintPy further performed pixel-wise screening and optimization of interferograms that satisfy the spatial and temporal baseline constraints to mitigate the effects of spatial and temporal decorrelation on deformation monitoring [46]. In this study, the pixel-wise coherence threshold for interferometric network selection was set to 0.4.
Within the SBAS-InSAR framework, MintPy applies an unbiased weighted least-squares estimator to invert the interferometric network and derive displacement time series. Four weighting strategies are available: uniform or no weighting [13], spatial coherence at the pixel level [47], the inverse of the phase variance [48], and the nonparametric Fisher information matrix [49]. In this study, the inverse of the phase variance was adopted to ensure the robustness of the inversion results. The phase closure method was employed to constrain and correct the temporal evolution of interferometric phases. To compensate for deterministic components such as tropospheric delay and the residual topographic phase, atmospheric delay corrections were implemented using ERA5 numerical weather model data, while topographic residuals were estimated and corrected according to the proportional relationship between the DEM error and the perpendicular baseline length. Furthermore, the influence of residual phase errors, including uncorrected tropospheric and ionospheric effects as well as residual decorrelation noise, was quantitatively evaluated by calculating the root mean square error (RMSE) of residual phases. Interferograms exhibiting excessive phase noise were consequently removed from the time series. Finally, a denoised surface deformation rate map and its corresponding standard deviation were derived, providing a reliable foundation for subsequent spatiotemporal deformation interpretation and mining-induced subsidence analysis. Considering the nonlinear temporal evolution of mining subsidence, no a priori deformation model was assumed during deformation inversion in this study. In addition, reference points in both study areas were manually selected following the principle of choosing stable artificial structures as close as possible to the subsidence zone.

3.2. Accuracy Validation

To verify the accuracy of InSAR-derived surface subsidence, ground leveling measurements were used as reference data, with which the deformation results obtained under the two mining conditions were quantitatively compared. Four commonly used accuracy metrics, namely the minimum absolute error, maximum absolute error, mean absolute error (MAE), and RMSE, were employed to comprehensively assess the accuracy and applicability of InSAR in deformation monitoring in caving and OIGB conditions. The MAE and RMSE are calculated as follows:
V M A E = 1 n ∑ i = 1 n x i − x ^ i
V R M S E = ∑ i = 1 n x i − x ⌢ i 2 n
In the above equations, V M A E and V R M S E represent the MAE and RMSE, respectively; x i denotes the subsidence value of the i-th ground monitoring point obtained from InSAR results; x ^ i represents the subsidence value measured by leveling; and n denotes the total number of ground monitoring points.

4. InSAR Monitoring Results and Analysis

4.1. Time-Series Deformation Monitoring Results

In this study, Sentinel-1A SAR scenes were processed using GAMMA software and the MintPy InSAR time-series analysis package. A total of 174 Sentinel-1A scenes acquired over the Wu’an mining area from 1 February 2018 to 22 April 2021 were used for SBAS-InSAR processing to retrieve the line-of-sight (LOS) deformation time series. Subsequently, based on the radar imaging geometry, the LOS deformation d L O S was converted to the vertical deformation component according to d v = d L O S / cos θ , where θ denotes the radar incidence angle. This procedure yielded the time-series cumulative subsidence of working face 132153 during the study period. Similarly, 50 Sentinel-1A SAR scenes acquired over the Fengfeng mining area between 11 February 2023 and 8 November 2024 were processed using the same SBAS-InSAR workflow to obtain the LOS deformation time series. Using the same geometric conversion, the time-series cumulative subsidence of the selected working face in the Fengfeng mining area was obtained for the corresponding period.

4.1.1. Wu’an Mining Area

Figure 4 illustrates the spatiotemporal evolution of cumulative subsidence within the 132153 working face of the Wu’an mining area under the caving method from 1 February 2018 to 22 April 2021. As shown in Figure 4a–e, from 1 February 2018 to 3 January 2019, subsidence was primarily confined to the working face. As mining progressed from north to south, the subsidence-affected area expanded southward along the working face. Figure 4f–k show that after 4 March 2019, the eastern portion of the working face exhibited a marked increase in subsidence magnitude. Mining records indicate that an adjacent working face was mined from January to August 2019. Under the combined influence of these two working faces, the subsidence area continued to expand, ultimately forming a subsidence basin centered on the two working faces.

4.1.2. Fengfeng Mining Area

Figure 5 illustrates the spatiotemporal distribution of cumulative subsidence in the area affected by the selected working face in the Fengfeng mining area under OIGB conditions from 11 February 2023 to 8 November 2024. As shown in the figure, the InSAR analysis captured the full time series of cumulative subsidence. Beginning on 23 June 2023, slight subsidence was first detected along the working face. With the progression of mining operations, both its magnitude and the affected area gradually increased, exhibiting an overall elliptical spatial distribution. As of 8 November 2024, the maximum cumulative subsidence within the affected area reached approximately 300 mm. During the period from 11 February 2023 to 8 December 2023, the subsidence within the village adjacent to the working face ranged from 5 mm to 45 mm, with an overall small subsidence magnitude, indicating that the village area was not significantly affected by mining activities during this period. Since 18 February 2024, the subsidence-affected zone has gradually expanded into the village area, where the maximum subsidence has reached approximately 200 mm.

