Prescribed wildland and agricultural fires are common in the eastern United States (U.S.), but their small size and short durations have led polar‐orbiting satellite data sets to underestimate their emissions due to omission. The higher temporal resolution of the GOES‐16 Advanced Baseline Imager (ABI) improves fire detection, enabling development of a new biomass burning emissions inventory: GOES Eastern U.S. Fire Emissions (GEUFE, pronounced “goofy”). GEUFE is based on geostationary fire radiative power (FRP) observations, incorporates improved crop type classification for agricultural fires, and includes a new emission factor (EF) compilation for 16 land cover types in the eastern U.S., including forest, crop, and grassland types that differ markedly from globally reported EF values. GEUFE provides daily estimates of dry matter (DM) consumption and emissions of CO 2 , CO, NO x , NH 3 , SO 2 , organic carbon (OC), black carbon (BC), and fine particulate matter (PM 2.5 ) at 2 km resolution. An intercomparison of 10 biomass burning emissions inventories (GEUFE, GBBEPx, QFED, NEI‐2017, RAVE, FEER, VFEI, GFAS, FINN, GFED) showed that OC estimates in the eastern U.S. during August 2019–July 2020 ranged from 0.05 to 1.32 Tg yr −1 , with both EFs and DM consumption driving the large spread in aerosol estimates. GEUFE, at 0.64 Tg yr −1 , is at the high end of this range due both to region‐specific EFs and continuous GOES‐16 monitoring, reducing missed short‐lived fires. GEUFE is a process‐based inventory that eliminates reliance on empirical scaling factors, such as QFED's aerosol optical depth‐based adjustments, while explaining previously inferred “missing emissions” from small fires.

Background. Accurately estimating burned area from satellites is key to improving biomass burning emission models, studying fire evolution and assessing environmental impacts. Previous studies have found that current methods for estimating burned area of fires from satellite active-fire data do not always provide an accurate estimate.Aims and methods. In this work, we develop a novel algorithm to estimate hourly accumulated burned area based on the area from boundaries of non-convex polygons containing the accumulated Visible Infrared Imaging Radiometer Suite (VIIRS) active-fire detections. Hourly time series are created by combining VIIRS estimates with Fire Radiative Power (FRP) estimates from GOES-17 (Geostationary Operational Environmental Satellite) data.Conclusions, key results and implication. We evaluate the performance of the algorithm for both accumulated and change in burned area between airborne observations, and specifically examine sensitivity to the choice of the parameter controlling how much the boundary can shrink towards the interior of the area polygon. Results of the hourly accumulation of burned area for multiple fires from 2019 to 2020 generally correlate strongly with airborne infrared (IR) observations collected by the United States Forest Service National Infrared Operations (NIROPS), exhibiting correlation coefficient values usually greater than 0.95 and errors <20%.

Biomass burning influences atmospheric composition and regional air quality. The hourly biomass-burning emissions are usually required by air quality models, yet most available emission inventories provide daily or monthly estimates in 0.1 degrees or coarser grids, limiting the prediction accuracy. The Advanced Baseline Imager (ABI) on the Geostationary Operational Environmental Satellites - R Series (GOES-R) observes fires across the conterminous United States (CONUS) every 5 min at a spatial resolution of 2 km, which allows for characterizing fires and emissions on diurnal scale. In this study, we developed a new operational algorithm to generate regional hourly 3 km fire emission across the CONUS by fusing temporally resolved ABI fire radiative power (FRP) and fine spatial-resolution (375 m) FRP from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Joint Polar Satellite System (JPSS) satellites. To do this, ABI FRP was first calibrated against and fused with VIIRS FRP in 3 km grids. Then, FRP diurnal cycles at an interval of 5 min were reconstructed using the fused ABI-VIIRS FRP and the land cover-ecoregion-specific FRP diurnal climatologies. The reconstructed FRP diurnal cycles were applied to estimate hourly emissions of eight species (e.g., carbon monoxide (CO) and fine particulate matter with di-ameters <2.5 mu m (PM2.5)). The accuracy was verified by comparing with CO observations from the TROPO-spheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite, and with PM2.5 emissions from eight other inventories. The results of the ABI-VIIRS estimates during one year from April 2020 to March 2021 indicate that fires burned 221 Tg dry matter and emit 2.25 Tg PM2.5 emissions across the CONUS. The seasonal and diurnal patterns of emissions vary with land cover types. The largest and smallest seasonal variations are shown in forest and agriculture fire emissions, respectively. The diurnal emission patterns of different land cover types share similar shapes but differ largely in magnitude. Moreover, the diurnal pattern of forest fire emissions suggests that emissions are dominated during daytime in the eastern U.S. but strong during both daytime and nighttime in the western U.S. The evaluation shows that the fused ABI-VIIRS based CO agrees well with the TROPOMI CO, with a difference of 11%. However, the agreement between fused ABI-VIIRS emissions and other inventories varies for different fire events.

