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Use raster data to analyze temperature | BigQuery

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This product or feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA products and features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

This tutorial describes how to perform geospatial analysis on raster data.

• Find publicly available Google Earth Engine data in BigQuery sharing (formerly Analytics Hub).
• Use the ST_REGIONSTATS function to calculate the average temperature in each country at a point in time.
• Visualize your results in BigQuery Geo Viz, which is a web tool for visualization of geospatial data in BigQuery using Google Maps APIs.

In this tutorial, you use the following billable components of Google Cloud:

• BigQuery
• Google Earth Engine

We recommend that you create a Google Cloud project for this tutorial. Make sure that you have the required roles to complete this tutorial.

1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

3. Verify that billing is enabled for your Google Cloud project.

4. Enable the BigQuery, BigQuery sharing, and Google Earth Engine APIs.

   Enable the APIs

5. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

6. Verify that billing is enabled for your Google Cloud project.

7. Enable the BigQuery, BigQuery sharing, and Google Earth Engine APIs.

   Enable the APIs

To get the permissions that you need to perform the tasks in this tutorial, ask your administrator to grant you the following IAM roles on your project:

• Earth Engine Resource Viewer (roles/earthengine.viewer)
• Service Usage Consumer (roles/serviceusage.serviceUsageConsumer)
• BigQuery Data Editor (roles/bigquery.dataEditor)

For more information about granting roles, see Manage access to projects, folders, and organizations.

These predefined roles contain the permissions required to perform the tasks in this tutorial. To see the exact permissions that are required, expand the Required permissions section:

The following permissions are required to perform the tasks in this tutorial:

• earthengine.computations.create
• serviceusage.services.use
• bigquery.datasets.create

You might also be able to get these permissions with custom roles or other predefined roles.

To find the dataset used for this tutorial, follow these steps:

1. Go to the Sharing (Analytics Hub) page.

   Go to Sharing (Analytics Hub)

2. Click search Search listings.

3. In the Search for listings field, enter "ERA5-Land Daily Aggregated".

4. Click the result. A details pane opens with information about the ERA5-Land climate reanalysis dataset, including a description, a link to band information, the availability, the pixel size, and the terms of use.

5. Click Subscribe.

6. Optional: Update the Project.

7. Update the Linked dataset name to era5_climate_tutorial.

8. Click Save. The linked dataset is added to your project and contains a single table called climate.

Each row in the era5_climate_tutorial.climate table contains metadata for a raster image that has climate data for a particular day. Run the following query to extract the raster ID of the raster image for January 1, 2025:

SELECT
  assets.image.href
FROM
  `era5_climate_tutorial.climate`
WHERE
  properties.start_datetime = '2025-01-01';

The result is ee://ECMWF/ERA5_LAND/DAILY_AGGR/20250101. In the next section, you use this for the raster_id argument to the ST_REGIONSTATS function.

Run the following query to compute the average temperature of each country on January 1, 2025 using the ST_REGIONSTATS function:

WITH SimplifiedCountries AS (
  SELECT
    ST_SIMPLIFY(geometry, 10000) AS simplified_geometry,
    names.primary AS name
  FROM
    `bigquery-public-data.overture_maps.division_area`
  WHERE
    subtype = 'country'
)
SELECT
  sc.simplified_geometry AS geometry,
  sc.name,
  ST_REGIONSTATS(
    sc.simplified_geometry,
    'ee://ECMWF/ERA5_LAND/DAILY_AGGR/20250101',
    'temperature_2m'
  ).mean - 273.15 AS mean_temperature
FROM
  SimplifiedCountries AS sc
ORDER BY
  mean_temperature DESC;

This query runs on the publicly available division_area table that contains GEOGRAPHY values representing the boundaries of various regions on Earth, including countries. The ST_REGIONSTATS function uses the temerature_2m band of the raster image, which contains the temperature of the air at 2 meters above the surface of the land at the given pixel.

To visualize your results in BigQuery, follow these steps:

1. In the Query results pane, click the Visualization tab.

2. For Data column, select mean_temperature.

   A world map appears, styled by a color gradient for the average temperature of each country.

You can also visualize your results using BigQuery Geo Viz.

Before using Geo Viz, you must authenticate and grant access to data in BigQuery.

To set up Geo Viz, do the following:

1. Open the Geo Viz web tool.

   Open Geo Viz

   Alternatively, in the Query results pane, click Open in > GeoViz.

2. In step one, Query, click Authorize.

3. In the Choose an account dialog, click your Google Account.

4. In the access dialog, click Allow to give Geo Viz access to your BigQuery data.

After you authenticate and grant access, the next step is to run the query in Geo Viz.

To run the query, do the following:

1. For step one, Select data, enter your project ID in the Project ID field.

2. In the query window, enter the following GoogleSQL query. If you opened Geo Viz from your query results, this field is already populated with your query.

   WITH SimplifiedCountries AS (
     SELECT
       ST_SIMPLIFY(geometry, 10000) AS simplified_geometry,
       names.primary AS name
     FROM
       `bigquery-public-data.overture_maps.division_area`
     WHERE
       subtype = 'country'
   )
   SELECT
     sc.simplified_geometry AS geometry,
     sc.name,
     ST_REGIONSTATS(
       sc.simplified_geometry,
       'ee://ECMWF/ERA5_LAND/DAILY_AGGR/20250101',
       'temperature_2m'
     ).mean - 273.15 AS mean_temperature
   FROM
     SimplifiedCountries AS sc
   ORDER BY
     mean_temperature DESC;
   

3. Click Run.

The Style section provides a list of visual styles for customization. For more information about each style, see Format your visualization.

To format your map, do the following:

1. To open the fillColor panel, click step 3, Style.

2. Click the Data-driven toggle to the on position.

3. For Function, choose linear.

4. For Field, choose mean_temperature.

5. For Domain, enter -20 in the first box and 32 in the second box.

6. For Range, click the first box and enter #0006ff in the Hex box, and then click the second box and enter #ff0000. This changes the color of each country based on its average temperature on January 1, 2025. Blue indicates a colder temperature and red indicates a warmer temperature.

7. Click fillOpacity.

8. In the Value field, enter .5.

9. Click Apply style.

10. Examine your map. If you click a country, the country's name, average temperature, and simplified geometry are displayed.

Caution: Deleting a project has the following effects:
• Everything in the project is deleted. If you used an existing project for the tasks in this document, when you delete it, you also delete any other work you've done in the project.
• Custom project IDs are lost. When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as an appspot.com URL, delete selected resources inside the project instead of deleting the whole project.

If you plan to explore multiple architectures, tutorials, or quickstarts, reusing projects can help you avoid exceeding project quota limits.

1. In the Google Cloud console, go to the Manage resources page.

   Go to Manage resources

2. In the project list, select the project that you want to delete, and then click Delete.
3. In the dialog, type the project ID, and then click Shut down to delete the project.

• To learn more about how to visualize options for geospatial analytics, see Visualizing geospatial data.
• To work with raster data, see Work with raster data.
• To learn more about the geography functions you can use in geospatial analytics, see Geography functions in GoogleSQL.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2025-08-14 UTC.

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