Satellite-based infrared (IR) sensors have been used since the early 1980s to detect actively burning vegetation fires, primarily using observations made in middle infrared (MIR; 3–5 µm) and longwave infrared (LWIR; 8–12 µm) spectral regions [1
]. The MIR spectral region is extremely sensitive to sub-pixel sized hot sources, and can be used to detect active fire (AF) pixels covering as little as 10–4
of the pixel area [1
]. Latterly, such remotely sensed AF detections have often been augmented with assessments of their fire radiative power (FRP), representing the rate of release of thermal radiant energy liberated via the process of combustion (e.g., [2
]). FRP measures are often used as a means of estimating rates of landscape-scale fuel consumption, and thus its temporal integral (fire radiative energy; FRE) is considered linearly proportional to the amount of fuel consumed over the integration period [4
], which itself relates to the amount of smoke emitted to the atmosphere. Many studies have thus gone on to relate remotely sensed FRP and FRE metrics to, respectively, the rates and totals of particulate and trace gas emission (e.g., [8
]), thus providing a relatively direct approach to estimating biomass burning smoke emissions that can be applied in real-time whilst the fires are still burning (e.g., [12
Spaceborne and airborne remote sensing instruments possessing a suitable MIR spectral channel of adequate pixel size, and with a dynamic range that largely prevents saturation over even high intensity fires, can be used to generate FRP values across a very wide range of magnitudes [2
]. However, all fire regimes examined so far using spaceborne active fire (AF) detection have all been characterised by a far greater number of low-FRP fires than high FRP fires (e.g., [14
]). This is a direct result of the dominance of smaller, and often lower intensity, fires in most landscapes and these low-FRP fires are more difficult to discriminate from the type of ‘thermal clutter’ (i.e., locational variations in IR brightness temperature) that typically characterises the ambient background upon which AF pixels are superimposed. Even with the ability to discriminate highly sub-pixel AFs using suitably performing sensors with a MIR channel and relatively small pixel sizes, there will be some fires that remain too small and/or too weakly burning at the observation time to be readily detected from space [3
]. Assuming application of the same AF detection algorithm, minimum detectable FRP scales with the sensing instruments ground pixel area [7
]. This means that moving, for example, from 1 km to 500 m pixels lowers the minimum FRP detection limit by a factor of four, ignoring for now any influence from potential changes in background thermal clutter the intensity of which generally increases as pixels become smaller and thermal variability becomes less ‘averaged out’. Similarly, whilst 1 km type spatial resolution sensors operating on many polar orbiting spacecraft can detect active fire pixels burning below 10 MW, geostationary satellite sensors with their pixel sizes of typically many km have minimum FRP detection limits of many tens of MW [16
The most widely used spaceborne FRP product is that generated from observations made by the Moderate Resolution Imaging Spectroradiometer (MODIS) carried onboard the polar orbiting Terra and Aqua satellites [17
]. MODIS provides a 1 km pixel size at nadir, resulting in a minimum FRP detection limit somewhat below 10 MW at the nadir point with the detection limit increasing as one moves towards the scan edge and pixel size increases [5
]. Yet this remains insufficient to detect some of the fires burning on the landscape at the satellite overpass time. Regions where small fires show a particular dominance, such as agricultural residue burning areas, are likely to have the greatest proportion of their fires burning below this minimum FRP detection limit; because of this, total FRP measures derived for such regions may fail to include much of the FRP that is actually being released. Any fuel consumption and/or smoke emissions estimates derived from such landscape-scale FRP measures are thus likely to be at risk of underestimation unless suitable (but yet largely unknown, and very likely highly variable) bias-corrections are undertaken. The Visible Infrared Imaging Radiometer Suite (VIIRS) operates on board the Suomi National Polar-orbiting Partnership (Suomi NPP) and the NOAA-20 polar orbiting satellites, and offers pixel sizes smaller than those of MODIS at 750 m and 375 m. It has been shown [19
] that in the eastern China’s agricultural regions the use of VIIRS FRP data derived from these two channels provides FRP totals many times that provided by MODIS due to the ability to detect more of the lowest FRP agricultural residue fires burning below MODIS’ minimum FRP detection limit.
