Imagine if you could tell how healthy the soil is and how much carbon it's trapping without having to dig thousands of holes and send dirt to a lab. This tech uses satellite photos and a massive library of existing soil data to guess the carbon levels from space. It's like using a high-tech scanner instead of doing a manual biopsy for every inch of a field.
Traditional soil carbon monitoring is too slow and expensive, acting as a bottleneck for the growth of the voluntary carbon market and regenerative farming.
A satellite-based SaaS platform and a predictor data access pipeline that uses machine learning to quantify soil organic carbon.
If you are a carbon offset provider dealing with the high cost of verifying soil carbon, this project developed a SaaS solution that is up to 90% lower in cost than established services. This allows you to certify more projects with less manual labor.
If you are a farming consultant dealing with slow and expensive soil testing for clients, this project developed a machine learning tool that is up to 30% more accurate than current comparable approaches. You can now provide faster, data-driven proof of soil health improvements.
If you are an NCS developer dealing with monitoring, reporting, and verification (MRV) as a limiting factor for new projects, this project developed a satellite-based monitoring tool. It removes the bottleneck of manual sampling to accelerate project deployment.
The solution is designed to be low cost, offering a reduction of up to 90% compared to established services.
Yes, the project focuses on cost-effective monitoring, reporting, and verification of soil organic carbon sequestration at scale using satellite-based remote sensing.
Based on available project data, the solution is delivered as a SaaS (Software as a Service) model.
The objective states it is up to 30% more accurate than current comparable approaches and aims to achieve similar accuracy and uncertainty as traditional soil sampling.
The system uses a database of millions of soil data records combined with satellite data, topography, weather, and climate data.
The project is led by a single German SME, SEQANA GmbH, which received an EU contribution of EUR 2,410,174. The 100% industry ratio indicates a strong commercial drive, focusing on a SaaS product rather than academic research.
Contact SEQANA GmbH in Germany for SaaS licensing
Request a demo of the satellite-based SOC monitoring tool
