首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >Advances in the estimation of high Spatio-temporal resolution pan-African top-down biomass burning emissions made using geostationary fire radiative power (FRP) and MAIAC aerosol optical depth (AOD) data
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Advances in the estimation of high Spatio-temporal resolution pan-African top-down biomass burning emissions made using geostationary fire radiative power (FRP) and MAIAC aerosol optical depth (AOD) data

机译:利用地球静止火辐射电力(FRP)和毛利波光学深度(AOD)数据估算高时空分辨率泛 - 非持续生物量燃烧排放的进展

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We provide major updates to the 'top down' Fire Radiative Energy Emissions (FREM) approach to biomass burning emissions calculations, bypassing the estimation of fuel consumption that is a major source of uncertainty in widely used 'bottom up' approaches. The FREM approach links satellite observations of fire radiative power (FRP) to emission rates of total particulate matter (TPM) via spatially varying smoke emissions coefficients (g.MJ(-1)) - each derived from matchups of FRP and smoke plume aerosol optical depth (AOD). In the original FREMv1 approach, FRP data came from the geostationary Meteosat satellite and AOD data from the 10 km spatial resolution MODIS MOD04 aerosol product. However, the latter often performs quite poorly close to biomass burning sources due to its large 10 km pixels, bias at high MODIS view zenith angles, and saturation and/or removal of areas of high AOD limitations introducing bias and uncertainty into the final FREM-derived smoke emissions estimates. We address each of these issues through a series of significant methodological and input data improvements, including exploitation of the 1 km MODIS MAIAC AOD product that performs far better close to fire sources. We use our FREMv2 methodology to generate a new pan-African fire emissions inventory for TPM and the carbonaceous gases CO2, CO and CH4, and our annual mean TPM emissions are within 11% of those of the MODIS-based FEER top-down approach, but significantly higher than those of GFASv1.2 and GFEDv4.1s (by 114% and 69% respectively) agreeing with independent assessments that aerosol emissions of GFASv1.2 require upscaling by a factor of 2 to 3.4 to deliver matching magnitudes between modelled and observed AODs. From our carbonaceous emissions totals we map dry matter consumed (DMC) across Africa, and dividing this by the FireCCISFD11 20 m burned area product we provide one of the first data-driven pan-African maps of fuel consumption per unit area (kg.m(-2)) which in many areas is higher than in GFEDv4.1s. Our estimates represent the highest spatio-temporal resolution biomass burning emissions data yet available over Africa, and significantly advance the aim of a pan-tropical and mid-latitude inventory based on FRP from the global geostationary satellite network (Meteosat, Meteosat IOD, GOES and Himawari).
机译:我们为“自上而下”消防辐射能源排放(FREM)方法提供了重大更新,以生物质燃烧排放计算,绕过燃料消耗的估计,这是广泛使用的“自下而上”方法的主要不确定性来源。 FREM方法通过空间不同的烟雾排放系数(G.mj(-1))将卫星观察的卫星观察消防力(FRP)与总颗粒物质(TPM)的排放率 - 每个来自FRP和烟雾羽流气雾光学的对决深度(AOD)。在原始的FREMV1方法中,FRP数据来自Geodationary Meteosat卫星和AOD数据,来自10公里的空间分辨率Modis Mod04气溶胶产品。然而,由于其大的10公里像素,后者往往靠近生物量燃烧来源,高模态视图Zenith角度,以及饱和度和/或去除高AOD限制区域,将偏差和不确定性转化为最终的FREM-衍生的烟雾排放估计。我们通过一系列显着的方法和输入数据改进来解决这些问题,包括利用1公里的Modis Maiac Aod产品,这些产品更靠近火源。我们使用FREMV2方法生成用于TPM的新的泛非火排放库存,CO2,CO和CH4,以及我们的年度平均TPM排放量在MODIS的FEER自上而下方法的11%以内,但显着高于GFASV1.2和GFEDV4.1(分别为114%和69%)同意GFASV1.2气溶胶排放的独立评估需要升高2至3.4倍以在建模和观察之间提供匹配的大小AODS。从我们的碳质排放总数从非洲映射干物质(DMC),并将这一点除以Fireccisfd11 20 M烧毁的区域产品,我们提供了每单位面积的第一个数据驱动的泛非洲燃料消耗的地图之一(kg.m (-2))在许多地区高于GFEDV4.1。我们的估计代表了最高的时空分辨率生物量燃烧的排放数据,尚未在非洲提供的排放数据,并根据全球地静止卫星网络(Meteosat,Meteosat Iod,Geed和Meteosat Iod)的FRP大大提高了泛热带和中纬度库存的目标Himawari)。

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