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Full catalog/SLOPE_GPP_CONUS_1786
SLOPE_GPP_CONUS_1786·v1·dataset

How much carbon plants capture, USA 2000-2019

MODIS-based GPP, PAR, fC4, and SANIRv estimates from SLOPE for CONUS, 2000-2019
land NASA ORNL_CLOUD Level 4 GeoTIFF
In plain English

What it measures. Estimates how much carbon plants capture across the continental U.S. (gross primary productivity) along with the sunlight available for photosynthesis and related vegetation measures, daily at 250 m, from 2000 to 2019.

How it's made. Produced by the SLOPE method, which feeds MODIS atmosphere and land data from Terra and Aqua into machine-learning and physical models to estimate plant productivity per pixel.

How & where you'd use it. Lets researchers study how much carbon U.S. ecosystems and croplands take up over two decades, useful for carbon-cycle and agricultural studies.

What's measured

LAND SURFACE › SURFACE RADIATIVE PROPERTIES › REFLECTANCEBIOSPHERE › VEGETATION › PHOTOSYNTHETICALLY ACTIVE RADIATIONBIOSPHERE › VEGETATION › PLANT CHARACTERISTICSSPECTRAL/ENGINEERING › INFRARED WAVELENGTHS › REFLECTED INFRAREDLAND SURFACE › SOILS › SOIL PRODUCTIVITY › GROSS PRIMARY PRODUCTION (GPP)

Coverage & cadence

  • Time span2000-01-01 → 2020-01-01
  • Measured byMODELS (Computer) · Aqua (MODIS) · Terra (MODIS)
  • Processing levelLevel 4
  • Spatial extent-155.573, 19.99, -52.216, 50.01
  • FormatsGeoTIFF
  • StatusCOMPLETE

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

This dataset contains estimated gross primary productivity (GPP), photosynthetically active radiation (PAR), soil adjusted near infrared reflectance of vegetation (SANIRv), the fraction of C4 crops in vegetation (fC4), and their uncertainties for the conterminous United States (CONUS) from 2000 to 2019. The daily estimates are SatelLite Only Photosynthesis Estimation (SLOPE) products at 250-m resolution. There are three distinct features of the GPP estimation algorithm: (1) SLOPE couples machine learning models with MODIS atmosphere and land products to accurately estimate PAR, (2) SLOPE couples gap-filling and filtering algorithms with surface reflectance acquired by both Terra and Aqua MODIS satellites to derive a soil-adjusted NIRv (SANIRv) dataset, and (3) SLOPE couples a temporal pattern recognition approach with a long-term Crop Data Layer (CDL) product to predict dynamic C4 crop fraction. PAR, SANIRv and C4 fraction are used to drive a parsimonious model with only two parameters to estimate GPP, along with a quantitative uncertainty, on a per-pixel and daily basis. The slope GPP product has an R2 = 0.84 and a root-mean-square error (RMSE) of 1.65 gC m-2 d-1.

Get the data

slope_gpp_conus_1786_access.py
import earthaccess
earthaccess.login(strategy="netrc")          # free Earthdata Login

results = earthaccess.search_data(
    short_name="SLOPE_GPP_CONUS_1786",
    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.