# LiCSBAS: An Open-Source InSAR Time Series Analysis Package Integrated with the LiCSAR Automated Sentinel-1 InSAR Processor

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## Abstract

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## 1. Introduction

## 2. LiCSAR and LiCSBAS

#### 2.1. LiCSAR: Automatic Sentinel-1 InSAR Processer

#### 2.2. LiCSBAS Overview

#### 2.3. Prepare Stack of Unwrapped Data

#### 2.3.1. Step 0-1: Download LiCSAR Products

#### 2.3.2. Step 0-2: Convert GeoTIFF (and Downsample)

#### 2.3.3. Step 0-3: Tropospheric Noise Correction Using GACOS (Optional)

#### 2.3.4. Step 0-4: Mask Interferograms (Optional)

#### 2.3.5. Step 0-5: Clip Interferograms (Optional)

#### 2.4. Time Series Analysis

#### 2.4.1. Step 1-1: Quality Check

#### 2.4.2. Step 1-2: Network Refinement by Loop Closure

#### 2.4.3. Step 1-3: Small Baseline Network Inversion

_{i}is the incremental displacement between time t

_{i}

_{-1}and t

_{i}) can be derived by solving

#### 2.4.4. Step 1-4: Estimate Standard Deviation of the Velocity by Bootstrap

#### 2.4.5. Step 1-5: Mask Noisy Pixels in the Time Series

#### 2.4.6. Step 1-6: Spatiotemporal Filtering of Time Series

#### 2.5. Visualization of the Results

## 3. Case Study: Entire Frame

#### 3.1. Southern Tohoku, Japan

_{w}9.0 Tohoku earthquake is located offshore, just beyond the frame (Figure 5a) [58], although the frame encompasses a region that experienced a few meters of the eastward coseismic motion based on continuous Global Navigation Satellite System (GNSS) observations [58]. The region has also experienced significant, ongoing postseismic deformation [59]. The rate of postseismic deformation has roughly converged to a constant velocity since around 2014 [60]. GNSS-derived velocities across the northern portion of the frame reveal significant eastward displacements at a rate of ~50 mm/yr from 2014–2019, compared to the south (Figure 5a). Vertical displacements are generally smaller than horizontal, but an east-to-west transition from uplift to subsidence with rates on the order of >10 mm/yr occurs across the region covered by the northern portion of the frame.

#### 3.2. Results

#### 3.3. Impact of Masking and a Network Gap

#### 3.4. Impact of the GACOS Correction

#### 3.5. Evolution of Velocity Uncertainty

## 4. Case Study: Clipped Area

#### 4.1. Echigo Plain

#### 4.2. Niigata City

#### 4.3. Ojiya City

#### 4.4. Sanjo City

#### 4.5. Annual Displacement in Echigo Plain

## 5. Discussion

#### 5.1. Processing Time and Disk Usage

#### 5.2. Limitations

## 6. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Workflow of LiCSBAS comprising the preparation of unwrapped (UNW) interferometric phases and coherence (COH) data (Steps 0-1 to 0-5) prior to the time series analysis (Steps 1-1 to 1-6). Optional steps of incorporating atmospheric corrections (Generic Atmospheric Correction Online Service, GACOS); masking; and clipping are denoted by the dashed lines. Checks are performed for poorly unwrapped interferograms prior to the small baseline (SB) inversion to generate velocities and time series. A range of noise indices are calculated to mask poorly constrained data points. Further masking and filtering occur to generate the final products.

**Figure 2.**Example of the loop closure for the region of the Echigo plain, Japan (Section 4). Top and bottom row images are interferograms consisting of five images and loop phases calculated from the interferograms by Equation (1), respectively. Black and red lines between them denote which three interferograms are used to calculate each loop phase. Three loop phases with a red frame (${\mathsf{\Phi}}_{124},{\mathsf{\Phi}}_{234},{\mathsf{\Phi}}_{245}$) have areas with ±2π phase and a large (>2 rad) root mean square (RMS). Then, the interferogram ${\varphi}_{24}$ with a red frame can be identified as a bad interferogram including significant unwrapping errors, because all loops associated with it are problematic, whilst the other interferograms generate at least one nonproblematic loop.

**Figure 3.**Example of the masking of velocities in noisy pixels derived from the time series analysis based upon a suite of indices for the region of the Echigo Plain (Section 4). Masked and unmasked velocities and the mask itself are shown along the top row (along with the average coherence from the input interferometric data), and the other images denote further noise indices used to create the mask (explanations and units given in Table 1). Numbers shown in the parentheses next to the titles of each noise index are the applied threshold value.

