Abstract
The Global Fire Emissions Database (GFED)—currently by far the most widely used global fire emissions inventory—is primarily driven by the 500 m MODIS MCD64A1 burned area (BA) product. This product is unable to detect many smaller fires, and the new v4.1s of GFED addresses this deficiency by using a ‘small fire boost’ (SFB) methodology that estimates the ‘small fire’ burned area from MODIS active fire (AF) detections. We evaluate the performance of this approach in two globally significant agricultural burning regions dominated by small fires, eastern China and north-western India. We find the GFED4.1s SFB can affect the burned area and fire emissions data reported by GFED very significantly, and the approach shows some potential for reducing low biases in GFED’s fire emissions estimates of agricultural burning regions. However, it also introduces several significant errors. In north-western India, the SFB slightly improves the temporal distribution of agricultural burning, but the magnitude of the additional burned area added by the SFB is far too low. In eastern China, the SFB appears to have some positive effects on the magnitude of agricultural burning reported in June and October, but significant errors are introduced in the summer months via false alarms in the MODIS AF product. This results in a completely inaccurate ‘August’ burning period in GFED4.1s, where false fires are erroneously stated to be responsible for roughly the same amount of dry matter fuel consumption as fires in June and October. Even without the SFB, we also find problems with some of the burns detected by the MCD64A1 burned area product in these agricultural regions. Overall, we conclude that the SFB methodology requires further optimisation and that the efficacy of GFED4.1s’ ‘boosted’ BA and resulting fire emissions estimates require careful consideration by users focusing in areas where small fires dominate.
Highlights
The Global Fire Emissions Database (GFED) is currently the most widely used global fire emissions inventory
The burned area (BA) maps used by GFED are provided by NASA’s 500-m spatial resolution MODIS BA product (MCD64A1), which classifies pixels as burned using a spectral reflectance-based change detection technique [3]
June and October are the periods of most intensive agricultural burning in this region of eastern China [17,21], which makes the ‘strongly boosted’ BA seen in August in GFED4.1s Remo(tehSigenhsl.ig20h1t8e,d10w, 8it2h3 the blue dashed rectangle in (f)) seem potentially erroneous, and at odds with a lack of fire activity reported by the VIIRS-IM product for the same 5 of 18 month (h)
Summary
The Global Fire Emissions Database (GFED) is currently the most widely used global fire emissions inventory. The BA signature of landscape fires typically lasts for several days to several months post-fire (depending on biome), making these BA maps somewhat immune to cloud-cover and satellite observation gaps [3] This contrasts with the active fire (AF) detection approach which can identify fires only if they are burning and the area is cloud-free at the time of the satellite observation [4,5]. The SFB was shown to have by far the greatest impact in agricultural regions of certain developing nations, where recurrent crop residue burning has major implications for regional air quality [12,13,14,15,16] In such regions, Randerson et al [11] indicated that the SFB strategy increased the BA detected by MCD64A1 by 80–90%, and based on this demonstration, an adaptation of the approach was introduced into the most recent version of GFED (GFED4.1s; [10]). We examine why some agricultural regions show far greater impacts from the GFED4.1s BA ‘boost’ than others
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