Moderate Resolution Imaging Spectroradiometer Fire Radiative Power 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.

Smoke Injection Heights from Forest and Grassland Fires in Southwest China Observed by CALIPSO

Smoke injection height (SIH) determines the distance and direction of smoke transport, thus impacting the atmospheric environment. In this study, we used Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations data coupled with Moderate Resolution Imaging Spectroradiometer (MODIS) data and the Hybrid Single-Particle Lagrangian Integrated Trajectory model to derive the SIH values during the peak forest and grassland fire seasons from 2012 to 2017 in Southwest China. The results suggest that the SIH values ranged from 2500 m to 2890 m. An analysis of the dependence of SIH on fire characteristics revealed no obvious correlation between SIH and fire radiative power (FRP) because other factors in addition to FRP have an important impact on SIH. Moreover, MODIS FRP data has a drawback in representing the energy released by real fires, also leading to this result. The topographic variables of forest and grassland fires in Southwest China are very different. Complex topography affects SIH by affecting fire intensity and interactions with wind. A comparison of the SIHs with boundary layer height reveals that 75% of the SIHs are above the boundary layer. Compared with other areas, a higher percentage of free troposphere injection occurs in Southwest China, indicating that smoke can cause air pollution over large ranges. Our work provides a better understanding of the transport and vertical distribution of smoke in Southwest China.

Correcting Swath-Dependent Bias of MODIS FRP Observations With Quantile Mapping

Active fire observations with satellite instruments exhibit a well-documented increase of the detection threshold with increasing pixel footprint size, i.e., distance from the sub-satellite point. This results in a viewing angle-dependent, negative bias in gridded representations of the observed Fire Radiative Power (FRP), which in turn is frequently being used for climate monitoring of biomass burning and for pyrogenic emission inventories. We present a method based on quantile mapping to alleviate this bias and apply it to the gridded-FRP from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instruments. The gridded-FRP observations are corrected with a correction function that depends on the satellite viewing angle and the magnitude of FRP in each grid cell. Assuming the fire observations at nadir to be the best representation of the truth, we derive a correction function by mapping cumulative distribution function (CDF) of off-nadir gridded-FRP to the CDF of near-nadir gridded-FRP. The method can be directly applied to correct the negative bias in gridded-FRP observations at a grid resolution of 1 ∘ or more. The performance of the correction methodology is confirmed through comparisons with co-located Visible Infrared Imaging Radiometer Suite (VIIRS) satellite observations: After bias correction, the gridded-FRP observations from both satellites agree better than before, particularly over savanna, tropical forests, and extra-tropical forests. Experiments with the Global Fire Assimilation System (GFAS) show that the impacts of the bias-corrected MODIS/Aqua gridded-FRP observations and VIIRS/Suomi-NPP gridded-FRP observations on regional FRP analyses are comparable, which confirms the potential for improving fire emission inventories and climate monitoring based on FRP.

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.

Intercomparison of MODIS AQUA and VIIRS I-Band Fires and Emissions in an Agricultural Landscape-Implications for Air Pollution Research.

Quantifying emissions from crop residue burning is crucial as it is a significant source of air pollution. In this study, we first compared the fire products from two different sensors, the Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product (VNP14IMG) and Moderate Resolution Imaging Spectroradiometer (MODIS) 1 km fire product (MCD14ML) in an agricultural landscape, Punjab, India. We then performed an intercomparison of three different approaches for estimating total particulate matter (TPM) emissions which includes the fire radiative power (FRP) based approach using VIIRS and MODIS data, the Global Fire Emissions Database (GFED) burnt area emissions and a bottom-up emissions approach involving agricultural census data. Results revealed that VIIRS detected fires were higher by a factor of 4.8 compared to MODIS Aqua and Terra sensors. Further, VIIRS detected fires were higher by a factor of 6.5 than Aqua. The mean monthly MODIS Aqua FRP was found to be higher than the VIIRS FRP; however, the sum of FRP from VIIRS was higher than MODIS data due to the large number of fires detected by the VIIRS. Besides, the VIIRS sum of FRP was 2.5 times more than the MODIS sum of FRP. MODIS and VIIRS monthly FRP data were found to be strongly correlated (r2 = 0.98). The bottom-up approach suggested TPM emissions in the range of 88.19–91.19 Gg compared to 42.0–61.71 Gg, 42.59–58.75 Gg and 93.98–111.72 Gg using the GFED, MODIS FRP, and VIIRS FRP based approaches, respectively. Of the different approaches, VIIRS FRP TPM emissions were highest. Since VIIRS data are only available since 2012 compared to MODIS Aqua data which have been available since May 2002, a prediction model combining MODIS and VIIRS FRP was derived to obtain potential TPM emissions from 2003–2016. The results suggested a range of 2.56–63.66 (Gg) TPM emissions per month, with the highest crop residue emissions during November of each year. Our results on TPM emissions for seasonality matched the ground-based data from the literature. As a mitigation option, stringent policy measures are recommended to curtail agricultural residue burning in the study area.