AbstractModeling smoke dispersion from wildland fires is a complex problem. Heat and emissions are released from a fire front as well as from post-frontal combustion, and both are continuously evolving in space and time, providing an emission source that is unlike the industrial sources for which most dispersion models were originally designed. Convective motions driven by the fire’s heat release strongly couple the fire to the atmosphere, influencing the development and dynamics of the smoke plume. This chapter examines how fire events are described in the smoke modeling process and explores new research tools that may offer potential improvements to these descriptions and can reduce uncertainty in smoke model inputs. Remote sensing will help transition these research tools to operations by providing a safe and reliable means of measuring the fire environment at the space and time scales relevant to fire behavior.

Corroboration of Geostationary Operational Environmental Satellite-17 (GOES-17) wildland fire detection capabilities occurred during the 24 October 2019 (evening of 23 October LST) ignition of the Kincade Fire in northern California. Post-analysis of remote sensing data compared to observations by the ALERTWildfire fire surveillance video system suggests that the emerging Kincade Fire hotspot was visually evident in GOES17 shortwave infrared imagery 52 s after the initial near-infrared heat source detected by the ground-based camera network. GOES-17 Advanced Baseline Imager Fire Detection Characteristic algorithms registered the fire 5 min after ignition. These observations represent the first documented comparative dataset between fire initiation and satellite detection, and thus provide context for GOES-16/17 wildland fire detections.

Wildfire smoke influences on air quality and atmospheric chemistry have been underscored by the increasing fire prevalence in recent years, and yet, the connection between fire, smoke emissions, and the subsequent transformation of this smoke in the atmosphere remains poorly constrained. Toward improving these linkages, we present a new method for coupling high time‐resolution satellite observations of fire radiative power with in situ observations of smoke aerosols and trace gases. We apply this technique to 13 fire plumes comprehensively characterized during the recent FIREX‐AQ mission and show that changes in fire radiative power directly translate into changes in conserved smoke tracers (CO2, CO, and black carbon aerosol) observed in the downwind smoke plume. The correlation is particularly strong for CO2 (mean r > 0.9). This method is important for untangling the competing effects of changing fire behavior versus the influence of dilution and atmospheric processing on the downwind evolution of measured smoke properties.

The Advanced Baseline Imager (ABI) on-board NOAA’s current Geostationary Operational Environmental Satellite (GOES-16/17) generates a suite of operational products. In October 2017, users and the developer of the fire product reported anomalously cold pixels around fires (CPAF) in the Level 1b 3.9 um channel imagery. Without correction, this anomaly can results in bias of hundreds of degrees for selected pixels. This anomaly was found to be very common in that imagery, though often not immediately discernable. The GOES Calibration Working Group (CWG) investigated this anomaly and found the root cause. Based on this analysis, the ABI vendor revised the re-sampling kernels for the 3.9 um channel, which was successfully implemented into the ground processing system in April 2019. The CPAF anomaly has been eliminated from the L1b 3.9 um imagery since then.

