Figure 2‑21 shows a visual comparison between the spatial patterns of daytime active fire pixel count and FRP contained within the Level-3 monthly daytime FRP products retrieved from one year’s Sentinel-3A and -3B data, and from Terra MODIS. Both products cover the period March 2022 to February 2023 and show very similar spatial patterns, indicating a broad degree of agreement even though the MODIS data includes all daytime observations and not just those collected near-simultaneously with SLSTR.
The AF pixel count data shown in Figure 2‑21 (a, c, e) indicate that both the Sentinel-3A and Sentinel-3B SLSTR Level 3 products show a similar AF pixel detection pattern to MODIS. However, as the grid-cell FRP totals shown in Figure 2‑21 (b, d, f) indicate, the MODIS active fire pixels contain a higher FRP total than either that of SLSTR from Sentinel-3A or Sentinel-3B – and in fact the value is quite close to the total of the two SLSTR records combined. These findings mirror those reported in Xu et al. (2023) and those for the March 2023 – Feb 2024 period detailed in Section 2.6.1, and result from the same reasons reported therein.
Figure 2‑21: Total active fire (AF) pixel count and total FRP of fires detected within 0.25° grid cells from March 2022 to Feb 2023 using all daytime Sentinel-3A, -3B SLSTR and MODIS Terra AF product data, not just those observed near simultaneously. Note that the MODIS Terra FRP has a scale reaching two times higher than that of S3A and S3B, due to the generally higher maximum grid-cell FRP totals from Terra, though once you add the S3A and S3B data together to approximate daily global coverage the total is quite similar to that from MODIS Terra which also provides that coverage.
Figure 2‑22 provides a visual comparison between the spatial patterns of active fire (AF) pixel count and FRP seen within the Level 3 Monthly Summary AF & FRP Night-time Products derived from data collected by the different instruments encompassing one year of data (March 2022 to February 2023). Shown are the data based on observations from SLSTR onboard Sentinel-3A, on -3B and from Terra MODIS. Sentinel and MODIS products covering this March 2022 to February 2023 period show very similar spatial patterns that indicates a broad degree of agreement, and this is even with the MODIS data including all night-time observations and not just those collected near-simultaneously with SLSTR.
Figure 2‑22: Total active fire (AF) pixel count (left panels) and total FRP of fires detected (right panels) within 0.25° grid cells, as calculated for the March 2022 to February 2023 period using all Sentinel-3A SLSTR, -3B SLSTR and Terra MODIS data. Note that the Terra MODIS active fire pixel count map has a quantitative scale five times smaller than that of S3A and S3B due to its typically significantly lower AF pixel detection count.
The AF pixel count data show that the SLSTR products include many more AF pixel detections than the MODIS products, but the grid-cell FRP totals shown in Figure 2‑22 are similar between the two records. This is because the additional AF pixels that SLSTR detects over and above those detected by MODIS in many of the grid cells have mostly low FRP values. These findings mirror those reported in Xu et al. (2020) and the results from sections (2.4.1 - 2.3.1) – in that at the grid-cell level the recorded FRP is similar between compatible Sentinel-3 and MODIS products, but that the former product includes more AF pixels since it can detect lower FRP fires due to the AF detection algorithms sensitivity and the smaller mean SLSTR pixel area across the swath compared to MODIS.