Although the extent of MODIS’ landscape-scale FRP underestimation may be expected to be most significant at locations dominated by the obviously small fires characteristic of agricultural burning, the true extent to which landscape-scale FRP is underestimated by MODIS (or any other 1 km sensor) is poorly understood for most biome’s worldwide. A biome-dependency is assumed, since the level of FRP underestimation will be a function of a biomes fire regime characteristics in terms of fire size and intensity distributions. Furthermore, the relative number of low and high FRP fires will very likely vary over the typically very strong fire diurnal cycle [20
], making it different for the Terra MODIS and Aqua MODIS observations, which are collected from morning and late evening overpasses (Terra) and early afternoon and night-time (Aqua) overpasses.
Here we focus on demonstrating how FRP underestimation can be quantified for one of the most fire-prone ecosystems on Earth, namely the Brazilian cerrado which is a savannah-like environment covering 2 million km2
(>20%) of Brazil. In this region we have access to both spaceborne 1 km MODIS data and far higher spatial resolution observations from the MODIS Airborne Simulator (MAS). The MAS instrument was flown on the NASA ER-2 High-Altitude Airborne Science Aircraft, and in this configuration provides similar atmospheric effects to those which affect MODIS spaceborne data but on data with 50 m pixels. MAS possesses similar MIR and LWIR spectral bands as MODIS, and fires for which the area of active combustion exceeds just 0.5 m2
are detectable in the 50 m pixels. This enables the identification of essentially every active fire that was burning and which was not cloud covered during the ER-2 overflights. By spatially degrading the MAS data and re-running the active fire detection process we can (i) accurately quantify the FRP underestimation provided by MODIS-type 1 km sensors, (ii) identify how this varies over the diurnal cycle, and (iii) explore for the purposes of future sensor design the minimum spatial resolution required to deliver an FRP record that has negligible pixel-area related FRP underestimation in this strongly fire-affected environment. We organize the work as follows. In Section 2
we discuss the causes and significance of FRP underestimation present when observing landscapes from remote sensing satellites. This is followed by Section 3
where we present the materials and methods used to assess the degree to which this affects observations made over the Brazilian cerrado ecoregion. The results of the investigation are presented in Section 4
, followed by conclusions in Section 5
2. Causes and Significance of FRP Underestimation
In any particular biome, the number of active fires remaining undetected is likely to be strongly time-dependent, because the proportion of high- to low-FRP fires varies across the diurnal cycle [20
]. The impact of pixel area on active fire detection performance is well evidenced by analysis of the 375 m spatial resolution data provided by the experimental Bispectral and Infrared Remote Detection (BIRD) satellite mission, which carried the Hotspot Recognition Sensor (HSRS). This system had the task of capturing accurate snapshots of fire activity worldwide [22
], and could detect active fire pixels having FRP < 1 MW [3
], far lower than the minimum FRP detection threshold of 1 km class sensors [17
]. MODIS typically shows a 5–8 MW minimum detection limit depending on the nature of the ‘thermal clutter’ that defines the ambient background temperature variability [24
], though fires with an FRP < 10 MW appear generally undercounted [18
]. Furthermore, since the minimum FRP detection limit scales with pixel area, and since MODIS’ pixel area increases away from nadir as the scan angle increases, the FRP detection threshold also increases away from nadir. The MODIS pixel area at the swath edge is almost 10 km², and the minimum FRP detection limit here is thus around an order of magnitude higher than at nadir [24
