Finding NISAR Data with ASF Search

ASF Search Python Package¶

ASF’s asf_search Python package enables quick and customizable programmatic access to NISAR data.

ASF Search Version¶

The asf_search package is always under development to add functionality in support of new platforms and products, to manage dependencies, and to comply with security best practices. In particular, NISAR support in asf_search will be evolving as more data products are added to the archive and preferred search patterns emerge.

As such, we recommend using the latest release of the package whenever possible. You can update your installation of asf_search via pip with:

python -m pip install --upgrade asf_search

or via conda with:

conda update asf_search

Search for NISAR Data¶

To quickly begin exploring NISAR data, search by setting the dataset parameter to 'NISAR' and the processingLevel parameter to the four-letter acronym corresponding to your desired product. For example, to search for NISAR GCOV products, use the following Python code:

import asf_search as asf

results = asf.search(dataset='NISAR', processingLevel='GCOV')

A list of accepted processingLevel constants for data from all missions hosted by ASF, including NISAR, is available here.

Refer to the Searching page of the ASF Data Search Manual for more details on available search filters and their possible values.

Download data¶

Downloading NISAR data requires authentication through Earthdata Login (EDL). For more information, see Accessing Data Using EDL Accounts.

EDL credentials can be provided to asf_search using the ASFSession class. After creating an authenticated ASFSession, pass the session object to the download function along with the target directory path where the data will be saved:

session = asf.ASFSession().auth_with_creds('username', 'password')
results.download(path='path/to/data/', session=session) 

Alternatively, users may configure a local .netrc file to store their EDL credentials. Once the .netrc file is properly set up, downloads can be performed without explicitly passing credentials:

results.download(path='path/to/data/')