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Despite the ecological and socioeconomic impacts of wildfires, little attention has been paid to the spatiotemporal patterns of nighttime fire activity across the conterminous United States (CONUS). Daytime fire radiative power (FRP) detected by the Moderate Resolution Imaging Spectroradiometer (MODIS) was nearly evenly split (54% vs. 46%) between inside and outside wildfires from 2003 to 2020. In contrast, 94% of nighttime FRP was detected within wildfires, of which 95% was detected within large wildfires (> 2023 ha). Nighttime proportions (i.e., the proportion of total summed FRP detected by MODIS at night) were lowest (3%) outside wildfires when coincident 1000-hr fuel moistures were highest and vegetation fires were smaller and less intense. As 1000-hr fuel moistures decreased, MODIS active fire pixels shifted out of agricultural and prescribed fires and into wildfires with higher nighttime per-pixel values of FRP such that nighttime proportions peaked at 29% for the largest wildfires. Increases in nighttime proportions within larger wildfires were attributed to increases in nighttime persistence whereby under the driest conditions, daytime fire activity detected by MODIS was more likely to continue burning with sufficient vigour to be detected again at night. From 2003–2020, MODIS detected significant (p < 0.01) increasing trends in nighttime wildfire fire activity, with a +54%, +42% and +21% increase in the annual nighttime sum of FRP, annual nighttime active fire pixel counts and annual mean nighttime per-pixel values of FRP, respectively, detected in the latter half of the study period. Nighttime trends were corroborated using observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) as well annual wildfire statistics reported by U.S. federal, state and local agencies. Moreover, MODIS detected a significant positive trend in the nighttime proportion of FRP emitted from wildfires, indicating that in the absence of diurnal differences in detection biases, increases in nighttime fire activity since 2003 have outpaced daytime increases. However an analysis of MODIS omission rates revealed that increasing nighttime proportions were at least partially attributed to a relatively greater improvement in nighttime detection performance compared to the daytime for larger wildfires burning during drier conditions. Nighttime fire activity already poses additional risks to firefighters and communities, and this work suggests that projected increases in the frequency of large wildfires will be accompanied by increases in the extent and intensity of nighttime fire activity.

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.

Most active fire detection algorithms applied to data from geostationary Earth Observation (EO) satellites are adjustments of those originally developed for polar-orbiting systems, and thus the high temporal imaging frequencies offered from geostationary systems are often not fully utilized within such detection approaches. Here we present a new active fire detection algorithm that fully exploits geostationary data's temporal dimension, including both for detecting actively burning fires and quantifying their fire radiative power (FRP). The approach uses a robust matching algorithm to model each pixels diurnal temperature cycle (DTC) in the middle infra-red (MIR) spectral band, the most important band for active fire detection. For each pixel, a Kalman filter (KF) is used to blend the set of basis DTCs with the actual geostationary observations during times of confirmed cloud- and fire-free measurements, allowing for estimates of the pixels true non-fire ‘background’ signal to be provided throughout the day, even when a fire maybe present. This is different to the standard ‘spatial contextual’ approach, where the non-fire background signal is estimated from nearby non-fire pixels. A series of spectral thresholds are then applied to the analyzed pixel in order to identify whether the actual observation departs sufficiently from the estimated non-fire signal to confidently suggest that the pixel contains an active fire. If it does, the difference between the fire pixel and non-fire background signal estimate in the MIR is used to estimate the fire radiative power (FRP) output. We apply this new ‘Kalman Filter Algorithm’ (KFA) to one month of African imagery acquired by the Spinning Enhanced Visible and Infrared Imager (SEVIRI), which is carried onboard the geostationary Meteosat satellite. We compare the resulting active fire detections and FRPs to those produced using the prototype (offline) version of the ‘spatial contextual’ based ‘Fire Thermal Anomalies’ (FTA) algorithm, here termed the pFTA, now used to generate the operational SEVIRI FRP-PIXEL product in the EUMETSAT Land Surface Analysis Satellite Applications Facility (http://landsaf.meteo.pt/), and also compare results to simultaneous detections from the MODIS MOD14/MYD14 active fire products. The KFA shows some advantages over the pFTA algorithm, detecting a greater number of fire pixels, up to ~ 80% more at the peak of the diurnal fire cycle. These additional fire pixels are primarily low FRP fires (