]. VIIRS data limit off-nadir pixel growth in the 375 and 750 m spatial resolution channels via a pixel aggregation approach, limiting pixel area growth to a factor of 4.5 by the swath edge, which together with the fundamentally smaller pixel areas compared to MODIS enables far more of the lowest FRP fires to be detected [19
BIRD data of multiple fire-affected biomes indicates that 64% of HSRS-detected active fires (AFs) had FRP < 10 MW [22
], most far lower than this, indicating that MODIS would have missed the majority of the AFs that BIRD HSRS detected. However, these sub-10 MW fires only represented around 2% of the total FRP detected by BIRD HSRS over these same regions, so the fires that would have been missed by MODIS appear in this case to be responsible for very little of the overall landscape-scale FRP (and thus for a minority of fuel consumption and smoke emission). From this analysis it appears that MODIS did not significantly underestimate landscape-scale FRP, despite failing to detect probably most of the fires that were actually burning, because the higher FRP fires it could detect were responsible for the vast majority of FRP release. However, this contrasts with the findings of Zhang et al. (2017) [19
], who found very significant landscape-scale FRP underestimation by MODIS when comparing near simultaneous MODIS- and VIIRS-derived FRP values in eastern China (with VIIRS’ 375 m pixel minimum FRP detection limit being very similar to that of the BIRD HSRS). The reason for these contrasting findings is that [19
] focused on a ‘small fire’ dominated agricultural burning region, whereas the BIRD-HSRS acquisitions were primarily targeted at far larger and sometimes quite extreme fire events that often included regions showing some extremely high FRP fires. The true extent to which 1 km MODIS-type sensors underestimate landscape-scale FRPs in different biomes therefore remains an open question, and how this characteristic changes between the multiple MODIS overpass times and across the MODIS swath is similarly poorly understood. We address these questions by using remote sensing observations of fires burning in the Brazilian cerrado, which both provides answers for this type of fire-prone environment that are likely to be relatively indicative of savannahs more generally (which are Earth’s most fire-affected environment), and which provides a potential method for future application in other important fire-affected biomes.
compares the numbers of active fires (AFs) and fire radiative power (FRP) detected in the study region using the coarsest (1 km) spatially degraded MAS data (i.e., that which best simulates spaceborne MODIS nadir observations) with that identified using the original 50 m resolution MAS data. Only daytime data are considered, but fires in these and other environments are almost always far more common by day than by night [20
]. In terms of AF detection, peak performance occurs with the 1 km data at 15:45 local time, soon after the local diurnal fire count peak that the MAS data also identifies at ~ 15:00 local time, which is very close to the 15:36 local time peak reported previously for Brazilian deforestation (evergreen broadleaf forest and savannah) fires [20
]. Even so, at this time the 1 km spatially degraded MAS data provide an active fire count just 17% of that provided by the original 50 m MAS data (Figure 4
). Whilst some of the undercounting is the result of multiple individual fires discernible at 50 m resolution being grouped together as a single fire when resampled to 1 km, much is due to originally detectable fires falling below the minimum detection limit when the data are degraded to 1 km pixels. Furthermore, at times far away from the afternoon diurnal peak of fire activity, the fire count proportion falls further. At 12:40 local time, the 1 km MAS data show an AF count less than 5% of that of the original 50 m resolution data. Together, the data of Figure 4
suggest that by day the MODIS sensors onboard the Terra and Aqua satellites fail to detect a very significant fraction of active fires burning in the Brazillian cerrado at their respective overpass times, but that the best performance (in terms of detecting the highest fraction of actually burning fires) occurs at the early afternoon Aqua overpass time.