Controls on variations in MODIS fire radiative power in Alaskan boreal forests: Implications for fire severity conditions

Fire activity in the Alaskan boreal forest, though episodic at annual and intra-annual time scales, has experienced an increase over the last several decades. Increases in burned area and fire severity are not only releasing more carbon to the atmosphere, but likely shifting vegetation composition in the region towards greater deciduous dominance and a reduction in coniferous stands. While some recent studies have addressed qualitative differences between large and small fire years in the Alaskan boreal forest, the ecological effects of a greater proportion of burning occurring during large fire years and during late season fires have not yet been examined.Some characteristics of wildfires that can be detected remotely are related to fire severity and can provide new information on spatial and temporal patterns of burning. This analysis focused on boreal wildfire intensity (fire radiative power, or FRP) contained in the Moderate Resolution Imaging Spectroradiometer (MODIS) daily active fire product from 2003 to 2010. We found that differences in FRP resulted from seasonality and intra-annual variability in fire activity levels, vegetation composition, latitudinal variation, and fire spread behavior.Our studies determined two general categories of active fire detections: new detections associated with the spread of the fire front and residual pixels in areas that had already experienced front burning. Residual pixels had a lower average FRP than front pixels, but represented a high percentage of all pixels during periods of high fire activity (large fire years, late season burning, and seasonal periods of high fire activity). As a result, the FRP from periods of high fire activity was less intense than those from periods of low fire activity. Differences related to latitude were greater than expected, with higher latitudes burning later in the season and at a higher intensity than lower latitudes. Differences in vegetation type indicate that coniferous vegetation is the most fire prone, but deciduous vegetation is not particularly fire resistant, as the proportion of active fire detections in deciduous stands is roughly the same as the fraction of deciduous vegetation in the region.Qualitative differences between periods of high and low fire activity are likely to reflect important differences in fire severity. Large fire years are likely to be more severe, characterized by more late season fires and a greater proportion of residual burning. Given the potential for severe fires to effect changes in vegetation cover, the shift toward a greater proportion of area burning during large fire years may influence vegetation patterns in the region over the medium to long term.

Space‐based observational constraints for 1‐D fire smoke plume‐rise models

We use a plume height climatology derived from space‐based Multiangle Imaging Spectroradiometer (MISR) observations to evaluate the performance of a widely used plume‐rise model. We initialize the model with assimilated meteorological fields from the NASA Goddard Earth Observing System and estimated fuel moisture content at the location and time of the MISR measurements. Fire properties that drive the plume‐rise model are difficult to constrain, and we test the model with four estimates each of active fire area and total heat flux, obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) fire radiative power (FRP) thermal anomalies available for each MISR plume and other empirical data. We demonstrate the degree to which the fire dynamical heat flux (related to active fire area and sensible heat flux) and atmospheric stability structure influence plume rise, although entrainment and possibly other less well constrained factors are also likely to be significant. Using atmospheric stability conditions, MODIS FRP, and MISR plume heights, we find that smoke plumes reaching high altitudes are characterized by higher FRP and weaker atmospheric stability conditions than those at low altitude, which tend to remain confined below the boundary layer, consistent with earlier results. However, over the diversity of conditions studied, the model simulations generally underestimate the plume height dynamic range observed by MISR and do not reliably identify plumes injected into the free troposphere, key information needed for atmospheric models to simulate smoke dispersion. We conclude that embedding in large‐scale atmospheric studies an advanced plume‐rise model using currently available fire constraints remains a difficult proposition, and we propose a simplified model that crudely constrains plume injection height based on two main physical factors for which some observational constraints often exist. Field experiments aimed at directly measuring fire and smoke plume properties in detail are likely to produce the next major advances in plume‐rise modeling.

Estimating smoke emissions over the US Southern Great Plains using MODIS fire radiative power and aerosol observations

A newly developed method, which involves the use of satellite measurements of energy released by fires, was used to estimate smoke emissions in the United States (US) Southern Great Plains (SGP). This SGP region was chosen because extensive agricultural and planned burning occurs there annually. Moderate resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) and fire radiative energy (FRE) release rates (R FRE), acquired in 2004 from the Terra and Aqua satellites, were used to derive a FRE-based smoke emission coefficient ( C e kg MJ −1), which when multiplied by R FRE (MJ s −1) gives the rate of smoke emission (kg s −1). Correlations between the smoke emission rates and the R FRE were significant for Terra-MODIS ( R 2=0.645, n=146, p<0.0001) and Aqua-MODIS ( R 2=0.752, n=178, p<0.0001). Furthermore, the C e values derived independently from Terra and Aqua were in close agreement, and the average C e for this area is 0.049±0.024 kg MJ −1. A Monte Carlo (MC) probabilistic approach was used to approximate uncertainties from the smoke emission and resulting C e. For the first time, smoke emission estimates have been derived for the US SGP using observations of energy released by fires. Although more work is necessary, the present study demonstrates the feasibility of using R FRE for smoke emission estimation in that region. Burning peaked during the spring and fall seasons. Moreover, qualitative examination of smoke emission patterns side-by-side with local air-quality measurements indicated that the impact of smoke from local biomass burning activities was significant on the regional air-quality.