# Refined InSAR Mapping Based on Improved Tropospheric Delay Correction Method for Automatic Identification of Wide-Area Potential Landslides

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Study Area and Datasets

#### 2.1. Study Area

^{2}. The reservoir area extends from Leibo, Sichuan province, in the north to Dongchuan, Yunnan province, in the south, covering a length of 270.24 km along the Jinsha River. The area includes the Baihetan hydropower station, which is the second-largest hydropower station in China after the Three Gorges hydropower station [40].

#### 2.2. Datasets

## 3. Methodology

#### 3.1. InSAR Processing

#### 3.1.1. SBAS-InSAR Processing

#### 3.1.2. Stacking-InSAR Processing

#### 3.2. Improved Tropospheric Delay Correction Method

#### 3.2.1. Adaptive Window Selection

#### 3.2.2. The MMVM-Based Tropospheric Delay Correction Method

#### 3.3. Hotspot Analysis and Spatial Clustering

## 4. Results

^{2}, while the smallest covers an area of 8642 m

^{2}. The Wozitou landslide exhibits the maximum deformation rate of −57.75 mm/year, which corresponds to a cumulative deformation of −113.923 mm and covers an area of 0.039 km

^{2}. This landslide required additional attention as it threatened villages, farmland, and roads.

## 5. Discussion

#### 5.1. Performance Evaluation of MMVM-Based Correction Method

#### 5.1.1. Statistical Evaluation of Corrected Interferograms

#### 5.1.2. Improvement in Derived Deformation Rate

#### 5.1.3. Stability of Time-Series Deformation

#### 5.2. Advantages and Limitations of the Proposed Method

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Varnes, D.; David, J. Slope movement types and processes. Transp. Res. Board Spec. Rep.
**1978**, 176, 11–33. [Google Scholar] - Luo, S.; Feng, G.; Xiong, Z.; Wang, H.; Zhao, Y.; Li, K.; Deng, K.; Wang, Y. An Improved Method for Automatic Identification and Assessment of Potential Geohazards Based on MT-InSAR Measurements. Remote Sens.
**2021**, 13, 3490. [Google Scholar] [CrossRef] - Ran, P.; Li, S.; Zhuo, G.; Wang, X.; Meng, M.; Liu, L.; Chen, Y.; Huang, H.; Ye, Y.; Lei, X. Early Identification and Influencing Factors Analysis of Active Landslides in Mountainous Areas of Southwest China Using SBAS-InSAR. Sustainability
**2023**, 15, 4366. [Google Scholar] [CrossRef] - Zhu, K.; Xu, P.; Cao, C.; Zheng, L.; Liu, Y.; Dong, X. Preliminary Identification of Geological Hazards from Songpinggou to Feihong in Mao County along the Minjiang River Using SBAS-InSAR Technique Integrated Multiple Spatial Analysis Methods. Sustainability
**2021**, 13, 1017. [Google Scholar] [CrossRef] - Notti, D.; Davalillo, J.C.; Herrera, G.; Mora, O. Assessment of the performance of X-band satellite radar data for landslide mapping and monitoring: Upper Tena Valley case study. Nat. Hazards Earth Syst. Sci.
**2010**, 10, 1865–1875. [Google Scholar] [CrossRef] - Ren, T.; Gong, W.; Bowa, V.; Tang, H.; Chen, J.; Zhao, F. An Improved R-Index Model for Terrain Visibility Analysis for Landslide Monitoring with InSAR. Remote Sens.
**2021**, 13, 1938. [Google Scholar] [CrossRef] - Fournier, T.; Pritchard, M.; Finnegan, N. Accounting for Atmospheric Delays in InSAR Data in a Search for Long-Wavelength Deformation in South America. IEEE Trans. Geosci. Remote Sens.
**2011**, 49, 3856–3867. [Google Scholar] [CrossRef] - Ferretti, A.; Prati, C. Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry. IEEE Trans. Geosci. Remote Sens.