4.2. Accuracy Verification of Deformation Monitoring

To validate the accuracy and reliability of the InSAR monitoring results, leveling measurements were used as the benchmark in this study. Accuracy assessment was conducted using the cumulative surface subsidence derived from InSAR for the Wu’an mining area from 25 June 2018 to 26 July 2019 and for the Fengfeng mining area from 11 February 2023 to 28 August 2024. The corresponding leveling observation periods for the Wu’an and Fengfeng mining areas were from 27 June 2018 to 27 July 2019 and from 11 February 2023 to 26 August 2024, respectively. A total of 48 monitoring points were deployed in the Wu’an mining area; however, monitoring points Z23–Z26 were located in areas lacking valid InSAR results and therefore could not be included in the comparison. Consequently, 44 leveling monitoring points were ultimately used for accuracy validation in the Wu’an mining area. In the Fengfeng mining area, all 12 monitoring points were included in the accuracy assessment. The error statistics for the two mining areas are presented in Table 2.
As shown in Table 2, the maximum absolute errors between InSAR and leveling along the strike and dip observation lines in the Wu’an mining area reached 404 mm and 514 mm, respectively, indicating substantial discrepancies. The MAE values for the strike (Z1–Z22) and dip (Q1–Q22) lines were 82 mm and 142 mm, while the corresponding RMSE values were 132 mm and 245 mm, respectively, reflecting comparatively low overall accuracy. Further analysis revealed that, in the strike direction (Z1–Z18), the MAE and RMSE decreased markedly to 17 mm and 22 mm, respectively, and in the dip direction (Q10–Q22), they decreased to 18 mm and 70 mm. This demonstrates a high level of consistency between the InSAR-derived and leveling-measured results within these segments. As shown in Figure 6, the subsidence values at monitoring points Z1–Z18 and Q10–Q22 are all less than 400 mm, and the InSAR-derived results are in good agreement with the leveling measurements. In contrast, the leveling-observed subsidence at points Z19–Z22 and Q1–Q9 increased markedly. In particular, the maximum subsidence at point Q6 reached 814 mm, whereas the corresponding InSAR-derived value is only 328 mm, indicating a substantial discrepancy between the two. The above analysis indicates that, under the Sentinel-1 data and conventional SBAS-InSAR processing workflow adopted in this study, InSAR can provide monitoring results with relatively high accuracy and good stability in areas where the cumulative surface subsidence does not exceed 400 mm. However, when subsidence exceeds 400 mm, the inversion accuracy and reliability of InSAR in large-magnitude subsidence areas decrease significantly, making it difficult to accurately characterize the subsidence characteristics of such areas.
In comparison, the Fengfeng mining area exhibited a much closer fit between InSAR-derived and leveling-measured results, with a maximum absolute error of only 76 mm, an MAE of 38 mm, and an RMSE of 42 mm. As shown in Figure 7, the overall subsidence magnitude in the Fengfeng mining area was relatively small, with a maximum leveling-measured subsidence of 229 mm, while the InSAR-derived value was 172 mm, corresponding to a difference of 57 mm. Overall, using Sentinel-1 data and the conventional SBAS-InSAR processing workflow, the time-series deformation results in the Fengfeng mining area show relatively high accuracy and reliability and can effectively capture the spatiotemporal evolution of surface subsidence under OIGB conditions.

4.3. Comparison of Different Mining Methods

4.3.1. Coherence Analysis Comparison

In InSAR processing, interferometric coherence quantifies the degree of phase correlation between the primary and secondary SAR images, typically ranging from 0 to 1. A higher coherence value indicates stronger correlation between image pairs, implying less surface change and thus higher deformation retrieval accuracy [50,51]. To evaluate the applicability of InSAR under caving and OIGB conditions, two sets of coherence maps with 24-day and 36-day temporal baselines were selected for comparative analysis.
To minimize the influence of vegetation cover on the comparative analysis of coherence, coherence data with interferometric temporal baselines of 24 and 36 days acquired in mid- and late January, when vegetation coverage was relatively low in both mining areas, were selected for analysis. On this basis, the central parts of the subsidence basins in the two mining areas, defined as the areas within approximately one-third of the maximum subsidence influence range, were further selected as the statistical analysis regions to investigate the relationship between surface subsidence and coherence variation. Figure 8a–h illustrate the spatial distributions of coherence for both mining areas: Figure 8a,c,e,g correspond to Wu’an (characterized by the caving method), while Figure 8b,d,f,h represent Fengfeng (characterized by OIGB). As shown in Figure 8a,b, the 24-day coherence statistics for mid-January indicate that the average coherence in the Wu’an area, mined using the traditional caving method, was 0.79, which was significantly lower than that in the Fengfeng area, which reached 0.90. When the interferometric temporal baseline was extended to 36 days, coherence decreased overall as surface deformation increased. The average coherence values dropped to 0.67 for Wu’an and 0.79 for Fengfeng (Figure 8c,d), and the latter remained noticeably higher. Similarly, the results from late January (Figure 8e–h) show that the Fengfeng mining area maintained relatively high coherence, while Wu’an exhibited more pronounced coherence degradation. Spatially, the low-coherence zones in Wu’an were concentrated around the center of the 132153 working face and closely corresponded to the region of maximum subsidence. Interestingly, coherence within the subsidence center remained relatively high, likely because, despite large deformation magnitudes, the deformation gradient in this area was comparatively low, thereby reducing phase decorrelation effects. Although both datasets were acquired during the winter season, when vegetation cover was sparse, the overall coherence in the Fengfeng mining area may still have been affected to some extent by residual vegetation-induced decorrelation.

4.3.2. Deformation Analysis Comparison

To investigate the deformation monitoring performance of InSAR under different mining methods, in this study, we selected the 132153 working face in the Wu’an mining area (characterized by the caving method) and a representative working face in the Fengfeng area (characterized by OIGB) as case studies. The time-series surface deformation was retrieved using InSAR, and the results were compared with ground leveling measurements.
Under caving mining conditions, InSAR achieves relatively high accuracy in monitoring small-magnitude deformation in the subsidence boundary area and shows good agreement with leveling measurements. However, constrained by the wavelength characteristics of C-band SAR and the conventional SBAS-InSAR processing workflow, InSAR has clear limitations in retrieving deformation in the large-gradient zone at the center of the subsidence basin. As a result, it is difficult to fully capture the deformation characteristics of such large-gradient subsidence areas, leading to incomplete deformation information over the subsidence zone.
In contrast, OIGB technology can effectively mitigate mining-induced damage to the overburden structure and maintain relatively gentle surface subsidence gradients. As a result, using Sentinel-1 data and the conventional SBAS-InSAR processing workflow, InSAR is able to retrieve continuous and complete time-series deformation information over the subsidence area with relatively high monitoring accuracy and reliability. Compared with ground leveling measurements, InSAR provides superior spatial and temporal resolution, enabling the acquisition of high-frequency, multitemporal datasets that provide a more detailed characterization of the evolution of surface subsidence under OIGB conditions. Furthermore, this technology enables dynamic assessment of mining-induced impacts on surface infrastructure, such as buildings in nearby villages, thereby providing a scientific basis for safety evaluation and risk mitigation in mining subsidence monitoring.
In summary, OIGB technology can effectively mitigate both the magnitude and gradient of surface deformation induced by underground mining, thereby reducing the adverse effect of deformation gradients on coherence and helping to maintain relatively high coherence. Therefore, using Sentinel-1 data and the conventional SBAS-InSAR processing workflow adopted in this study, InSAR demonstrates higher reliability and applicability for surface subsidence monitoring in mining areas under OIGB conditions than in those under caving mining conditions.