**Figure 4.**Example of the interactive time series viewer used to interpret the cumulative displacements, both spatially and through time, for the LiCSAR frame ID 046D_05292_131313 (Section 3). (

**a**) Image window for the velocities, cumulative displacements, and noise indices (displayed in a nongeographic spatial reference given in pixels). A black dashed rectangle denotes the reference area, which can be changed by mouse operations. A black dot denotes the selected point of which the displacement time series is displayed in the time series window. Cumulative displacements at each acquisition epoch can be displayed by sliding the lower time panel. (

**b**) Time series window for a selected point in the map denoted by a black dot. The red cumulative displacement points and model lines (with a linear or linear plus annual term shown here and a quadratic or quadratic plus annual term also available) indicate the unfiltered data, whilst those shown in blue have a spatial filter of 2 km and a temporal filter of 49 days applied, in this case. The model lines are calculated by post-fitting to the displacement time series and irrelevant to the temporal constraint function used in Step 1-3. A vertical black line denotes the gap in the interferogram network.

**Figure 5.**(

**a**) Topography of the target area: Southern Tohoku and Northern Kanto, Japan. A black polygon denotes the area of the frame ID 046D_05292_131313. Black dots, black arrows, and red and blue bars denote Global Navigation Satellite System (GNSS) stations and associated horizontal and vertical velocities from 25 November 2014 to 14 July 2019. A small black square denotes the 950217 GNSS station, which is used as the reference point for the both GNSS- and synthetic aperture radar interferometry (InSAR)-derived surface displacements. (Inset) Location of the area of interest on Honshu in Japan. A black polygon and red star indicate the frame ID 046D_05292_131313 and the epicenter of the 2011 M

_{w}9.0 Tohoku Earthquake, respectively. (

**b**) Perpendicular baseline configuration and network of the 306 SB interferograms formed from 104 Sentinel-1A images used in this study.

**Figure 6.**(

**a**) Line-of-sight (LOS) velocity of InSAR (background colors) and GNSS (circles). Positive values mean displacement toward the satellite (i.e., uplift and/or eastward displacement, in this case). A black square denotes the reference GNSS point (950217). (

**b**) Difference of the LOS velocity between InSAR and GNSS (colors in circles) and the estimated best-fit quadratic polynomial ramp. Note that the color scale is different from Figure 6a. (

**c**) Displacement time series at 950179 (150 km away from the reference). The same ones for all the GNSS stations, and after removing the constant velocity (35.1 mm/yr) at 950179, are shown in Figures S5 and S6, respectively. (

**d**) STD of the difference of the time series between InSAR and GNSS as a function of the distance from the reference point.

**Figure 7.**Correlation diagram of the STD of unwrapped phases in the 306 interferograms before and after the GACOS correction. The grey line denotes a 1:1. The STD decreased from 6.7 rad to 4.2 rad on average and from 6.0 rad to 3.9 rad on median by the GACOS correction.

**Figure 9.**Temporal evolution of velocity uncertainties from the first acquisition (i.e., 25 November, 2014) to each subsequent acquisition with a log scale on the y-axis. Solid and dashed lines denote the theoretical values calculated from Eequation (4) with ${\sigma}_{e}$ of 7.5 mm and 9.0 mm, respectively, and sampling intervals of 70, 24, 12, and 6 days. Blue dots denote the STD of the difference between InSAR and GNSS LOS velocities. Orange dots and error bars denote the average and STD of the InSAR LOS velocity STD computed by the bootstrap method.

**Figure 10.**(

**a**) Landsat 8 image of the Echigo Plain. (

**b**) LOS velocity. Black dots denote GNSS stations. A black square denotes the reference point (960566).

**Figure 11.**(

**a**) Vertical velocities derived from InSAR (background colors) and leveling surveys (circles) in Niigata Ccity. (

**b**) Correlation diagram of the vertical velocity between InSAR and leveling. The grey line denotes a 1:1. (

**c**) Time series of the vertical displacement at benchmark A just east of the mouth of the Agano River.

**Figure 12.**(

**a**) Map of Ojiya City. Background color denotes the LOS velocity. Gray lines denote the distribution of the snow-melting system. (

**b**) LOS displacement on 13 February 2018. (

**c**) Cumulative LOS displacements with reference to the first acquisition of 25 November 2014. The acquisition dates (yyyymmdd) are shown on the top of each figure. The color range is the same as Figure 12b. All the acquisitions are shown in Figure S11. (

**d**) Time series of the LOS displacement derived from InSAR and GNSS at 950240, and the groundwater level at Well I.

**Figure 13.**(

**a**) LOS velocity in Sanjo City. (

**b**) Time series of the LOS displacement at SA-SD of which locations are shown in (a), and snow depth at the Nagaoka snow gauge (the location is shown in Figure 10b).