Satellite-based active fire data provide indispensable information for monitoring global fire activity and understanding its impacts on climate and air quality. Yet the limited spatiotemporal sampling capacities of current satellites result in considerable uncertainties in fire observation and emissions estimation. The mitigation of these uncertainties mainly relies on new remote-sensing technology. The Advanced Baseline Imager (ABI) onboard the Geostationary Operational Environmental Satellite-R (GOES-R) Series observes fires across North and South Americas at an unprecedentedly spatiotemporal resolution of nominal 2 km every 5-15 min. This study evaluated the GOES-16 (the first GOES-R satellite) ABI active fire product using active fire data derived from the 30-m Landsat-8 and the 375-m and 750-m Visible Infrared Imaging Radiometer Suite (VIIRS), and ground-based burning data across the southeastern Conterminous United States (CONUS) during the 2018 peak fire season. Specifically, we characterized the overall fire detection performance of the ABI active fire detections, estimated omission and commission errors, and evaluated ABI fire radiative power (FRP). The results showed that the ABI fire detection probability and its omission and commission errors were highly related to fire size and temporal period. ABI detection probability was higher than 95% for the fire pixel that contained over 114 Landsat-8 (30 m) fire detections or 11 VIIRS (375 m) detections. During a period of +/- 8 h, ABI detected 19% and 29% more fires observed by Landsat-8 and 375-m VIIRS, respectively. Correspondingly, the omission error could reduce by up to 33%. Further, ABI was able to detect 6-22% and 31-42% more ground-recorded fires than VIIRS in Georgia and Florida States, respectively, but ABI still missed many very small fires because ABI was hard to detect fires smaller than similar to 34.5 MW. Additionally, compared with 750-m VIIRS FRP, ABI FRP was similar to 30-50% larger in individual fire events but was overall similar at a regional scale.

AbstractThe Geostationary Operational Environmental Satellite-14 (GOES-14) imager was operated by the National Oceanic and Atmospheric Administration (NOAA) in an experimental rapid scan 1-min mode during parts of the summers of 2012 and 2013. This scan mode, known as the super rapid scan operations for GOES-R (SRSOR), emulates the high-temporal-resolution sampling of the mesoscale region scanning of the Advanced Baseline Imager (ABI) on the next-generation GOES-R series. This paper both introduces these unique datasets and highlights future satellite imager capabilities. Many phenomena were observed from GOES-14, including fog, clouds, severe storms, fires and smoke (including the California Rim Fire), and several tropical cyclones. In 2012 over 6 days of SRSOR data of Hurricane Sandy were acquired. In 2013, the first two days of SRSOR in June observed the propagation and evolution of a mid-Atlantic derecho. The data from August 2013 were unique in that the GOES imager operated in nearly continuous 1-min...