4.4. Deformation Characteristics Under OIGB Conditions

The above analysis indicates that the SBAS-InSAR technique adopted in this study failed to retrieve the large-gradient deformation in the central part of the subsidence basin in the Wu’an mining area under caving mining, and thus could not fully reveal the spatiotemporal evolution characteristics of the subsidence basin. In contrast, OIGB technology can effectively control both the magnitude of surface subsidence and the deformation gradient. Using Sentinel-1 data and the conventional SBAS-InSAR processing workflow, InSAR exhibits higher monitoring accuracy and applicability under such mining conditions. Therefore, this study further analyzes the deformation characteristics of a mine in the Fengfeng mining area under OIGB conditions.
To further investigate the subsidence mitigation performance and the spatiotemporal evolution patterns of surface deformation under OIGB conditions, time-series cumulative subsidence data were extracted along two representative profiles, C–C′ and D–D′, as illustrated in Figure 9. The corresponding time-series subsidence curves reveal that both profiles exhibit similar deformation evolution trends. Between 11 February 2023 and 3 September 2023, as mining operations progressed along the working face, cumulative subsidence gradually increased, and its center migrated along the direction of face advancement, consistent with the typical subsidence behavior observed in traditional mining. However, for 2 November 2023 and 17 June 2024, compared to those for 23 September 2023 and 5 June 2024, the subsidence curves show slight localized uplift, with a maximum magnitude of approximately 30 mm. During the period from 8 December 2023 to 28 August 2024, the magnitude increased markedly, and the subsidence-affected area expanded rapidly, with the profiles developing into a typical U-shaped cross-section. After 28 August 2024, although minor additional subsidence was observed up to 8 November 2024, the overall rate decreased significantly, and the surface deformation tended toward stabilization.
The C–C′ profile, which traverses the village zone between 1030 m and 1610 m, indicates that, during the period from 11 February 2023 to 8 December 2023, the maximum cumulative subsidence within the village area was approximately 45 mm, representing minor surface deformation that posed no significant risk to building stability. As mining operations approached the village, the subsidence-influenced zone gradually extended into the residential area, leading to a modest increase in deformation magnitude. By 8 November 2024, roughly six months after the completion of extraction, the maximum cumulative subsidence within the village reached 200 mm, while the gradient remained gentle.

5. Discussion

5.1. Surface Subsidence Control Performance

According to the geological and mining conditions of the working face, the Code for Coal Pillar Retention and Coal Mining in Buildings, Water Bodies, Railways and Main Shafts and Lanes [52], and the general laws of surface movement and deformation established through long-term observation and practice in the Fengfeng mining area, the parameters for surface movement prediction were determined (Table 3). As shown in Figure 10a, after adopting OIGB technology, the maximum surface subsidence along the strike principal section was significantly reduced from the 1.8 m predicted under caving mining to 0.3 m, while the subsidence Coefficient decreased from 0.84 to 0.12, corresponding to a surface subsidence reduction of approximately 83.3%. To further quantify the surface subsidence control effect of OIGB, the deformation gradients along the strike principal section under the caving-mining prediction scenario and under OIGB conditions were extracted for comparative analysis, as shown in Figure 10b. The results indicate that, under the caving-mining prediction scenario, along the 100–650 m and 950–1500 m sections of the strike principal section, surface subsidence increased rapidly from 0 to 1.8 m, while the corresponding deformation gradient also increased sharply from 0 to approximately 6. After implementing OIGB, however, the overall deformation gradient was significantly reduced, with the maximum deformation gradient decreasing to approximately 1.6, representing a reduction of about 73.3% relative to the caving-mining prediction. Combined with the field investigation results, no obvious new building cracks or structural damage were identified within the village area. In summary, OIGB technology exhibits significant advantages in controlling surface subsidence and deformation gradients and can provide reliable engineering and technical support for the safe, green, and efficient implementation of coal mining under the “three-under” conditions.

5.2. Coherence Response to Deformation Gradient

As shown in Figure 4, the surface subsidence-affected area of working face 132153 in the Wu’an mining area expanded rapidly during the period from November 2018 to July 2019. In particular, since March 2019, the surface deformation in the area to the east of the working face increased sharply. To further investigate the coupling relationship among coherence in mining areas, surface deformation, and deformation gradient under different mining methods, this study presents in Figure 11 the filtered mean coherence maps of the Wu’an mining area for two periods, November 2018 to March 2019 and March 2019 to July 2019, together with the cumulative deformation map from November 2018 to July 2019. In addition, comparative analysis was conducted by combining the subsidence values, deformation gradients, and coherence at the ground monitoring points arranged along the strike direction during the above two periods. Meanwhile, Figure 12 presents the filtered mean coherence map of the Fengfeng mining area from February 2023 to August 2024 under OIGB conditions, together with a comprehensive analysis of the subsidence values, deformation gradients, and coherence derived from the corresponding ground monitoring points during the same period.
As shown in Figure 11a, during the period from November 2018 to March 2019, the central part of working face 132153 exhibited pronounced low coherence, with a mean coherence value of approximately 0.4. It is noteworthy that some local high-coherence patches still existed within the center of this low-coherence zone. Further examination of monitoring point Z26, located at the boundary between the low- and high-coherence zones in Figure 11d,e, indicates that from November 2018 to March 2019, although subsidence at point Z26 was relatively large, coherence increased because the deformation gradient decreased sharply. By contrast, from March 2019 to July 2019, the deformation gradient at point Z26 continued to increase, while coherence exhibited a continuous decline. Therefore, even though the coherence around point Z26 remained relatively high during the earlier stage, InSAR still failed to retrieve subsidence information in this area (Figure 11c). As shown in Figure 4 and Figure 11b, since March 2019, with the rapid increase in surface deformation around working face 132153, the low-coherence zone expanded quickly, and its spatial distribution was highly consistent with the data gaps in the cumulative InSAR deformation results (Figure 11c). To reveal the relationship between coherence, surface subsidence, and deformation gradient, the coherence values, leveling-derived subsidence values, and deformation gradients at the surface subsidence monitoring points along strike (Z1–Z26) were extracted for the two periods and comparatively analyzed (Figure 11d,e). The results indicate that, in areas where both subsidence and deformation gradient remained at relatively low levels (Z1–Z18), the InSAR coherence was maintained at about 0.9. During the period from March 2019 to July 2019, however, the deformation gradients at monitoring points Z8–Z14 increased significantly due to the influence of mining in an adjacent working face, and the coherence decreased accordingly. In contrast, in areas where subsidence increased markedly and the deformation gradient rose sharply (Z19–Z26), coherence exhibited a pronounced spatial decline.
As shown in Figure 5, mining-induced surface deformation in the Fengfeng mining area was mainly distributed in the village area and in the area to the east of the working face. However, except for the village building area, which maintained relatively high coherence, no obvious large-scale coherence reduction was observed within the subsidence area relative to the non-subsidence area, as shown in Figure 12a. The coherence values, deformation gradients, and leveling-derived subsidence values at the ground monitoring points were further extracted for comparative analysis. The results show that OIGB technology effectively controlled surface subsidence, constraining the maximum surface subsidence in the Fengfeng mining area to within 300 mm. The deformation gradient across the study area remained generally stable, and coherence was maintained at a relatively high level, with no significant coherence decay caused by changes in subsidence magnitude.
In summary, using the Sentinel-1 data and SBAS-InSAR processing workflow adopted in this study, InSAR generally exhibits high coherence in areas with small subsidence values and low deformation gradients, thereby enabling surface subsidence monitoring with high spatial and temporal resolution. In areas with large deformation gradients, however, coherence often decreases significantly, making it difficult to obtain continuous and reliable subsidence information, which in turn limits the applicability of InSAR in areas with large deformation variations. Caving mining commonly induces large-gradient deformation in the central part of the subsidence basin, thereby reducing coherence and affecting the monitoring accuracy and applicability of InSAR under this mining method. In contrast, OIGB technology, by implementing separated-layer grouting in the goaf, can significantly control surface subsidence and deformation gradients, maintain relatively high coherence, and thus improve the monitoring accuracy and applicability of InSAR under such mining conditions.