The AF detection process is inherently a trade-off between attempting to detect the lowest FRP AF pixels (which are typically the most common type in the majority of geographic areas), whilst also minimising false alarms (i.e., the triggering of the AF detection algorithm by non-fire phenomena). Raising the minimum FRP detection limit of the contextual AF detection algorithm used to generate the Level 2 SLSTR AF products was required by day (Xu et al., 2023) as compared to at night (Xu et al., 2020) due to the fact that the noisier F1 had to be used by day more often for AF detection due to saturation of the more sensitive S7 band (Xu et al., 2023). This increase in minimum FRP detection limit very likely reduces the number of false alarms, which are commonly of low FRP magnitude, but will also prevent some real fires having low FRP from being identified. Xu et al. (2023) already compared global daytime AF pixel data from MODIS Terra taken within a scan angle limit of 30° and within ±6 minutes of a Sentinel-3 SLSTR acquisition, and found that 70% of MODIS’ identified active fire pixels had a matching Sentinel-3 AF pixel detection, representing an apparent SLSTR AF detection omission error compared to MODIS of 30% (this assumes all MODIS AF pixel detections are correct; whereas at a global scale it is more like a 1.2% commission error rate, Giglio et al., (2016)). Conversely, of the Sentinel-3 daytime AF pixel detections present in the same dataset, 84% had a matched MODIS AF pixel detection – representing an apparent MODIS product AF pixel omission error compared to SLSTR of 16%. The analysis of daytime global S3 SLSTR and MODIS Terra AF fire data from March 2023 to March 2024 (see Figure 2‑4) showed that overall, the two combined S3A and S3B datasets include around twice the number of AF pixels than does the matching MODIS Terra data, but that the additional AF pixels have mostly low FRP and are often weakly radiating and found at the edge of an active fire cluster that contains some AF pixels having an FRP significantly higher than the daytime algorithms minimum FRP detection limit. Conversely, single pixel fires with a low FRP, or indeed fires with a few AF pixels but all of which are low FRP, that are detected by MODIS relatively close to nadir are quite often missed by SLSTR – because of the raised minimum FRP detection limit of the daytime S3 product by day compared to at night. Indeed, this latter performance is somewhat the opposite of that shown by the S3 night-time product, where single pixel ‘low FRP’ fires appear in general more likely detected by S3 than by MODIS, even when imaged by MODIS close to nadir (Xu et al., 2020).
Figure 2‑23a directly compares the FRP of fire clusters (a set of spatially discrete AF pixels) imaged near simultaneously (within ± 10 mins) by SLSTR on Sentinel-3B and by MODIS Terra in March 2022, each with a maximum view zenith angle of 30°. Figure 2‑23a presents the per-fire analysis, based on comparing the near-simultaneous FRP retrievals of the same fire cluster in the same way as in Xu et al. (2020, 2023). The near unity slope (0.97) demonstrates low bias between the two FRP measures, and also a strong degree of agreement (r2 =0.73). Overall, 53% of the per-fire SLSTR-to-MODIS FRP matchups show an FRP difference of less than 50%, and 36% less than 30%. This result should be considered in the context of MODIS FRP data alone having a per-pixel FRP uncertainty of 26.6%, estimated via the standard deviation of the FRP differences derived from consecutive MODIS scans of the same fire pixel (Freeborn et al., 2014b), reducing as the cluster contains larger numbers of AF pixels. Similarly, Figure 2‑23b shows the comparison from matching regions observed near-simultaneously by SLSTR and MODIS Terra. Each measurement shown represents the total FRP of a region observed in March 2022. Figure 2‑23b indicates that a strong degree of agreement (r2=0.89) still exists, but the slope of 0.91 indicates a still relatively low bias but one that is larger than that derived on a per fire-cluster basis. This is because, on balance, when observing a region, MODIS generally detects more low FRP AF pixels that SLSTR does not detect.
Figure 2‑23: Inter-comparison of daytime global FRP records obtained from SLSTR and Terra MODIS within ± 10 minutes of each other. (a) Fire cluster-based comparison. Each measurement is for a single fire cluster observed during daytime in March 2022, representing a spatially contiguous set of active fire pixels. Both datasets are atmospherically corrected. (b) Region-based comparison. Each measurement shows total FRP from the regions detected at almost the same time by both SLSTR and MODIS in March 2022.
The C3S Level 3a Daily Gridded AF & FRP Daytime Products are intended primarily for large scale analysis of fire patterns, seasonality, anomalies and trends. Such characteristics are part of a regional ‘fire regime’, which describes the role of landscape fires within an area over time, and under this broad definition, the fires’ physical attributes such as frequency, size, intensity, type and seasonality. Fire regimes may change with changing climate and with other changes in human impact associated with, for example, land use management and land use change (Moritz 2012; Flannigan et al., 2009; Archibald et al., 2013; Hantson et al, 2015). Characterizing past and current fire regimes has historically been performed by analysing field data such as charcoal records, fire-scar networks and fire occurrence databases. However, the regular and continuous information on landscape fires that can be provided by EO satellites is increasingly being used to determine certain fire regime characteristics (e.g., Chuvieco et al., 2008; Freeborn et al., 2014). Seasonality is one of the most important component characteristics of a region fire regime, and one which products such as the C3S Level 3 gridded FRP and the MODIS Active Fire Products are well suited to determine. To evaluate the C3S Level 3 Gridded FRP Products we therefore used them to derive fire regime seasonality characteristics, and compared these to the same metrics derived from the MOD14 data used to create the similar MODIS MOD14CMQ and MOD14C8Q climate modelling grid (CMG) products (Justice et al., 2002).