Channel-to-channel co-registration is an important performance metric for the Geostationary Operational Environmental Satellite (GOES) Imager, and large co-registration errors can have a significant impact on the reliability of derived products that rely on combinations of multiple infrared (IR) channels. Affected products include the cloud mask, fog and fire detection. This is especially the case for GOES-13, in which the co-registration error between channels 2 (3.9 mu m) and 4 (10.7 mu m) can be as large as 1 pixel (or similar to 4 km) in the east-west direction. The GOES Imager IR channel-to-channel co-registration characterization (GII4C) algorithm is presented, which allows a systematic calculation of the co-registration error between GOES IR channel image pairs. The procedure for determining the co-registration error as a function of time is presented. The algorithm characterizes the co-registration error between corresponding images from two channels by spatially transforming one image using the fast Fourier transformation resampling algorithm and determining the distance of the transformation that yields the maximum correlation in brightness temperature. The GII4C algorithm is an area-based approach which does not depend on a fixed set of control points that may be impacted by the presence of clouds. In fact, clouds are a feature that enhances the correlations. The results presented show very large correlations over the majority of Earth-viewing pixels, with stable algorithm results. Verification of the algorithm output is discussed, and a global spatial-spectral gradient asymmetry parameter is defined. The results show that the spatial-spectral gradient asymmetry is strongly correlated to the co-registration error and can be an effective global metric for the quality of the channel-to-channel co-registration characterization algorithm. Implementation of the algorithm in the GOES ground system is presented. This includes an offline component to determine the time dependence of the co-registration errors and a real-time component to correct the co-registration errors based on the inputs from the offline component. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Current and planned wildfire detection systems are impressive but lack both sensitivity and rapid response times. A small telescope with modern detectors and significant computing capacity in geosynchronous orbit can detect small (12 m2) fires on the surface of the earth, cover most of the western United States (under conditions of moderately clear skies) every few minutes or so, and attain very good signal-to-noise ratio against Poisson fluctuations in a second. Hence, these favorable statistical significances have initiated a study of how such a satellite could operate and reject the large number of expected systematic false alarms from a number of sources. Here we present both studies of the backgrounds in Geostationary Operational Environmental Satellites (GOES) 15 data and studies that probe the sensitivity of a fire detection satellite in geosynchronous orbit. We suggest a number of algorithms that can help reduce false alarms, and show efficacy on a few. Early detection and response would be of true value in the United States and other nations, as wildland fires continue to severely stress resource managers, policy makers, and the public, particularly in the western US. Here, we propose the framework for a geosynchronous satellite with modern imaging detectors, software, and algorithms able to detect heat from early and small fires, and yield minute-scale detection times.

Abstract Geostationary Operational Environmental Satellite (GOES)-14 imager was operated by National Oceanic and Atmospheric Administration (NOAA) in an experimental rapid scan 1-min mode that emulates the high-temporal resolution sampling of the Advanced Baseline Imager (ABI) on the next generation GOES-R series. Imagery with a refresh rate of 1 min of many phenomena were acquired, including clouds, convection, fires, smoke, and hurricanes, including 6 days of Hurricane Sandy through landfall. NOAA had never before operated a GOES in a nearly continuous 1-min mode for such an extended period of time, thereby making these unique datasets to explore the future capabilities possible with GOES-R. The next generation GOES-R imager will be able to routinely take mesoscale ( 1000     km × 1000     km ) images every 30 s (or two separate locations every minute). These images can be acquired even while scanning continental United States and full disk images. These high time-resolution images from the GOES-14 imager are being used to prepare for the GOES-R era and its advanced imager. This includes both the imagery and quantitative derived products such as cloud-top cooling. Several animations are included to showcase the rapid change of the many phenomena observed during super rapid scan operations for GOES-R (SRSOR).

'Natural' (or 'true') colour imagery, so-called for its qualitative likeness to colour photography, is one of the most visually intuitive and readily communicable forms of satellite information. It is constructed by combining solar reflectance measurements from three narrow spectral bands defining the red, green and blue wavelengths of visible light. Natural colour facilitates the interpretation of multiple components in the complex earth/atmosphere scene and, therefore, it is widely used by experts and non-experts alike to visualize many forms of geophysical phenomena. Although sensors on board low-Earth-orbiting (LEO) satellites have long-demonstrated the superior quality of natural colour imagery over various other 'false colour' renditions, similar capabilities currently do not exist on sensors operating in geostationary orbits that offer distinct advantages over LEO in terms of high temporal refresh. The Advanced Baseline Imager (ABI) of the next-generation Geostationary Operational Environmental Satellite (GOES)-R series will include the blue and red bands, but is missing the 0.55 mu m green band necessary for producing natural colour. The emphases of this article are twofold. First, we consider the merits of natural colour imagery from the standpoints of both science and operational users, and the philosophical roadblocks of a system definition process that seems inherently ill-equipped to consider qualitative user requirements. Second, we present a mitigation strategy for GOES-R ABI that entails synthesizing the missing ABI green band information via its correlation with spectrally adjacent available bands, with a first-order account for surface type dependencies. The technique is developed, demonstrated and evaluated here using Moderate-resolution Imaging Spectroradiometer (MODIS) data.

