Awesome

ARIA-tools

ARIA-tools is an open-source package in Python which contains tools to manipulate ARIA standard InSAR products. This software is open source under the terms of the Apache 2.0 License. Its development was funded under the NASA Sea-level Change Team (NSLCT) program and the Earth Surface and Interior (ESI) program.

For a full overview of available ARIA standard products and their specification, see the products page on the ARIA website. Currently, support for the ARIA Geocoded Unwrapped Interferogram (GUNW) product is included. Products can be downloaded for free from the ARIA-products page and the ASF DAAC vertex page under missions and beta-products, but require log-on using the NASA Earthdata credentials. The ARIA-tools package includes functionality to crop/merge data and meta-data layers for multiple standard products, extraction of data and meta-data layers from these products, and the set-up and the preparation for time-series.

Actual time-series processing is not supported in ARIA-tools. However, outputs are compatible with third-party time-series InSAR packages such as the "Generic InSAR Analysis Toolbox" (GIAnT) and the "Miami INsar Time-series software in PYthon" (MintPy).

Contents

1. Software Dependencies
2. Installation
   • Conda
   • Other installation options
   • ARIA-tools with support for S3 virtual data access
3. Running ARIA-tools
   • Commandline download of GUNW Products
   • Manipulating GUNW Products
   • Baseline and quality control plots for GUNW Products
   • Time-series set-up of GUNW Products
4. Documentation
5. Citation
6. Contributors and community contributions

Software Dependencies

Below we list the dependencies for ARIA-tools

Packages

* Python >= 3.5  (3.6 preferred)
* [PROJ 4](https://github.com/OSGeo/proj) github) >= 6.0
* [GDAL](https://www.gdal.org/) and its Python bindings >= 3.0

Python dependencies

* [SciPy](https://www.scipy.org/)
* [netcdf4](http://unidata.github.io/netcdf4-python/netCDF4/index.html)
* [requests](https://2.python-requests.org/en/master/)

Python Jupyter dependencies

* py3X-jupyter
* py3X-jupyter_client
* py3X-jupyter_contrib_nbextensions
* py3X-jupyter_nbextensions_configurator
* py3X-hide_code
* py3X-RISE

Optional Third-party packages

* RelaxIV available from [Min-Cost-Flow-Class](https://github.com/frangio68/Min-Cost-Flow-Class)

Installation

ARIA-tools has been tested on the following system:

• Linux v.7 and up

Below we demonstrate how to build and setup an environment from scratch through Linux through a TCSH shell.

ARIA-tools package can be easily installed and used after the dependencies are installed and activated.

Conda is a cross-platform way to use Python that allows you to setup and use "virtual environments," which allows for the easy installation and management of all of the required dependencies. We recommend using the Miniforge conda environment manager, which uses conda-forge as its default code repo. Alternatively, see here for help installing Anaconda and here for installing Miniconda.

Conda

Below we outline the different steps for setting up the ARIA-tools while leveraging Miniforge for installation of the requirements.

Run the commands below to download and setup your Miniforge environment manager:

cd ~/tools
wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh
bash Miniforge3-Linux-x86_64.sh -b -p miniforge
miniforge/bin/mamba init tcsh
# reset shell
csh

Run the commands below to download/clone the ARIA-tools package to your local directory:

cd ~/tools
git clone https://github.com/aria-tools/ARIA-tools.git
cd ARIA-tools

Run the commands below to install dependencies to a new conda environment ARIA-tools and activate it. Make sure to activate your environment each time you open a new session:

mamba env create -f environment.yml
conda activate ARIA-tools

Or run the commands below to install dependencies to an existing conda environment (base by default):

mamba install -c conda-forge --yes --file requirements.txt

We have included a setup.py script which allows for easy compilation and installation of third-party dependencies (c-code), as well as for setting up the ARIA-tools package itself (python and command line tools).

python -m pip install -e .

If not using the setup.py, users should compile third-party packages manually and ensure ARIA-tools and dependencies are included on their PATH and PYTHONPATH. For TCSH shell this can be done as follows (replace {$PWD}/tools/ARIAtools to the location where you have cloned the ARIAtools repository):

setenv PYTHONPATH ${PYTHONPATH}:{$PWD}/tools/ARIAtools
setenv PATH ${PATH}:${PWD}/tools/bin

To avoid potential issues associated with dependencies when cloning new ARIA-tools commits, it is advised to regularly maintain your conda environment as so (making sure to adjust the conda environment argument name --name ARIA-tools as appropriate):

mamba env update --name ARIA-tools --file environment.yml --prune

GNU Parallel (https://www.gnu.org/software/parallel/) will write output to stdout that requests that the user cite their paper. We can use this command to suppress this output:

echo 'will cite' | parallel --citation

Other installation options

The following pages might be of use to those trying to build third party packages from source.

• Installing dependencies from source on linux
• Installing dependencies from source on mac

ARIA-tools with support for S3 virtual data access

GDAL Virtual File Systems capabilities (vsicurl) can be leveraged in ARIA-tools to avoid download of product during processing.

Minimum requirements:

* [GDAL](https://www.gdal.org/) and its Python bindings >= 3.0
* Linux kernel >=4.3 
* libnetcdf >=4.5 

A '~/.netrc' file with earthdata credential included

echo "machine urs.earthdata.nasa.gov login myUsername password myPassword" > ~/.netrc
chmod 600 ~/.netrc

In addition, users should set the following environment variables:

export GDAL_HTTP_COOKIEFILE=/tmp/cookies.txt
export GDAL_HTTP_COOKIEJAR=/tmp/cookies.txt
export VSI_CACHE=YES

Running ARIA-tools

The ARIA-tools scripts are highly modulized in Python and therefore allows for building your own processing workflow. Below, we show how to call some of the functionality. For detailed documentation, examples, and Jupyter notebooks see the ARIA-tools-docs repository. We welcome the community to contribute other examples on how to leverage the ARIA-tools (see here for instructions).

• NOTE, currently ARIA-tools does not support deduplication (we are working to add it soon though), so for each commandline input please make sure to specify a fresh output directory with the --workdir option.

Commandline download of GUNW Products

GUNW products can be downloaded through the commandline using the ariaDownload.py program, which wraps around the ASF DAAC api.

Manipulating GUNW Products

GUNW product can be manipulated (cropped, stitched, extracted) using the ariaExtract.py program.

Baseline and quality control plots for GUNW Products

Quality and baseline plots for spatial-temporal contiguous interferograms can be made using the ariaPlot.py program.

Time-series set-up of GUNW Products

Time-series set-up with spatial-temporal contiguous unwrapped interferograms and coherence can be done using the ariaTSsetup.py program.

Documentation

See the ARIA-tools-docs repository for all documentation and Jupyter Notebook Tutorials.

Citation

Buzzanga, B., Bekaert, D. P. S., Hamlington, B. D., & Sangha, S. S. (2020). Towards Sustained Monitoring of Subsidence at the Coast using InSAR and GPS: An Application in Hampton Roads, Virginia. Geophysical Research Letters, 47, e2020GL090013. https://doi.org/10.1029/2020GL090013

Contributors

• David Bekaert
• Simran Sangha
• Emre Havazli
• Brett Buzzanga
• Alexander Fore
• Marin Govorcin
• Charles Marshak
• Joseph Kennedy
• other community members

We welcome community contributions. For instructions see here.