ORCaSa attends a workshop in California
On 28th of August, Eric Ceschia (INRAE/CESBIO) took part in a workshop on “Managing soil organic carbon for climate change mitigation – multiscale quantification through remote sensing, AI and biogeochemical models”, organised by Michigan State University, Colorado State University, US Department of Agriculture, California Institute of Technology, and Jet Propulsion Laboratory.
This event was a unique opportunity to meet the scientific community involved in soil carbon modelling and monitoring in the United States, but also to develop a shared vision of Monitoring, Reporting & Verification (MRV) for cropland between the ORCaSa European partners and the United States.
“It is a fantastic opportunity to set the basis of a strong and long term collaboration on those topics in the frame of the forthcoming International Research Consortium (IRC) on soil carbon“, said Eric Ceschia.
Soils offer an attractive ‘natural capital solutions’ approach to atmospheric CO2 removal and climate change mitigation, but accurately quantifying changes in soil organic carbon (SOC) stock (and hence CO2 removal) at landscape to regional scales is a major challenge. On the whole, this workshop will contribute to the development of a credible framework for a comprehensive SOC data analysis, modelling and prediction system that will enable accurate tracking of SOC changes from landscape to regional and national soils.
Focus on the AgriCarbon-EO processing chain
The aim of the workshop is to design a new scalable SOC quantification platform that integrates remote sensing, field experimentation and on-farm monitoring data, with overall modelling based on processes and machine learning, assimilation/fusion of remote sensing observation data and activity data at farm level. In this context, Eric Ceschia presented the AgriCarbon-EO* processing chain developed by INRAE/CESBIO.
Lastly, this workshop will review the current state of model-data systems for soil carbon measurements, assess key knowledge and technical gaps, and develop an operational plan for a novel integrated modelling and data fusion system that can be applied at landscape and regional scales.