Near‐real‐time estimates of biomass burning emissions are crucial for air quality monitoring and forecasting. We present here the first near‐real‐time global biomass burning emission product from geostationary satellites (GBBEP‐Geo) produced from satellite‐derived fire radiative power (FRP) for individual fire pixels. Specifically, the FRP is retrieved using WF_ABBA V65 (wildfire automated biomass burning algorithm) from a network of multiple geostationary satellites. The network consists of two Geostationary Operational Environmental Satellites (GOES) which are operated by the National Oceanic and Atmospheric Administration, the Meteosat second‐generation satellites (Meteosat‐09) operated by the European Organisation for the Exploitation of Meteorological Satellites, and the Multifunctional Transport Satellite (MTSAT) operated by the Japan Meteorological Agency. These satellites observe wildfires at an interval of 15–30 min. Because of the impacts from sensor saturation, cloud cover, and background surface, the FRP values are generally not continuously observed. The missing observations are simulated by combining the available instantaneous FRP observations within a day and a set of representative climatological diurnal patterns of FRP for various ecosystems. Finally, the simulated diurnal variation in FRP is applied to quantify biomass combustion and emissions in individual fire pixels with a latency of 1 day. By analyzing global patterns in hourly biomass burning emissions in 2010, we find that peak fire season varied greatly and that annual wildfires burned 1.33 × 10 12 kg dry mass, released 1.27 × 10 10 kg of PM2.5 (particulate mass for particles with diameter <2.5 μ m) and 1.18 × 10 11 kg of CO globally (excluding most parts of boreal Asia, the Middle East, and India because of no coverage from geostationary satellites). The biomass burning emissions were mostly released from forest and savanna fires in Africa, South America, and North America. Evaluation of emission result reveals that the GBBEP‐Geo estimates are comparable with other FRP‐derived estimates in Africa, while the results are generally smaller than most of the other global products that were derived from burned area and fuel loading. However, the daily emissions estimated from GOES FRP over the United States are generally consistent with those modeled from GOES burned area and MODIS (Moderate Resolution Imaging Spectroradiometer) fuel loading, which produces an overall bias of 5.7% and a correlation slope of 0.97 ± 0.2. It is expected that near‐real‐time hourly emissions from GBBEP‐Geo could provide a crucial component for atmospheric and chemical transport modelers to forecast air quality and weather conditions.

Biomass burning patterns over the Maritime Continent of Southeast Asia are examined using a new active fire detection product based on application of the Wildfire Automated Biomass Burning Algorithm (WF_ABBA) to data from the imagers on the MTSAT geostationary satellites operated by the Japanese space agency JAXA. Data from MTSAT-1R and MTSAT-2 covering 34months from September 2008 to July 2011 are examined for a study region consisting of Indonesia, Malaysia, and nearby environs. The spatial and temporal distributions of fires detected in the MTSAT WF_ABBA product are described and compared with active fire observations from MODIS MOD14 data. Land cover distributions for the two instruments are examined using a new 250m land cover product from the National University of Singapore. The two products show broadly similar patterns of fire activity, land cover distribution of fires, and pixel fire radiative power (FRP). However, the MTSAT WF_ABBA data differ from MOD14 in important ways. Relative to MODIS, the MTSAT WF_ABBA product has lower overall detection efficiency, but more fires detected due to more frequent looks, a greater relative fraction of fires in forest and a lower relative fraction of fires in open areas, and significantly higher single-pixel retrieved FRP. The differences in land cover distribution and FRP between the MTSAT and MODIS products are shown to be qualitatively consistent with expectations based on pixel size and diurnal sampling. The MTSAT WF_ABBA data are used to calculate coverage-corrected diurnal cycles of fire for different regions within the study area. These diurnal cycles are preliminary but demonstrate that the fraction of diurnal fire activity sampled by the two MODIS sensors varies significantly by region and vegetation type. Based on the results from comparison of the two fire products, a series of steps is outlined to account for some of the systematic biases in each of these satellite products in order to produce a successful merged fire detection product.