The fire season metrics derived from the daytime C3S Level 3a Daily Gridded AF & FRP Daytime Product files were compared to those derived from daytime MOD14 data gridded to the same 0.1° spatial resolution grid. As with the prior analyses, the comparisons were made globally as well as within the geographic regions of the Global Fire Emission Database (GFED) (see Figure 1‑6). Comparisons were made in terms of AF detections but also FRP (as in Figure 2‑21). The 14 GFED regions are defined based on areas having similar biomass burning regimes rather than on country borders (Van der Werf et al., 2017). Since, as shown above, the MODIS and SLSTR FRP records are more similar than the AF pixel count records, and as shown in the daytime C3S FRP validation reports the FRP patterns are more in agreement with the seasonality patterns seen in Giglio et al. (2006), we focus on the FRP metric for fire season analysis.
As an example at the global scale, Figure 2‑24a shows daytime daily global total daytime FRP as derived from the daily gridded global AF products of Sentinel-3A, -3B SLSTR and MODIS Terra. All three products show a very similar temporal development. The cumulative percentage FRP plot shown in Figure 2‑24b shows a similar trend from each sensor, and at the global scale the fire season starts in June and ends in November according to each of S3A, S3B and MODIS – based on defining the start and end of the fire season as the point at which 25% and 75% of the total FRP is reached respectively.
The peak and duration of the global "fire season" generally shows good consistency between SLSTR and MODIS, with one notable exception: the global FRP peak detected by MODIS Terra occurred on 19th April due to an ongoing ‘mega fire’ happening in Mongolia that resulted in a total FRP measure of approximately 400,000 MW across an area of approximately 20,000 km2. Terra MODIS quantified this very significant fire on that day via two morning overpasses, one at 02:10 UTC (54,354 MW) and another at 03:45 UTC (347,866 MW) – these being roughly equivalent to one month's total FRP from all global wildfires on average. By contrast, SLSTR made only one overpass of this fire on 19th April (at 02:33 UTC) and reported an FRP of 53,575 MW – very close to the MODIS value taken within 25 mins of that time but missing the peak of the fire that occurred more than an hour later as observed by MODIS.
Figure 2‑24: Intercomparison of the daytime daily global FRP records derived from the C3S Level 3a Daily Gridded AF & FRP Daytime Product files for S3A and S3B SLSTR and the MOD14 daytime records from Terra MODIS. (a) Total global daily FRP from each sensor; (b) Cumulative percentage of total global daily FRP. Note that there are a few days in June, August 2022 and February 2023 when S3 SLSTR provides no input Level-2 FRP data, and in October 2022 there are few days when MODIS Terra provides no data due to a changing orbit.
Figure 2‑25 shows the fire season analysis, both globally and for the GFED regions defined in Figure 1‑6. Results from the daytime C3S Level 3a Daily Gridded AF & FRP Daytime Product files generated from Sentinel-3 SLSTR and for the MODIS Terra data are shown. Globally, the three satellite datasets (S3A, S3B and MODIS Terra) that agree very well, all indicating a start and end of the fire season within a few days of one another as derived from these daytime records. A very similar fire duration also therefore results from each dataset. A very similar fire duration also therefore results from this. Globally for example, the fire season starts towards the end of June 2022 and ends in the middle of November 2022, lasting a period of approximately six months. For the GFED regions, 11 out of the 14 regions show a very good agreement between the data of the three satellites, with a fire season start, end and duration identical to within a few days. The peak of the fire season exhibits greater variability between the datasets compared to the other three fire season metrics, particularly for the global region (as illustrated in Figure 2‑24a). This is due to the aforementioned ‘mega-fire’ in Mongolia whose FRP peaked at an enormous 347,866 MW. Among the GFED regions, 7 out of 14 still demonstrate a fire season peak occurring within a few days of one another from all sensors. Note that landscape fire is a highly dynamic phenomenon, as demonstrated by the FRP of the Mongolian ‘mega-fire’ apparently increasing by more than 500% in the ~ 90 mins that occurred between the two MODIS views, and thus often shows a very high daily variance. Further analysis would therefore be needed to study the cause of the differences in regions showing greater disparity between each of the datasets, and it may just be that the daily fire activity was sufficiently dissimilar at the various overpass times of the sensors to results in the differences shown.
