Getting Started with the AρρEEARS API: Submitting and Downloading an Area Request

This tutorial demonstrates how to use Python to connect to the AρρEEARS API

The Application for Extracting and Exploring Analysis Ready Samples (AρρEEARS) offers a simple and efficient way to access and transform geospatial data from a variety of federal data archives in an easy-to-use web application interface. AρρEEARS enables users to subset geospatial data spatially, temporally, and by band/layer for point and area samples. AρρEEARS returns not only the requested data, but also the associated quality values, and offers interactive visualizations with summary statistics in the web interface. The AρρEEARS API offers users programmatic access to all features available in AρρEEARS, with the exception of visualizations. The API features are demonstrated in this notebook.

Example: Submit an area request using a U.S. National Park boundary as the region of interest for extracting elevation, vegetation and land surface temperature data

Connect to the AρρEEARS API, query the list of available products, submit an area sample request, download the request, become familiar with the AρρEEARS Quality API, and import the results into Python for visualization. AρρEEARS area sample requests allow users to subset their desired data by spatial area via vector polygons (shapefiles or GeoJSONs). Users can also reproject and reformat the output data. AρρEEARS returns the valid data from the parameters defined within the sample request.

Data Used in the Example:

Topics Covered:

  1. Getting Started
    1a. Set Up the Working Environment
    1b. Login [Login]
  2. Query Available Products [Product API]
    2a. Search and Explore Available Products [List Products]
    2b. Search and Explore Available Layers [List Layers]
  3. Submit an Area Request [Tasks]
    3a. Import a Shapefile
    3b. Search and Explore Available Projections [Spatial API]
    3c. Compile a JSON [Task Object]
    3d. Submit a Task Request [Submit Task]
    3e. Retrieve Task Status [Retrieve Task]
  4. Download a Request [Bundle API]
    4a. Explore Files in Request Output [List Files]
    4b. Download Files in a Request (Automation) [Download File]
  5. Explore AρρEEARS Quality Service [Quality API]
    5a. List Quality Layers [List Quality Layers]
    5b. Show Quality Values [List Quality Values]
    5c. Decode Quality Values [Decode Quality Values]
  6. BONUS: Import Request Output and Visualize
    6a. Import a GeoTIFF
    6b. Plot a GeoTIFF

Dependencies:

AρρEEARS Information:

To access AρρEEARS, visit: https://appeears.earthdatacloud.nasa.gov/

For comprehensive documentation of the full functionality of the AρρEEARS API, please see the AρρEEARS API Documentation

Throughout the tutorial, specific sections of the API documentation can be accessed by clicking on the bracketed [] links in the section headings.

Files Used in this Tutorial:

Source Code used to Generate this Tutorial:

1. Getting Started

1a. Set Up the Working Environment

Import the required packages, set the input/working directory, and create an output directory for the results.

If you are missing any of the packages above, download them in order to use the full functionality of this tutorial.

If you plan to execute this tutorial on your own OS, `inDir` above needs to be changed.

1b. Login[Login]

To submit a request, you must first login to the AρρEEARS API. The AρρEEARS API requires the same NASA Earthdata Login as the AρρEEARS user interface. Use the getpass package to enter your NASA Earthdata login Username and Password. When prompted after executing the code block below, enter your username followed by your password.

Use the requests package to post your username and password. A successful login will provide you with a token to be used later in this tutorial to submit a request. For more information or if you are experiencing difficulties, please see the API Documentation.

Above, you should see a Bearer token. Notice that this token will expire approximately 48 hours after being acquired.

2. Query Available Products [Product API]

2a. Search and Explore Available Products [List Products]

The product API provides details about all of the products and layers available in AρρEEARS. Below, call the product API to list all of the products available in AρρEEARS.

Next, create a dictionary indexed by product name, making it easier to query a specific product.

The product service provides many useful details, including if a product is currently available in AρρEEARS, a description, and information on the spatial and temporal resolution.

Below, make a list of all product+version names, and search for products containing Leaf Area Index in their description.

Using the info above, start a list of desired products by using the highest temporal resolution LAI product, MCD15A3H.006.

2b. Search and Explore Available Layers [List Layers]

The product API allows you to call all of the layers available for a given product. Each product is referenced by its ProductAndVersion property. For a list of the layer names only, print the keys from the dictionary below.

Use the dictionary key 'LST_Day_1km' to see the information for that layer in the response.

AρρEEARS also allows subsetting data spectrally (by band). Create a tupled list with product name and specific layers desired.

Next, request the layers for the MCD15A3H.006 product.

Above, Lai_500m is the desired layer within the MCD15A3h.006 product.

Next, append Lai_500m to the tupled list of desired product/layers.

Thirdly, request the layers for the SRTMGL1_NC.003 product.

