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Soil Organic Carbon Stock Estimates with Uncertainty across Latin America

Metadata Updated: December 7, 2023

This dataset provides 5 x 5 km gridded estimates of soil organic carbon (SOC) across Latin America that were derived from existing point soil characterization data and compiled environmental prediction factors for SOC. This dataset is representative for the period between 1980 to 2000s corresponding with the highest density of observations available in the WoSIS system and the covariates used as prediction factors for soil organic carbon across Latin America. SOC stocks (kg/m2) were estimated for the SOC and bulk density point measurements and a spatially explicit measure of the SOC estimation error was also calculated. A modeling ensemble, using a linear combination of five statistical methods (regression Kriging, random forest, kernel weighted nearest neighbors, partial least squared regression and support vector machines) was applied to the SOC stock data at (1) country-specific and (2) regional scales to develop gridded SOC estimates (kg/m2) for all of Latin America. Uncertainty estimates are provided for the two model predictions based on independent model residuals and their full conditional response to the SOC prediction factors.

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

Graphic Preview

Country-specific (a) and regional (b) predictions of SOC across Latin America based on a linear ensemble of methods. The units are presented in units of Mg per ha for visualization purposes. The data provided with the dataset are in units of kg per m2. Image is from Guevara et al. (2018).

Additional Metadata

Resource Type Dataset
Metadata Created Date December 1, 2022
Metadata Updated Date December 7, 2023
Publisher ORNL_DAAC
Maintainer
Identifier C2389081666-ORNL_CLOUD
Data First Published 2019-03-07
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 Guevara, M., G.F. Olmedo, E. Stell, Y. Yigini, C.A. Hernandez, G. Arevalo, C.E. Arroyo-cruz, A. Bolivar, S. Bunning, N.B. Canas, C.O. Cruz-gaistardo, F. Davila, M.D. Acqua, A. Encina, F. Fontes, J.A.H. Herrera, A.R.I. Navarro, V. Loayza, A.M. Manueles, F.M. Jara, C. Olivera, G. Pereira, P. Prieto, I.A. Ramos, J.C.R. Brina, R. Rivera, J. Rodriguez-Rodriguez, R. Roopnarine, A. Rosales, K.A.R. Rivero, G.A. Schulz, A. Spence, G.M. Vasques, R.R. Vargas, and R. Vargas. 2019. Soil Organic Carbon Stock Estimates with Uncertainty across Latin America. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1615
Graphic Preview Description Country-specific (a) and regional (b) predictions of SOC across Latin America based on a linear ensemble of methods. The units are presented in units of Mg per ha for visualization purposes. The data provided with the dataset are in units of kg per m2. Image is from Guevara et al. (2018).
Graphic Preview File https://daac.ornl.gov/CMS/guides/Country_SOC_Latin_America_Fig1.png
Harvest Object Id c302d811-563a-4800-9c32-b3bf32ce951c
Harvest Source Id 58f92550-7a01-4f00-b1b2-8dc953bd598f
Harvest Source Title NASA Data.json
Homepage URL https://doi.org/10.3334/ORNLDAAC/1615
Metadata Type geospatial
Old Spatial -121.29 -58.58 -31.24 35.56
Program Code 026:001
Source Datajson Identifier True
Source Hash b89512ca70028fc3b6abcb3616af53ba555cd4839e7b15ba6ef82c3e34052473
Source Schema Version 1.1
Spatial
Temporal 2018-06-14T00:00:00Z/2018-06-14T23:59:59Z

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