Last updated
Developer: ecometric
Sectoral Scope: Carbon Removal
Abstract:
This methodology protocol proposes the use of remotely sensed multispectral imagery and soil sample results to train an Artificial Neural Network (ANN) to monitor changes in Soil Organic Carbon (SOC) stocks, within a project area, through time. The project area will be defined in the credit class document. The SOC change will be reported as CO2 equivalent. SOC is crucial to soil health, fertility and ecosystem services including food production, making its preservation and restoration essential. This methodology will concentrate on the assessment of SOC sequestration as a major soil health characteristic, consisting of:
SOC stocks.
CO2 equivalents (CO2e)
Soil contains approximately 2344 Gt of organic carbon globally and is the largest terrestrial pool of organic carbon (Stockmann et al., 2013). Small changes in the soil organic carbon stock could result in significant impacts on the atmospheric carbon concentration. By monitoring the carbon levels in the soil, farmers and landowners will be able to measure the impact of their stewardship.
Update V1.2.2 of the ecometric Methodology for Soil Organic Carbon Estimation in Regenerative Cropping and Managed Grassland Ecosystems proposes the following changes:
The inclusion of Loss on Ignition laboratory test in geographies where there is no regional availability of DUMAS testing.
Using a sampling simulation based on the estimated SOC value range within a Project Area to calculate and report sampling uncertainty for a range of Compact Geographic Stratification strata sizes, for each monitoring round. This tool will be used to balance sampling uncertainty with sampling cost on large project areas and to transparently report the uncertainty associated with the selected sampling density for verifiers and auditors.
Methodology Proposal for Soil Organic Carbon Estimation in Regenerative Cropping and Managed Grasslands Ecosystems