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Full catalog/LAI_Africa_2325
LAI_Africa_2325·v1·dataset

How leafy the vegetation is, Africa 2002-2022

MODIS-derived Aggregate, Woody and Herbaceous Leaf Area Index for Africa, 2002-2022
biosphere NASA ORNL_CLOUD Level 3 netCDF-4
In plain English

What it measures. Maps of how leafy the vegetation is (leaf area index) across Sub-Saharan Africa from 2002 to 2022, with separate layers for woody plants like trees and shrubs, herbaceous plants like grasses, and the two combined.

How it's made. Derived from MODIS satellite leaf-coverage data, then smoothed, gap-filled, and split into woody versus herbaceous components.

How & where you'd use it. Helps researchers track vegetation growth, monitor ecosystems and grazing lands, and study how Africa's plant cover changes over two decades.

What's measured

BIOSPHERE › VEGETATION › LEAF CHARACTERISTICS › LEAF AREA INDEX (LAI)BIOSPHERE › VEGETATION › VEGETATION INDEX › LEAF AREA INDEX (LAI)

Coverage & cadence

  • Time span2002-07-05 → 2022-07-29
  • Measured byTerra (MODIS) · Aqua (MODIS)
  • Processing levelLevel 3
  • Spatial extent-21.2839, -40.02, 63.8625, 20.02
  • FormatsnetCDF-4
  • 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 dataset provides leaf area index (LAI) estimates for Sub-Saharan Africa for woody, herbaceous, and aggregate vegetation types. The estimates were derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4 and the native MODIS LAI product (MCD15A2H Version 6.1), which provides LAI measurements every 8 days at 500-m pixel size. Data from the MCD15A2H product were processed further to generate three layers including: a smoothed and gap filled LAI layer referred to as aggregate leaf area index and two additional layers processed to separate woody LAI (tree and shrubs) and herbaceous LAI (grass and forbs). The data include 31 MODIS 10-degree tiles and cover 2002 to 2022. The data are provided in NetCDF format.

Get the data

lai_africa_2325_access.py
import earthaccess
earthaccess.login(strategy="netrc")          # free Earthdata Login

results = earthaccess.search_data(
    short_name="LAI_Africa_2325",
    version="1",
    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.