5.3. Subsidence Evolution Pattern Under OIGB Conditions

To further reveal the evolution pattern of surface subsidence in the Fengfeng mining area under OIGB conditions, monitoring points P1 and P2 were selected for a comparative analysis of leveling- and InSAR-derived subsidence time series. As shown in Figure 13b, the red solid line represents the InSAR-derived subsidence time series, whereas the blue solid line denotes the leveling-derived subsidence time series. Under both monitoring approaches, P1 and P2 exhibit highly consistent subsidence evolution patterns: during the mining stage, the subsidence rate increased significantly and surface subsidence developed rapidly; moreover, before the subsidence values increased markedly, both datasets showed a short-term “uplift” during the same period. After the completion of mining, the subsidence rate decreased noticeably, and the surface subsidence gradually stabilized after experiencing a brief “uplift–subsidence” fluctuation.
Considering differences in surface-cover types and mining-induced disturbance, monitoring point P3 was selected within the village area, point P4 was selected at a surface slurry outflow location, and points P5 and P6 were selected along the strike principal section to analyze the surface subsidence evolution at typical locations. Meanwhile, to determine whether the “uplift” observed in the subsidence curves was caused by external factors such as atmospheric delay, a reference monitoring point located outside the influence range of mining subsidence and grouting backfilling was selected for comparative analysis. As shown in Figure 13c, the overall subsidence process in the Fengfeng mining area can be divided into two stages. The first stage spans from 11 February 2023 to 8 December 2023, during which the subsidence evolution is characterized by a pattern of “subsidence–stabilization–uplift–stabilization”, which is consistent with the subsidence pattern summarized by Yao et al. [53] for a coal mine in Huaibei, Anhui Province, where OIGB technology was adopted. The second stage spans from 8 December 2023 to 8 November 2024, during which the subsidence evolution shows a pattern of “subsidence–uplift–subsidence–stabilization”.
These results indicate that both monitoring datasets exhibit the “uplift” phenomenon in both stages, further enhancing the reliability of this observation. Combined with field investigations, multiple surface slurry outflow events were observed in the study area during the grouting process (Figure 13d–f), and the time series at point P4, located in the slurry outflow area, also showed an “uplift” feature. It is therefore inferred that this “uplift” phenomenon may be related to the process and associated effects of OIGB technology. However, at the present stage, this interpretation is mainly based on a preliminary analysis of the InSAR monitoring results, leveling observations, and field investigation findings, and its deformation mechanism has not yet been systematically demonstrated. The physical mechanism of this “uplift” phenomenon warrants further investigation through the integration of geological conditions, engineering grouting records, and hydrological monitoring data, so as to further clarify the evolution pattern and formation mechanism of surface subsidence under OIGB conditions.

5.4. Limitations and Future Prospects

5.4.1. Limitations and Prospects of InSAR Monitoring in Caving-Mined Areas

The above analysis indicates that, when C-band SAR data and a conventional SBAS-InSAR processing workflow are used to monitor large-gradient deformation in caving-mined areas, the phase gradient between adjacent pixels increases sharply and exceeds the threshold that phase-unwrapping algorithms can reliably handle [18,19]. This, in turn, causes rapid coherence decay or even complete decorrelation in the central part of the subsidence basin, making it difficult to retrieve continuous, stable, and reliable deformation information and thus significantly constraining the engineering applicability of InSAR monitoring. By contrast, L-band demonstrates a stronger tolerance to large-gradient deformation in InSAR applications, as the measurable deformation within a single phase cycle is approximately four times that of C-band, thereby substantially reducing the risk of phase-unwrapping failure during rapid deformation processes. The NISAR satellite, carrying both L- and S-band sensors, was successfully launched on 30 July 2025, and its high resolution and enhanced tolerance to large-gradient deformation can help reduce phase-unwrapping difficulty. In combination with existing SAR sensors, multi-orbit and multi-angle coordinated observations can also be used to retrieve three-dimensional deformation fields [54]. These advances provide new observational opportunities and technical support for the application of InSAR to large-gradient deformation monitoring. In addition, previous studies have attempted to retrieve large-gradient deformation in mining areas by introducing prior models to assist phase unwrapping [55,56], adopting POT techniques [23,24,25,26], integrating multi-source data [57,58,59], and applying deep-learning-based phase-unwrapping methods, thereby offering new technical approaches for the application of InSAR to surface deformation monitoring in caving-mined areas characterized by large-gradient deformation.

5.4.2. Applicability and Application Prospects of InSAR Monitoring in Backfill Mining Areas

This study shows that OIGB technology has significant advantages in controlling surface movement and subsidence, as the induced surface deformation is generally small in magnitude and characterized by relatively low gradients. Under such conditions, the use of C-band SAR data and a conventional SBAS-InSAR processing workflow can continuously and comprehensively capture the spatiotemporal evolution of surface deformation, demonstrating good applicability for deformation monitoring. Although this study mainly analyzes the surface subsidence control effect and the applicability of InSAR monitoring in mining areas under OIGB conditions, the findings of this study may also provide useful guidance for other backfill mining methods that can effectively control both the magnitude and gradient of surface deformation under similar geological and mining conditions, such as solid backfilling, paste backfilling, and super-high-water-material backfilling technologies. In recent years, to support green mine construction and the safe extraction of coal resources under the “three-under” conditions, backfill mining technologies have been gradually promoted in coal mining [60]. Given that the conclusions of this study are mainly based on only two representative cases, the sample size remains limited, and the generalizability of these findings to other mining areas, different geological conditions, and different backfill mining technologies still requires further verification. Future research should further validate and compare the applicability of InSAR monitoring under different backfill mining conditions.