**2000**, 38, 2202–2212. [Google Scholar] [CrossRef] - 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] - Su, Y.; Yang, H.; Peng, J.; Liu, Y.; Zhao, B.; Shi, M. A Novel Near-Real-Time GB-InSAR Slope Deformation Monitoring Method. Remote Sens.
**2022**, 14, 5585. [Google Scholar] [CrossRef] - Liao, M.; Jiang, H.; Wang, Y.; Wang, T.; Zhang, L. Improved topographic mapping through high-resolution SAR interferometry with atmospheric effect removal. ISPRS J. Photogramm.
**2013**, 80, 72–79. [Google Scholar] [CrossRef] - Zhang, X.; Li, Z.; Liu, Z. Reduction of Atmospheric Effects on InSAR Observations through Incorporation of GACOS and PCA Into Small Baseline Subset InSAR. IEEE Trans. Geosci. Remote Sens.
**2023**, 61, 5209115. [Google Scholar] [CrossRef] - Zhao, Y.; Zuo, X.; Li, Y.; Guo, S.; Bu, J.; Yang, Q. Evaluation of InSAR Tropospheric Delay Correction Methods in a Low-Latitude Alpine Canyon Region. Remote Sens.
**2023**, 15, 990. [Google Scholar] [CrossRef] - Tymofyeyeva, E.; Fialko, Y. Mitigation of atmospheric phase delays in InSAR data, with application to the eastern California shear zone. J. Geophys. Res. Solid Earth.
**2015**, 120, 5952–5963. [Google Scholar] [CrossRef] - Darvishi, M.; Cuozzo, G.; Bruzzone, L.; Nilfouroushan, F. Performance Evaluation of Phase and Weather-Based Models in Atmospheric Correction with Sentinel-1 Data: Corvara Landslide in the Alps. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens.
**2020**, 13, 1332–1346. [Google Scholar] [CrossRef] - Liang, H.; Zhang, L.; Lu, Z.; Li, X. Correction of spatially varying stratified atmospheric delays in multitemporal InSAR. Remote Sens. Environ.
**2023**, 285. [Google Scholar] [CrossRef] - Liu, Q.; Zeng, Q.; Zhang, Z. Evaluation of InSAR Tropospheric Correction by Using Efficient WRF Simulation with ERA5 for Initialization. Remote Sens.
**2023**, 15, 273. [Google Scholar] [CrossRef] - Owerko, T.; Kuras, P.; Ortyl, Ł. Atmospheric Correction Thresholds for Ground-Based Radar Interferometry Deformation Monitoring Estimated Using Time Series Analyses. Remote Sens.
**2020**, 12, 2236. [Google Scholar] [CrossRef] - Chen, Y.; Li, Z.; Penna, N. Interferometric synthetic aperture radar atmospheric correction using a GPS-based iterative tropospheric decomposition model. Remote Sens. Environ.
**2018**, 204, 109–121. [Google Scholar] [CrossRef] - Xiao, R.; Yu, C.; Li, Z.; He, X. Statistical assessment metrics for InSAR atmospheric correction: Applications to generic atmospheric correction online service for InSAR (GACOS) in Eastern China. Int. J. Appl. Earth Obs. Geoinf.
**2021**, 96, 102289. [Google Scholar] [CrossRef] - Karanam, V.; Mahdi, M.; Shagun, G.; Kamal, J. Multi-sensor remote sensing analysis of coal fire induced land subsidence in Jharia Coalfields, Jharkhand, India. Int. J. Appl. Earth Obs. Geoinf.
**2021**, 102, 102439. [Google Scholar] [CrossRef] - Onn, F. Correction for interferometric synthetic aperture radar atmospheric phase artifacts using time series of zenith wet delay observations from a GPS network. Geophys. Res. Solid Earth.
**2006**, 111, B09102. [Google Scholar] [CrossRef] - Bekaert, D.; Hooper, A.; Wright, T. A spatially variable power law tropospheric correction technique for InSAR data. Geophys. Res. Solid Earth.
**2015**, 120, 1345–1356. [Google Scholar] [CrossRef] - Chen, Y.; Li, Z.; Penna, N. Triggered afterslip on the southern Hikurangi subduction interface following the 2016 Kaikura earthquake from InSAR time series with atmospheric corrections. Remote Sens. Environ.