Figure 2‑25: Fire season metrics, as determined from the daytime Level 3a Daily Gridded FRP Product of S3A and S3B and from daily MODIS Terra data. Fire Season (a) Start, (b) Peak), (c) End and (d) Duration. Temporal resolution of the data used in the calculations is one day. The GFED regions are shown in Figure 1‑6 and are Boreal North America (BONA); Temperate North America (TENA); Central America (CEAM); NH South America (NHSA); SH South America (SHSA); Europe (EURO); Middle East (MIDE); NH Africa (NHAF); SH Africa (SHAF); Boreal Asia (BOAS); Central Asia (CEAS); SE Asia (SEAS); Equatorial Asia (EQAS); Australia and New Zealand (AUST). Using the different S3A, S3B and MODIS daytime data, the generally very good match between the different metrics for the majority of the GFED areas is apparent.
The daytime C3S Level 3a 27-Day Gridded AF & FRP Daytime Product is simply the accumulation of twenty-seven of the daytime C3S Level 3a Daily Gridded AF & FRP Daytime Products used in Section 2.5.3, so the evaluation of the former simply focuses on verifying the correctness of the lower temporal resolution (27-day) statistical summary derived from the daily data. The daytime 27-Day products were verified thoroughly in this way, with the active fire pixel count, FRP and all other parameters being found to agree with the 27-day accumulation of the daytime daily product data. This verification indicates the correctness of the data contained within the C3S Level 3a 27-Day Gridded AF & FRP Daytime Products.
An example of this verification is shown in Figure 2‑26, where the active fire pixel count from 3 to 29 December 2022 derived from the daytime C3S Level 3a 27-Day Gridded AF & FRP Daytime Product perfectly agrees with that derived from the accumulation of the appropriate twenty seven individual daily products.
Figure 2‑26: Example of verification of the Level 3a 27-Day Gridded AF & FRP Daytime Product. (a) Total active fire pixel count from the C3S Level 3a 27-Day Gridded AF & FRP Daytime Product covering 3 Dec to 29 Dec 2022; (b) Total active fire pixel from the Level 3a 27-Day Gridded AF & FRP Daytime Product covering the same period. The active fire pixel count is identical in all the grid cells between (a) and (b).
The daytime C3S Level 3 Monthly Summary AF & FRP Daytime Product was compared to the data contained within the MODIS MOD14 products covering the same area and time. As the spatial patterns of absolute AF pixel counts and FRP have been analysed already in Section 2.6.1, we focus here on the analysis of fire season and metrics defined at the global and GFED region scale. Degrees of agreement between the C3S and MODIS products are quantified using the coefficient of determination (r2) and slope of the linear best fit.
Figure 2‑27 and Figure 2‑28 (image has been divided into two distinct parts to enhance user accessibility and clarity) show the seasonal pattern of cumulative monthly FRP at both the global and GFED region scale, as derived from both the daytime C3S FRP products and from daytime MODIS Terra. As with the C3S daily product (Figure 2‑24 and Figure 2‑25), the seasonal pattern seen from the monthly C3S product appears very close to that derived from MODIS. Examining the global scale data, the r2 value between the S3A and Terra data is 1.0 and the slope of the ordinary least squares (OLS) linear best fit between them is 0.98, and that between S3B and Terra is the same. For the GFED regions, the r2 value between the S3A and MODIS data ranges from 0.88 to 1.00, and with slopes of the linear best fits lying between 0.74 and 1.02, whilst for S3B and MODIS the r2 value lies between 0.91 to 1.00 and the slope 0.71 to 1.03.
Figure 2‑27: Seasonal pattern of cumulative daytime monthly FRP at the global and GFED region scales, as derived from the C3S Level 3 Monthly Summary FRP Product and from MODIS Terra. The abbreviation of the names is as follows: Boreal North America (BONA); Temperate North America (TENA); SH South America (SHSA); Europe (EURO); Middle East (MIDE); Boreal Asia (BOAS); Central Asia (CEAS); SE Asia (SEAS) – see Figure 1‑6. The statistics of the r2 values between the datasets, and of the slope of the linear best fit between them, are also shown.
