Simultaneous Characterization of Wildfire Smoke and Surface Properties With Imaging Spectroscopy During the FIREX-AQ Field Campaign

We introduce and evaluate an approach for the simultaneous retrieval of aerosol and surface properties from Airborne Visible/Infrared Imaging Spectrometer Classic (AVIRIS-C) data collected during wildfires. The joint National Aeronautics and Space Administration (NASA) National Oceanic and Atmospheric Administration Fire Influence on Regional to Global Environments and Air Quality field campaign took place in August 2019, and involved two aircraft and coordinated ground-based observations. The AVIRIS-C instrument acquired data from onboard NASA's high altitude ER-2 research aircraft, coincident in space and time with aerosol observations obtained from the Aerosol Robotic Network (AERONET) DRAGON mobile platform in the smoke plume downwind of the Williams Flats Fire in northern Washington in August 2019. Observations in this smoke plume were used to assess the capacity of optimal-estimation based retrievals to simultaneously estimate aerosol optical depth (AOD) and surface reflectance from Visible Shortwave Infrared (VSWIR) imaging spectroscopy. Radiative transfer modeling of the sensitivities in spectral information collected over smoke reveal the potential capacity of high spectral resolution retrievals to distinguish between sulfate and smoke aerosol models, as well as sensitivity to the aerosol size distribution. Comparison with ground-based AERONET observations demonstrates that AVIRIS-C retrievals of AOD compare favorably with direct sun AOD measurements. Our analyses suggest that spectral information collected from the full VSWIR spectral interval, not just the shortest wavelengths, enables accurate retrievals. We use this approach to continuously map both aerosols and surface reflectance at high spatial resolution across heterogeneous terrain, even under relatively high AOD conditions associated with wildfire smoke.

Theoretical Uncertainty Analysis of Satellite Retrieved Aerosol Optical Depth Associated with Surface Albedo and Aerosol Optical Properties

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.

Estimating the Columnar Concentrations of Black Carbon Aerosols in China Using MODIS Products

Black carbon (BC), the strongest light-absorbing particle, is believed to play substantial roles in regional air quality and global climate change. In this study, taking advantage of the high quality of moderate resolution imaging spectroradiometer products, we developed a new algorithm to estimate the BC columnar concentrations over China by simulating the BC and non-BC aerosol mixing states in detail. The results show that our new algorithm produces a reliable estimation of BC aerosols, in which BC columnar concentrations and their related parameters (aerosol absorption and BC surface concentration) show reasonable agreements and low biases compared with ground-based measurements. The uncertainties of BC retrievals are mainly associated with the surface and aerosol assumptions used in the algorithm, ranging from -14 to 44% at higher aerosol optical depth (AOD > 0.5). The proposed algorithm can improve the capability of space-borne aerosol remote sensing by successfully distinguishing BC from other aerosols. The acquired BC columnar concentrations enable the spatial pattern of serious BC aerosol pollution over East China to be characterized, showing that it exhibits higher levels in winter. These nationwide results are beneficial for estimating BC emissions, proposing mitigation strategies for air pollution, and potentially reducing the uncertainties of climate change studies.

Unmanned Aerial Vehicle-Borne Sensor System for Atmosphere-Particulate-Matter Measurements: Design and Experiments

An unmanned aerial vehicle (UAV) particulate-matter (PM) monitoring system was developed that can perform three-dimensional stereoscopic observation of PM2.5 and PM10 in the atmosphere. The UAV monitoring system was mainly integrated by modules of data acquisition and processing, wireless data transmission, and global positioning system (GPS). Particularly, in this study, a ground measurement-control subsystem was added that can display and store collected data in real time and set up measurement scenarios, data-storage modes, and system sampling frequency as needed. The UAV PM monitoring system was calibrated via comparison with a national air-quality monitoring station; the data of both systems were highly correlated. Since rotation of the UAV propeller affects measured PM concentration, this study specifically tested this effect by setting up another identical monitoring system fixed at a tower as reference. The UAV systems worked simultaneously to collect data for comparison. A correction method for the propeller disturbance was proposed. Averaged relative errors for the PM2.5 and PM10 concentrations measured by the two systems were 6.2% and 6.6%, respectively, implying that the UAV system could be used for monitoring PM in an atmosphere environment.

Assessing the Potential of Geostationary Satellites for Aerosol Remote Sensing Based on Critical Surface Albedo

Geostationary satellites are increasingly used for the detection and tracking of atmospheric aerosols and, in particular, of the aerosol optical depth (AOD). The main advantage of these spaceborne platforms in comparison with polar orbiting satellites is their capability to observe the same region of the Earth several times per day with varying geometry. This provides a wealth of information that makes aerosol remote sensing possible when combined with the multi-spectral capabilities of the on-board imagers. Nonetheless, the suitability of geostationary observations for AOD retrieval may vary significantly depending on their spatial, spectral, and temporal characteristics. In this work, the potential of geostationary satellites was assessed based on the concept of critical surface albedo (CSA). CSA is linked to the sensitivity of each spaceborne observation to the aerosol signal, as it is defined as the value of surface albedo for which a varying AOD does not alter the satellite measurement. In this study, the sensitivity to aerosols was determined by estimating the difference between the surface albedo of the observed surface and the corresponding CSA (referred to as dCSA). The values of dCSA were calculated for one year of observations from the Meteosat Second Generation (MSG) spacecraft, based on radiative transfer simulations and information on the satellite acquisition geometry and the properties of the observed surface and aerosols. Different spectral channels from MSG and the future Meteosat Third Generation-Imager were used to study their distinct capabilities for aerosol remote sensing. Results highlight the significant but varying potential of geostationary observations across the observed Earth disk and for different time scales (i.e., diurnal, seasonal, and yearly). For example, the capability of sensing multiples times during the day is revealed to be a notable strength. Indeed, the value of dCSA often fluctuates significantly for a given day, which makes some instants of time more suitable for aerosol retrieval than others. This study determines these instants of time as well as the seasons and the sensing wavelengths that increase the chances for aerosol remote sensing thanks to the variations of dCSA. The outcomes of this work can be used for the development and refinement of AOD retrieval algorithms through the use of the concept of CSA. Furthermore, results can be extrapolated to other present-day geostationary satellites such as Himawari-8/9 and GOES-16/17.

Characterizing elemental, equivalent black, and refractory black carbon aerosol particles: a review of techniques, their limitations and uncertainties

Elemental-, equivalent black- and refractory black-carbon are terms that have been defined in order to dissect the more general term, black carbon, into its component parts related to its specific chemical and optical properties and its impact on climate and health. Recent publications have attempted to clarify the meaning of these terms with respect to their environmental impact, particularly on climate. Here, we focus on the measurement aspects, reviewing the most commonly implemented techniques for the direct and indirect derivation of black carbon properties, their strengths, limitations, and uncertainties, and provide a non-exhaustive bibliography where the reader can find more detailed information. This review paper is designed as a guide for those wishing to learn about the current state of black carbon measurement instrumentation, how calibration is carried out, when one instrument may have the advantage over another, and where new techniques are needed to fill important knowledge gaps.