Deriving aerosol optical depth (AOD) from space-borne observations is still challenging due to uncertainties associated with sensor calibration drift, cloud screening, aerosol type classification, and surface reflectance characterization. As an initial step to understanding the physical processes impacting these uncertainties in satellite AOD retrievals, this study outlines a theoretical approach to estimate biases in the satellite aerosol retrieval algorithm affected by surface albedo and prescribed aerosol optical properties using a simplified radiative transfer model with a traditional error propagation approach. We expand the critical surface reflectance concept to obtain the critical surface albedo (CSA), critical single scattering albedo (CSSA), and critical asymmetry parameter (CAP). The top-of-atmosphere (TOA) reflectance is not sensitive to significant variability in aerosol loading (AOD) at the critical value; thus, the AOD cannot be determined. Results show that 5% bias in surface albedo (A), single scattering albedo (SSA), or asymmetry parameter (g) lead to large retrieved AOD errors, especially high under conditions when A, SSA, or g are close to their critical values. The results can be useful for future research related to improvements of satellite aerosol retrieval algorithms and provide a preliminary framework to analytically quantify AOD uncertainties from satellite retrievals.

We analyze new aerosol products from NASA satellite retrievals over the western USA during August 2013, with special attention to locally generated wildfire smoke and downwind plume structures. Aerosol optical depth (AOD) at 550 nm from MODerate Resolution Imaging Spectroradiometer (MODIS) (Terra and Aqua Collections 6 and 6.1) and Visible Infrared Imaging Radiometer Suite (VIIRS) Deep Blue (DB) and MODIS (Terra and Aqua) Multi‐Angle Implementation of Atmospheric Correction (MAIAC) retrievals are evaluated against ground‐based AErosol RObotic NETwork (AERONET) observations. We find a significant improvement in correlation with AERONET and other metrics in the latest DB AOD (MODIS C6.1 r² = 0.75, VIIRS r² = 0.79) compared to MODIS C6 (r² = 0.62). In general, MAIAC (r² = 0.84) and DB (MODIS C6.1 and VIIRS) present similar statistical evaluation metrics for the western USA and are useful tools to characterize aerosol loading associated with wildfire smoke. We also evaluate three novel NASA MODIS plume injection height (PIH) products, one from MAIAC and two from the Aerosol Single scattering albedo and layer Height Estimation (ASHE) (MODIS and VIIRS) algorithm. Both Terra and Aqua MAIAC PIHs statistically agree with ground‐based and satellite lidar observations near the fire source, as do ASHE, although the latter is sensitive to assumptions about aerosol absorption properties. We introduce a first‐order approximation Smoke Height Boundary Layer Ratio (SHBLR) to qualitatively distinguish between aerosol pollution within the planetary boundary layer and the free troposphere. We summarize the scope, limitations, and suggestions for scientific applications of surface level aerosol concentrations specific to wildfire emissions and smoke plumes using these novel NASA MODIS and VIIRS aerosol products.

As wildfires intensify and fire seasons lengthen across the western US, the development of models that can predict smoke plume concentrations and track wildfire-induced air pollution exposures has become critical. Wildfire smoke plume height is a key indicator of the vertical placement of plume mass emitted from wildfire-related aerosol sources in climate and air quality models. With advancements in Earth observation (EO) satellites, spaceborne products for aerosol layer height or plume injection height have recently emerged with increased global-scale spatiotemporal resolution. However, to evaluate column radiative effects and refine satellite algorithms, vertical profiles of regionally representative aerosol properties from wildfires need to be measured directly. In this study, we conducted the first comprehensive evaluation of four passive satellite remote-sensing techniques specifically designed for retrieving plume height. We compared these satellite products with the airborne Wyoming Cloud Lidar (WCL) measurements during the 2018 Biomass Burning Flux Measurements of Trace Gases and Aerosols (BB-FLUX) field campaign in the western US. Two definitions, namely, “plume top” and “extinction-weighted mean plume height”, were used to derive the representative heights of wildfire smoke plumes, based on the WCL-derived vertical aerosol extinction coefficient profiles. Using these two definitions, we performed a comparative analysis of multisource satellite-derived plume height products for wildfire smoke. We provide a discussion related to which satellite product is most appropriate for determining plume height characteristics near a fire event or estimating downwind plume rise equivalent height, under multiple aerosol loadings. Our findings highlight the importance of understanding the sensitivity of different passive remote-sensing techniques on space-based wildfire smoke plume height observations, in order to resolve ambiguity surrounding the concept of “effective smoke plume height”. As additional aerosol-observing satellites are planned in the coming years, our results will inform future remote-sensing missions and EO satellite algorithm development. This bridges the gap between satellite observations and plume rise modeling to further investigate the vertical distribution of wildfire smoke aerosols.