Earth Observation (EO) sensors play an important role in quantifying biomass burning related fuel consumption and carbon emissions, and capturing their spatial and temporal dynamics. Typically, biomass burning emissions inventories are developed by exploiting either burned area (BA) or active fire (AF) measures of fire radiative energy (FRE). These approaches have both advantages and limitations. For example, methods based on burned area data typically require hard-to-obtain estimates of fuel load and combustion completeness, and the accuracy of the BA algorithm may deteriorate for small fires or those in densely forested terrain. Conversely, ‘raw’ FRE-based methods are typically low-biassed due to the non-detection of low intensity fires, and are also hindered by cloud cover. Here we develop a methodology integrating these two EO data types to deliver a high temporal resolution emissions inventory, maximising the benefit of each data type without requiring additional information. We focus on Africa, the most fire affected continent, and combine daily FRE observations provided by Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI) with BA measures delivered by Moderate Resolution Imaging Spectroradiometer (MODIS). For individual fires detected by both types of data, we estimate fuel consumption per unit area (FCA: g·m− 2) via the ratio of FRE-derived total fuel consumption (FCT) to BA. These values are then extrapolated to fires that were mapped using the BA data but which remained undetected in the SEVIRI AF product, thus correcting for the ‘low spatial resolution bias’ inherent in geostationary AF datasets. Calculated daily continental scale FCT for Africa varies between 0.3 and 20 Tg for the period February 2004–January 2005. We estimate annual continental FCT to be 1418 Tg, far closer to the 2272 Tg provided by the widely used Global Fire Emissions Database (version 3; GFEDv3) than is obtained when using ‘raw’ FRE data alone. This synergistic approach has substantially narrowed the gap between GFEDv3 and FRE-derived emissions inventories, whilst the geostationary FRP observations offer the advantage that the daily emissions estimates can be distributed more accurately over the diurnal fire cycle if required for linking to atmospheric transport models.

Satellite-based estimates of the fire radiative power (FRP) and energy (FRE) emitted from open biomass burning are affected by the spatiotemporal resolution of polar-orbiting and geostationary sensors. Here the impacts of the MODIS sampling design on estimates of FRE are characterized by superimposing the timing and extents of the Terra and Aqua granules onto the SEVIRI active fire product. Results for different land-cover types across Africa indicate that the FRE measured by SEVIRI during eight days is linearly related to the sum of FRP measured by SEVIRI within the MODIS granules. These relationships are least variable during the height of the fire season when diurnal cycles of FRP measured by SEVIRI are most consistent. Relationships between FRE and the sum of FRP developed using the SEVIRI active fire product are directly applied to the sum of FRP retrieved from the MODIS Terra and Aqua Climate Modeling Grid (CMG) fire products. Estimates of FRE from MODIS herein agree within 5% of those obtained from previously published methods, but remain a factor of 0.72 times those obtained by adjusting SEVIRI measurements of FRE to account for low spatial resolution detection biases. An examination of the MODIS scan geometry suggests that the latter underestimation is attributed to the coupling between a MODIS imaging artefact referred to as the “bow-tie” effect and the typical calculation used to retrieve the sum of FRP from the MODIS CMG fire products. Depending on the availability of MODIS scan angle information, we offer rigorous and simplified calculations to account for the bow-tie effect. Applying the simplified adjustment to the MODIS CMG fire products yields national estimates of monthly FRE that are 1.44 times greater than originally predicted.