Figure 2‑28: Seasonal pattern of daytime cumulative monthly FRP at the global and GFED region scales, as derived from the C3S Level 3 Monthly Summary FRP Product and MODIS Terra. The abbreviation of the names is as follows: Central America (CEAM); NH South America (NHSA); NH Africa (NHAF); SH Africa (SHAF); Equatorial Asia (EQAS); Australia and New Zealand (AUST) – see Figure 1‑6. The statistics of the r2 values between the datasets, and of the slope of the linear best fit between them, are also shown.
Figure 2‑29 shows the fire season start, peak, end and duration as derived from the C3S Level 3 Monthly Summary FRP Product and from MODIS Terra, both globally and from GFED regions. As before, a threshold of 25% of the total cumulative FRP was used to define the start of the fire season, and 75% to define the end of the fire season. Our analysis shows that the fire season duration and intensity are more accurately captured by the 25% and 75% percentiles of the annual cumulative FRP total than by the 10% and 90% percentiles for the GFED regions. This is because the lower and upper percentiles reflect the onset and cessation of fire activity more closely, while the intermediate percentiles may include periods of low or no fire occurrence. Overall, the start, end and duration of the fire season agree very well between the S3A, S3B and MODIS data.
At the global scale, the fire season begins and ends at similar months according to the different data sources, and therefore the duration is also very close. However, there are some discrepancies between SLSTR and MODIS in terms of the peak of the fire season. The Sentinel-3 product analysis reports a fire season peak in July 2022, whilst MODIS shows a peak in September 2022. For all the GFED regions, ~ 25% of the 14 regions have 100% agreement for all the four fire season metrics analysed, whilst ~ 80% have a difference of one month or very occasionally more. Almost all the regions have a difference in the metrics of less than two months, except occasionally there are some regions like CEAS where fire activity such as the Mongolia fire in this case results in a larger difference.
Figure 2‑29: Fire season metrics as determined from Level 3 Monthly Gridded FRP Product of S3A and S3B and from daily MODIS Terra data. The temporal resolution of the data is one month. Analysis is conducted globally and for the GFED regions. (a) Start of the fire season; (b) Peak of the fire season; (c) End of the fire season; (d) Duration of the fire season. The abbreviation of the names is as follows: Boreal North America (BONA); Temperate North America (TENA); Central America (CEAM); NH South America (NHSA); SH South America (SHSA); Europe (EURO); Middle East (MIDE); NH Africa (NHAF); SH Africa (SHAF); Boreal Asia (BOAS); Central Asia (CEAS); SE Asia (SEAS); Equatorial Asia (EQAS); Australia and New Zealand (AUST) – see Figure 1‑6.
The AF detection process is inherently a trade-off between attempting to detect the lower FRP active fire pixels (which are typically the most common type in most geographic areas), whilst also minimising false alarms (i.e., the triggering of the AF detection algorithm by a non-fire phenomena). Raising the minimum FRP detection limit in the contextual AF detection algorithm of Xu et al. (2020) very likely reduces the number of false alarms, but will also prevent some lower FRP fires from being identified. Comparison to global data from Terra MODIS taken within a scan angle limit of 30° and within ±6 minutes of a Sentinel-3 SLSTR acquisition (see section 2.3.1 and Xu et al., 2020) shows that > 90% of MODIS-identified active fire pixels (MOD14 Collection 6 product) had a matching Sentinel-3 AF pixel detection, representing an apparent SLSTR product omission error of <10% compared to MODIS. Conversely, of the Sentinel-3 AF pixel detections present in the same dataset, only around 60% had a matching MODIS AF pixel detection, and around 80% of the additional AF pixel detections made by SLSTR had an FRP of less than 5 MW. These additional AF pixel detections provided by Sentinel-3 SLSTR are mostly below the minimum MODIS FRP AF pixel detection limit, and SLSTR can provide this benefit because it has a slightly lower minimum FRP detection limit than MODIS due to the smaller mean SLSTR pixel area and the intricacies of the AF pixel detection algorithm used to generate the Setinel-3 AF products (Xu et al., 2020).
Figure 2‑30 shows a visual comparison between the spatial patterns of active fire pixel count and FRP contained within the Level-3 monthly Night-time FRP products retrieved from Sentinel-3A and -3B data, and from Terra MODIS in September 2022. Very similar spatial patterns are seen, indicating a broad degree of agreement despite the MODIS data including all night-time observations and not just those made contemporaneously with SLSTR. The errors of omission and commission are expected to be similar to what was reported in section 2.3.1 and indeed also in Xu et al. (2020), and the spatial pattern of AF pixel detections are indeed similar between SLSTR and MODIS. For the same reason, the spatial pattern of FRP is similar between SLSTR and MODIS in Figure 2‑22 and Figure 2‑30, and the FRP between SLSTR and MODIS is expected to be similar to what is reported in section 2.3.1 and Xu et al. (2020).
