Fire Radiative Power Retrievals Research Articles

Mitigating underestimation of fire emissions from the Advanced Himawari Imager: A machine learning and multi-satellite ensemble approach

The accurate estimation of biomass burning emissions has played a crucial role in air quality and climate forecast modeling. Satellite-based fire radiative power (FRP) has proven effective for calculating biomass burning emissions. However, FRP-based emission estimations in East Asia often rely on polar-orbiting satellites owing to the unstable performance of Japan Aerospace Exploration Agency Advanced Himawari Imager (JAXA AHI) from poor detection capability and unproper FRP retrieval method. To address this, we improve the FRP by machine learning based on mid-infrared (MIR) radiance method, leveraging the superior fire detection model developed in our previous study. In addition, we propose a multi-satellite distance-based weighted ensemble FRP estimation method. Compared to traditional MIR radiance methods, the machine learning-based FRP estimation model exhibited promising performance (correlation coefficient: 1, mean bias error: 0.2, mean absolute percentage error: 1.9%). The integration of machine learning-based FRP estimation and fire detection model dramatically mitigated the underestimation issues from the JAXA AHI. The machine learning-based FRP was combined with the Moderate Resolution Imaging Spectroradiometer FRP to create a multi-satellite ensemble FRP. Comparative assessments using the TROPOspheric Monitoring Instrument and conventional bottom-up method demonstrated that the proposed method produced reliable output. Furthermore, impact analysis revealed that missing peaks or underestimated burn scars could lead to fatally low emissions; however, the proposed method was relatively robust against missing data owing to its multi-satellite ensemble. By identifying potential FRP problems and their impact on emission estimations, this study provides valuable insights for FRP-based emission estimation studies.

Improved estimation of fire particulate emissions using a combination of VIIRS and AHI data for Indonesia during 2015–2020

Smoke aerosol emissions from Indonesian fires frequently cause adverse environmental consequences across southeast Asia. Satellite observations provide us with a great opportunity to monitor such emissions at large scales. However, existing satellite-based estimates of Indonesian fire emissions vary considerably in magnitude, differing by a factor of four. Here, we aim to improve Indonesian fire emissions estimates through improved calculations of fire radiative energy (FRE: time-integrated fire radiative power (FRP)) and smoke aerosol emission coefficients (Ce) using multiple new-generation satellite observations. Specifically, peatland and non-peatland Ce values were derived from FRP and emission rates of smoke aerosols based on Visible Infrared Imaging Radiometer Suite (VIIRS) active fire and aerosol products. FRE was calculated from the diurnal FRP cycle that was reconstructed by fusing cloud-corrected FRP retrievals from the high temporal-resolution Himawari-8 Advanced Himawari Imager (AHI) with those from high spatial-resolution VIIRS. Then, fuel type-specific Ce values and fused AHI-VIIRS FRE were used to produce hourly and daily fire emission data from 2015 to 2020 across Indonesia. To evaluate AHI-VIIRS estimates, we generated a reference dataset of total particulate matter (TPM) by computing Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol observation differences from successive Terra and Aqua satellites overpasses over a set of manually-selected smoke plumes. AHI-VIIRS-based fire emissions correlated significantly with the MODIS reference data (r = 0.84; p < 0.001). The interannual time series analysis of AHI-VIIRS emissions showed extreme variability (~26 fold), with the greatest amount in 2015 (6.09 Tg) and the least in 2020 (0.23 Tg), during the study period. Mean annual fire emissions distributed across Indonesia's islands were primarily from Kalimantan (43.8%) and Sumatra (41.7%), followed by Sulawesi (5.6%), Java (4.5%), and Papua (4.4%). Additionally, contributions of Indonesian fire emissions from peatland, forest, cropland, and savanna/grassland fuel types were 51.4%, 37.8%, 7.0%, and 3.8%, respectively, during the fire season of the strong 2015 El Niño event. The analysis suggests that the majority of Indonesian fire emissions are very likely associated with large-scale land use conversion from peatland to agriculture, as well as prolonged droughts induced by El Niño events.

