Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Skip to content
  • Feedback

    • Global Gridded 1-km Annual Soil Respiration and Uncertainty Derived from SRDB V3

    Metadata Updated: December 7, 2023

    This dataset provides six global gridded products at 1-km resolution of predicted annual soil respiration (Rs) and associated uncertainty, maps of the lower and upper quartiles of the prediction distributions, and two derived annual heterotrophic respiration (Rh) maps. A machine learning approach was used to derive the predicted Rs and uncertainty data using a quantile regression forest (QRF) algorithm trained with observations from the global Soil Respiration Database (SRDB) version 3 spanning from 1961 to 2011. The two Rh maps were derived from the predicted Rs with two different empirical equations. These products were produced to support carbon cycle research at local- to global-scales, and highlight the immense spatial variability of soil respiration and our ability to predict it across the globe.

    Access & Use Information

    Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

    Downloads & Resources

    Dates

    Metadata Created Date December 1, 2022
    Metadata Updated Date December 7, 2023

    Metadata Source

    Harvested from NASA Data.json

    Other Data Resources

    Graphic Preview

    A global map of predicted annual soil respiration (Rs) at 1-km spatial resolution created by applying the QRF model to gridded covariates. To the right is a plot of the latitudinal mean predicted annual Rs. (Figure from Warner et al., In Review).

    Additional Metadata

    Resource Type Dataset
    Metadata Created Date December 1, 2022
    Metadata Updated Date December 7, 2023
    Publisher ORNL_DAAC
    Maintainer
    Identifier C2389104778-ORNL_CLOUD
    Data First Published 2020-01-09
    Language en-US
    Data Last Modified 2023-06-12
    Category CMS, geospatial
    Public Access Level public
    Bureau Code 026:00
    Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
    Metadata Catalog ID https://data.nasa.gov/data.json
    Schema Version https://project-open-data.cio.gov/v1.1/schema
    Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
    Citation Warner, D.L., B.P. Bond-Lamberty, J. Jian, E. Stell, and R. Vargas. 2019. Global Gridded 1-km Annual Soil Respiration and Uncertainty Derived from SRDB V3. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1736
    Graphic Preview Description A global map of predicted annual soil respiration (Rs) at 1-km spatial resolution created by applying the QRF model to gridded covariates. To the right is a plot of the latitudinal mean predicted annual Rs. (Figure from Warner et al., In Review).
    Graphic Preview File https://daac.ornl.gov/CMS/guides/CMS_Global_Soil_Respiration_Fig1.png
    Harvest Object Id 169fe760-439f-4091-ac2f-c0fbbd5ee573
    Harvest Source Id 58f92550-7a01-4f00-b1b2-8dc953bd598f
    Harvest Source Title NASA Data.json
    Homepage URL https://doi.org/10.3334/ORNLDAAC/1736
    Metadata Type geospatial
    Old Spatial -180.0 -90.0 180.0 90.0
    Program Code 026:001
    Source Datajson Identifier True
    Source Hash 489ecce836322420d5c2e46a0a4375d896274a8182234f363fa5b8625a7b5f2b
    Source Schema Version 1.1
    Spatial
    Temporal 1963-01-01T00:00:00Z/2011-12-31T23:59:59Z

    Didn't find what you're looking for? Suggest a dataset here.