CEOS ODC-GEE Technical Documentation

Operation Requirements

The ODC-GEE integration package ([GitHub]( has a few requirements to operate properly.

Operation Requirement 1: Supported Metadata Types

The ODC index database used by your environment must support the `eo3` metadata type. This can be checked by running datacube metadata list in your ODC environment. If eo3 is not listed, then you will need to add it. The default eo3 type definition can be found [here]( To add it, save the eo3 definition in a .yaml file then run the following command: datacube metadata add <your_eo3.yaml>.

Operation Requirement 2: Earth Engine Credentials

To use the ODC-GEE integration package, you must be registered as an Earth Engine developer. If not, you may submit an [application to Google](

You will need [GEE service account credentials]( for your account - specifically the private key JSON file. Put your version of this JSON file here: odc_gee/config/credentials.json.

(Optional) You can also create a JSON file defining the spatial areas associated with region names. An example is provided in odc_gee/opt/config/odc-gee/regions.json. Put your version of this JSON file here: odc_gee/config/regions.json.

Adding New GEE Datasets as ODC Products

To use a GEE dataset from the [Earth Engine Data Catalog](, a new product must be created using the new_product command. Format: new_product –asset <asset_id> <product_name.yaml> where the asset_id is provided in the “Earth Engine Snippet” string on the dataset’s page on the catalog and <product_name.yaml> is the path to the output YAML file containing the ODC product definition. For example, to index [Landsat 8 Level 2 Collection 2 Tier 1]( data: new_product –asset LANDSAT/LC08/C02/T1_L2 ls8_l2_c2_gee.yaml. The full process is as follows:

  1. Run the new_product command to create the product definition.

  2. Reformat the product definition to match a standard format, such as [this one from the ODC Indexer](

  3. Change the aliases field for the measurements as desired. Do NOT change the name field of any measurement - creating the product will fail if the name fields are changed.

  4. Run datacube product add <path-to-product-definition-file> to add the product.

Notably, you will need to add a storage section with crs and resolution entries to avoid having to specify the output_crs and resolution each time data is loaded from the product.

After adding the product, it is a non-indexed GEE product (or [“undefined product”]( It must be loaded using the [ODC-GEE datacube.Datacube wrapper class](

Once you have confirmed that data is loading for the newly defined product, you are welcome to add a pull request to the [ODC Indexer]( repository to add the new product definition to the prod_defs directory. Prefix the product definition file with _google.

The data can be indexed using the index_gee command, making it an indexed GEE product, but this is deprecated. Format: index_gee –asset <asset_id> –product <product_name> [–latitude (lat1, lat2) –longitude (lon1, lon2) –time (YYYY-MM-DD, YYYY-MM-DD) –region <region_name>] (for information on the optional arguments and others not listed here, run index_gee –help). For example, to index [Landsat 8 Level 2 Collection 2 Tier 1]( data for the United States: index_gee –asset LANDSAT/LC08/C02/T1_L2 –product <product_name> –latitude (25.3168, 49.4885) –longitude (-125.2052, -66.6657). Data for these products can be loaded by the normal datacube.Datacube class.

Using GEE vs non-GEE Data

To load data from non-GEE products, use the datacube.Datacube class as always:

`python from datacube import Datacube dc = datacube.Datacube() `

To load data from GEE products, use the odc_gee.earthengine.Datacube class:

`python from odc_gee.earthengine import Datacube as GEE_Datacube dc = GEE_Datacube() `

To load data from indexed GEE products (remember this is deprecated), use the datacube.Datacube class.

GEE versus Alternative Datasources

These are the benefits and penalties of loading data from Google Earth Engine through the ODC-GEE module instead of from other datasources such as S3.


  1. Data does not need to be indexed before loading, which allows new datasets to be added and queried quickly, which allows faster prototyping. This also results in a much smaller ODC index database.

  2. There is no cost to loading data from GEE.


  1. Data has a very low throughput - just a few MiB per second.

  2. (Advanced) Using dask_chunks in the load() call does not work as expected. Normally, loads specifying the dask_chunks parameters will not immediately load data - instead creating the plan to load data with Dask. Instead, the data immediately begins loading as if dask_chunks was not specified. This can be problematic for datasets that are larger than the amount of available memory. This problem does not occur when loading data using the normal datacube.Datacube class.