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Full catalog/MOD09A1G_NDVI
MOD09A1G_NDVI·v6·dataset

Greenness index NDVI, gap-filled (Terra, 500 m)

MODIS/Terra Gap-Filled, Smoothed NDVI 8-Day L4 500m SIN Grid
land NASA LAADS Level 4 active HDF-EOS
In plain English

What it measures. A vegetation greenness index (NDVI, which indicates how lush and active plant cover is) every 8 days at 500-meter detail, with gaps and poor-quality spots filled in to give a clean, continuous map.

How it's made. Derived from Terra MODIS surface-reflectance data, then smoothed and gap-filled with an algorithm that replaces cloudy or missing values using the best available observations.

How & where you'd use it. Provides gap-free greenness data for vegetation and ecosystem studies, and as reliable input for models that can't handle missing data.

What's measured

LAND SURFACE › LAND USE/LAND COVER › LAND USE/LAND COVER CLASSIFICATION › VEGETATION INDEX › NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI)

Coverage & cadence

  • Time span2001-01-01 → ongoing
  • Measured byTerra (MODIS)
  • Processing levelLevel 4
  • Spatial extent-180, -90, 180, 90
  • FormatsHDF-EOS
  • StatusACTIVE

What you can do with it

  • Track deforestation, fire scars and land-cover change
  • Monitor crop and vegetation health (NDVI/EVI)
  • Map how built-up vs. green an area is over time
Official description

The MODIS/Terra Gap-Filled, Smoothed NDVI 8-Day L4 500m SIN Grid product, with short-name MOD09A1G_NDVI is calculated from MODIS surface reflectance products (MOD09), at 500-m resolution. MODIS time series contains occasional lower quality data, gaps from persistent clouds, cloud contamination, and other gaps. Many modeling efforts, such as those used in NACP, that use MODIS data as input, require gap-free data. The procedure contains two algorithm stages, one for smoothing and one for gap filling, which attempt to maximize the use of high-quality data to replace missing or poor-quality observations.

Get the data

mod09a1g_ndvi_access.py
import earthaccess
earthaccess.login(strategy="netrc")          # free Earthdata Login

results = earthaccess.search_data(
    short_name="MOD09A1G_NDVI",
    version="6",
    bounding_box=(-122.5, 37.2, -121.8, 37.9),  # your area (W,S,E,N)
    temporal=("2024-01-01", "2024-12-31"),       # your dates
)
files = earthaccess.open(results)   # stream straight from LAADS
Browsing CMR needs no login. Downloading or streaming bytes needs a free Earthdata Login + the earthaccess package.