Estimation of Byram’s Fire Intensity and Rate of Spread from Spaceborne Remote Sensing Data in a Savanna Landscape

Fire behavior is well described by a fire’s direction, rate of spread, and its energy release rate. Fire intensity as defined by Byram (1959) is the most commonly used term describing fire behavior in the wildfire community. It is, however, difficult to observe from space. Here, we assess fire spread and fire radiative power using infrared sensors with different spatial, spectral and temporal resolutions. The sensors used offer either high spatial resolution (Sentinel-2) for fire detection, but a low temporal resolution, moderate spatial resolution and daily observations (VIIRS), and high temporal resolution with low spatial resolution and fire radiative power retrievals (Meteosat SEVIRI). We extracted fire fronts from Sentinel-2 (using the shortwave infrared bands) and use the available fire products for S-NPP VIIRS and Meteosat SEVIRI. Rate of spread was analyzed by measuring the displacement of fire fronts between the mid-morning Sentinel-2 overpasses and the early afternoon VIIRS overpasses. We retrieved FRP from 15-min Meteosat SEVIRI observations and estimated total fire radiative energy release over the observed fire fronts. This was then converted to total fuel consumption, and, by making use of Sentinel-2-derived burned area, to fuel consumption per unit area. Using rate of spread and fuel consumption per unit area, Byram’s fire intensity could be derived. We tested this approach on a small number of fires in a frequently burning West African savanna landscape. Comparison to field experiments in the area showed similar numbers between field observations and remote-sensing-derived estimates. To the authors’ knowledge, this is the first direct estimate of Byram’s fire intensity from spaceborne remote sensing data. Shortcomings of the presented approach, foundations of an error budget, and potential further development, also considering upcoming sensor systems, are discussed.

Improvements in high-temporal resolution active fire detection and FRP retrieval over the Americas using GOES-16 ABI with the geostationary Fire Thermal Anomaly (FTA) algorithm

Geostationary imaging sensors offer unique high temporal resolution capabilities with which to characterise the fast-changing dynamics of landscape fires. The new R-Series of Geostationary Operational Environmental Satellite (GOES-R) are the most advanced geostationary weather satellites currently operating, and each carry the new Advanced Baseline Imager (ABI) which greatly improves on the spatial, temporal and radiometric characteristics of the previous GOES Imager. Here we develop and evaluate the first landscape fire radiative power (FRP) observation system for the first of the GOES-R series (GOES-16), comparing its outputs to those generated near simultaneously from the forerunner GOES-13 Imager and from the far higher spatial resolution, but far less frequently imaging, Moderate Spatial Resolution Imaging Spectroradiometer (MODIS). Across the Americas, when examining near-simultaneous data from August to September 2017, the geostationary Fire Thermal Anomaly (FTA) algorithm originally developed for use with Meteosat’s Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) enables GOES-16 ABI to detect substantially more (6%) active fire (AF) pixels than GOES-13 Imager, due mainly to the former sensors‘ smaller pixel area. ABI detects even greater numbers of AF pixels due to its higher temporal resolution, but here we are only comparing results from near-simultaneous imagery. The majority of fires having an FRP substantially less than ~30 ​MW remain undetected even by ABI, and since these are the most common fire type ABI still shows a high error of omission (68%) compared to MODIS centre of swath data (±30° scan angle). ABI AF pixel errors of commission are far lower than errors of omission however at ~12% when using MODIS as a benchmark. This appears far more favorable than results found when using the Fire Detection and Characterization (FDC) algorithm, an adaptation of the Wildfire Automated Biomass Burning Algorithm (WF_ABBA) algorithm long-applied to GOES, which when applied to ABI data was recently assessed to have an 88% commission error. When ABI and MODIS both successfully detect a fire at the same time there is strong agreement and low bias in terms of their retrieved FRP. Overall therefore we find that the FTA approach to generating AF data from GOES-16 ABI sensor appears well performing across the Americas, and is compatible with the data from this system joining that from Meteosat, Meteosat over India Ocean and Himawari-8 to become part of the global geostationary active fire observation system now reaching maturity. The AF detection and FRP data provided from GOES-16 operating as GOES-East, and also now GOES-17 operating from the GOES-West position, are now available in near real-time for use worldwide.

