Abstract
Active fire observations with satellite instruments exhibit a well-documented increase of the detection threshold with increasing pixel footprint size, i.e., distance from the sub-satellite point. This results in a viewing angle-dependent, negative bias in gridded representations of the observed Fire Radiative Power (FRP), which in turn is frequently being used for climate monitoring of biomass burning and for pyrogenic emission inventories. We present a method based on quantile mapping to alleviate this bias and apply it to the gridded-FRP from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instruments. The gridded-FRP observations are corrected with a correction function that depends on the satellite viewing angle and the magnitude of FRP in each grid cell. Assuming the fire observations at nadir to be the best representation of the truth, we derive a correction function by mapping cumulative distribution function (CDF) of off-nadir gridded-FRP to the CDF of near-nadir gridded-FRP. The method can be directly applied to correct the negative bias in gridded-FRP observations at a grid resolution of 1 ∘ or more. The performance of the correction methodology is confirmed through comparisons with co-located Visible Infrared Imaging Radiometer Suite (VIIRS) satellite observations: After bias correction, the gridded-FRP observations from both satellites agree better than before, particularly over savanna, tropical forests, and extra-tropical forests. Experiments with the Global Fire Assimilation System (GFAS) show that the impacts of the bias-corrected MODIS/Aqua gridded-FRP observations and VIIRS/Suomi-NPP gridded-FRP observations on regional FRP analyses are comparable, which confirms the potential for improving fire emission inventories and climate monitoring based on FRP.
Highlights
Satellite-based active fire observation is important for monitoring fires and their emissions
The inherent limitations of the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments introduce a spurious variability in the observations which in turn affects the performance of the model outputs: the fire detection threshold of MODIS instruments increases with the increasing pixel size towards the swath edges, the small fires with magnitudes below the detection limit are not detected
If ni,j is the number of grid cells with viewing angle θ in viewing angle bin i and Fire Radiative Power (FRP) in FRP bin j, the probabilities of observations in the FRP and viewing angle bins are described by the following probability density function (PDF)
Summary
Satellite-based active fire observation is important for monitoring fires and their emissions. The missing fires introduce an underestimation in FRP-based biomass burning inventories like the ’Global Fire Assimilation System (GFAS)’ for grid cells that are observed off-nadir or even near the swath edges. When the missing fire pixels are aggregated with neighbouring non-zero fire pixels, the FRP in that grid cell is underestimated Both these effects when combined with the MODIS observational frequency introduces a spurious fluctuation in the GFAS FRP analyses. In this study, we present a correction methodology characterised by the fire type/magnitude and the viewing angle This correction procedure provides a grid-level mitigation of the swath-dependent bias, and is applicable to MODIS gridded-FRP in various emission inventories. In order to evaluate the efficiency of the methodology, we assess the similarities and differences between the gridded-FRP from Visible Infrared Imaging Radiometer Suite (VIIRS) Suomi National Polar-orbiting Partnership (Suomi-NPP) and the bias adjusted MODIS gridded-FRP
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