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Full catalog/AirMOSS_L4_Regional_NEE_1423
AirMOSS_L4_Regional_NEE_1423·v1·dataset

How much carbon US ecosystems absorb or release (2012-2014)

AirMOSS: L4 Modeled Net Ecosystem Exchange (NEE), Continental USA, 2012-2014
biosphere NASA ORNL_CLOUD Level 4 netCDF-4 classic
In plain English

What it measures. Estimates of net ecosystem carbon exchange across the continental United States, meaning whether land is absorbing or releasing carbon dioxide, given hourly and monthly from 2012 to 2014 at 50-km detail.

How it's made. Produced by an ecosystem model (the Ecosystem Demography model) that was improved by feeding in soil-moisture data from the AirMOSS airborne campaign.

How & where you'd use it. Helps scientists understand how US ecosystems take up or give off carbon and how soil moisture affects those carbon fluxes.

What's measured

BIOSPHERE › VEGETATION › CARBONLAND SURFACE › SOILS › CARBONBIOSPHERE › ECOLOGICAL DYNAMICS › ECOSYSTEM FUNCTIONS › BIOGEOCHEMICAL CYCLESBIOSPHERE › ECOLOGICAL DYNAMICS › ECOSYSTEM FUNCTIONS › PRIMARY PRODUCTION

Coverage & cadence

  • Time span2012-01-01 → 2014-10-31
  • Measured byMODELS (Computer)
  • Processing levelLevel 4
  • Spatial extent-124.938, 25.062, -66.937, 53.062
  • 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 Level 4 estimates of Net Ecosystem Exchange (NEE) of CO2 across the conterminous USA at a spatial resolution of 50 km. Modeled estimates are provided at hourly and monthly temporal resolutions, from January 2012 through October 2014. The AirMOSS L4 Regional NEE data were produced by the Ecosystem Demography Biosphere Model (ED2) augmented by the AirMOSS-derived L2/3 root zone soil moisture data as an additional input. The AirMOSS soil moisture data were used to estimate the sensitivity of carbon fluxes to soil moisture and to diagnose and improve estimation and prediction of NEE by constraining the model's predictions of soil moisture and its impact on above- and below-ground fluxes.

Get the data

airmoss_l4_regional_nee_1423_access.py
import earthaccess
earthaccess.login(strategy="netrc")          # free Earthdata Login

results = earthaccess.search_data(
    short_name="AirMOSS_L4_Regional_NEE_1423",
    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.