**Figure 14.**(

**a**) Amplitude of the annual displacement in the Echigo Plain. (

**b**) Timing of the lower peak of the annual displacement.

Noise index | Meaning |
---|---|

coh_avg | Average value of the interferometric coherence across the stack (0–1). |

n_unw | Number of unwrapped data used in the time series calculation. |

vstd | Standard deviation of the velocity (mm/yr) estimated in Step 1-4. |

maxTlen | Maximum time length of the connected network (years).The larger the value, the better the precision in the velocity estimate (see Section 3.5). |

n_gap | Number of gaps in the interferogram network and time series breaks. |

stc | Spatiotemporal consistency (mm) [55], which is the minimum root mean square (RMS) of the double differences of the time series in space and time between the pixel of interest and an adjacent pixel among all adjacent pixels. |

n_ifg_noloop | Number of interferograms with no loops that cannot be checked by the loop closure and possibly have unidentified unwrapping errors. |

n_loop_err | Number of the unclosed loops after the network refinement in Step 1-2. |

resid_rms | RMS of residuals in the small baseline (SB) inversion (mm). |

Entire Frame | Echigo Plain | Entire Frame (10 × 10 Downsampled) | |
---|---|---|---|

Size of Image | 3338 × 2685 | 732 × 922 | 333 × 268 |

# of Images | 104 | 104 | 104 |

# of Interferograms | 306 | 306 | 306 |

# of Inverted Pixels (at Step 1-3) | 2,503,334 | 288,079 | 24,579 |

# of Remaining Pixels (after Step 1-6) | 1,166,756 | 167,269 | 15,704 |

Step 0-1 | 10 min | — | — |

Step 0-2 | 10 min | — | 3 min |

Step 0-3 | 30 min | — | 5 min |

Step 0-4 | 5 min | 2 min | 1 min |

Step 0-5 | — | 5 min | — |

Step 1-1 | 2 min | <1 min | <1 min |

Step 1-2 | 20 min | 4 min | 3 min |

Step 1-3 | 3 hr 30 min | 25 min | 5 min |

Step 1-4 | 20 min | 2 min | <1 min |

Step 1-5 | <1 min | <1 min | <1 min |

Step 1-6 | 30 min | 5 min | 2 min |

Total Time for Step 1 | ~5 hr | ~40 min | ~10 min |

Size of Downloaded GeoTIFF | 5 GB | — | — |

Size of GACOS Data | 5 GB | — | — |

Size of Converted Data | 21 GB | 2 GB | 0.2 GB |

Size of Created Data in Step 1 | 10 GB | 1 GB | 0.2 GB |

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**MDPI and ACS Style**

Morishita, Y.; Lazecky, M.; Wright, T.J.; Weiss, J.R.; Elliott, J.R.; Hooper, A.
LiCSBAS: An Open-Source InSAR Time Series Analysis Package Integrated with the LiCSAR Automated Sentinel-1 InSAR Processor. *Remote Sens.* **2020**, *12*, 424.
https://doi.org/10.3390/rs12030424

**AMA Style**

Morishita Y, Lazecky M, Wright TJ, Weiss JR, Elliott JR, Hooper A.
LiCSBAS: An Open-Source InSAR Time Series Analysis Package Integrated with the LiCSAR Automated Sentinel-1 InSAR Processor. *Remote Sensing*. 2020; 12(3):424.
https://doi.org/10.3390/rs12030424

**Chicago/Turabian Style**

Morishita, Yu, Milan Lazecky, Tim J. Wright, Jonathan R. Weiss, John R. Elliott, and Andy Hooper.
2020. "LiCSBAS: An Open-Source InSAR Time Series Analysis Package Integrated with the LiCSAR Automated Sentinel-1 InSAR Processor" *Remote Sensing* 12, no. 3: 424.
https://doi.org/10.3390/rs12030424