Enabling sustainable farming through world-leading MRV
With Hummingbird Technologies in the family, we offer remote sensing data to boost our customers’ environmental impact and business bottom line.
Scalable MRV is key to enabling sustainable supply chains
With an urgent need to scale regenerative agriculture globally, MRV (Measurement, Reporting and Verification) is the critical capability for any platform, institution or corporation committed to being part of the solution.
Carbon markets
Verify farm data for carbon certification of highest integrity
Agrifood sector
Monitor supply chains, build traceability, and boost climate-impact
Regulators
Verify agricultural practices for subsidy programmes
NGOs
Monitor practices for water quality risk management
Financial sector
Provide data for green bank loans and monitor portfolios of agri-investors
Our MRV capabilities are integrated into the AgreenaCarbon platform, and also available as stand-alone products for corporations seeking cost-effective, reliable solutions.
Field boundary detection
Accurate field boundaries are fundamental to any farm management system including our own soil carbon programme. For agrifood corporations looking to monitor their supply chains, they are pivotal too. Using satellite imagery from multiple points in the growing season we can deliver data to automatically delineate field boundaries – with the rest of our MRV suite but also as a stand-alone API product.
Cover crop detection
A key practice of regenerative agriculture is the use of cover crops – improving soil structure, reducing erosion, and naturally storing carbon in the soil. Via satellites, we can detect cover crops by analysing vegetation indices during the non-growing season, enabling agrifood stakeholders or water resource managers to monitor and verify the prevalence of cover crops over large areas.
Tillage detection
Minimising soil disturbance and retaining crop residue on the soil surface is another vital component of regenerative agriculture, improving soil structure and mitigating carbon emissions. We can classify different types of tillage practice, such as no-till, min-till and conventional tillage, by analysing the percentage of reflectance at key wavelength bands in the near-infrared and shortwave-infrared spectral zones.
Crop rotation detection
By applying our crop type classification models to analyse geospatial and temporal trends in crop types we are able to identify crop rotations and diversity. This allows users to monitor these management practices at a large scale, as a component of sustainable agriculture verification or supply chain monitoring.
