A novel approach to mapping and monitoring land carbon sinks by combining remote sensing and biogeochemical modeling: A case study in Burkina Faso

Accurate and timely estimation of carbon sequestration in soil and forest biomass is crucial for applications such as carbon stock assessment, forest degradation monitoring, and climate change mitigation. Traditional methods such as field inventories, remote sensing, and biogeochemical models each have strengths and limitations, particularly in data-scarce regions. To address these challenges, this study integrates the light-use efficiency based ETLook model, which is driven by remotely sensed data, with the biogeochemical model DayCent, which is driven by management and weather data, to spatially model aboveground biomass and carbon sequestration. This novel approach aims to improve carbon sequestration estimates in a case study area in Burkina Faso, where ongoing political instability severely limits the availability of field data. In the absence of ground-truth data, we compare the outputs from DayCent and ETLook across time and space to build confidence in our estimates. Our findings indicate that, despite being driven by different input data, the DayCent model closely matches the aboveground biomass patterns observed in the ETLook model, with an r${}^2$ value of 0.81, a Kling-Gupta efficiency (KGE) of 0.77, low bias, and consistent seasonal patterns. Since ETLook lacks a soil carbon module, combining its Net Primary Productivity (NPP) and growth estimates with DayCent’s soil organic carbon (SOC) outputs provides a more robust estimate of total carbon sequestration than either model alone. Future work will focus on applying this hybrid approach across different ecological and geographical regions to evaluate its broader applicability.