Figure 2‑30: Total active fire (AF) pixel count (left panels) and total FRP of fires detected (right panels) within 0.25° grid cells from September 2022 using all Sentinel-3A, -3B SLSTR and Terra MODIS data. Note the Terra MODIS active fire pixel count has a scale that is five times smaller than that of S3A and S3B due to the lower number of AF pixels the former sensor typically detects.
Similar to what is reported in section 2.3.3, the fire season metrics derived from the C3S Level 3a Daily Gridded AF & FRP Night-time Product files were compared to those derived from MOD14 data. The comparisons are made globally as well as within the geographic regions of the Global Fire Emission Database (GFED) (e.g., see Figure 2‑31). Comparisons were made in terms of AF detections as well as FRP.
Figure 2‑31a shows daily global total FRP as derived from data collected by SLSTR onboard Sentinel-3A, -3B and by Terra MODIS and stored in the various daily gridded global products. All three products show a very similar temporal development. At the global scale, the fire season peak occurs in August, starts in May and end in November for each of S3A, S3B and MODIS. The cumulative percentage in Figure 2‑36b shows a similar trend, and using 10% and 90% of the total FRP as the definitions of the start and end of the fire season respectively we identify May 2022 as the start of season via data from the two sentinel-3 satellites and Terra MODIS, and the end of the fire season as January 2023 for two Sentinel-3 satellites and Terra MODIS. These timings, as well as the duration of the global “fire season”, agrees with findings reported by Giglio et al. (2006).
Figure 2‑31: Daily FRP timeseries comparison between SLSTR and MODIS. (a) Total global daily FRP; (b) Cumulative percentage of total global daily FRP; . Note that there are a few days in June and August 2022 when SLSTR has no input Level-2 FRP data and in Oct. 2022 there are a few days when Terra MODIS has no FRP data.
Figure 2‑32: Peak timing of the fire season as determined from Level 3a Daily Gridded FRP Product of S3A and S3B and from daily Terra MODIS data. Temporal resolution of the data is one day. The abbreviated names are as follows: Boreal North America (BONA); Temperate North America (TENA); Central America (CEAM); NH South America (NHSA); SH South America (SHSA); Europe (EURO); Middle East (MIDE); NH Africa (NHAF); SH Africa (SHAF); Boreal Asia (BOAS); Central Asia (CEAS); SE Asia (SEAS); Equatorial Asia (EQAS); Australia and New Zealand (AUST).
Figure 2‑32 shows the monthly peak of the fire season both globally and in the individual GFED regions defined in Figure 1‑5, both from SLSTR and from Terra MODIS. Globally, the three satellite datasets (S3A, S3B and Terra MODIS) agree well: S3A & Terra MODIS peak at the beginning of September 2022 and S3B at the end of August 2022, for example. For the GFED regions, 13 out of the 14 regions show a very good agreement between the data from the two sensors, and the three different satellites show a fire season peak that is identical to within a few days in Sept 2022. Note that landscape fire is a dynamic phenomenon having a high daily variance, and further analysis is needed to study the difference for regions showing greater disparity between each of the datasets.
The C3S Level 3a 27-Day Gridded AF & FRP Night-time Product is simply the accumulation of 27 C3S Level 3a Daily Gridded FRP Products, so its evaluation will focus on verifying the correctness of its lower temporal resolution statistical summary as derived from the former data. The 27-Day products were verified thoroughly in this way, with the active fire pixel count, FRP and all other parameters being found to agree with the accumulation of the daily product data. This indicates the correctness of the C3S 27-Day products. An example is shown in Figure 2‑38, where the active fire pixel count from 3 to 29 December 2022 derived from the 27-Day product perfectly agrees with that derived from the accumulation of the 27 individual daily products.
Figure 2‑33: Example verification of the Level-3a 27-Day gridded FRP product. (a) Total active fire pixel count from the C3S 27-Day gridded AF & FRP Night-time product covering 3 to 29 Dec 2022; (b) Total active fire pixel from the 27 daily C3S daily products covering the same period. The active fire pixel count is identical in all the grid cells between the data shown in (a) and that in (b).
