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Full catalog/ndvi-global-2022
ndvi-global-2022·dataset

Global Plant Greenness, 1982-2022

Bi-Monthly NDVI GIMMS-3G+
biosphere NASA VEDA COG
In plain English

What it measures. Tracks plant greenness worldwide twice a month from 1982 to 2022, using a vegetation index (NDVI) that rises with healthier, denser plant cover.

How it's made. Built from decades of AVHRR satellite measurements, carefully corrected for sensor changes, orbital drift, and volcanic haze.

How & where you'd use it. Gives scientists a consistent 40-year record to study long-term shifts in vegetation, climate, and land use.

What's measured

ndviglobal2022

Coverage & cadence

  • Time span2022-01-08 → 2022-06-23
  • Spatial extent-180, -90, 180, 90
  • FormatsCOG

What you can do with it

  • Map vegetation, forests and biomass
  • Monitor ecosystem productivity and carbon
  • Support habitat and biodiversity studies
Official description

This dataset holds the Global Inventory Modeling and Mapping Studies-3rd Generation V1.2 (GIMMS-3G+) data for the Normalized Difference Vegetation Index (NDVI). NDVI was based on corrected and calibrated measurements from Advanced Very High Resolution Radiometer (AVHRR) data with a spatial resolution of 0.0833 degree and global coverage for 1982 to 2022. Maximum NDVI values are reported within twice monthly compositing periods (two values per month). The dataset was assembled from different AVHRR sensors and accounts for various deleterious effects, such as calibration loss, orbital drift, and volcanic eruptions. The data are provided in NetCDF format.

Get the data

veda_access.py
# NASA VEDA — open STAC API, anonymous (cloud-optimized GeoTIFFs)
from pystac_client import Client

cat = Client.open("https://openveda.cloud/api/stac")
col = cat.get_collection("ndvi-global-2022")
items = list(col.get_items())          # browse the analysis-ready COGs
# open an asset with rioxarray:
# import rioxarray; da = rioxarray.open_rasterio(items[0].assets["cog_default"].href)
NASA VEDA is an open STAC catalog — browse and stream the cloud-optimized GeoTIFFs anonymously (no login).