New GOES imager algorithms for cloud and active fire detection and fire radiative power assessment across North, South and Central America

Vegetation fires are a key global terrestrial disturbance factor and a major source of atmospheric trace gases and aerosols. Therefore, many earth-system science and operational monitoring applications require access to repetitive, frequent and well-characterized information on fire emissions source...

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Veröffentlicht in:Remote sensing of environment 2010-09, Vol.114 (9), p.1876-1895
Hauptverfasser: Xu, W., Wooster, M.J., Roberts, G., Freeborn, P.
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container_issue 9
container_start_page 1876
container_title Remote sensing of environment
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creator Xu, W.
Wooster, M.J.
Roberts, G.
Freeborn, P.
description Vegetation fires are a key global terrestrial disturbance factor and a major source of atmospheric trace gases and aerosols. Therefore, many earth-system science and operational monitoring applications require access to repetitive, frequent and well-characterized information on fire emissions source strengths. Geostationary imagers offer important temporal advantages when studying rapidly changing phenomena such as vegetation fires. Here we present a new algorithm for detecting and characterising active fires burning within the imager footprints of the Geostationary Operational Environmental Satellites (GOES), including consideration of cloud-cover and calculation of fire radiative power (FRP), a metric shown to be strongly related to fuel consumption and smoke emission rates. The approach is based on a set of algorithms now delivering near real time (NRT) operational FRP products from the Meteosat Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) imager (available from http://landsaf.meteo.pt/), and the GOES processing chain presented here is designed to deliver a compatible fire product to complete geostationary coverage of the Western hemisphere. Results from the two GOES imagers are intercompared, and are independently verified against the well regarded MODIS cloud mask and active fire products. We find that the detection of cloud and active fires from GOES matches that of MODIS very well for fire pixels having FRP > 30 MW, when the GOES omission error falls to less than 10%. The FRP of fire clusters detected near simultaneously by both GOES and MODIS have a bias of only 22 MW, and a similar bias is found when comparing near-simultaneous GOES East and GOES West FRP observations. However, many fire pixels having FRP < 30 MW remain undetected by GOES, probably unavoidably since it has a much coarser spatial resolution than MODIS. Adjustment using data from the less frequent but more accurate views obtained from high spatial resolution polar orbiting imagers could be used to bias correct regional FRP totals. Temporal integration of the GOES FRP record indicates that during the summer months, biomass burning combusts thousands of millions of tonnes of fuel daily across the Americas. Comparison of these results to those of the Global Fire Emissions Database (GFEDv2) indicate strong linear relationships ( r² > 0.9), suggesting that the timely FRP data available from a GOES real-time data feed is likely to be a suitable fire emissions source strength te
doi_str_mv 10.1016/j.rse.2010.03.012
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subjects Algorithms
America
Animal, plant and microbial ecology
Applied geophysics
Atmospherics
Bias
Biological and medical sciences
Cloud
Clouds
Earth sciences
Earth, ocean, space
Exact sciences and technology
Fiber reinforced plastics
Fire radiative power
Fires
Fundamental and applied biological sciences. Psychology
General aspects. Techniques
GOES
Internal geophysics
MODIS
Remote sensing
Spatial resolution
Strength
Teledetection and vegetation maps
title New GOES imager algorithms for cloud and active fire detection and fire radiative power assessment across North, South and Central America
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