Finally, append SRTMGL1_DEM to the tupled list of desired products/layers.

Below, take the tupled list (layers) and create a list of dictionaries to store each layer+product combination. This will make it easier to insert into the json file used to submit a request in Section 3.

3. Submit an Area Request [Tasks]

The Submit task API call provides a way to submit a new request to be processed. It can accept data via JSON, query string, or a combination of both. In the example below, compile a json and submit a request. Tasks in AρρEEARS correspond to each request associated with your user account. Therefore, each of the calls to this service requires an authentication token (see Section 1c.), which is stored in a header below.

3a. Import a Shapefile

In this section, begin by importing a shapefile using the geopandas package. The shapefile is publically available for download from the NPS website.

Below, query the geopandas dataframe for the national park that you are interested in using for your region of interest, here Grand Canyon National Park.

3b. Search and Explore Available Projections [Spatial API]

The spatial API provides some helper services used to support submitting area task requests. The call below will retrieve the list of supported projections in AρρEEARS.

Create a dictionary of projections with projection Name as the keys.

3c. Compile a JSON [Task Object]

In this section, begin by setting up the information needed to compile an acceptable json for submitting an AρρEEARS area request. For detailed information on required json parameters, see the API Documentation.

Below, compile the JSON to be submitted as an area request. Notice that nps_gc is inserted from the shapefile transformed to a json via the geopandas and json packages above in section 3a.

3d. Submit a Task Request [Submit Task]

Below, post a call to the API task service, using the task json created above.

3e. Retrieve Task Status [Retrieve Task]

This API call will list all of the requests associated with your user account, automatically sorted by date descending with the most recent requests listed first.

The AρρEEARS API contains some helpful formatting resources. Below, limit the API response to 2 entries and set pretty to True to format the response as an organized json, making it easier to read. Additional information on AρρEEARS API pagination and formatting can be found in the API documentation.

Next, take the task id returned from the task_response that was generated when submitting your request, and use the AρρEEARS API status service to check the status of your request.

Above, if your request is still processing, you can find information on the status of how close it is to completing.

Below, call the task service for your request every 20 seconds to check the status of your request.

4. Download a Request [Bundle API]

Before downloading the request output, set up an output directory to store the output files, and examine the files contained in the request output.

4a. Explore Files in Request Output [List Files]

The bundle API provides information about completed tasks. For any completed task, a bundle can be queried to return the files contained as a part of the task request. Below, call the bundle API and return all of the output files.

4b. Download Files in a Request (Automation) [Download File]

Now, use the contents of the bundle to select the file name and id and store as a dictionary.

Use the files dictionary and a for loop to automate downloading all of the output files into the output directory.

5. Explore AρρEEARS Quality Service [Quality API]

The quality API provides quality details about all of the data products available in AρρEEARS. Below are examples of how to query the quality API for listing quality products, layers, and values. The final example (Section 5c.) demonstrates how AρρEEARS quality services can be leveraged to decode pertinent quality values for your data.

First, reset pagination to include offset which allows you to set the number of results to skip before starting to return entries. Next, make a call to list all of the data product layers and the associated quality product and layer information.

5a. List Quality Layers [List Quality Layers]

This API call will list all of the quality layer information for a product.

5b. Show Quality Values [List Quality Values]

This API call will list all of the values for a given quality layer.

5c. Decode Quality Values [Decode Quality Values]

This API call will decode the bits for a given quality value.

6. BONUS: Import Request Output and Visualize

Here, import one of the output GeoTIFFs and show some basic visualizations using the matplotlib package.

6a. Import a GeoTIFF

To perform the next step below, you will need to have GDAL installed on your OS. Open the GeoTIFF file for the SRTM DEM, and read in as an array.

Next, query the metadata for the fill value, and set fill value equal to nan.

6b. Plot a GeoTIFF

In this section, begin by highlighting the functionality of the matplotlib plotting package.

First, make a basic plot of the DEM data.

Next, add some additional parameters to the plot.

Finally, add important map items including a legend and title.

This example can provide a template to use for your own research workflows. Leveraging the AρρEEARS API for searching, extracting, and formatting analysis ready data, and importing it directly into Python means that you can keep your entire research workflow in a single software program, from start to finish.

Contact Information

Material written by LP DAAC$^{1}$

Contact: LPDAAC@usgs.gov

Voice: +1-605-594-6116

Organization: Land Processes Distributed Active Archive Center (LP DAAC)

Website: https://lpdaac.usgs.gov/

Date last modified: 05-06-2022

$^{1}$Innovate! Inc., contractor to the U.S. Geological Survey, Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, 57198-001, USA. Work performed under USGS contract G15PD00467 for LP DAAC$^{2}$.

$^{2}$LP DAAC Work performed under NASA contract NNG14HH33I.