Satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS).
MODIS provides Earth observation data in a wide spectral range, from 1999 to the present. The MODIS satellites image the Earth every one to two days, though individual products derived from MODIS data may have lower temporal resolutions. MODIS is administered by the National Aeronautics and Space Administration (NASA) and the US Geological Survey (USGS). We currently mirror the MCD43A4 (500m-resolution global daily surface reflectance) product on Azure dating back to 2000, and we will be on-boarding select additional MODIS products.
Data are stored in blobs in the East US data center, in the following blob container:
Within that container, data are organized according to:
product is the MODIS product name; currently
MCD43A4 is available on Azure.
vtile refer to tile numbers in the MODIS sinusoidal grid system. The notebook available under “Data Access” demonstrates one way to map latitude and longitude into this grid system.
daynum is a four-digit year plus a three-digit day of year (from 001 to 365), e.g.
2019001 represents January 1, 2019.
…for example, the folder:
…contains images from Jan 10, 2019.
Images are stored in GeoTIFF format, with one image per MODIS channel. The mapping from channels to spectral bands is product-specific; for MCD43A4, mappings are available here.
As per that document, spectral band 1 corresponds channel 7 for MCD43A4, so in the above directory, the file:
…contains information from spectral band 1.
A complete Python example of accessing and plotting a MODIS image is available in the notebook provided under “data access”.
We also provide a read-only SAS (shared access signature) token to allow access to MODIS data via, e.g., BlobFuse, which allows you to mount blob containers as drives:
Mounting instructions for Linux are here.
MODIS data can consume hundreds of terabytes, so large-scale processing is best performed in the East US Azure data center, where the images are stored. If you are using MODIS data for environmental science applications, consider applying for an AI for Earth grant to support your compute requirements.
Imagery of the Chicago area on May 15, 2019.
For questions about this dataset, contact
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This dataset is provided under the original terms that Microsoft received source data. The dataset may include data sourced from Microsoft.
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Quickly explore the dataset with Jupyter notebooks hosted on Azure or your local machine.