How Well Does the ‘Small Fire Boost’ Methodology Used within the GFED4.1s Fire Emissions Database Represent the Timing, Location and Magnitude of Agricultural Burning?

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 northwestern 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 northwestern 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.

Trends in eastern China agricultural fire emissions derived from a combination of geostationary (Himawari) and polar (VIIRS) orbiter fire radiative power products

“…During the summer months of May-June, all three inventories (GFAS, GFED, and VIIRS-IM/Himawari) show a clear peak in DMB, but the GFAS and VIIRS-IM/Him show a much sharper peak in June, while the GFED's summer burning season extends 1 month earlier (May) and later (July). This extended summer fire season reported by the GFED is likely the result false fire reporting, discussed at length in Zhang et al (2018). VIIRS-IM/Him shows a June DMB peak ranging from 3.30 to 11.2 Tg, 2 times higher than GFED4.1s (1.89-5.34 Tg) and GFAS (2.00 to 4.30 Tg).…”

“…Due to this boost, GFED4.1s shows higher values of dry matter burned (DMB) in most eastern China grid cells compared to the "unboosted" GFED4, and a more extensive fire distribution. However, Zhang et al (2018) show that the boosting procedure can introduce significant anomalies into the GFED dataset at certain times of year, generated when the MODIS AF detection procedure incorrectly identifies urban features in eastern China as fires.…”

“…A major source of this uncertainty stems from the hitherto relatively poor ability of Earth observation (EO) satellite instruments to adequately detect biomass burning activity in many agricultural areas due to the small size of the fires usually found in these areas. Many agricultural fields in eastern China are typically only around 700 m 2 in area (NBSC, 2012); fires ignited to burn across the stubble left in place after harvest are therefore hard to detect with moderate-spatialresolution burned area (BA) mapping from sensors such as MODIS (Moderate Resolution Imaging Spectroradiometer) and are made even more elusive by the common farming practice of piling up residues into an even smaller area before igniting them (Zhang et al, 2017(Zhang et al, , 2018. As most BA mapping methods require ∼ >20 % of a pixel to be burned in order for it to be classified as "fire-affected" (Giglio et al, 2006, BA-based emissions inventories such as the GFED (Global Fire Emissions Database) tend to significantly underestimate fire activity in areas such as eastern China (Zhang et al, 2018).…”

On the Use of Satellite Sentinel 2 Data for Automatic Mapping of Burnt Areas and Burn Severity

“…The remote sensing of burned areas has traditionally been based on optical data and carried out using vegetation indices (see for example [4][5][6][7][8][9][10][11][12][13][14][15][16][17]) that are a spectral combination of diverse bands, devised to emphasize the spectral changes caused by fire on vegetation in the short-and long-term. The vegetation indices operate by contrasting intense chlorophyll pigment absorption in the red (RED in Equation (1)) against the high reflectance of leaf mesophyll in the most commonly used spectral channels for vegetation monitoring at near infrared (NIR in Equation (1)) and shortwave (SWIR in Equation 2).…”

Correcting Swath-Dependent Bias of MODIS FRP Observations With Quantile Mapping

“…However, in all these studies, the bias correction approach is derived from MODIS FRP observations over large biomass burning areas like Africa, which hinders its application to FRP observations on smaller and global scales. The sensor limitation significantly affects the smaller fires, e.g., agricultural waste burning [14], and a separate analysis for each fire type is necessary.…”