**2020**, 251, 112097. [Google Scholar] [CrossRef] - Kinoshita, Y. Development of InSAR Neutral Atmospheric Delay Correction Model by Use of GNSS ZTD and Its Horizontal Gradient. IEEE Trans. Geosci. Remote Sens.
**2022**, 60, 5231414. [Google Scholar] [CrossRef] - Liang, H.; Zhang, L.; Ding, X.; Lu, Z.; Li, X. Toward Mitigating Stratified Tropospheric Delays in Multitemporal InSAR: A Quadtree Aided Joint Model. IEEE Trans. Geosci. Remote Sens.
**2019**, 57, 291–303. [Google Scholar] [CrossRef] - Wang, Y.; Dong, J.; Zhang, L.; Zhang, L.; Deng, S.; Zhang, G.; Liao, M.; Gong, J. Refined InSAR tropospheric delay correction for wide-area landslide identification and monitoring. Remote Sens. Environ.
**2022**, 275, 113013. [Google Scholar] [CrossRef] - Shi, M.; Peng, J.; Chen, X.; Zheng, Y.; Yang, H.; Su, Y.; Wang, G.; Wang, W. An Improved Method for InSAR Atmospheric Phase Correction in Mountainous Areas. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens.
**2021**, 14, 10509–10519. [Google Scholar] [CrossRef] - Barnhart, W.; Lohman, R. Characterizing and estimating noise in InSAR and InSAR time series with MODIS. Geochem. Geophys. Geosyst.
**2013**, 14, 4121–4132. [Google Scholar] [CrossRef] - Bekaert, D.; Walters, R.; Wright, T.; Hooper, A.; Parker, D. Statistical comparison of InSAR tropospheric correction techniques. Remote Sens. Environ.
**2015**, 170, 40–47. [Google Scholar] [CrossRef] - Cavalié, O.; Doin, M.; Lasserre, C.; Briole, P. Ground motion measurement in the Lake Mead area, Nevada, by differential synthetic aperture radar interferometry time series analysis: Probing the lithosphere rheological structure. J. Geophys. Res. Solid Earth.
**2007**, 112, B03403. [Google Scholar] [CrossRef] - Delacourt, C.; Briole, P.; Achache, J. Tropospheric corrections of SAR interferograms with strong topography. Application to Etna. Geophys. Res. Lett.
**1998**, 25, 2849–2852. [Google Scholar] [CrossRef] - Lin, Y.; Simons, M.; Hetland, E.; Muse, P.; DiCaprio, C. A multiscale approach to estimating topographically correlated propagation delays in radar interferograms. Geochem. Geophys. Geosyst.
**2010**, 11, 9. [Google Scholar] [CrossRef] - Li, Z.; Cao, Y.; Wei, J.; Duan, M.; Wu, L.; Hou, J.; Zhu, J. Time-series InSAR ground deformation monitoring: Atmospheric delay modeling and estimating. Earth-Sci. Rev.
**2019**, 192, 258–284. [Google Scholar] [CrossRef] - Lu, P.; Casagli, N.; Catani, F.; Tofani, V. Persistent scatterers interferometry hotspot and cluster analysis (PSI-HCA) for detection of extremely slow-moving landslides. Int. J. Remote Sens.
**2012**, 33, 466–489. [Google Scholar] [CrossRef] - Dai, H.; Zhang, H.; Dai, H.; Wang, C.; Tang, W.; Zou, L.; Tang, Y. Landslide Identification and Gradation Method Based on Statistical Analysis and Spatial Cluster Analysis. Remote Sens.
**2022**, 14, 4504. [Google Scholar] [CrossRef] - Ni, W.; Zhao, L.; Zhang, L.; Xing, K.; Dou, J. Coupling Progressive Deep Learning with the AdaBoost Framework for Landslide Displacement Rate Prediction in the Baihetan Dam Reservoir, China. Remote Sens.
**2023**, 15, 2296. [Google Scholar] [CrossRef] - Liu, H.; Luo, Y.; Feng, W.; Wang, Y.; Ma, H.; Hu, P. Site response of ancient landslides to initial impoundment of Baihetan Reservoir (China) based on ambient noise investigation. Soil Dyn. Eearthq. Eng.
