Dynamic savanna burning emission factors based on satellite data using a machine learning approach
Roland Vernooij,Tom Eames,Jeremy Russell-Smith,Cameron Yates,Robin Beatty,Jay Evans,Andrew Edwards,Natasha Ribeiro,Martin Wooster,Tercia Strydom,Marcos Vinicius Giongo,Marco Assis Borges,Máximo Menezes Costa,Ana Carolina Sena Barradas,Dave van Wees,and Guido R. Van der Werf
Savannas account for over half of global landscape fire emissions. Although environmental and fuel conditions affect the ratio of species the fire emits, these dynamics have not been implemented in global models. We measured CO2, CO, CH4, and N2O emission factors (EFs), fuel parameters, and fire severity proxies during 129 individual fires. We identified EF patterns and trained models to estimate EFs of these species based on satellite observations, reducing the estimation error by 60–85 %.
Savannas account for over half of global landscape fire emissions. Although environmental and...