As wildfires intensify and fire seasons lengthen across the western U.S., the development of applicable models that can predict the density of smoke plumes and track wildfire-induced air pollution exposures has become critical. Wildfire smoke plume height is a key indicator of the vertical placement of plume mass emitted from wildfire-related aerosol sources in climate and air quality models. With advancements in Earth observation (EO) satellites, spaceborne products for aerosol layer height or plume injection height have recently emerged with increased global-scale spatiotemporal resolution. However, to evaluate column radiative effects and refine satellite algorithms, vertical profiles of regionally representative aerosol data from wildfire emissions need to be measured directly in the field. In this study, we conduct the first comprehensive evaluation of four passive satellite remote sensing techniques specifically designed to retrieve plume height distribution for wildfire smoke. We compare these satellite products with the airborne Wyoming Cloud Lidar (WCL) measurements during the 2018 Biomass Burning Flux Measurements of Trace Gases and Aerosols (BB-FLUX) field campaign in the western U.S. Two definitions, namely “plume top” and “extinction-weighted mean plume height”, are used to derive representative heights of wildfire smoke plumes, based on the WCL-retrieved vertical aerosol extinction coefficient profiles. We also perform a comparative analysis of multisource satellite-derived plume height products for wildfire smoke using these two definitions. With the aim to discuss which satellite product is most appropriate under various aerosol loadings and in determining plume height characteristics near a fire-event location or downwind plume rise equivalent height. Our findings highlight the importance of understanding the sensitivity of different passive remote sensing techniques to space-based wildfire smoke plume height observations, in order to resolve ambiguity surrounding the concept of “effective smoke plume height”. As additional aerosol-observing satellites are expected to be launched in the coming years, our results will inform future remote sensing missions and EO data selection. This will help bridge the gap between satellite observations and plume rise modeling to further investigate the vertical distribution of wildfire smoke aerosols.

As wildfires intensify and fire seasons lengthen across the western U.S., the development of applicable models that can predict the density of smoke plumes and track wildfire-induced air pollution exposures has become critical. Wildfire smoke plume height is a key indicator of the vertical placement of plume mass emitted from wildfire-related aerosol sources in climate and air quality models. With advancements in Earth observation (EO) satellites, spaceborne products for aerosol layer height or plume injection height have recently emerged with increased global-scale spatiotemporal resolution. However, to evaluate column radiative effects and refine satellite algorithms, vertical profiles of regionally representative aerosol data from wildfire emissions need to be measured directly in the field. In this study, we conduct the first comprehensive evaluation of four passive satellite remote sensing techniques specifically designed to retrieve plume height distribution for wildfire smoke. We compare these satellite products with the airborne Wyoming Cloud Lidar (WCL) measurements during the 2018 Biomass Burning Flux Measurements of Trace Gases and Aerosols (BB-FLUX) field campaign in the western U.S. Two definitions, namely “plume top” and “extinction-weighted mean plume height”, are used to derive representative heights of wildfire smoke plumes, based on the WCL-retrieved vertical aerosol extinction coefficient profiles. We also perform a comparative analysis of multisource satellite-derived plume height products for wildfire smoke using these two definitions. With the aim to discuss which satellite product is most appropriate under various aerosol loadings and in determining plume height characteristics near a fire-event location or downwind plume rise equivalent height. Our findings highlight the importance of understanding the sensitivity of different passive remote sensing techniques to space-based wildfire smoke plume height observations, in order to resolve ambiguity surrounding the concept of “effective smoke plume height”. As additional aerosol-observing satellites are expected to be launched in the coming years, our results will inform future remote sensing missions and EO data selection. This will help bridge the gap between satellite observations and plume rise modeling to further investigate the vertical distribution of wildfire smoke aerosols.

The southeastern US accounts for over 60% of national prescribed (Rx) burning acreage and relies on Rx fire to reduce wildfire risk and sustain forest biodiversity. While ecologically beneficial, Rx fires may introduce smoke‐related risks to surrounding communities. Using a burn permit geodatabase integrated with atmospheric, emissions, and chemical transport models, we estimated daily fine particulate matter (PM _2.5 ) attributable to the reported 2017–2019 Rx burns in Florida, Georgia, and South Carolina. Rx fires across this region contributed 1.39 ± 0.57 μg/m ³ to daily PM _2.5 , increasing to 3.24 ± 1.45 μg/m ³ (37% of total PM _2.5 ) during the January–April high‐burn season. Integrating permit data with air quality modeling improved smoke estimates relative to satellite‐based inventories in some regional and seasonal comparisons. Our framework refines Rx fire smoke exposure assessment and can inform state‐specific smoke management and public health planning.