**2023**, 167, 107590. [Google Scholar] [CrossRef] - Cheng, Z.; Liu, S.; Fan, X.; Shi, A.; Yin, K. Deformation behavior and triggering mechanism of the Tuandigou landslide around the reservoir area of Baihetan hydropower station. Landslides
**2023**, 20, 1679–1689. [Google Scholar] [CrossRef] - Yi, X.; Feng, W.; Li, B.; Yin, B.; Dong, X.; Xin, C.; Wu, M. Deformation characteristics, mechanisms, and potential impulse wave assessment of the Wulipo landslide in the Baihetan reservoir region, China. Landslides
**2023**, 20, 615–628. [Google Scholar] [CrossRef] - Liu, M.; Yang, Z.; Xi, W.; Guo, J.; Yang, H. InSAR-based method for deformation monitoring of landslide source area in Baihetan reservoir, China. Front. Earth Sci.
**2023**, 11, 1253272. [Google Scholar] [CrossRef] - Dun, J.; Feng, W.; Yi, X.; Zhang, G.; Wu, M. Detection and Mapping of Active Landslides before Impoundment in the Baihetan Reservoir Area (China) Based on the Time-Series InSAR Method. Remote Sens.
**2021**, 13, 3213. [Google Scholar] [CrossRef] - Karabork, H.; Makineci, H.; Orhan, O.; Karakus, P. Accuracy assessment of DEMs derived from multiple SAR data using the InSAR technique. Arab. J. Sci. Eng.
**2021**, 46, 5755–5765. [Google Scholar] [CrossRef] - Xu, Y.; Li, T.; Tang, X.; Zhang, X.; Fan, H.; Wang, Y. Research on the Applicability of DInSAR, Stacking-InSAR and SBAS-InSAR for Mining Region Subsidence Detection in the Datong Coalfield. Remote Sens.
**2022**, 14, 3314. [Google Scholar] [CrossRef] - Li, Y.; Zhang, J.; Li, Z.; Luo, Y.; Jiang, W.; Tian, Y. Measurement of subsidence in the Yangbajing geothermal fields, Tibet, from TerraSAR-X InSAR time series analysis. Int. J. Digit. Earth.
**2016**, 9, 697–709. [Google Scholar] [CrossRef] - Xiao, R.; Yu, C.; Li, Z.; Jiang, M.; He, X. InSAR stacking with atmospheric correction for rapid geohazard detection: Applications to ground subsidence and landslides in China. Int. J. Appl. Earth Obs. Geoinf.
**2022**, 115, 103082. [Google Scholar] [CrossRef] - Jia, H.; Wang, Y.; Ge, D.; Deng, Y.; Wang, R. InSAR Study of Landslides: Early Detection, Three-Dimensional, and Long-Term Surface Displacement Estimation-A Case of Xiaojiang River Basin, China. Remote Sens.
**2022**, 14, 1759. [Google Scholar] [CrossRef] - Zhang, B.; Hestir, E.; Yunjun, Z.; Reiter, M.; Viers, J.; Schaffer-Smith, D.; Sesser, K.; Oliver-Cabrera, T. Automated Reference Points Selection for InSAR Time Series Analysis on Segmented Wetlands. IEEE Geosci. Remote Sens. Lett.
**2024**, 21, 4008705. [Google Scholar] [CrossRef] - Bekaert, D.; Handwerger, A.; Agram, P.; Kirschbaum, D. InSAR-based detection method for mapping and monitoring slow-moving landslides in remote regions with steep and mountainous terrain: An application to Nepal. Remote Sens. Environ.
**2020**, 249, 111983. [Google Scholar] [CrossRef] - Zhang, G.; Xu, Z.; Chen, Z.; Wang, S.; Cui, H.; Zheng, Y. Predictable Condition Analysis and Prediction Method of SBAS-InSAR Coal Mining Subsidence. IEEE Trans. Geosci. Remote Sens.
**2022**, 60, 5232914. [Google Scholar] [CrossRef] - Chen, Z.; White, L.; Banks, S.; Behnamian, A.; Montpetit, B.; Pasher, J.; Duffe, J.; Bernard, D. Characterizing marsh wetlands in the Great Lakes Basin with C-band InSAR observations. Remote Sens. Environ.
**2020**, 242, 111750. [Google Scholar] [CrossRef] - Hu, Z.; Mallorquí, J. An Accurate Method to Correct Atmospheric Phase Delay for InSAR with the ERA5 Global Atmospheric Model. Remote Sens.
**2019**, 11, 1969. [Google Scholar] [CrossRef] - Yu, C.; Li, Z.; Penna, N.; Crippa, P. Generic atmospheric correction model for interferometric synthetic aperture radar observations. J. Geophys. Res. Solid Earth
**2018**, 123, 9202–9222. [Google Scholar] [CrossRef]

