Abstract

Seagrass ecosystems are globally significant hot spots of blue carbon storage, coastal biodiversity and coastal protection, rendering them a so‐called natural climate solution. Their potential as a natural climate solution has been largely overlooked in national and international climate strategies and financing. This stems mainly from the lack of standardized, spatially explicit mapping and region‐specific carbon inventories. Here, we introduce a novel seagrass ecosystem accounting framework that harnesses machine learning, big satellite data analytics and open region‐specific reference data within the Google Earth Engine cloud computing platform. Leveraging a biennial percentile composite, assembled from 16 453 Sentinel‐2 surface reflectance image tiles at 10‐m spatial resolution, and 20 820 reference data points, we applied the cloud‐native framework to produce the first national inventories of seagrass extent and total seagrass carbon stocks in Kenya, Tanzania, Mozambique and Madagascar. We estimated 4316 km2 of regional seagrass extent (mean F1‐score of 59.3% and overall accuracy of 84.3%) up to 23 m of depth. Pairing country‐specific in situ carbon data and our spatially explicit seagrass extents, we calculated total regional seagrass blue carbon stocks between 11.2–40.2 million MgC, with the largest national carbon pool in Kenya (8–29.2 million MgC). We envisage that improvements in the remote sensing components of the framework guided by a necessary influx of region‐specific data on seagrass stocks and fluxes could reduce uncertainties in our current spatially explicit ecosystem extent and carbon accounts, enhancing the incorporation of seagrasses into Multilateral Environmental Agreements for future resilient ecosystems, societies and economies.