Fire Detection and Fire Radiative Power in Forests and Low-Biomass Lands in Northeast Asia: MODIS versus VIIRS Fire Products

Fire omission and commission errors, and the accuracy of fire radiative power (FRP) from satellite moderate-resolution impede the studies on fire regimes and FRP-based fire emissions estimation. In this study, we compared the accuracy between the extensively used 1-km fire product of MYD14 from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the 375-m fire product of VNP14IMG from the Visible Infrared Imaging Radiometer Suite (VIIRS) in Northeastern Asia using data from 2012–2017. We extracted almost simultaneous observation of fire detection and FRP from MODIS-VIIRS overlapping orbits from the two fire products, and identified and removed duplicate fire detections and corresponding FRP in each fire product. We then compared the performance of the two products between forests and low-biomass lands (croplands, grasslands, and herbaceous vegetation). Among fire pixels detected by VIIRS, 65% and 83% were missed by MODIS in forests and low-biomass lands, respectively; whereas associated omission rates by VIIRS for MODIS fire pixels were 35% and 53%, respectively. Commission errors of the two fire products, based on the annual mean measurements of burned area by Landsat, decreased with increasing FRP per fire pixel, and were higher in low-biomass lands than those in forests. Monthly total FRP from MODIS was considerably lower than that from VIIRS due to more fire omission by MODIS, particularly in low-biomass lands. However, for fires concurrently detected by both sensors, total FRP was lower with VIIRS than with MODIS. This study contributes to a better understanding of fire detection and FRP retrieval performance between MODIS and its successor VIIRS, providing valuable information for using those data in the study of fire regimes and FRP-based fire emission estimation.

First study of Sentinel-3 SLSTR active fire detection and FRP retrieval: Night-time algorithm enhancements and global intercomparison to MODIS and VIIRS AF products

The Sea and Land Surface Temperature Radiometer (SLSTR) now operates concurrently onboard the European Sentinel-3A and 3B satellites. Its observations are expected ultimately to become the main global source of active fire (AF) detections and fire radiative power (FRP) retrievals for the mid-morning and evening low earth orbit timeslots – data currently supplied by the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra. Here we report for the first-time the significant adjustments made to the pre-launch Sentinel-3 AF detection and fire characterisation algorithm required to optimize its performance with real SLSTR data collected from the Sentinel-3A and 3B satellites. SLSTR possesses both an S7 ‘standard’ and an F1 ‘fire’ channel that operate in the same middle infrared (MIR) waveband, but which use different detectors with differing dynamic ranges and which are located at different focal plane locations. When S7 provides saturated observations, for example over higher FRP active fire pixels, F1 must be used to provide a reliable MIR spectral measurement. However, the two channels differing data characteristics (slightly different size, shape and spatial location of the matching pixels) means that swapping between their measurements is non-trivial. The main algorithm enhancement has therefore been the addition of a dedicated active fire pixel clustering component, required to cluster the detected AF pixels into individual fires as a solution to this issue. Focusing on night-time data due to the added complexity of daytime implementation, we compare AF information derived with this updated SLSTR algorithm to that from near-simultaneous MODIS Terra, and we find that SLSTR has a lower minimum FRP detection limit which enables more lower FRP active fire pixels to be identified than is the case with MODIS. When both sensors detect the same fire cluster at the same time, SLSTR typically measures a slightly higher FRP due to it being able to detect more of the low FRP AF pixels lying at the cluster edge (the OLS linear best fit between matched SLSTR and MODIS per-fire FRP matchups has a slope of 1.08). At the regional scale, SLSTR detects 90% of the AF pixels that the matching MODIS data contains, but also identifies an additional 44% more AF pixels – the vast majority of which have FRP < 5 MW. Regional FRP totals derived from SLSTR appear slightly higher than those from MODIS because of this, and the OLS linear best fit between these regional FRP matchup datasets has a slope of 1.10. Global fire mapping at 1° grid cell resolution for January 2019 shows very similar fire patterns and FRP totals from SLSTR onboard of Sentinel-3B and MODIS Terra, with SLSTR detecting seven times more AF pixels but very similar FRP totals. Case studies in 5° grid cell areas show the same pattern, and longer-term comparisons like these will provide the data required to mesh MODIS and SLSTR data into a single compatible time-series for long-term trend analysis. The night-time SLSTR AF product based largely on this algorithm has been fully operational from March 2020 and is available from near real-time feeds. A non-time critical (NTC) version based on a similar processing chain will follow shortly after, with products available from the Sentinel-3 Data Hub.