6. Conclusions

To evaluate the applicability and engineering potential of InSAR for deformation monitoring under OIGB conditions, this study employed a combined processing framework integrating the commercial GAMMA software and the open-source InSAR time-series analysis package MintPy. Two representative cases were selected for analysis: the 132153 working face of the Wu’an mining area, under caving mining conditions, and a working face of the Fengfeng mining area, under OIGB conditions. The SBAS-InSAR approach was applied to both datasets to retrieve time-series surface deformation, and the results were validated using ground leveling measurements. On this basis, we comparatively analyzed the relationship between InSAR coherence and surface deformation gradients under different mining conditions, thereby elucidating the advantages and monitoring potential of InSAR in OIGB relative to traditional caving mining. Finally, we comprehensively assessed the spatiotemporal evolution and surface subsidence characteristics of the Fengfeng mining area under OIGB conditions. The main findings are summarized as follows:
(1)
Under similar surface conditions, surface deformation gradients exert a significant influence on coherence. As deformation magnitude and gradient increase sharply, interferometric coherence decreases markedly.
(2)
Comparison with leveling measurements indicates that the RMSEs in the strike and dip directions for the Wu’an mining area under caving mining are 132 mm and 245 mm, respectively, whereas the RMSE for the Fengfeng mining area under OIGB conditions is 42 mm, representing a reduction of at least 68.2% relative to that of the Wu’an mining area. This demonstrates that InSAR achieves higher monitoring accuracy under OIGB conditions.
(3)
The OIGB technique demonstrates a pronounced subsidence-mitigation effect, effectively limiting the maximum surface subsidence within the mining influence zone of the Fengfeng working face to no more than 300 mm and restricting subsidence in the village area to no more than 200 mm, thereby substantially reducing the impact of mining-induced deformation.
(4)
Under OIGB conditions, both the magnitude and gradient of surface deformation remain relatively low, thereby effectively reducing the impact of deformation gradients on coherence and maintaining relatively high coherence. Therefore, using Sentinel-1 data and the conventional SBAS-InSAR processing workflow adopted in this study, InSAR demonstrates higher reliability and applicability for surface subsidence monitoring in mining areas under OIGB conditions than in those under caving mining conditions. These findings may also provide useful guidance for subsidence monitoring under similar geological and mining conditions.

Author Contributions

Conceptualization, Y.N. and Z.L.; methodology, Y.N., Z.L. and Z.Z. (Zhengpei Zhou); software, Y.N. and X.Y.; validation, Z.Z. (Zhengpei Zhou); formal analysis, Y.N. and Z.Z. (Zhengpei Zhou); investigation, Y.N., Z.Z. (Zhengpei Zhou), X.Y., Z.J. and J.Z.; resources, Z.Z. (Zhaojiang Zhang); data curation, Z.Z. (Zhengpei Zhou); writing—original draft preparation, Y.N. and Z.Z. (Zhengpei Zhou); writing—review and editing, Y.N., Z.L., Z.Z. (Zhengpei Zhou), X.Y., Z.Z. (Zhaojiang Zhang), Z.J. and J.Z.; visualization, Z.Z. (Zhengpei Zhou); supervision, Y.N., Z.L. and J.Z.; project administration, Y.N.; funding acquisition, Y.N. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Science Research Project of Hebei Education Department (QN2024231); the National Natural Science Foundation of China (NSFC) (grant number: 42307255); the Hebei Natural Science Foundation (D2023402033); and the Technologies R&D Program from the Bureau of Science and Technology of Handan (grant number: 21422903219).

Data Availability Statement

The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.

Acknowledgments

We would like to express our sincere appreciation for the anonymous reviewers and editors for their valuable comments and suggestions, which have significantly improved the quality of this paper. We are also grateful to ESA for providing Sentinel-1A/B data and to JAXA for providing AW3D30 DEM data.