Spaceborne instruments provide a unique view of global vegetation fire activity many times a day. In this study, we assessed the fire characterization information provided by two major products: the Terra and Aqua MODIS Thermal Anomalies product (MOD14 and MYD14, respectively) and the Wildfire Automated Biomass Burning Algorithm (WF_ABBA) product derived from GOES East Imager. Using higher spatial resolution imagery data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Landsat Enhanced Thematic Mapper Plus (ETM+) instruments, we analyzed the characterization of subpixel fires detected by MOD14, MYD14, and WF_ABBA over parts of Brazilian Amazonia. Our results suggest that MODIS and GOES fire radiative power (FRP) estimates derived for individual fire‐pixel clusters are subject to errors due to the effects of the point spread function of those instruments (underestimation of up to 75%), improper fire background characterization (overestimation of up to 80% assuming a 10 K cold bias in background temperature), and omission of small fire lines. Detection limits were approximately 11 and 9 MW for MOD14 and MYD14, respectively, and were equivalent to 27 and 19 MW for WF_ABBA data acquired coincidently with MOD14 and MYD14, respectively. We found a positive correlation between FRP and percentage tree cover indicating that FRP is sensitive to biomass density. Fire area and temperature estimates derived from the application of Dozier's (1981) approach to GOES data did not agree with our reference data (i.e., ASTER and ETM+ active fire masks and in situ fire temperature data), suggesting that large and variable errors could affect the retrieval of those parameters.

In this study, we discuss the early characterization of the active fire products derived from NPOESS/VIIRS and GOES-R ABI proxy data generated using ASTER and MODIS L1B input data, respectively. Improvements in spatial resolution of both VIIRS and ABI instruments result in higher probability of detection compared to their predecessors (MODIS and GOES Imager, respectively). The onboard pixel aggregation scheme that will be applied to VIIRS M15 band used by the fire algorithm shall reduce the probability of fire detection for relatively small fires compared to the unaggregated data, whereas the lower saturation temperature of band M15 could result in more frequent ambiguous fire detection information.

Recently, global biomass-burning research has grown from what was primarily a climate field to include a vibrant air quality observation and forecasting community. While new fire monitoring systems are based on fundamental Earth Systems Science (ESS) research, adaptation to the forecasting problem requires special procedures and simplifications. In a reciprocal manner, results from the air quality research community have contributed scientifically to basic ESS. To help exploit research and data products in climate, ESS, meteorology and air quality biomass burning communities, the joint Navy, NASA, NOAA, and University Fire Locating and Modeling of Burning Emissions (FLAMBE) program was formed in 1999. Based upon the operational NOAA/NESDIS Wild-Fire Automated Biomass Burning Algorithm (WF_ABBA) and the near real time University of Maryland/NASA MODIS fire products coupled to the operational Navy Aerosol Analysis and Prediction System (NAAPS) transport model, FLAMBE is a combined ESS and operational system to study the nature of smoke particle emissions and transport at the synoptic to continental scales. In this paper, we give an overview of the FLAMBE system and present fundamental metrics on emission and transport patterns of smoke. We also provide examples on regional smoke transport mechanisms and demonstrate that MODIS optical depth data assimilation provides significant variance reduction against observations. Using FLAMBE as a context, throughout the paper we discuss observability issues surrounding the biomass burning system and the subsequent propagation of error. Current indications are that regional particle emissions estimates still have integer factors of uncertainty.