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This Jupyter Book contains data recipes for loading ASF HyP3 INSAR_GAMMA and INSAR_ISCE_BURST stacks into MintPy and performing Small Baseline Subset (SBAS) line-of-sight, displacement time series analyses. It also provides options for error analysis, plotting, and outputting data to GeoTiff.

MintPy

The Miami INsar Time-series software in PYthon (MintPy as /mɪnt paɪ/) is an open-source package for Interferometric Synthetic Aperture Radar (InSAR) time series analysis. It reads the stack of interferograms (coregistered and unwrapped) in ISCE, ARIA, FRInGE, HyP3, GMTSAR, SNAP, GAMMA or ROI_PAC format, and produces three dimensional (2D in space and 1D in time) ground surface displacement in line-of-sight direction

https://github.com/insarlab/MintPy

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How To Use This Jupyter Book

1. Install the software environment needed to run the notebooks

   • Run the Install Required Software with Conda notebook (1_Software_Environment.ipynb)
   • Rerun this step periodically. Updates to environment config files will not take effect unless you update or recreate your environment.

2. Configure Climate Data Store Access (optional)

   • Run the Set Up Climate Data Store Access notebook (2_CDS_Access.ipynb)
   • Configure CDS access if you will perform tropospheric correction
   • If you do not wish to perform tropospheric correction, you must set the following config option: mintpy.troposphericDelay.method = no

3. Access HyP3 Data

   • Run the Access & Subset HyP3 SBAS Stack (InSAR or Burst-InSAR) notebook (3_Access_HyP3_Data.ipynb)
   • How-to: order interferograms from HyP3

4. Load Data with MintPy

   • Run the A. Load HyP3 SBAS Stack into MintPy notebook (a_Load_HyP3_Data.ipynb)
   • Run once per SBAS stack

5. Configure a Time Series Analysis

   • Run the B. Configure (or Reconfigure) MintPy Time Series Analysis notebook (b_Update_Configuration.ipynb)
   • Run anytime you wish to update the configuration of your time series

6. Perform the Time Series Analysis

   • Run the C. Perform MintPy Time Series Analysis notebook (c_MintPy_Time_Series.ipynb)

7. Run Post-Time Series Workflows

   • Error Analysis (Error_Analysis.ipynb)
   • Plotting (Plots.ipynb)
   • Output Results to GeoTiff (Output_GeoTiff.ipynb)

8. Update Your Configuration and Reprocess the Time Series

   • Use the results of your time series and error analyses to make configuration adjustments and reprocess the time series by repeating steps 5 and 6.

Practical Notes About Using MintPy

MintPy loads data from any source or processor into two HDF5 datasets:

• geometryGeo.h5
• ifgramStack.h5

As you run steps in the time series script, smallbaselineapp.py, additional HDF5 data sets will be created, which become inputs for following steps.

You only need to load your data once

• If you have loaded your times series from a bunch of large GeoTiffs, you can delete them to conserve space after loading your data.
• You will be able to reconfigure and rerun your time series without needing to reload your data.
• If you reconfigure your time series, you must rerun the time series for the updates to take effect.
• Once you have run your time series, until you wish to reconfigure it, you can rerun any data recipes in the Extras section without having to rerun the time series.