Dynamic savanna burning emission factors based on satellite data using a machine learning approach
Authors: Vernooij Roland , Eames Tom , Russell-Smith Jeremy , Yates Cameron , Beatty Robin , Evans Jay , Edwards Andrew , Ribeiro Natasha , Wooster Martin , Strydom Tercia , Giongo Marcos Vinicius , Borges Marco Assis , Menezes Costa Máximo , Barradas Ana Carolina Sena , van Wees Dave , Van der Werf Guido R. 
Published: October 2023

Abstract. Landscape fires, predominantly found in the frequently burning
global savannas, are a substantial source of greenhouse gases and aerosols.
The impact of these fires on atmospheric composition is partially
determined by the chemical breakup of the constituents of the fuel into
individual emitted chemical species, which is described by emission factors
(EFs). These EFs are known to be dependent on, amongst other things, the
type of fuel consumed, the moisture content of the fuel, and the
meteorological conditions during the fire, indicating that savanna EFs are
temporally and spatially dynamic. Global emission inventories, however, rely
on static biome-averaged EFs, which makes them ill-suited for the estimation
of regional biomass burning (BB) emissions and for capturing the effects of
shifts in fire regimes. In this study we explore the main drivers of
EF variability within the savanna biome and assess which geospatial proxies
can be used to estimate dynamic EFs for global emission inventories. We made
over 4500 bag measurements of CO2, CO, CH4, and N2O EFs using
a UAS and also measured fuel parameters and fire-severity proxies during 129 individual fires. The measurements cover a
variety of savanna ecosystems under different seasonal conditions sampled
over the course of six fire seasons between 2017 and 2022. We complemented
our own data with EFs from 85 fires with locations and dates provided in the
literature. Based on the locations, dates, and times of the fires we retrieved
a variety of fuel, weather, and fire-severity proxies (i.e. possible
predictors) using globally available satellite and reanalysis data. We then
trained random forest (RF) regressors to estimate EFs for CO2, CO,
CH4, and N2O at a spatial resolution of 0.25∘ and a
monthly time step. Using these modelled EFs, we calculated their
spatiotemporal impact on BB emission estimates over the 2002–2016 period
using the Global Fire Emissions Database version 4 with small fires
(GFED4s). We found that the most important field indicators for the EFs of
CO2, CO, and CH4 were tree cover density, fuel moisture content, and
the grass-to-litter ratio. The grass-to-litter ratio and the nitrogen-to-carbon ratio were important indicators for N2O EFs. RF models using
satellite observations performed well for the prediction of EF variability
in the measured fires with out-of-sample correlation coefficients between
0.80 and 0.99, reducing the error between measured and modelled EFs by
60 %–85 % compared to using the static biome average. Using dynamic EFs,
total global savanna emission estimates for 2002–2016 were 1.8 % higher
for CO, while CO2, CH4, and N2O emissions were, respectively,
0.2 %, 5 %, and 18 % lower compared to GFED4s. On a regional scale we
found a spatial redistribution compared to GFED4s with higher CO, CH4,
and N2O EFs in mesic regions and lower ones in xeric regions. Over the
course of the fire season, drying resulted in gradually lower EFs of these
species. Relatively speaking, the trend was stronger in open savannas than
in woodlands, where towards the end of the fire season they increased again.
Contrary to the minor impact on annual average savanna fire emissions, the
model predicts localized deviations from static averages of the EFs of CO,
CH4, and N2O exceeding 60 % under seasonal conditions.