MODIS/Terra: Global Vegetation Indices at 500m Resolution

The MOD13A1 V6.1 product provides two primary vegetation layers—Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)—at a 500-meter spatial resolution. It is produced by the National Aeronautics and Space Administration (NASA) from Terra MODIS satellite data, with the latest metadata update noted as 2026-03-12. The algorithm selects the best pixel value from all acquisitions within a 16-day period based on criteria including low clouds, low view angle, and the highest NDVI/EVI value.

Use Cases

• Monitor global vegetation health and phenology based on the 16-day composite NDVI and EVI layers.
• Assess biomass changes in high-density regions using the Enhanced Vegetation Index (EVI) which has improved sensitivity.
• Perform quality-controlled remote sensing analysis using the provided quality assurance (QA) layers and reflectance bands.
• Study land surface reflectance properties using the included red, near-infrared, blue, and mid-infrared bands.

Strengths

• Provides two standardized vegetation indices (NDVI and EVI) at a consistent 500-meter spatial resolution.
• Includes quality assurance layers and four observation layers to support data reliability assessment.
• Version 6.1 incorporates specific calibration improvements, including a polarization correction and updates for the period 2012-2017.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and total dataset size are unknown, which may limit suitability assessment.

Provenance

Source

National Aeronautics and Space Administration

Collection Method

Satellite remote sensing from the Terra MODIS instrument, with a pixel selection algorithm based on low clouds, low view angle, and highest NDVI/EVI over a 16-day period.

Time Range

Temporal coverage is not explicitly stated in the provided input.

Freshness

Last updated 2026-03-12 23:36:01.352491; freshness should be verified.

Geography

Global coverage using a sinusoidal grid projection.