Estimation of biomass-burning emissions by fusing the fire radiative power retrievals from polar-orbiting and geostationary satellites across the conterminous United States

Biomass burning is an important source of atmospheric greenhouse gases and aerosols, and its emissions can be estimated using Fire Radiative Power (FRP) retrievals from polar-orbiting and geostationary satellites. Accurate and timely estimation of biomass-burning emissions (BBE) requires high-spatiotemporal-resolution FRP that is characterized by accurate diurnal FRP cycle. This study is to estimate hourly reliable BBE in a 0.25° × 0.3125° grid across the conterminous United States (CONUS) to be used in chemical transport models for air quality forecast. To do this, this study for the first time fused FRP retrievals from the Geostationary Operational Environmental Satellite (GOES) with those from Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 after GOES FRP was angularly adjusted and was further calibrated against MODIS FRP. The FRP data was obtained from Terra and Aqua MODIS 1 km active fire products with fire observations of four times a day and from 4 km GOES WF_ABBA (WildFire Automated Biomass Burning Algorithm) fire products for GOES-W (GOES-11 and 15) and GOES-E (GOES-13) with observations every 5–15 min across the CONUS from 2011 to 2015. The diurnal FRP cycles at an interval of 15 min for a grid were reconstructed using the ecosystem-specific diurnal FRP climatology and actually available MODIS-GOES fused FRP, which were applied to estimate hourly BBE across the CONUS. The results indicate that the reconstructed diurnal FRP cycle varied significantly in magnitude and shape among 45 CONUS ecosystems. The biomass burning released 717 Gg particulate matter smaller than 2.5 μm in diameter (PM2.5) in the CONUS each year; however, it presented significant temporal (diurnal, seasonal, and interannual) and spatial variations. Finally, the BBE estimates were evaluated using available data sources and compared well (a difference of ∼4%) with emissions derived from Landsat burned areas in the western CONUS and with hourly carbon monoxide emissions simulated using a biogeochemical model over the Rim Fire in California (difference < 1%). The BBE estimates showed similar seasonal variation to six available BBE inventories but with variable magnitude.

Investigating the impact of overlying vegetation canopy structures on fire radiative power (FRP) retrieval through simulation and measurement

Fire radiative power (FRP) retrievals are now routinely made from polar and geostationary instruments, providing a means to estimate fuel consumption and trace gas and aerosol emissions directly from remotely sensed observations. This study presents the first investigation of the impact of vegetation canopy structure (percentage canopy cover and leaf area index, LAI) on FRP retrievals, based on 3D radiative transfer model simulations. The Discrete Anisotropic Radiative Transfer (DART) model is used to simulate above-canopy observations made through 3D vegetation canopies with different structural arrangements, under which a centrally positioned uniform landscape fire is burning. The vegetation canopy is modelled in two ways, as an opaque structure and as a hybrid turbid medium. The percentage canopy cover in each simulated scene is varied between 5 and 95%, and the FRP retrieved above the canopy is found to decrease in proportion to percentage canopy cover when the canopy is opaque, a finding that is in agreement with a series of small scale outdoor measurements conducted to evaluate the realism of the simulations. However, when the canopy is modelled as a turbid medium, which is in some ways a more realistic representation of a real ‘gappy’ vegetation canopy, the degree of FRP interception occurring at any particular canopy cover decreases by ~ 14%, due to some fire emitted thermal energy being transmitted through the canopy gaps. The simulations also reveal the impact of canopy LAI on above-canopy FRP retrievals, reducing these by 6% when both canopy cover and LAI are low (5% and < 1.0 respectively), but by up to 92% when canopy cover and scene LAI are high (95% and ~8 respectively). We use the derived relationships between FRP interception and canopy structure, along with MODIS LAI and percentage tree cover data, to adjust 2004–2012 fire radiative energy (FRE) estimates calculated from FRP data collected by the geostationary Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument. The adjusted annual FRE is on average 15% greater than estimated, and is characterized by low inter-annual variability as result of the majority of fire activity occurring in areas where percentage tree cover remains below 40%. Landscape burning occurs more frequently in areas of higher tree cover in southern hemisphere rather than northern hemisphere Africa, leading to a larger annual FRE adjustment (18.5% compared to 16.3%). This study illustrates the impact that canopy interception has on FRP for the first time at the satellite scale, and over Africa demonstrates a large but temporally consistent underestimation which can be accounted for using LAI and percentage tree cover metrics when estimating fuel consumption and atmospheric emissions from the FRP retrievals.

