Monitoring land from geostationary satellites

GEONEX Product Suite

From Photons to Pixels, Leveraging EOS Algorithms

Cloud-Based Processing

For Convenient Cummunity Access and Collaboration

What is GEONEX


GEONEX is a collaborative effort for generating land surface products from the new generation of geostationary satellite sensors such as GOES16/ABI.

In collaboration with the NASA Earth Exchange (NEX), GEONEX serves as a platform for scientific partnership, knowledge sharing and research for the Earth science community.


Key Features


A generation shift in geostationary satelites

New generation geostationary platforms offer capabilities similar to MODIS/VIIRS for Land Monitoring

  • ABI – Advanced Baseline Imager on GOES-R/T
  • AHI – Advanced Himawari Imager on Himawari
  • AMI – Advanced Meteorological Imager on GEO-KOMPSAT2
  • FCI – Flexible Combined Imager on MTG
  • AGRI – Advanced Geosynchronous Radiation Imager on Fengyun-4


Adapting MAIAC Algorithm for GEONEX



GEONEX Data Processing Flowchart


Data Access

Potential Land Surface Products from Geostationary Sensors and Corresponding Algorithms


Projection and Data Tile System

  • All GEONEX products are in the Geographic Projection (i.e., Latitude/Longitude grid) with a spatial resolution of 0.01x0.01 degrees (visible/near infrared bands) or 0.02x0.02 degrees (Shortwave/Thermal Infrared bands);
  • The geospatial domain of the GOES16/ABI is the region from (135oW, 60oN) to (15oW, 60oS), which is then divided into 20x20 rectangular tiles, numbered from 0 to 19 in both horizontal and vertical dimensions. Each tile contains 600x600 or 300x300 pixles;
  • The time resolution of the products are 15 minutes (Full Domain) or 5 minutes (CONUS);


HDF Format

  • The Naming Convention of the files is:
    GO16_ABI<Product ID>_<YYYYMMDD>_<HHMM>_NSAG_h<hid>v<vid>_<version>.hdf
    • <Product ID>: Two digit number assigned to each GEONEX product (Document here). For instance, Product ID of “05” indicates “Top-of-Atmosphere Reflectance”
    • <YYYYMMDD>: Year-Month-Day, e.g., 20180501
    • <HHMM>: Hour-Minute (UTC time), e.g., 2030
    • <hid>, <vid>: Horizontal or Vertical Tile ID (0-19)
    • <version>: Product version code. For now it is “001”
  • The data products are in the format of HDF files, we used the version of HDF-EOS2 based on HDF4 (details of HDF format can be found here);


Data Access

Near Real-time Data

  • The GEONEX products are hosted on a public AWS S3 bucket. They can be accessed with AWS Command Line Interface or standard tools including wget, curl, or simply a web browser.
  • To access a product file requires a full URL, which can be in either or the following two format:
    • fulldisk/h01v01/2018/197/GO16_ABI05_20180716_2215_ NSAG_h01v01_001.hdf
    • or
    • s3://geonexpub/ABI05/fulldisk/h01v01/ 2018/197/GO16_ABI05_20180716_2215_ NSAG_h01v01_001.hdf
  • Command examples to download a single data file:
    • Using a web-browser: copy and paste the URL (using the https protocal) to the address bar;
    • Using AWS command line interface:
              aws s3 cp s3://geonexpub/ABI05/fulldisk/h01v01/2018/197/GO16_ABI05_20180716_2215_NSAG_h01v01_001.hdf
    • Using Wget
  • We also provide "inventory" files in text or json formats for users to batch download product files

Historical Data

All the historical data are archived on NEX.


This data is considered provisional and subject to change. This data is provided as is without any warranty of any kind, either express or implied, arising by law or otherwise, including but not limited to warranties of completeness, non-infringement, accuracy, merchantability, or fitness for a particular purpose. The user assumes all risk associated with the use of, or inability to use, this data.

Near Real-time Fire


The Fire/Hot Spot Characterization product will make use of both visible and IR spectral bands to locate fires and retrieve sub-pixel fire characteristics. The product will greatly improve upon the currently available Fire Detection product by taking advantage of the higher spatial and temporal resolution which will be available with the GOES-R ABI. Forecasters will be able to use this product to monitor wildfires, and more importantly, rapid changes in individual fires. Forecasters will use this product as part of an arsenal of forecasting tools aimed at helping firefighting efforts.

Cloud-adapted NOAA WFABBA (Fire) Algorithm


Land Surface Temperature (LST)

The Land Surface Temperature (LST) product is derived from GOES-R ABI longwave infrared spectral channels and is expected to be used in a number of applications in hydrology, meteorology, and climatology. Forecasters will use it to forecast the occurrence of fog and frost. The land surface product is of fundamental importance to the net radiation budget at the Earth’s surface and to monitoring the state of crops and vegetation. It is an important indicator of both the greenhouse effect and the energy flux between the atmosphere and ground. Furthermore, it can be assimilated into climate, atmospheric, and land surface models to estimate sensible heat flux and latent heat flux.

           Eye on the heat wave, Adelaide, Australia





Fraction of Photosynthetic Active Radiation