Remote-C dMRV tool

Function

Two digital tools have been developed within F4C by CITIMAP/UCSC within TomaTO-C LL.

Remote-C dMRV tool is a Tier 3 hybrid (M)MRV technology designed to overcome scalability and cost-accuracy challenges in carbon farming (CF) programs. By leveraging data assimilation and Roth-C modelling frameworks, the simulation of SOC stocks under baseline and CF scenarios can be conducted at high resolution across multiple scales to measure C removals. Remote-C is designed to configure and support MRV activities of initiatives such as TomaTO-C living lab and any upcoming Carbon Farming (CF) programs that will follow CRCF, GHG L&S, SBTi FLAG, or Verra VM0042 standards.

Within Remote-C another digital tools has been developed and tested: a proximal sensing-based SOC stock accounting tool. This tool is based on an ML-based prediction model producing high-resolution SOC and BD maps at the field level, which can serve as inputs for calculating SOC stock maps with its associated uncertainty maps.

Utility

Remote-C dMRV tool functions as an Operational Processing Chain (OPC) for a defined project domains in Northern Italy. In the ex-ante phase, it establishes regional standardized baselines by combining RS-based SOC retrieval with Roth-C simulations. On the enrolled fields, Roth-C is initialized using ESM-derived SOC stock data or maps obtained trough smart soil sampling. In the ex-post phase, it quantifies parcel-level C removals using a calibrated Roth-C model fed by FMIS/IACS data, remotely sensed activity data (e.g., C-factor, tillage events), and plant C input data. Crop biomass and yields are estimated using LUE model, which assimilates RS data for crop phenology and fAPAR estimation.

Within this tool CITIUMPA has developed a proximal sensing-based SOC stock accounting tool that leverages a regional machine learning (ML)-based calibrated and validated prediction models to estimate key soil properties, such as clay content, SOC concentration, and BD for on-the- go SOC stock mapping. The tool can use EC, vis-NIR proximal soil sensor data and integrates bare soil S2 imageries. Additionally, a Smart Soil Sampling approach is introduced to optimize soil sampling at the field level, minimizing the number of samples required while enabling local calibration procedures. This approach provides valuable tools for precise, field-scale SOC stock spatial accounting for measure-remeasure approaches, or support model initialization in hybrid MRV approaches and/or climate smart fertilization strategies.

Beneficiaries: CF project developers, agrifood industries and MRV developers (both for insetting and offsetting program and/or Scope 3 emission reporting) serving as digital enablers of any decarbonization action or program in agricultural sectors.

Status

Remote-C dMRV tool "In development" (some R packages available, tested and operational, automation of input data assimilation from multiple sources is ongoing)

Proximal sensing SOC accounting tool "Completed and in use " (calibration and validation of prediction models using other proximal sensors data streams is ongoing)

Last update: 10 February 2025

Access

Access requirements: Upon request (for a demo). However soon a Git Repository will be published in 2 peer-review papers that are under submission.

Contact person

Andrea Ferrarini (Università Cattolica del Sacro Cuore) - andrea.ferrarini@unicatt.it