Comparison of Fire Radiative Power Estimates From VIIRS and MODIS Observations

AbstractSatellite‐based active fire data are a viable tool to understand the role of global fires in the biosphere and atmosphere. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on Aqua and Terra satellites are nearing the end of their lives. The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar‐orbiting Partnership satellite and the subsequent Joint Polar Satellite System series is expected to extend the MODIS active fire record. Thus, understanding the similarities of and discrepancies between the two data sets during their overlap period is important for existing applications. This study investigated the dependence of the MODIS and VIIRS fire characterization capabilities on satellite view zenith angle and the relationship between the two sensors' fire radiative power (FRP) from individual fire clusters to fire data on continental and global scales. The results indicate that the VIIRS fire characterization capability is similar across swath, whereas MODIS is strongly dependent on view zenith angle. Statistical analyses reveal that the VIIRS and MODIS FRP relationship varies between different spatial scales. In fire clusters, MODIS and VIIRS FRP estimates are very comparable, except for large boreal forest fires where VIIRS FRP is approximately 47% smaller. At the continental scale, the contemporaneous FRP retrievals from MODIS and VIIRS are generally comparable and strongly correlated, but VIIRS FRP is slightly larger and their differences vary across seasons. At global 1° × 1° grids, the FRP difference between the two sensors is, on average, approximately 20% in fire‐prone regions but varies significantly in fire‐limited regions.

Shortwave IR Adaption of the Mid-Infrared Radiance Method of Fire Radiative Power (FRP) Retrieval for Assessing Industrial Gas Flaring Output

The radiative power (MW) output of a gas flare is a useful metric from which the rate of methane combustion and carbon dioxide emission can be inferred for inventorying purposes and regular global surveys based on such assessments are now being used to keep track of global gas flare reduction efforts. Several multispectral remote sensing techniques to estimate gas flare radiative power output have been developed for use in such surveys and single band approaches similar to those long used for the estimation of landscape fire radiative power output (FRP) can also be applied. The MIR-Radiance method, now used for FRP retrieval within the MODIS active fire products, is one such single band approach—but its applicability to gas flare targets (which are significantly hotter than vegetation fires) has not yet been assessed. Here we show that the MIR-Radiance approach is in fact not immediately suitable for retrieval of gas flare FRP due to their higher combustion temperatures but that switching to use data from a SWIR (rather than MWIR) spectral channel once again enables the method to deliver unbiased FRP retrievals. Over an assumed flaring temperature range of 1600–2200 K we find a maximum FRP error of ±13.6% when using SWIR observations at 1.6 µm and ±6.3% when using observations made at 2.2 µm. Comparing these retrievals to those made by the multispectral VIIRS ‘NightFire’ algorithm (based on Planck Function fits to the multispectral signals) we find excellent agreement (bias = 0.5 MW, scatter = 1.6 MW). An important implication of the availability of this new SWIR radiance method for gas flare analysis is the potential to apply it to long time-series from older and/or more spectrally limited instruments, unsuited to the use of multispectral algorithms. This includes the ATSR series of sensors operating between 1991–2012 on the ERS-1, ERS-2 and ENVISAT satellites and such long-term data can be used with the SWIR-Radiance method to identify key trends in global gas flaring that have occurred over the last few decades.