**Figure 1.**The location of the study area and data coverage. (

**a**) The location of the study area in China. (

**b**) The areas covered by the Sentinel-1 image. (

**c**) The terrain of the study area. Red rectangle represents the coverage of SAR image. Blue polygon represents the Jinsha River. Black solid lines represent the provincial boundaries. Black triangles represent historical geological disasters.

**Figure 5.**(

**a**) The original identification results of at-risk zones. (

**b**) The MMVM-corrected identification results of at-risk zones. Black dashed lines with a gray background represent the provincial boundaries. Red solid lines represent the boundaries of identified at-risk zones. Blue polygon represents the Jinsha River.

**Figure 6.**(

**a**) The original identification results by the HSA in area S1. (

**b**) The MMVM-corrected identification results by the HSA in area S1. (

**c**) The original identification results by the HSA in area S2. (

**d**) The MMVM-corrected identification results by the HSA in area S2. Red solid lines represent the boundaries of identified at-risk zones. Blue polygon represents the Jinsha River.

**Figure 7.**The original unwrapped phases and those corrected by various methods for the interferometric pair of 1–13 November 2021. (

**a**) The original unwrapped phases. (

**b**–

**g**) The phases corrected by the Exp, ERA5, GACOS, SBAS, MMVM(5), and MMVM(10) methods, respectively. (

**h**) The terrain of the study area.

**Figure 8.**The mean and SD values of unwrapped phases before and after correction by different methods within stable areas are shown in (

**a**,

**b**), respectively.

**Figure 9.**The coverage of the reservoir area, the original deformation rate, and the deformation rate after correction by various methods. Enlarged views of area S1 are presented in the upper left corner of the subplot. (

**a**) The reservoir area. (

**b**–

**g**) The original deformation rate and the deformation rate after correction by the Exp, ERA5, GACOS, SBAS, and MMVM methods, respectively. (

**h**) The legend.

**Figure 10.**The distributions of LOS deformation rate for MPs derived from the original, Exp, ERA5, GACOS, SBAS, and MMVM methods are shown in (

**a**–

**e**), respectively. In each subfigure, red line represents the mean value, and blue line represents the standard deviation. The SD value of MMVM is highlighted in bold red in (

