earthaccess is a python library to search, download or stream NASA Earth science data with just a few lines of code.
In the age of cloud computing, the power of open science only reaches its full potential if we have easy-to-use workflows that facilitate research in an inclusive, efficient and reproducible way. Unfortunately —as it stands today— scientists and students alike face a steep learning curve adapting to systems that have grown too complex and end up spending more time on the technicalities of the tools, cloud and NASA APIs than focusing on their important science.
During several workshops organized by NASA Openscapes, the need to provide easy-to-use tools to our users became evident. Open science is a collaborative effort; it involves people from different technical backgrounds, and the data analysis to solve the pressing problems we face cannot be limited by the complexity of the underlying systems. Therefore, providing easy access to NASA Earthdata regardless of the data storage location (hosted within or outside of the cloud) is the main motivation behind this Python library.
You will need Python 3.8 or higher installed.
Install the latest release using conda
With earthaccess we can login, search and download data with a few lines of code and even more relevant, our code will work the same way if we are running it in the cloud or from our laptop. earthaccess handles authentication with NASA's Earthdata Login (EDL), search using NASA's CMR and access through
The only requirement to use this library is to open a free account with NASA EDL.
earthaccess with automatically look for your EDL account credentials in two locations:
If neither of these options are configured, you can authenticate by calling the
and manually entering your EDL account credentials.
Note you can pass
earthaccess.login() to have the EDL account credentials you enter
automatically saved to a
~/.netrc file for future use.
Once you are authenticated with NASA EDL you can:
- Get a file from a DAAC using a
- Request temporary S3 credentials from a particular DAAC (needed to download or stream data from an S3 bucket in the cloud).
- Use the library to download or stream data directly from S3.
- Regenerate CMR tokens (used for restricted datasets)
Searching for data
Once we have selected our dataset we can search for the data granules using doi, short_name or concept_id. If we are not sure or we don't know how to search for a particular dataset, we can start with the "Introducing NASA earthaccess" tutorial or through the NASA Earthdata Search portal. For a complete list of search parameters we can use visit the extended API documentation.
Now that we have our results we can do multiple things: We can iterate over them to get HTTP (or S3) links, we can download the files to a local folder, or we can open these files and stream their content directly to other libraries e.g. xarray.
Accessing the data
Option 1: Using the data links
If we already have a workflow in place for downloading our data, we can use earthaccess as a search-only library and get HTTP links from our query results. This could be the case if our current workflow uses a different language and we only need the links as input.
# if the data set is cloud hosted there will be S3 links available. The access parameter accepts "direct" or "external", direct access is only possible if you are in the us-west-2 region in the cloud.
data_links = [granule.data_links(access="direct") for granule in results]
# or if the data is an on-prem dataset
data_links = [granule.data_links(access="external") for granule in results]
Note: earthaccess can get S3 credentials for us, or auhenticated HTTP sessions in case we want to use them with a different library.
Option 2: Download data to a local folder
This option is practical if you have the necessary space available on disk. The earthaccess library will print out the approximate size of the download and its progress.
Option 3: Direct S3 Access - Stream data directly to xarray
This method works best if you are in the same Amazon Web Services (AWS) region as the data (us-west-2) and you are working with gridded datasets (processing level 3 and above).
And that's it! Just one line of code, and this same piece of code will also work for data that are not hosted in the cloud, i.e. located at NASA storage centers.
More examples coming soon!
Only Python 3.8+ is supported.
Please see the Contributing Guide.
earthaccess is licensed under the MIT license. See LICENSE.
Level of Support
This repository is supported by a joint effort of NSIDC, NASA DAACs, and the Earth science community, and we welcome any contribution in the form of issue submissions, pull requests, or discussions. Issues labeled as https://github.com/nsidc/earthaccess/labels/good%20first%20issue are a great place to get started.