Conflicts of Interest

Author Xuhai Yang was employed by the company Moonbristar (Chengdu) Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. Ge, S.; Liu, S.; Fan, J.; Zhao, Y.; Teng, T. Key technologies for low-carbon modern coal-based energy. J. China Coal Soc. 2024, 49, 2949–2972. [Google Scholar]
  2. Yuan, L.; Zhang, T.; Wang, Y.; Wang, X.; Wang, Y.; Hao, X. Scientific problems and key technologies for safe and efficient mining of deep coal resources. J. China Coal Soc. 2025, 50, 1–12. [Google Scholar]
  3. Fan, H.; Xin, J.; Lin, T.; Wang, J. Automatic Detection and Calculation of Mining Subsidence in Large-Scale Interferograms with Transformer-CNN Model. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2025, 18, 15086–15100. [Google Scholar] [CrossRef]
  4. Hu, L.; Tang, X.; Tomás, R.; Li, T.; Zhang, X.; Li, Z.; Yao, J.; Lu, J. Monitoring surface deformation dynamics in the mining subsidence area using LT-1 InSAR interferometry: A case study of Datong, China. Int. J. Appl. Earth Obs. Geoinf. 2024, 131, 103936. [Google Scholar] [CrossRef]
  5. Chen, Y.; Zhao, D.; Huang, P.; Yin, B.; Gao, Z.; Tan, W.; Qi, Y.; Xu, W. Enhanced surface deformation monitoring in open-pit mines via dynamic filtering and multiscale adaptive interpolation of D-InSAR/offset tracking fusion. Int. J. Digit. Earth 2025, 18, 2538218. [Google Scholar] [CrossRef]
  6. Zhu, S.; Zuo, X.; Shi, K.; Li, Y.; Guo, S.; Li, C. Surface subsidence monitoring in Kunming City with time-series InSAR and GNSS. Appl. Sci. 2022, 12, 12752. [Google Scholar] [CrossRef]
  7. Ashraf, T.; Yin, F.; Liu, L.; Zhang, Q. Land Subsidence Detection Using SBAS-and Stacking-InSAR with Zonal Statistics and Topographic Correlations in Lakhra Coal Mines, Pakistan. Remote Sens. 2024, 16, 3815. [Google Scholar] [CrossRef]
  8. Gojković, Z.; Kilibarda, M.; Brajović, L.; Marjanović, M.; Milutinović, A.; Ganić, A. Ground surface subsidence monitoring using Sentinel-1 in the “Kostolac” open pit coal mine. Remote Sens. 2023, 15, 2519. [Google Scholar] [CrossRef]
  9. Dong, L.; Wang, C.; Tang, Y.; Tang, F.; Zhang, H.; Wang, J.; Duan, W. Time series InSAR three-dimensional displacement inversion model of coal mining areas based on symmetrical features of mining subsidence. Remote Sens. 2021, 13, 2143. [Google Scholar] [CrossRef]
  10. Peng, M.; Motagh, M.; Lu, Z.; Xia, Z.; Guo, Z.; Zhao, C.; Liu, Q. Characterization and prediction of InSAR-derived ground motion with ICA-assisted LSTM model. Remote Sens. Environ. 2024, 301, 113923. [Google Scholar] [CrossRef]
  11. Carnec, C.; Massonnet, D.; King, C. Two examples of the use of SAR interferometry on displacement fields of small spatial extent. Geophys. Res. Lett. 1996, 23, 3579–3582. [Google Scholar] [CrossRef]
  12. Ferretti, A.; Prati, C.; Rocca, F. Permanent scatterers in SAR interferometry. IEEE Trans. Geosci. Remote Sens. 2001, 39, 8–20. [Google Scholar] [CrossRef]
  13. Berardino, P.; Fornaro, G.; Lanari, R.; Sansosti, E. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Trans. Geosci. Remote Sens. 2002, 40, 2375–2383. [Google Scholar] [CrossRef]
  14. Rocca, F. Modeling interferogram stacks. IEEE Trans. Geosci. Remote Sens. 2007, 45, 3289–3299. [Google Scholar] [CrossRef]
  15. Przyłucka, M.; Kowalski, Z.; Perski, Z. Twenty years of coal mining-induced subsidence in the Upper Silesia in Poland identified using InSAR. Int. J. Coal Sci. Technol. 2022, 9, 86. [Google Scholar] [CrossRef]
  16. Wang, Y.; Zhang, J.; Liu, Y. Feature Parameters Computation of Underground Coal Mining Space Coupled Probability Integral Model with SBAS-InSAR Subsidence Data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2025, 18, 13986–13999. [Google Scholar] [CrossRef]
  17. Zhao, J.; Yang, X.; Zhang, Z.; Niu, Y.; Zhao, Z. Mine Subsidence Monitoring Integrating DS-InSAR with UAV Photogrammetry Products: Case Studies on Hebei and Inner Mongolia. Remote Sens. 2023, 15, 4998. [Google Scholar] [CrossRef]
  18. Massonnet, D.; Feigl, K.L. Radar interferometry and its application to changes in the Earth’s surface. Rev. Geophys. 1998, 36, 441–500. [Google Scholar] [CrossRef]
  19. Wu, Y.-Y.; Madson, A. Error sources of interferometric synthetic aperture radar satellites. Remote Sens. 2024, 16, 354. [Google Scholar] [CrossRef]
  20. Chen, B.; Deng, K.; Fan, H.; Hao, M. Large-scale deformation monitoring in mining area by D-InSAR and 3D laser scanning technology integration. Int. J. Min. Sci. Technol. 2013, 23, 555–561. [Google Scholar] [CrossRef]
  21. Chen, Y.; Dong, X.; Qi, Y.; Huang, P.; Sun, W.; Xu, W.; Tan, W.; Li, X.; Liu, X. Integration of DInSAR-PS-stacking and SBAS-PS-InSAR methods to monitor mining-related surface subsidence. Remote Sens. 2023, 15, 2691. [Google Scholar] [CrossRef]
  22. Zhao, J.; Niu, Y.; Zhou, Z.; Lu, Z.; Wang, Z.; Zhang, Z.; Li, Y.; Ju, Z. Construction of mining subsidence basin and inversion of predicted subsidence parameters based on UAV photogrammetry products considering horizontal displacement. Remote Sens. 2024, 16, 4283. [Google Scholar] [CrossRef]
  23. Lou, J.; Zhao, C.; Li, G.; Li, D. Inversion of Time Series Elevation Changes in Open Pit Mining Areas Based on Adjacent Orbital Sentinel-2 Images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2024, 17, 6126–6133. [Google Scholar] [CrossRef]
  24. Ma, J.; Yang, J.; Zhu, Z.; Cao, H.; Li, S.; Du, X. Decision-making fusion of InSAR technology and offset tracking to study the deformation of large gradients in mining areas-Xuemiaotan mine as an example. Front. Earth Sci. 2022, 10, 962362. [Google Scholar] [CrossRef]
  25. Yang, B.; Du, W.; Zou, Y.; Zhang, H.; Chai, H.; Wang, W.; Song, X.; Zhang, W. Reconstruction of coal mining subsidence field by fusion of SAR and UAV LiDAR deformation data. Remote Sens. 2024, 16, 3383. [Google Scholar] [CrossRef]
  26. Zhao, C.; Lu, Z.; Zhang, Q. Time-series deformation monitoring over mining regions with SAR intensity-based offset measurements. Remote Sens. Lett. 2013, 4, 436–445. [Google Scholar] [CrossRef]
  27. Hu, B.; Liu, P.; Cui, F.; Wang, H. Review and development status of backfill coal mining technology in China. Coal Sci. Technol. 2020, 48, 39–47. [Google Scholar]
  28. Lu, G.; Meguid, M. Cemented Backfill Applications in Challenging Geothermal Environments: A Brief Review and Future Perspectives. Rock Mech. Rock Eng. 2025, 59, 2789–2809. [Google Scholar] [CrossRef]
  29. Yu, X.; Yang, K.; He, X.; Hou, Y.; Fang, J.; He, S. Research progress on multi-source coal-based solid waste (MCSW) resource utilization and backfill mining basic theory: A systematic literature review. Process Saf. Environ. Prot. 2025, 195, 106670. [Google Scholar] [CrossRef]