**f**).

**Figure 11.**The locations and time-series deformations at FP1 and FP2. (

**a**,

**b**) The locations of the optical images at FP1 and FP1, respectively. (

**c**,

**d**) The time-series deformations using various correction methods at FP1 and FP1, respectively.

Method | At-Risk Points | At-Risk Zones | Accuracy |
---|---|---|---|

Original | 3301 | 176 | 47.159% |

After Correction | 2297 | 133 | 96.241% |

No. | Name | Longitude | Latitude | Area | Maximum Rate | Maximum Deformation | Aspect | Threat Object |
---|---|---|---|---|---|---|---|---|

(°) | (°) | (km^{2}) | (mm/year) | (mm) | ||||

1 | Luotianba | 103.58 | 27.98 | 0.031 | −24.098 | −36.274 | W | River, villages |

2 | Sunjialiangzi | 103.59 | 27.94 | 0.017 | −28.744 | −40.041 | SW | Villages, roads |

3 | Lijiaping | 103.52 | 27.93 | 0.168 | −25.042 | −57.358 | NW | Villages, river, and roads |

4 | Wozitou | 103.52 | 27.71 | 0.039 | −57.750 | −113.923 | W | Villages, farmlands, and roads |

5 | Qianligou | 103.32 | 27.69 | 0.022 | −24.243 | −34.381 | S | Villages, farmlands, and roads |

6 | Gongshan | 103.24 | 27.59 | 0.055 | −27.401 | −48.558 | SW | Villages, farmlands |

7 | Liangshanjing | 103.23 | 27.48 | 0.025 | −41.725 | −75.183 | SW | Villages, farmlands |

8 | Yujiapingzi | 103.25 | 27.46 | 0.022 | −25.286 | −66.031 | W | Villages, farmlands, and roads |

9 | Dayadong | 103.25 | 27.40 | 0.032 | −26.053 | −38.562 | S | Villages, roads |

10 | Galuo | 102.95 | 27.45 | 0.336 | −31.396 | −54.545 | W | Villages, roads |

11 | Shanshu | 103.25 | 27.38 | 0.001 | −29.191 | −58.825 | S | Villages, roads |

12 | Niupingyan | 103.12 | 27.39 | 0.034 | −35.932 | −72.767 | SW | Villages, roads |

13 | Ertun | 103.00 | 27.40 | 0.098 | −32.063 | −72.047 | SE | Villages, roads |

14 | Youyicun | 102.85 | 27.14 | 0.130 | −34.710 | −51.933 | SE | Villages, farmlands |

15 | Huodi | 103.06 | 26.90 | 0.570 | −30.339 | −52.472 | SW | Villages, farmlands |

16 | Xintian | 103.12 | 26.67 | 0.015 | −22.794 | −48.206 | E | River, villages |

Method | Number with Reduced SD | SD (rad) | Number with Mean Closer to 0 | Mean (rad) |
---|---|---|---|---|