In terms of landscape-scale FRP underestimation, the situation is far better than for active fire counts. Even though the active fire detection algorithm fails to identify the majority of the actively burning fires when applied to the 1 km MAS data, these undetected fires each typically have a low FRP and thus their absence in the 1 km dataset has a much more limited effect on overall landscape-scale FRP underestimation. The most significant underestimation occurs furthest away from the ~ 15:00 local time diurnal peak, when at 11:50 local time the spatially degraded 1 km MAS data detects only 38% of the FRP able to be quantified by the 50 m observations (leftmost blue bar in Figure 4
). However, at this time the landscape-scale FRP total is less than one sixth of that observed at the diurnal FRP peak (see dashed line in Figure 4
), and so the increased level of FRP underestimation see away from this peak has a limited effect on the overall underestimation of landscape-scale fire radiative energy (FRE; calculated via the temporal integration of FRP). We calculate that the FRE identified over the period of the MAS flights using the 1 km MAS-detected active fires represents in fact 72% of that detected with the original 50 m MAS data. This is because, by around 13:00 local time, the local fire regime has seen enough of the fire fires grow in number, size and/or intensity that the 1 km AF detections are able to capture the majority (i.e., blue bar proportion > 0.5 in Figure 4
) of the total landscape FRP being emitted, and by the diurnal FRP peak (~ 15:00 local time) the active fires successfully detected using the 1 km MAS data represent 82% of the FRP identified using the 50 m data.
shows for each overflight time the total landscape-scale FRP detected with the original 50 m resolution MAS data and the increasingly spatially degraded datasets (i.e. the 100 m, 200 m, 500 m and 1 km data, examples of which were shown in Figure 3
). As introduced in Section 1
, the minimum FRP detection limit of a sensor and algorithm essentially scales with pixel area [24
], and even though the minimum FRP detection limits for the 100 m and 200 m versions of the MAS data are thus about 4× and 16× those of the original 50 m data respectively, these detection limits remain low enough that in both cases the undetected active fires are responsible for only a very small proportion of the total landscape scale FRP. Therefore, for these 100 m and 200 m spatially degraded MAS datasets, the total landscape FRP sensed by these data lie extremely close to the ‘true’ landscape-scale FRP value calculated from the fires detectable with the original 50 m data (Figure 5
shows the frequency-densities of FRP constructed from the original 50 m MAS data and calculated down to a 0.01 to 0.1 MW FRP range (i.e., a 0.05 MW bin centre). Additionally shown are the FRP frequency-densities from both 1 km spatially degraded MAS data, and from the spaceborne Aqua MODIS data (both calculated down to a 20 MW bin centre, representing the 10 MW to 30 MW FRP range). The latter two sets of frequency-densities appear very similar, suggesting that the degree of FRP underestimation present in the 1 km spatially degraded MAS data is broadly consistent with that in the real Aqua MODIS data. We can therefore use the former degree of underestimation, which we know via comparison between the results obtained with the 50 m and 1 km MAS data (e.g., Figure 4
), to estimate the latter currently unknown quantity.
Fitting and extrapolating the FRP frequency-density distribution to the real MODIS data of Figure 6
, which include observations of fires at nadir and off-nadir, provides another means of estimating the FRP remaining undetected by Aqua MODIS’ daytime observations because their constituent AF pixels lie below its minimum FRP detection limit. The extrapolation of the best-fit power-law distribution indicates that these undetected fires are responsible for around 35% of the total landscape fire FRP present at early afternoon Aqua overpass, which is close to the figure of 71% derived when comparing landscape FRP totals from the 1 km and 50 m versions of the MAS data captured around 14:10 local time (blue vertical bars, Figure 4
), which is the MAS data collection timed closest to the afternoon Aqua overpass of this region. Of course, the MAS and MODIS data are captured in different years, so an exact match would not be expected, and furthermore the FRP frequency densities in Figure 6
may not follow a single strict power-law. Specifically, the exponents of the power-law best fits to the 50 m MAS FRP frequency-density distribution data appear to differ slightly either side of a ~ 10 MW threshold, a ‘break-of-exponent characteristic that has already been observed in wildfire burned area frequency-density statistics (e.g., [36
]). Nevertheless, we can be reasonably sure that from these results that the FRP measures provided by Aqua MODIS appear to represent around two thirds of the real landscape fire FRP emitted in this biome and season at the time of the afternoon Aqua MODIS overpass.
