To evaluate soil carbon sequestration and land degradation neutrality policies and measures there is a need for a global web-based platform to inform on the status and trends of soil organic carbon (SOC). Among others, such a platform requires a statistical methodology that allows to predict SOC in space and time from SOC point observations and spatial and spatio-temporal maps of environmental covariates.
This project develops, implements and applies a statistical space-time SOC mapping methodology. In the first phase Argentina was used as a pilot and SOC concentration and stocks were predicted on annual basis from 1982 to 2017. In phase 2 an extension to the globe is made, both using a UNCCD modified IPCC approach and machine learning. Web-based visualization of the resulting time series SOC maps is led by a partner in the project.
- Assemble soil profile data and covariates.
- Develop space-time statistical models and calibrate these using the available data.
- Use the calibrated models to make space-time predictions of SOC concentration and SOC stocks for a chosen time period.
- Quantify the uncertainties associated with the predictions and predicted temporal trends.
- Share resulting time series SOC maps publicly in a web-based interactive portal.
Deliverables (not all freely accessible)
Project reports phase 1 and journal manuscript: Description of the machine learning methodology and application to the Argentina pilot.
Project reports phase 2: Description of the UNCCD modified IPCC approach and application to the globe.
The project is commissioned by The Nature Conservancy (USA).