Vegetation fires are a key global terrestrial disturbance factor and a major source of atmospheric trace gases and aerosols. Therefore, many earth-system science and operational monitoring applications require access to repetitive, frequent and well-characterized information on fire emissions source strengths. Geostationary imagers offer important temporal advantages when studying rapidly changing phenomena such as vegetation fires. Here we present a new algorithm for detecting and characterising active fires burning within the imager footprints of the Geostationary Operational Environmental Satellites (GOES), including consideration of cloud-cover and calculation of fire radiative power (FRP), a metric shown to be strongly related to fuel consumption and smoke emission rates. The approach is based on a set of algorithms now delivering near real time (NRT) operational FRP products from the Meteosat Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) imager (available from http://landsaf.meteo.pt/), and the GOES processing chain presented here is designed to deliver a compatible fire product to complete geostationary coverage of the Western hemisphere. Results from the two GOES imagers are intercompared, and are independently verified against the well regarded MODIS cloud mask and active fire products. We find that the detection of cloud and active fires from GOES matches that of MODIS very well for fire pixels having FRP > 30 MW, when the GOES omission error falls to less than 10%. The FRP of fire clusters detected near simultaneously by both GOES and MODIS have a bias of only 22 MW, and a similar bias is found when comparing near-simultaneous GOES East and GOES West FRP observations. However, many fire pixels having FRP 0.9), suggesting that the timely FRP data available from a GOES real-time data feed is likely to be a suitable fire emissions source strength term for inclusion in schemes aiming to forecast the concentrations of atmospheric constituents affected by biomass burning

We explore the ability to enhance landscape fire detection and characterization by constructing a ‘virtual’ fire product from a synthesis of geostationary and polar orbiting satellite data. Active fire pixels detected by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) and the Moderate Resolution Imaging Spectroradiometer (MODIS) were spatially and temporally collated across Africa between February 2004 and January 2005. Coincident fire pixels detected by SEVIRI and MODIS were used to populate an empirical database of frequency density (f-D) distributions of fire radiative power (FRP). Frequency density distributions of FRP measured by SEVIRI at 5.0° grid cell resolution and 15-minute temporal resolution were then cross referenced in the database to a set of counterpart f-D distributions of FRP measured by MODIS. This procedure resulted in the first generation of a ‘virtual’ fire product that exhibits the full continental coverage and high temporal resolution of SEVIRI whilst quantifying fire pixel counts and FRP with accuracies approaching those of MODIS. Diurnal cycles extracted from the virtual fire product indicate that SEVIRI measures a greater proportion of the active fire pixels and FRP potentially detectable by MODIS during the day due to the increased prevalence and stronger radiant contribution of highly energetic fire pixels. On a daily basis (sample size n = 365) the peak magnitude in the diurnal cycle of the virtual FRP occurred within the same 15-minute timeslot as in the native SEVIRI fire product. Continental-scale ignition and extinction events, however, were detected on average 44 min earlier (standard deviation s.d. = 40 min) and 137 min later (s.d. = 92 min), respectively. It is anticipated that the methodology developed here can be used to cross-calibrate active fire products between a variety of different satellite platforms.

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 (TF), effective fire area (AF) 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 TF or AF. 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 TF 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.

We evaluate the operational MODIS Leaf Area Index (LAI) product using field-sampled data collected at five sites in southern Africa in March 2000. One site (Mongu, Zambia) was sampled monthly throughout the year. All sites were along the International Geosphere Biosphere Programme's (IGBP) Kalahari Transect, which features progressively lower annual precipitation, and hence, lower vegetation productivity, from north to south. The soils are consistently sandy. At each site, we sampled the vegetation overstory along three 750-m transects using the Tracing Radiation and Architecture in Canopies (TRAC) instrument. The resulting plant area index values were adjusted with ancillary stem area data to estimate LAI. Despite some instrument characterization and production issues in the first year of MODIS operations, our results suggest the first-year MODIS LAI algorithm correctly accommodates structural and phenological variability in semiarid woodlands and savannas, and is accurate to within the uncertainty of the validation approach used here. Limitations of this study and its conclusions are also discussed.