The C3S Level 3 Monthly Summary AF & FRP Night-time Product was compared to the MODIS MOD14 product. As the spatial patterns of absolute AF pixel counts and FRP have been analysed already in section 2.5.1, we focus here on the analysis of fire season and metrics defined at the global and GFED regional scale. Degrees of agreement between the C3S and MODIS products are quantified using the coefficient of determination (r2) and slope of the linear best fit.
Figure 2‑34 and Figure 2‑35 (image has been divided into two distinct parts to enhance user accessibility and clarity) show the seasonal pattern of cumulative monthly FRP at both the global and GFED region scale, from both the C3S FRP products and Terra MODIS. As with the C3S daily (Figure 2‑31 and Figure 2‑32), the seasonal pattern from SLSTR appears very close to that derived from MODIS. The r2 value of cumulative FRP formed by the S3A and Terra data is 1.00 and the slope of the OLS linear best fit, 1.02, whilst that from S3B and Terra is 1.00 and 1.00 respectively. For the GFED regions, the r2 between the S3A and MODIS values range from 0.74 to 1.00 and slopes 0.61 to 1.15, whilst S3B and MODIS are 0.75 to 1.00 and 0.63 to 1.15.
Figure 2‑34: Seasonal pattern of cumulative monthly FRP (%), both at the global and GFED region scales, as derived from the C3S Level 3 Monthly Summary FRP Product and Terra MODIS. The abbreviation of the names is as follows: Boreal North America (BONA); Temperate North America (TENA); SH South America (SHSA); Europe (EURO); Middle East (MIDE); Boreal Asia (BOAS); Central Asia (CEAS); SE Asia (SEAS).
Figure 2‑35: Seasonal pattern of cumulative monthly FRP (%) at the global and GFED region scales, as derived from the C3S Level 3 Monthly Summary FRP Product and Terra MODIS. The abbreviation of the names is as follows: Central America (CEAM); NH South America (NHSA); NH Africa (NHAF); SH Africa (SHAF); Equatorial Asia (EQAS); Australia and New Zealand (AUST).
Figure 2‑36 shows the fire season start, peak, end and duration as derived from the C3S Level 3 Monthly Summary FRP Product and from Terra MODIS, both globally and for GFED regions. We used 25% of the total FRP to define the beginning of the fire season, and 75% the end of the fire season. Our analysis shows that the fire season duration and intensity are more accurately captured by the 25% and 75% percentiles of the annual cumulative FRP curve than by the 10% and 90% percentiles for the GFED regions. This is because the lower and upper percentiles reflect the onset and end of the fire activity more closely, while the intermediate percentiles may include periods of low or no fire occurrence. Overall, the start, peak, end and duration of the fire season agree very well between S3A, S3B and MODIS. At the global scale, according to these satellite data, the fire season begins and reaches its peak at the same time globally. However, there are some discrepancies between SLSTR and MODIS in terms of the end and duration of the fire season. SLSTR shows that the ‘global fire season’ ends one month later and lasts one month longer than does MODIS. For all the GFED regions, around 25% of the 14 regions have 100% agreement for all the four fire season metrics analysed, and around 80% have a difference of one month. Almost all the regions have a difference of less than two months.
Figure 2‑36: Fire season metrics as determined from Level 3 Monthly Gridded FRP Product of S3A and S3B and from daily Terra MODIS data. The temporal resolution of the data is one month. Analysis is conducted globally and for the GFED regions. (a) Start of the fire season; (b) Peak of the fire season; (c) End of the fire season; (d) Duration of the fire season. The abbreviation of the names is as follows: Boreal North America (BONA); Temperate North America (TENA); Central America (CEAM); NH South America (NHSA); SH South America (SHSA); Europe (EURO); Middle East (MIDE); NH Africa (NHAF); SH Africa (SHAF); Boreal Asia (BOAS); Central Asia (CEAS); SE Asia (SEAS); Equatorial Asia (EQAS); Australia and New Zealand (AUST).
This document has been produced in the context of the Copernicus Climate Change Service (C3S). The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of C3S on behalf of the European Union (Delegation Agreement signed on 11/11/2014 and Contribution Agreement signed on 22/07/2021). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose. The users thereof use the information at their sole risk and liability. For the avoidance of all doubt , the European Commission and the European Centre for Medium - Range Weather Forecasts have no liability in respect of this document, which is merely representing the author's view. |