  30. Peng, L.; Zhang, G.; Wang, H.; Sun, S.; Zhang, Y.; Liu, Z.; Wang, Z.-y. Research on strata movement control in deep areas of super-thick and weak-cemented overlying strata during local backfill mining—A case study based on Yingpanhao Coal Mine. Results Eng. 2025, 27, 105579. [Google Scholar] [CrossRef]
  31. Sun, X. Present situation and prospect of green backfill mining in mines. Coal Sci. Technol. 2020, 48, 48–55. [Google Scholar]
  32. Yang, Z.; Zhai, S.; Gao, Q.; Li, M. Stability analysis of large-scale stope using stage subsequent filling mining method in Sijiaying iron mine. J. Rock Mech. Geotech. Eng. 2015, 7, 87–94. [Google Scholar] [CrossRef]
  33. Zhang, J.; Yang, K.; He, X.; Zhao, X.; Wei, Z.; He, S. Research status of comprehensive utilization of coal-based solid waste (CSW) and key technologies of filling mining in China: A review. Sci. Total Environ. 2024, 926, 171855. [Google Scholar] [CrossRef]
  34. Liu, J.; Qin, K. Progress of green coal mining technology in China. Min. Saf. Environ. Prot. 2023, 50, 7–15. [Google Scholar]
  35. Qian, M.; Xu, J.; Wang, J. Further on the sustainable mining of coal. J. China Coal Soc. 2018, 43, 1–13. [Google Scholar]
  36. Guo, W.; Yang, W.; Wu, D. Current status and progress of subsidence control technology in China coal mines. J. Min. Strat. Control Eng. 2024, 6, 5–20. [Google Scholar]
  37. Sun, X. Research on paste backfilling mining technology of coal mining under buildings, water bodies and railways. Coal Sci. Technol. 2021, 49, 218–224. [Google Scholar]
  38. Wang, F.; Liu, C.; Hao, W.; Hu, S.; Feng, G.; Li, H.; Guo, G. Progress and Prospect for Disaster Mitigation and Damage Reduction Green Mining Theory and Technology of Super-high Water Material Backfill. Met. Mine 2023, 5, 14–30. [Google Scholar]
  39. Xuan, D.; Xu, J. Longwall surface subsidence control by technology of isolated overburden grout injection. Int. J. Min. Sci. Technol. 2017, 27, 813–818. [Google Scholar] [CrossRef]
  40. Xu, J.; Xuan, D.; Li, J. Application and research prospects of overburden isolated grouting backfilling technology. China Coal 2024, 50, 74–80. [Google Scholar]
  41. Xuan, D.; Xu, J.; Wang, B. Green mining technology of overburden isolated grout injection. J. China Coal Soc. 2022, 47, 4265–4277. [Google Scholar]
  42. Teng, H.; Xu, J.; Xuan, D.; Wang, B. Surface subsidence characteristics of grout injection into overburden: Case study of Yuandian No. 2 coalmine, China. Environ. Earth Sci. 2016, 75, 530. [Google Scholar] [CrossRef]
  43. Chen, Y.; Li, J.; Li, H.; Gao, Y.; Li, S.; Chen, S.; Guo, G.; Wang, F.; Zhao, D.; Zhang, K. Revealing land surface deformation over the Yineng backfilling mining area, China, by integrating distributed scatterer SAR interferometry and a mining subsidence model. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2023, 16, 3611–3634. [Google Scholar] [CrossRef]
  44. Jiang, C.; Guo, G.; Li, H.; Wang, F.; Guo, K.; Wei, T.; Jiang, Q. Study on Surface Movement Characteristics and Building Damage of Backfilling Mining Based on DInSAR. Coal Technol. 2022, 41, 142–145. [Google Scholar]
  45. Di, M.; Zhang, L.; He, Y.; Xu, N.; Qiao, L.; Li, X. Deformation Monitoring of Subsidence Areas in Wu′an City Based on SBAS-InSAR Technology. Geogr. Geo-Inf. Sci. 2021, 37, 52–56. [Google Scholar]
  46. Ahadov, B.; Fielding, E.; Kadirov, F. Far-Field Earthquake-Induced Crustal Deformation and Mud Volcano Activity in Azerbaijan Based on the InSAR Technique. Remote Sens. 2025, 17, 1421. [Google Scholar] [CrossRef]
  47. Pepe, A.; Yang, Y.; Manzo, M.; Lanari, R. Improved EMCF-SBAS processing chain based on advanced techniques for the noise-filtering and selection of small baseline multi-look DInSAR interferograms. IEEE Trans. Geosci. Remote Sens. 2015, 53, 4394–4417. [Google Scholar] [CrossRef]
  48. Tough, J.; Blacknell, D.; Quegan, S. A statistical description of polarimetric and interferometric synthetic aperture radar data. Proc. R. Soc. London. Ser. A Math. Phys. Sci. 1995, 449, 567–589. [Google Scholar] [CrossRef]
  49. Samiei-Esfahany, S.; Martins, J.E.; Van Leijen, F.; Hanssen, R.F. Phase estimation for distributed scatterers in InSAR stacks using integer least squares estimation. IEEE Trans. Geosci. Remote Sens. 2016, 54, 5671–5687. [Google Scholar] [CrossRef]
  50. Hu, X.; Song, C.; Yu, C.; Li, Z.; Du, J.; Zhou, J.; Peng, J. Long-term deformation monitoring and analysis of Changbaishan Tianchi volcano based on time-series InSAR technique. Chin. J. Geophys. 2025, 68, 1660–1677. [Google Scholar]
  51. Jiang, M.; Ding, X.; Li, Z.; Wang, C.; Zhu, W.; Ke, L. InSAR coherence magnitude estimation based on data stack. Chin. J. Geophys. 2013, 56, 799–811. [Google Scholar]
  52. Safety, S.A.o.W. Code for Coal Pillar Retention and Coal Mining in Buildings, Water Bodies, Railways and Main Shafts and Lanes; China Coal Industry Publishing House: Beijing, China, 2017. [Google Scholar]
  53. Yao, S.; Zhang, Y.; Yang, K.; Shi, X.; Wang, J. Spatial-temporal evolution of surface deformation in grouting mining area by SMD-InSAR monitoring-analysis method. Acta Geod. Cartogr. Sin. 2020, 49, 893–906. [Google Scholar]
  54. Lu, Z.; Zhao, J.; Niu, Y.; Chen, L.; Fan, Q.; Si, J.; Wang, Z.; Gao, Y.; Wang, S.; Qu, F.; et al. The NISAR mission: Innovations in earth observation and applications in surface deformation monitoring. Acta Geod. Cartogr. Sin. 2026, 55, 261–274. [Google Scholar]
  55. Liu, Z.; Peng, J.; Su, H.; Li, X.; Wang, C.; Peng, Y. InSAR monitoring of large gradient deformation in coalfield and phase unwrapping research. Geod. Geodyn. 2025, 17, 130–144. [Google Scholar] [CrossRef]
  56. Ng, A.H.-M.; Wen, B.; Ma, Y.; Guo, L.; Dai, Y.; Wang, H.; Ge, L.; Du, Z. Integrating spatial modeling-assisted InSAR phase unwrapping with temporal analysis for advanced mine subsidence time series mapping. Int. J. Appl. Earth Obs. Geoinf. 2024, 133, 104143. [Google Scholar] [CrossRef]
  57. Wang, R.; Huang, S.; He, Y.; Wu, K.; Gu, Y.; He, Q.; Yan, H.; Yang, J. Construction of High-Precision and Complete Images of a Subsidence Basin in Sand Dune Mining Areas by InSAR-UAV-LiDAR Heterogeneous Data Integration. Remote Sens. 2024, 16, 2752. [Google Scholar] [CrossRef]
  58. Zhou, D.; Wang, L.; An, S.; Wang, X.; An, Y. Integration of unmanned aerial vehicle (UAV)-based photogrammetry and InSAR for mining subsidence and parameters inversion: A case study of the Wangjiata Mine, China. Bull. Eng. Geol. Environ. 2022, 81, 343. [Google Scholar] [CrossRef]