5. Summary and Conclusions
Research conducted here has exploited MODIS Airborne Simulator (MAS) data from the 1995 SCAR-B experiment to develop an approach to help optimize some aspects of the design of fire-focused future spaceborne missions. Work in [2
] with these same data already helped lay the foundations for the extremely successful spaceborne Terra and Aqua MODIS MOD14/MYD14 Active Fire (AF) detection algorithm and AF products. We have specifically focused on gaining improved understanding of the impact that pixel size has on active fire detection and FRP underestimation in the key fire affected landscape focused on by SCAR-B, the savannah-like Brazilian cerrardo that covers around 20% of the country. Using the original 50 m spatial resolution MAS data collected across a significant part of the cerrardo fire diurnal cycle we have identified pixels containing actively burning fires and estimated the fires FRP. We have then spatially degraded the MAS data to simulate spaceborne remote sensing data of varying pixel sizes (100 m to 1 km), and reapplied our AF detection and FRP estimation techniques to compare the AF detection omission error rates and resulting levels of FRP underestimation coming from use of different spatial resolution data. Finally, we have compared our AF data to those coming from real spaceborne Aqua MODIS observations, attempting to understand the potential degree of landscape-scale FRP underestimation occurring in real MODIS data of this same cerrado region.
Our results confirm observations [2
] that the 50 m spatial resolution MAS instrument operated from the high-altitude ER-2 aircraft can detect almost every landscape fire burning in this cerrado environment (cloud cover permitting). Active fire pixels with an FRP as low as 0.01 MW can be discriminated with the 50 m MAS data (equivalent to a flaming fire covering only ~ 0.25 m2
), but when the MAS data is spatially degraded the number of fires that remain undetected increases with increasing pixel size. The minimum FRP detection limit scales directly with pixel area [7
], and at the diurnal fire cycle peak (~ 15:00 local time) we find that very many of the active fires detectable with the 50 m MAS data become undetectable with the 1 km (MODIS-like) version of the dataset. Away from the diurnal peak of fire activity, the fraction of detectable fires falls even further because more of the fires are of lower intensity and size. Further decreases in the fraction of detectable fires may occur at times outside the 11:50 to 15:45 local time window sampled herein by the ER-2.
However, because it is the smaller and/or more weakly burning active fires that remain undetected by the coarser spatial resolution data, landscape scale underestimation of FRP is far less severe than are the AF errors of omission. The afternoon overpass of the Aqua satellite provides the MODIS observations capturing FRP closest to the diurnal peak of fire activity (~ 15:00 local time), and using two different approaches we find that this afternoon overpass captures around two thirds (our estimates are 65 to 71%) of the true FRP being emitted from fires in this savannah-like landscape, a lower estimate than the 80 to 100% suggested by [2
]. The mid-morning Terra MODIS overpass shows a greater degree of FRP underestimation than the Aqua afternoon overpass, due to its imaging at a time much further away from the peak of the diurnal fire cycle. Mid-morning is a time when the fires that are burning are typically significantly smaller and/or more weakly radiant than later in the day, so more of them are missed, but since the absolute fire count and FRP total is also significantly lower at this earlier time of day the overall FRE and/or daily average FRP used by others (e.g., [10
]) to estimate daily mean smoke emissions are less impacted by this level of FRP underestimation than may initially appear the case. Overall, we find that the FRE derived using the 1 km version of the MAS data over the 5 hours of the ER-2 flight campaign is 72% of that derived with the full resolution (50 m) version of the same dataset.
