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Python wrapper for ASF's SearchAPI

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Python wrapper for the ASF SearchAPI

import asf_search as asf

results = asf.granule_search(['ALPSRS279162400', 'ALPSRS279162200'])

wkt = 'POLYGON((-135.7 58.2,-136.6 58.1,-135.8 56.9,-134.6 56.1,-134.9 58.0,-135.7 58.2))'
results = asf.geo_search(platform=[asf.PLATFORM.SENTINEL1], intersectsWith=wkt, maxResults=10)


In order to easily manage dependencies, we recommend using dedicated project environments via Anaconda/Miniconda or Python virtual environments.

asf_search can be installed into a conda environment with

conda install -c conda-forge asf_search

or into a virtual environment with

python3 -m pip install asf_search

To install pytest/cov packages for testing, along with the minimal packages:

python3 -m pip install asf_search[test]


Full documentation is available at

Programmatically searching for ASF data is made simple with asf_search. Several search functions are provided:

  • geo_search() Find product info over an area of interest using a WKT string
  • granule_search() Find product info using a list of scenes
  • product_search() Find product info using a list of products
  • search() Find product info using any combination combination of search parameters
  • stack() Find a baseline stack of products using a reference scene
  • Additionally, numerous constants are provided to ease the search process

Additionally, asf_search support downloading data, both from search results as provided by the above search functions, and directly on product URLs. An authenticated session is generally required. This is provided by the ASFSession class, and use of one of its three authentication methods:

  • auth_with_creds('user', 'pass)
  • auth_with_token('EDL token')
  • auth_with_cookiejar(http.cookiejar)

That session should be passed to whichever download method is being called, can be re-used, and is thread safe. Examples:

results = asf_search.granule_search([...])
session = asf_search.ASFSession()
session.auth_with_creds('user', 'pass')'/Users/SARGuru/data', session=session)

Alternately, downloading a list of URLs contained in urls and creating the session inline:

urls = [...]
asf_search.download_urls(urls=urls, path='/Users/SARGuru/data', session=ASFSession().auth_with_token('EDL token'))

Also note that and the generic download_urls() function both accept a processes parameter which allows for parallel downloads.

Further examples of all of the above can be found in examples/



Instance Branch Description, Instructions, Notes
Stable stable Accepts merges from Working and Hotfixes
Working master Accepts merges from Features/Issues and Hotfixes
Features/Issues topic-* Always branch off HEAD of Working
Hotfix hotfix-* Always branch off Stable

For an extended description of our workflow, see

Enable Logging

We use standard the standard logging in our package for output.

Heres a basic example for hooking into it with your application:

import asf_search as asf
import logging
ASF_LOGGER = logging.getLogger("asf_search")
formatter = logging.Formatter('[ %(asctime)s (%(name)s) %(filename)s:%(lineno)d ] %(levelname)s - %(message)s')

# Get output to the console:
stream_handle = logging.StreamHandler()
# If you want it write to a file too:
file_handle = logging.FileHandler('MyCustomApp.log')
# Only see messages that might affect you
# Test if the logger throws an error, you see it as expected:
ASF_LOGGER.error("This is only a drill. Please do not panic.")
# Should output this:
# [ 2023-01-17 10:04:53,780 (asf_search) ] ERROR - This is only a drill. Please do not panic.

For more configure options on logging, please visit their howto page.

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