  59. Zhou, W.; Zhang, W.; Yang, X.; Wu, W. An Improved GNSS and InSAR Fusion Method for Monitoring the 3D Deformation of a Mining Area. IEEE Access 2021, 9, 155839–155850. [Google Scholar] [CrossRef]
  60. Guo, W.; Hu, Y.; Hu, C.; Li, L.; Wu, D.; Ge, Z. System and engineering practice of coal mining technology under buildings, water bodies and railways in China. Coal Sci. Technol. 2025, 53, 19–38. [Google Scholar]
Remotesensing 18 01476 g001
Figure 1. Overview map of the study areas.
Figure 1. Overview map of the study areas.
Remotesensing 18 01476 g001
Remotesensing 18 01476 g002
Figure 2. Technical flowchart.
Figure 2. Technical flowchart.
Remotesensing 18 01476 g002
Remotesensing 18 01476 g003
Figure 3. Spatiotemporal baseline configuration of interferometric pairs for the study areas. (a) Wu’an; (b) Fengfeng.
Figure 3. Spatiotemporal baseline configuration of interferometric pairs for the study areas. (a) Wu’an; (b) Fengfeng.
Remotesensing 18 01476 g003
Remotesensing 18 01476 g004
Figure 4. Time-series cumulative subsidence maps of the Wu’an mining area.
Figure 4. Time-series cumulative subsidence maps of the Wu’an mining area.
Remotesensing 18 01476 g004
Remotesensing 18 01476 g005
Figure 5. Time-series cumulative subsidence maps of the Fengfeng mining area.
Figure 5. Time-series cumulative subsidence maps of the Fengfeng mining area.
Remotesensing 18 01476 g005
Remotesensing 18 01476 g006
Figure 6. Comparison between InSAR and leveling results in the Wu’an mining area. (a) Strike; (b) dip.
Figure 6. Comparison between InSAR and leveling results in the Wu’an mining area. (a) Strike; (b) dip.
Remotesensing 18 01476 g006
Remotesensing 18 01476 g007
Figure 7. Comparison between InSAR and leveling results in the Fengfeng mining area.
Figure 7. Comparison between InSAR and leveling results in the Fengfeng mining area.
Remotesensing 18 01476 g007
Remotesensing 18 01476 g008
Figure 8. Statistical distributions of interferometric coherence.
Figure 8. Statistical distributions of interferometric coherence.
Remotesensing 18 01476 g008
Remotesensing 18 01476 g009
Figure 9. Time-series subsidence curves. (a) C–C′; (b) D–D′.
Figure 9. Time-series subsidence curves. (a) C–C′; (b) D–D′.
Remotesensing 18 01476 g009
Remotesensing 18 01476 g010
Figure 10. Subsidence control performance: (a) subsidence control along strike; (b) deformation-gradient control along dip.
Figure 10. Subsidence control performance: (a) subsidence control along strike; (b) deformation-gradient control along dip.
Remotesensing 18 01476 g010
Remotesensing 18 01476 g011
Figure 11. Coherence analysis in the Wu’an mining area: (a) coherence map from November 2018 to March 2019; (b) coherence map from March 2019 to July 2019; (c) deformation map from November 2018 to July 2019; (d) coherence map of monitoring points from November 2018 to March 2019; (e) coherence map of monitoring points from March 2019 to July 2019.
Figure 11. Coherence analysis in the Wu’an mining area: (a) coherence map from November 2018 to March 2019; (b) coherence map from March 2019 to July 2019; (c) deformation map from November 2018 to July 2019; (d) coherence map of monitoring points from November 2018 to March 2019; (e) coherence map of monitoring points from March 2019 to July 2019.
Remotesensing 18 01476 g011
Remotesensing 18 01476 g012
Figure 12. Coherence analysis in the Fengfeng mining area: (a) mean coherence map from February 2023 to August 2024; (b) coherence at monitoring points.
Figure 12. Coherence analysis in the Fengfeng mining area: (a) mean coherence map from February 2023 to August 2024; (b) coherence at monitoring points.
Remotesensing 18 01476 g012
Remotesensing 18 01476 g013
Figure 13. Time-series point analysis: (a) spatial distribution of time-series points; (b) comparison of leveling and InSAR time series; (c) time series of time-series points; (d–f) surface slurry outflow.
Figure 13. Time-series point analysis: (a) spatial distribution of time-series points; (b) comparison of leveling and InSAR time series; (c) time series of time-series points; (d–f) surface slurry outflow.
Remotesensing 18 01476 g013
Table 1. Principal parameters of datasets used for the two mining areas.
Table 1. Principal parameters of datasets used for the two mining areas.
Study AreaData TypeNumberResolution (m)Incidence Angle (°)OrbitStart DateEnd Date
Wu’anSAR174 5 × 2035.50Ascending1 February 201822 April 2021
Leveling48 ///27 June 201827 July 2019
DEM1 30//20062011
FengfengSAR50 5 × 2037.35Ascending11 February 20238 November 2024
Leveling12 ///11 February 202326 August 2024
DEM1 30//20062011
Table 2. Error statistics.
Table 2. Error statistics.
Study AreaDirectionMinimum Absolute Error (mm)Maximum Absolute Error (mm)MAE (mm)RMSE (mm)
Z1–Z18
(Q10–Q22)		Z1–Z22
(Q1–Q22)		Z1–Z18
(Q10–Q22)		Z1–Z22
(Q1–Q22)
Wu’anStrike1404178222132
Dip285141814270245
Fengfeng/3763842
Table 3. Predicted parameters of surface subsidence.
Table 3. Predicted parameters of surface subsidence.
No.NameSymbolParameter
1Subsidence Coefficientq0.84
2Tangent of Major Influence Angle tan β 2.2
3Deviation of Inflection PointS0.1H
4Propagation Angle θ 89
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zhou, Z.; Niu, Y.; Lu, Z.; Yang, X.; Zhang, Z.; Ju, Z.; Zhao, J. Applicability and Feasibility of InSAR-Based Mining Subsidence Monitoring Under Overburden Isolated Grouting Backfill Mining Conditions. Remote Sens. 2026, 18, 1476. https://doi.org/10.3390/rs18101476

AMA Style

Zhou Z, Niu Y, Lu Z, Yang X, Zhang Z, Ju Z, Zhao J. Applicability and Feasibility of InSAR-Based Mining Subsidence Monitoring Under Overburden Isolated Grouting Backfill Mining Conditions. Remote Sensing. 2026; 18(10):1476. https://doi.org/10.3390/rs18101476

Chicago/Turabian Style

Zhou, Zhengpei, Yufen Niu, Zhong Lu, Xuhai Yang, Zhaojiang Zhang, Ziheng Ju, and Jinqi Zhao. 2026. "Applicability and Feasibility of InSAR-Based Mining Subsidence Monitoring Under Overburden Isolated Grouting Backfill Mining Conditions" Remote Sensing 18, no. 10: 1476. https://doi.org/10.3390/rs18101476

APA Style

Zhou, Z., Niu, Y., Lu, Z., Yang, X., Zhang, Z., Ju, Z., & Zhao, J. (2026). Applicability and Feasibility of InSAR-Based Mining Subsidence Monitoring Under Overburden Isolated Grouting Backfill Mining Conditions. Remote Sensing, 18(10), 1476. https://doi.org/10.3390/rs18101476

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop