Modeling Aboveground Biomass and carbon sequestration at local and national scale with in-situ and remote sensing data

Monitoring carbon release and sequestration is now more important than ever. Not only to confirm that carbon sinks remain intact or vulnerable ecosystems do not further degrade, but also to keep track of our journey towards carbon neutrality, where increasing efforts to offset CO2 emissions have been initiated. Amongst a number of solutions, carbon trading schemes have been introduced, such as the EU Emission Trading System that is used in programs where local smallholder farmers benefit from transitioning towards agroforestry. Critical to the success of such programs is the use of accurate, scalable and transparent remote sensing technologies that objectively monitor the carbon that trees in a given plot of land have removed from the atmosphere.

At Satelligence, we exploit radar and optical satellite data worldwide and at scale to empower clients to combat deforestation and decrease carbon losses, as well as to protect biodiversity and prevent land degradation. In this contribution, we will present our approach to model Aboveground Biomass (AGB) over tropical moist forests at (sub) national scale based on data from several Earth Observation missions (GEDI, Sentinel-1, Sentinel-2, Landsat) and machine learning models. In-situ data, where available, are integrated to improve models at local and regional scales. We will show preliminary results of modeled AGB and carbon sequestration over large areas as well as individual agricultural plots for selected countries in Africa and South America.