DOI: 10.18742/26826448
title: Dataset used in 'Can remote sensing combustion phase improve estimates of landscape fire smoke emission rate and composition?' (AMT-2024-73)

description: Investigation into how the combustion phase (flaming vs. smouldering) of wildfires can be remotely sensed using hyperspectral and thermal sensors and how this information can be used to improve emissions estimates, given the strong influence this property has on smoke composition and production rate. This dataset includes all the data used to produce the manuscript that has been submitted to Atmospheric Measurement Techniques and published as a preprint (https://doi.org/10.5194/amt-2024-73). Please find the abstract below:<i>The proportion of flaming and smoldering activity occurring in landscape fires varies with fuel type and fuel </i><i>characteristics, which themselves are influenced by ecology, meteorology, time since the last fire etc. The proportion of </i><i>these combustion phases greatly influences the rate of fuel consumption and smoke emission, along with the chemical </i><i>composition of the smoke, which influences the effects on the atmosphere. Earth Observation (EO) has long been suggested </i><i>as a way to remotely map combustion phase, and here we provide the first known attempt at evaluating whether such </i><i>approaches can lead to the desired improvements in smoke emissions estimation. We use intensively measured laboratory </i><i>burns to evaluate two EO approaches hypothesized to enable remote determination of combustion phase and concurrent </i><i>measurements of the smoke to determine how well each is able to improve estimation of smoke emission rates, smoke </i><i>composition and the overall rate of fuel consumption. The first approach aims to estimate the sub-pixel ‘effective fire </i><i>temperature’, which has been suggested to differ between flaming and smoldering combustion, and the second detects the </i><i>potassium emission line (K-line) believed only to be present during flaming combustion. We find while the fire effective </i><i>temperature approach can be suited to estimating Fire Radiative Power (FRP), it does not significantly improve on current </i><i>approaches to estimate smoke chemical makeup and smoke emission. The K-line approach does however provide these </i><i>improvements when combined with the FRP data, improving the accuracy of the estimated CO2 emission rate by an average </i><i>of 17±4% and 42±15%, respectively, depending on whether the K-line detection is used to simply classify the presence of </i><i>flaming combustion, or whether its magnitude is also used to estimate its relative proportion. Estimates of CO and CH4 </i><i>emission rates were improved to a lesser extent than that of CO2, but the accuracy of the smoke modified combustion </i><i>efficiency (MCE) estimates increased by 30±15% and 46±10%, respectively. MCE is correlated to the emissions factors </i><i>(EFs) of many smoke constituents, so remotely deriving MCE provides a way to tailor these during smoke emissions </i><i>calculations. Whilst we derived and tested our approaches on laboratory burns, we demonstrate their wider efficacy using n</i><i>airborne EO data of a boreal forest wildfire where we find that combined used of K-line and FRP data significantly change </i><i>estimated smoke MCE and CO2 and CO emission rates compared to the standard approach. Our findings suggest that </i><i>satellite EO methods that jointly provide K-Line and FRP data could enable marked improvements in the mapping of </i><i>landscape fire combustion phase, fuel consumption and smoke emissions rate and composition.</i>All of the data in this dataset was collected by members of the Earth Observation and Wildfire research group in the Department of Geography, King's College London. The laboratory data was collected at the KCL Combustion Chamber, located at Rothamsted Research, and the aircraft data was recorded by the NCEO Airborne Earth Observatory, King's College London.