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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

How To Use This Jupyter Book

1. Install the software environment needed to run the notebooks

   (Option 1) Run in OpenSARLab or on Another JupyterHub Supporting User-Installable Pixi Environments

   1. Sign up for an OpenScienceLab account and request access to OpenSARLab.

   2. Navigate to ~/Data_Recipe_Jupyter_Books/opensarlab_MintPy_Recipe_Book to explore and execute the notebooks.

   (Option 2) Run on Your Own Machine

   If you are interested in running this material locally on your computer, you will need to follow this workflow:

   1. Install Pixi using one of the following options if not already installed:

      • macOS / Linux:

          curl -fsSL https://pixi.sh/install.sh | bash

      • Download the pixi installer

   2. Restart your terminal after installing Pixi so it is on your PATH.

   3. Clone the opensarlab_MintPy_Recipe_Book GitHub repository:

       git clone https://github.com/ASFOpenSARlab/opensarlab_MintPy_Recipe_Book.git

   4. Move into the opensarlab_MintPy_Recipe_Book directory

      cd opensarlab_MintPy_Recipe_Book

   5. Install the Pixi environment needed to execute the notebooks and launch JupyterLab with a single command:

      pixi run lab
      

      :::{tip} Navigate the NISAR Cookbook from its table of contents

      Click the "JB" tab on the left-hand sidebar if you wish to navigate the opensarlab_MintPy_Recipe_Book using a table of contents instead of the file browser.

      This feature is provided by the jb-toc JupyterLab extension. :::

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 Extra 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 Extra Workflows section without having to rerun the time series.