Full catalog/oco2-geos-l3-daily
oco2-geos-l3-daily·dataset

Daily Global Carbon Dioxide Maps

Gridded Daily OCO-2 Carbon Dioxide assimilated dataset
land NASA VEDA active COG
In plain English

What it measures. Provides gap-free daily maps of carbon dioxide (CO₂) concentrations across the whole globe, filling in the spots the satellite couldn't see directly.

How it's made. Built from NASA's OCO-2 satellite carbon measurements, combined with NASA's GEOS atmospheric model through data assimilation to fill gaps left by clouds and narrow viewing.

How & where you'd use it. Helps scientists study the global carbon cycle and where CO₂ builds up or disperses.

What's measured

oco2geosdaily

Coverage & cadence

  • Time span— → ongoing
  • Spatial extent-180, -90, 180, 90
  • FormatsCOG

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 OCO-2 mission provides the highest quality space-based XCO2 retrievals to date. However, the instrument data are characterized by large gaps in coverage due to OCO-2’s narrow 10-km ground track and an inability to see through clouds and thick aerosols. This global gridded dataset is produced using a data assimilation technique commonly referred to as state estimation within the geophysical literature. Data assimilation synthesizes simulations and observations, adjusting the state of atmospheric constituents like CO2 to reflect observed values, thus gap-filling observations when and where they are unavailable based on previous observations and short transport simulations by GEOS. Compared to other methods, data assimilation has the advantage that it makes estimates based on our collective scientific understanding, notably of the Earth's carbon cycle and atmospheric transport. OCO-2 GEOS (Goddard Earth Observing System) Level 3 data are produced by ingesting OCO-2 L2 retrievals every 6 hours with GEOS CoDAS, a modeling and data assimilation system maintained by NASA's Global Modeling and Assimilation Office (GMAO). GEOS CoDAS uses a high-performance computing implementation of the Gridpoint Statistical Interpolation approach for solving the state estimation problem. GSI finds the analyzed state that minimizes the three-dimensional variational (3D-Var) cost function formulation of the state estimation problem.

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("oco2-geos-l3-daily")
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).