Meet the newest additions to the Earth Engine Data Catalog
By Simon Ilyushchenko, Sulien O’Neill, and Justin Braaten on behalf of the Earth Engine Data team
The Earth Engine Data Catalog has continued to grow over this past year to over 35 petabytes containing over 700 datasets. Between updates and expansions to existing datasets and new data additions, the catalog grows at an approximate rate of one petabyte per month. Each day, the Earth Engine Ingestion team works hard to update and maintain the data catalog so community members can innovatively monitor our changing planet without interruption.
Through feature requests and input from our users, the Earth Engine Ingestion team ensures that the data catalog reflects the Earth Engine community’s needs. Members of the Earth Engine community can access our growing catalog of freely available data to perform impactful environmental analyses around the globe. These datasets provide imagery from the last 40 years, and preprocessed datasets spanning even more time, to support our community’s efforts, whether they focus on land, sea, or sky.
Exploring some of the new datasets
GOES Cloud and Moisture
The addition of the GOES-16 (East) and GOES-17 (WEST) Cloud and Moisture data to the Earth Engine data catalog supports near real-time monitoring of Earth and natural hazards like fires, volcanoes, floods, hurricanes, and tornadoes. Earth Engine provides both CONUS and full disk datasets from 2017 to present as collected by NOAA’s geostationary weather sensor, GOES-R Series Advanced Baseline Imager (ABI). The full disk product images the Western Hemisphere every 5 to 15 minutes, and the CONUS product images every 5 minutes, with 2km spatial resolution.
The GOES-R ABI sensor collects observations of Earth’s surface using 16 different spectral bands compared to the previous generation sensor’s five bands. The 16 bands include both reflective bands and emissive bands with wavelengths ranging from 0.45 to 13.6 µm with corresponding data quality flags. The brightness values (BVs) and brightness temperatures (BTs) of these bands are useful for characterizing clouds, vegetation, snow/ice, aerosols, and emissions over land and water.
Sentinel-2 Cloud Probability
The Sentinel-2 Cloud Probability dataset is the newest addition to the Sentinel-2 collection in the Earth Engine data catalog. By implementing Sentinel Hub’s cloud detector algorithm on individual Sentinel-2 scenes, this new collection provides per-pixel cloud probabilities ranging from 0 to 100%. Users can integrate these cloud probability layers into their workflows to seamlessly detect and mask clouds before image classification. Gone are the days of poorly cloud-masked Sentinel-2 observations!
Sentinel-5P Tropospheric Ozone and UV Aerosol Layer Heights
As we described in our April Medium article, Sentinel-5P has been an invaluable resource for monitoring changes in emissions over this past year. Since our initial upload, the Earth Engine Ingestion team has updated the data to reflect preprocessing recommendations (convection cloud differential and cloud slicing algorithms) made by the joint European Commission and European Space Agency (ESA) Copernicus program. Additionally, the team ingested the remaining Sentinel-5P variables so that our users can carry out all of the Sentinel-5P analyses possible.
The Offline Tropospheric Ozone dataset provides high-resolution imagery of ozone concentrations in the troposphere for the tropical band from 20°S to 20°N. High concentrations of tropospheric ozone can be harmful to the health of humans, animals, and vegetations. The Aerosol Layer Height, available as both Offline and Near Real-Time, provides high-resolution imagery representing the vertical location of aerosols in the troposphere. Although sensitive to clouds’ impact, once corrected, this dataset is particularly useful for identifying desert dust, biomass burning, or volcanic ash plumes. We are excited to see what other innovative analyses our community members complete using the Sentinel-5P products!
A snapshot of new datasets added since November 2019
- Copernicus Global Land Cover Layers (CGLS-LC100 collection 3): A newly updated version of the CGLS-LC100 now provided for 2015–2019 for the entire globe at 100m resolution. It is derived from PROBA-V time-series, land cover training sites, and ancillary datasets.
- CONUS drought indices: Drought indices derived from the 4 km daily Gridded Surface Meteorological dataset, expanding on (and replacing) IDAHO_EPSCOR/PDSI.
- FireCCI51: MODIS Fire_cci Burned Area: A monthly global ~250m resolution dataset containing information on burned area and ancillary data, based on surface reflectance in the Near-Infrared.
- Global Forest Cover Change: The GFCC Tree Cover Multi-Year Global dataset is available for four epochs centered on the years 2000, 2005, 2010, and 2015. This 30m resolution dataset is derived from the GFCC Surface Reflectance product.
- GOES-16 (East) and GOES-17 (West) Fire/Hot Spot data: The Fire Data Classification CONUS (FDCC) GOES data are an addition to the Fire Data Classification Full Disk (FDCF) GOES data that we added last year.
- LANDFIRE: LANDFIRE v1.2 and v1.4 vegetation and fire regime products, including vegetation type, height, and fire regime types for the contiguous U.S. and Alaska.
- MERIT DEM: This global DEM has ~90m spatial resolution at the equator and is a product of multiple satellite DEMs processed to ensure high accuracy and reduce error.
- MERIT Hydro: A global flow direction map with ~90m spatial resolution at the equator produced from the MERIT DEM elevation data and other water body datasets.
- Tsinghua FROM-GLC year of change to impervious surface (GAIA): Annual change information of global impervious surface area from 1985 to 2018 at a 30m resolution.
- USGS PAD-US v2.0: PAD-US is America’s official national inventory of U.S. terrestrial and marine-protected areas separated into four feature attributes: designation, easement, fee, and proclamation.
Check out the Earth Engine Data Catalog today to start using these newly available and updated datasets in your own analyses.