Major advances in geostationary fire radiative power (FRP) retrieval over Asia and Australia stemming from use of Himarawi-8 AHI

Characterising the highly variable temporal dynamics of landscape-scale fire activity is best achieved using geostationary satellites, and the Himawari-8 Advanced Himawari Imager (AHI) now provides views of Asian and Australian fires at an unprecedented 10min temporal resolution and 2km nadir thermal channel spatial resolution. We here develop the first processing system to identify active fires and retrieve their fire radiative power (FRP) from AHI data, based on the geostationary Fire Thermal Anomaly (FTA) algorithm and FRP retrieval method originally developed for use with Meteosat SEVIRI over Africa and Europe. This scheme detects active fires covering as little as 10−3 to 10−4 of an AHI pixel, and we compare performance to the same scheme applied to data from the forerunner geostationary MTSAT imager and the FengYun-2 (FY-2) Stretched Visible and Infrared Spin Scan Radiometer (S-VISSR), and also to 1km (at nadir) polar-orbiting MODIS active fire data. We find major benefits of Himawari-8 AHI over both MTSAT and FY-2, being able to detect a substantially greater proportion of fire activity and with little impact from sensor saturation. AHI-derived FRP retrievals of detected fires show a very strong agreement and a low (3MW) bias with respect to near-simultaneous MODIS retrievals, though fires having FRP≤40MW are undercounted by AHI due to its 4× larger pixel area (at nadir) than MODIS. Large parts of Asia are characterised by smaller/lower FRP fires associated with e.g. agricultural residue burning, meaning many are at or below this AHI minimum FRP detection limit, and during June 2015 AHI fails to detect around 66% of the hotspots that MODIS detects when both sensors view the same area simultaneously. However, AHI provides 144 observation opportunities per day compared to 4 typical observations from MODIS, and shows a low (8%) active fire detection error of commission. We demonstrate the unique value of the geostationary FRP retrievals made from AHI data for full fire diurnal cycle assessment and for Fire Radiative Energy (FRE) calculations. We conclude that these FRP data demonstrate major benefits for studies of active fires over Asia and Australia, and expect them to become an important component of the global geostationary active fire observation system.

Active fires from the Suomi NPP Visible Infrared Imaging Radiometer Suite: Product status and first evaluation results

AbstractThe Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar‐orbiting Partnership (S‐NPP) satellite incorporates fire‐sensitive channels, including a dual‐gain high‐saturation temperature 4 µm channel, enabling active fire detection and characterization. The active fire product, based on the 750 m moderate resolution “M” bands of VIIRS, is one of the standard operational products generated by the Interface Data Processing Segment of the S‐NPP ground system. The product builds on an earlier “Collection 4” version of the algorithm used for processing Moderate Resolution Imaging Spectroradiometer (MODIS) data. Following postlaunch quality assessments and corrections in the input VIIRS Sensor Data Record data processing, an initial low detection bias was removed and the product achieved Beta quality in April 2012. Daily spurious detections along‐scan lines were also significantly reduced as a result of further processing improvements in October 2012. Direct product comparison with MODIS over 4 months of data in 2013 has shown that VIIRS produces approximately 26% more detections than MODIS within the central 3 pixel VIIRS aggregation zone of approximately ±31° scan angle range and 70% more detections outside of that zone, mainly as a result of the superior VIIRS scanning and sampling characteristics. Further development is in progress to ensure high‐quality VIIRS fire products that continue the MODIS data record and better serve the user community by delivering a full image classification product and fire radiative power retrievals. Research is also underway to take advantage of the radiometric signal from the 375 m VIIRS imager “I” bands.