Whilst the analysis performed herein has focused on 1 km MODIS data, in fact the pixel area of MODIS increases markedly as you move far away from nadir, as demonstrated in [24
]. At the MODIS edge of swath, the minimum detectable per-pixel FRP is around an order of magnitude higher than at the nadir point, though the impact of this on landscape-scale FRP underestimation appears more than counteracted by the MODIS ‘bow-tie’ effect inducing double- or triple-counting of AF pixels towards the swath edge (such that more FRP is in fact detected at the MODIS edge of swath observations than at nadir) [24
]. Without this ‘bow-tie’ effect, our analysis of MAS data shows that in the cerrado landscape MODIS’ edge of swath observations would probably detect less than 50% of the true FRP being emitted. However, the more recent VIIRS sensor, with its dual 375 m and 750 m observations should be able to detect perhaps 90% of the FRP coming from landscape cerrado fires when imaging at nadir [19
], and the pixel aggregation scheme employed to limit VIIRS off-nadir pixel area growth to within around a factor of two helps limit falloff in this figure as view zenith angle increases.
Degrading the MAS data to a series of intermediate spatial resolutions and performing the same analyses as for the 1 km data informs us that reducing pixel sizes below around 200 m may offer limited additional advantages for regional FRP estimation, because active fires responsible for more than 97% of the total FRP emission are detected at this pixel size. This means that almost all the FRP coming from fires responsible for the vast majority of air pollutant and carbon emissions could be detected with spaceborne data collected at a 200 m spatial resolution, and thus we recommend this pixel size as potentially the minimum needed for future spaceborne missions aiming at AF detection and especially FRP retrieval, at least when targeting savannah-like biomes. At these types of pixel sizes, large swath instruments having relatively frequent revisit capabilities remain possible, especially when operating two or more such system simultaneously. Further investigation may indicate that even coarser pixel sizes maybe considered adequate for biomes such as the North American boreal forest, which typically show preponderance of higher FRP fires when compared to savannahs [14
These spatial resolution recommendations assume the use of MIR-channel equipped sensors having relatively low noise characteristics, so that even relatively low FRP active fire pixels can be clearly identified against their non-fire background ‘thermal clutter’. MODIS in fact has two 3.9 μm channels, with an NEdT of 0.07 and 2.0 K respectively, in order to provide both low noise for sensitivity to smaller (low FRP) fires and a wide dynamic range to avoid saturation over larger (higher FRP) fires [5
], whilst VIIRS operates MIR channels at two spatial resolutions (375 m and 750 m) for a similar reason [19
]. If sensors characterised by significantly higher noise levels and thus different detection sensitivities are being considered for use (e.g., MIR microbolometers) then these pixel size recommendations may need to be re-assessed. Any such considerations should also be traded off against the apparent potential for increasing false alarm rates as pixel size decreases and thermal clutter and other non-fire effects on the thermal signals potentially increase in magnitude [19
Assuming the low-noise detection capabilities of MAS and MODIS-type sensors, 200 m pixels enable fires with FRPs of around 0.2 MW—equivalent to a flaming area of ~ 5 m² or a smouldering area ~ ×10 larger—to be detected. The vast majority of FRP and smoke emissions come from fires larger than this, even when taking into account the increased non-CO2
trace gas and particulate emissions factors characteristic of smouldering fires [39
]. It is for this reason that we consider pixel sizes finer than 200 m potentially unnecessary for the FRP application, and areas of agricultural residue burning maybe among the only biomes where significant FRP release is commonly dominated by fires smaller than these limits, with sub-surface fires in peatlands and coal seams also potentially posing similar issues. However, even here the clustering of many such fires within a single pixel enables many of the fires to still be detected, e.g., as demonstrated via use of 375 m VIIRS I-Band data [19
]. Since pixel area is typically traded off against instrument swath width and temporal revisit time on polar orbiting spacecraft, the suggestion of an absolute minimum 200 m limit on pixel size should provide a useful guide for future mission developers aiming at active fire and FRP applications. Our methodology builds on that of [2
] to develop an approach to help optimize the design of potential future spaceborne missions with an active fire detection and FRP focus, and our findings should be further confirmed by collecting additional airborne observations that extend the dataset used herein to other times, seasons and biomes.