Earth Data School
About
Full catalog/US_MODIS_NDVI_1299
US_MODIS_NDVI_1299·v3·dataset

How green the land is, smoothed (US, 2000-2015)

MODIS NDVI Data, Smoothed and Gap-filled, for the Conterminous US: 2000-2015
biosphere NASA ORNL_CLOUD Level 4 netCDF-4 classic
In plain English

What it measures. A cleaned-up measure of how green and leafy the land was across the lower 48 US states, day by day from 2000 to 2015. The greenness index (NDVI) rises when plants are healthy and growing.

How it's made. Derived from MODIS greenness data collected by the Terra and Aqua satellites, then smoothed and gap-filled with a NASA tool to remove cloud interference and bad pixels.

How & where you'd use it. Handy for tracking growing seasons, droughts, crop conditions, and long-term vegetation trends without the noise that clouds normally add to satellite greenness records.

What's measured

BIOSPHERE › VEGETATION › PLANT PHENOLOGYBIOSPHERE › VEGETATION › VEGETATION INDEX

Coverage & cadence

  • Time span2000-01-01 → 2015-12-31
  • Measured byTerra (MODIS) · Aqua (MODIS)
  • Processing levelLevel 4
  • Spatial extent-129.892, 20.8458, -62.556, 50.5562
  • FormatsnetCDF-4 classic
  • StatusCOMPLETE

What you can do with it

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

This data set provides Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data, smoothed and gap-filled, for the conterminous US for the period 2000-01-01 through 2015-12-31. The data were generated using the NASA Stennis Time Series Product Tool (TSPT) to generate NDVI data streams from the Terra satellite (MODIS MOD13Q1 product) and Aqua satellite (MODIS MYD13Q1 product) instruments. TSPT produces NDVI data that are less affected by clouds and bad pixels.

Get the data

us_modis_ndvi_1299_access.py
import earthaccess
earthaccess.login(strategy="netrc")          # free Earthdata Login

results = earthaccess.search_data(
    short_name="US_MODIS_NDVI_1299",
    version="3",
    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 ORNL_CLOUD
Browsing CMR needs no login. Downloading or streaming bytes needs a free Earthdata Login + the earthaccess package.