Integrated active fire retrievals and biomass burning emissions using complementary near-coincident ground, airborne and spaceborne sensor data

Ground, airborne and spaceborne data were collected for a 450ha prescribed fire implemented on 18 October 2011 at the Henry W. Coe State Park in California. The integration of various data elements allowed near-coincident active fire retrievals to be estimated. The Autonomous Modular Sensor-Wildfire (AMS) airborne multispectral imaging system was used as a bridge between ground and spaceborne data sets providing high-quality reference information to support satellite fire retrieval error analyses and fire emissions estimates. We found excellent agreement between peak fire radiant heat flux data (<1% error) derived from near-coincident ground radiometers and AMS. Both MODIS and GOES imager active fire products were negatively influenced by the presence of thick smoke, which was misclassified as cloud by their algorithms, leading to the omission of fire pixels beneath the smoke, and resulting in the underestimation of their retrieved fire radiative power (FRP) values for the burn plot, compared to the reference airborne data. Agreement between airborne and spaceborne FRP data improved significantly after correction for omission errors and atmospheric attenuation, resulting in as low as 5% difference between Aqua/MODIS and AMS. Use of in situ fuel and fire energy estimates in combination with a collection of AMS, MODIS, and GOES FRP retrievals provided a fuel consumption factor of 0.261kgMJ−1, total energy release of 14.5e+06MJ, and total fuel consumption of 3.8e+06kg. Fire emissions were calculated using two separate techniques, resulting in as low as 15% difference for various species.

Hindcast experiments of tropospheric composition during the summer 2010 fires over western Russia

Abstract. The severe wildfires in western Russia during July–August 2010 coincided with a strong heat wave and led to large emissions of aerosols and trace gases such as carbon monoxide (CO), hydrocarbons and nitrogen oxides into the troposphere. This extreme event is used to evaluate the ability of the global MACC (Monitoring Atmospheric Composition and Climate) atmospheric composition forecasting system to provide analyses of large-scale pollution episodes and to test the respective influence of a priori emission information and data assimilation on the results. Daily 4-day hindcasts were conducted using assimilated aerosol optical depth (AOD), CO, nitrogen dioxide (NO2) and ozone (O3) data from a range of satellite instruments. Daily fire emissions were used from the Global Fire Assimilation System (GFAS) version 1.0, derived from satellite fire radiative power retrievals. The impact of accurate wildfire emissions is dominant on the composition in the boundary layer, whereas the assimilation system influences concentrations throughout the troposphere, reflecting the vertical sensitivity of the satellite instruments. The application of the daily fire emissions reduces the area-average mean bias by 63% (for CO), 60% (O3) and 75% (NO2) during the first 24 h with respect to independent satellite observations, compared to a reference simulation with a multi-annual mean climatology of biomass burning emissions. When initial tracer concentrations are further constrained by data assimilation, biases are reduced by 87, 67 and 90%. The forecast accuracy, quantified by the mean bias up to 96 h lead time, was best for all compounds when using both the GFAS emissions and assimilation. The model simulations suggest an indirect positive impact of O3 and CO assimilation on hindcasts of NO2 via changes in the oxidizing capacity. However, the quality of local hindcasts was strongly dependent on the assumptions made for forecasted fire emissions. This was well visible from a relatively poor forecast accuracy quantified by the root mean square error, as well as the temporal correlation with respect to ground-based CO total column data and AOD. This calls for a more advanced method to forecast fire emissions than the currently adopted persistency approach. The combined analysis of fire radiative power observations, multiple trace gas and aerosol satellite observations, as provided by the MACC system, results in a detailed quantitative description of the impact of major fires on atmospheric composition, and demonstrate the capabilities for the real-time analysis and forecasts of large-scale fire events.

Exploiting the power law distribution properties of satellite fire radiative power retrievals: A method to estimate fire radiative energy and biomass burned from sparse satellite observations