Original | 110 | 2.3228 | 110 | −0.0327 |

Exp | 110 | 2.0449 | 106 | 0.0101 |

ERA5 | 107 | 1.8854 | 61 | −0.0606 |

GACOS | 43 | 2.1990 | 42 | 0.0326 |

SBAS | 39 | 2.2711 | 36 | 0.0103 |

MMVM(5) | 110 | 1.1893 | 106 | 0.0040 |

MMVM(10) | 110 | 1.0123 | 107 | 0.0045 |

**Table 4.**Statistical characteristics of SBAS-InSAR deformation rate using various correction methods.

Method | MPs | Non-Deformed Area | Deformed Area | ||
---|---|---|---|---|---|

MPs | SD | Mean | SD | ||

(mm/year) | (mm/year) | (mm/year) | |||

Original | 173,349 | 61,530 | 2.8320 | −2.9009 | 13.3601 |

Exp | 218,352 | 81,333 | 2.8310 | −1.8157 | 13.4583 |

ERA5 | 170,706 | 62,201 | 2.8274 | −2.7907 | 13.3704 |

GACOS | 152,046 | 56,331 | 2.8346 | −2.5942 | 13.3697 |

SBAS | 245,442 | 95,205 | 2.8307 | −2.0812 | 12.8820 |

MMVM | 296,813 | 150,071 | 2.7633 | −1.3111 | 11.0191 |

**Table 5.**Statistical characteristics of Stacking-InSAR deformation rate within the entire study area.

Method | Non-Deformed Areas | Deformed Areas | ||
---|---|---|---|---|

MPs | SD | Mean | SD | |

(mm/year) | (mm/year) | (mm/year) | ||

Original | 4,329,987 | 0.6469 | −0.0020 | 2.5300 |

Exp | 5,601,942 | 0.6464 | −0.0248 | 3.3740 |

ERA5 | 4,772,330 | 0.6444 | 0.0006 | 2.3842 |

GACOS | 4,657,488 | 0.6478 | 0.0054 | 2.8266 |

SBAS | 4,644,076 | 0.6464 | −0.0017 | 2.5607 |

MMVM | 8,897,582 | 0.6236 | −0.0482 | 2.4970 |

Method | Non-Deformed Areas | Deformed Areas | ||
---|---|---|---|---|

MPs | SD | Mean | SD | |

(mm/year) | (mm/year) | (mm/year) | ||

Original | 98,255 | 0.6431 | 0.0008 | 2.2065 |

Exp | 107,778 | 0.6426 | −0.0004 | 2.7814 |

ERA5 | 118,150 | 0.6374 | 0.0007 | 2.0490 |

GACOS | 86,101 | 0.6347 | 0.0011 | 2.4283 |

SBAS | 97,495 | 0.6443 | 0.0008 | 2.2063 |

MMVM | 179,376 | 0.5882 | −0.0005 | 1.7713 |

Method | Standard Deviation from Fit | |
---|---|---|

FP1 (mm) | FP2 (mm) | |

Original | 6.278 | 4.213 |

Exp | 5.965 | 4.267 |

ERA5 | 6.259 | 4.321 |

GACOS | 6.303 | 4.863 |

SBAS | 6.684 | 5.306 |

MMVM | 4.773 | 3.022 |

Method | Plain | Steep Terrain | Computational Efficiency | External Data | Advantages | Disadvantages |
---|---|---|---|---|---|---|

Exp | Good | Good | Medium | No | Satisfies the spatial heterogeneity | Sensitive to deformed or turbulent signals, small-scale |

ERA5 | Good | Poor | Low | Yes | Wide-scale | Uncertainty in estimated tropospheric delay phases |

GACOS | Good | Poor | Medium | Yes | Wide-scale | Uncertainty in estimated tropospheric delay phases |

SBAS | Good | Good | High | No | Various monitoring scenarios | Unable to satisfy the spatial heterogeneity |

MMVM | Excellent | Excellent | Low | No | Wide-scale, various monitoring scenarios, and satisfies the spatial heterogeneity | Phase overcorrection in some areas |

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. |

© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Li, L.; Wang, J.; Zhang, H.; Zhang, Y.; Xiang, W.; Fu, Y.
Refined InSAR Mapping Based on Improved Tropospheric Delay Correction Method for Automatic Identification of Wide-Area Potential Landslides. *Remote Sens.* **2024**, *16*, 2187.
https://doi.org/10.3390/rs16122187

**AMA Style**

Li L, Wang J, Zhang H, Zhang Y, Xiang W, Fu Y.
Refined InSAR Mapping Based on Improved Tropospheric Delay Correction Method for Automatic Identification of Wide-Area Potential Landslides. *Remote Sensing*. 2024; 16(12):2187.
https://doi.org/10.3390/rs16122187

**Chicago/Turabian Style**

Li, Lu, Jili Wang, Heng Zhang, Yi Zhang, Wei Xiang, and Yuanzhao Fu.
2024. "Refined InSAR Mapping Based on Improved Tropospheric Delay Correction Method for Automatic Identification of Wide-Area Potential Landslides" *Remote Sensing* 16, no. 12: 2187.
https://doi.org/10.3390/rs16122187