[1] Instantaneous estimates of the power released by fire (fire radiative power, FRP) are available with satellite active fire detection products. The temporal integral of FRP provides an estimate of the fire radiative energy (FRE) that is related linearly to the amount of biomass burned needed by the atmospheric emissions modeling community. The FRE, however, is sensitive to satellite temporal and spatial FRP undersampling due to infrequent satellite overpasses, cloud and smoke obscuration, and failure to detect cool and/or small fires. Satellite FRPs derived over individual burned areas and fires have been observed to exhibit power law distributions. This property is exploited to develop a new way to derive FRE, as the product of the fire duration and the expected FRP value derived from the FRP power law probability distribution function. The method is demonstrated and validated by the use of FRP data measured with a dual-band radiometer over prescribed fires in the United States and by the use of FRP data retrieved from moderate resolution imaging spectroradiometer (MODIS) active-fire detections over Brazilian deforestation and Australian savanna fires. The biomass burned derived using the conventional FRP temporal integration and power law FRE estimation methods is compared with biomass burned measurements (prescribed fires) and available fuel load information reported in the literature (Australian and Brazilian fires). The results indicate that the FRE power law derivation method may provide more reliable burned biomass estimates under sparse satellite FRP sampling conditions and correct for satellite active-fire detection omission errors if the FRP power law distribution parameters and the fire duration are known.

Spaceborne detection and characterization of fires during the bi-spectral infrared detection (BIRD) experimental small satellite mission (2001–2004)

Wildfires have a range of significant environmental effects with respect to both Earth's surface and atmosphere. Spaceborne remote sensing of active fires has been undertaken for more than two decades, but the bi-spectral Infrared Detection (BIRD) Experimental Small Satellite (2001–04) is the first mission dedicated to this task. This paper summarizes the experience gained during the BIRD mission, which has focused both on active fire detection and active fire characterization, in terms of quantifying effective fire temperature ( T F), effective fire area ( A F) and fire radiative power (FRP). A detailed error analysis for each parameter is undertaken, and the accuracy of FRP retrieval is shown to be significantly better than that of T F or A F. For key fire-affected forest, bush and savanna environments (Australia, Benin, Borneo, Brazil, Canada-US, Portugal and Siberia) BIRD data allows FRP estimation to within 30% in 75% of fires examined, and for the first time from space BIRD is able to allow estimation of fireline length, effective fireline depth and radiative fireline intensity for the more pronounced fire fronts. Some indication of the predominant combustion regime (smoldering or flaming), which has implications for the relative concentrations of emitted pollutant products, is possible through use of the T F parameter. This experience demonstrates the advantages of the new infrared sensor technologies employed in BIRD, and offers suggestions for future fire monitoring sensors based on similar technologies.

Retrieval of biomass combustion rates and totals from fire radiative power observations: Application to southern Africa using geostationary SEVIRI imagery

Southern African wildfires are a globally significant source of trace gases and aerosols. Estimates of southern African wildfire fuel consumption have varied from hundreds to thousands of teragrams (Tg), and better‐constrained estimates are required to properly assess the effects of the pollutant emissions. A new approach for providing such estimates is via remote sensing observations of fire radiative power (FRP), a variable proportional to the rate of fuel consumption. The launch of the SEVIRI radiometer onboard the geostationary Meteosat‐8 platform presents a unique opportunity to monitor FRP at 15‐min intervals, allowing analysis of the complete diurnal cycle of biomass burning and calculation of the total fire radiative energy. Here we present the first FRP retrievals from SEVIRI and compare them to those derived from near‐coincident MODIS overpasses. Strong agreement is achieved on a per‐fire basis (r2 = 0.83, n = 139, p &lt; 0.0001), although at the regional scale SEVIRI typically underestimates FRP with respect to MODIS due primarily to its inability to confidently detect fire pixels with FRP &lt; ∼100 MW. Using relationships developed during ground‐based experiments, SEVIRI‐derived FRP measures are converted into estimates of the rate and total quantity of biomass combusted in southern Africa. During a 4.5 day monitoring period, and based on only the observed FRP recorded by SEVIRI, we infer that as a minimum estimate, peak combustion rates reached 50 tons/s and a total of 3.2 Tg of fuel was burnt in southern Africa. While provisional, we calculate that these figures maybe potentially increased upward by a factor of ∼3 to account for atmospheric absorption of the upwelling radiation and for fires that were potentially cloud covered or too weakly emitting to be detected by the geostationary imager. The new tool of SEVIRI‐derived FRP provides an insight into biomass burning on the African continent at a hitherto unobtainable temporal frequency, highly suited to the linking of pollutant emissions estimates to models of atmospheric transport.