Decentralized solar is an emerging strategy for advancing modern energy access among rural populations globally. However, both natural and anthropogenic aerosols can significantly worsen solar panel performance. Although the effect of aerosols is typically assessed using satellite or reanalysis data during system sizing, these datasets often underestimate extreme aerosol conditions in West Africa. This study evaluates the impact of aerosols on photovoltaic (PV) output by developing irradiance and generation models that accept as input three reanalysis and satellite-derived datasets with varying spatial and temporal resolution. The accuracy of each dataset is evaluated through comparisons to ground-based AERONET measurements. We find none of the aerosol datasets capture the highest aerosol loadings well, underestimating the 99th percentile aerosol optical depth (AOD) values between 18-49 %, which can lead to undersizing PV systems by up to 11 % for high-reliability designs. To capture total regional dust impacts, we combine dust aerosol with dust soiling loss modeling. Modeled irradiance shows that daily energy losses during the annual Harmattan dry season can reach 50 %, and seasonal energy losses caused by dust can be between 19-40 %. In locations within the Sahel, soiling dominates dust-associated losses (62-66 % of total losses), while for coastal locations near the Gulf of Guinea, dust aerosols drive losses (56 %). These findings highlight the need for location-specific mitigation strategies to effectively address PV dust losses. The modeling framework developed in this study can be used to improve the siting, sizing and maintenance strategies for PV systems in dry regions worldwide.

Fuels are a large source of uncertainty in fire emissions estimates due to variability in the physical and chemical properties of fuels and how they are represented. These uncertainties can be addressed using imaging spectroscopy and lidar data, that provide observations of the chemical and physical traits and spatial distribution of vegetation. Combined with ground fuel measurements, these data provide information on fuel distribution and quantity important for mapping and modeling fire effects. In this study, we present a methodology to develop models and continuous maps of pre-fire fuel characteristics for use in fire emissions modeling. We first addressed any spatial gaps over fire areas for Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) chemical trait data using Random Forests regression and for derived fractional cover. We used the AVIRIS fractional cover and chemical traits or AVIRIS estimates alongside lidar, multispectral, and topographic variables to build fuel characteristic models informed by ground measurements with partial least squares regression. We derived maps of predictive uncertainty alongside a suite of uncertainty statistics for each fuel characteristic that inform the use of fuels data within fire effects models. We used two study sites: the Williams Flats wildfire in eastern Washington state, USA and three prescribed crown fires in Utah, USA. The results show similar error between calibration and validation sets and NRMSE of around 20 % or lower for a majority of the fuel models. We present fuel characteristic and uncertainty maps for all fires. This study shows that the use of imaging spectroscopy and lidar data have the potential to represent fuel heterogeneity and continuously map fuel characteristics for fire effects modeling.

Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) was a field campaign aimed at better understanding the impact of wildfires and agricultural fires on air quality and climate. The FIREX-AQ campaign took place in August 2019 and involved two aircraft and multiple coordinated satellite observations. This study applied and evaluated a self-supervised machine learning (ML) method for the active fire and smoke plume identification and tracking in the satellite and sub-orbital remote sensing datasets collected during the campaign. Our unique methodology combines remote sensing observations with different spatial and spectral resolutions. With as much as a 10% increase in agreement between our produced masks and high-certainty hand-labeled pixels, relative to evaluated operational products, the demonstrated approach successfully differentiates active fire pixels and smoke plumes from background imagery. This enables the generation of a per-instrument smoke and active fire mask product, as well as smoke and fire masks created from the fusion of selected data from independent instruments. This ML approach has the potential to enhance operational active wildfire monitoring systems and improve decision-making in air quality management through fast smoke plume identification and tracking and could improve climate impact studies through fusion data from independent instruments.

Ammonia (NH3) plays an important role in atmospheric chemistry and air quality, but its emissions remain poorly constrained due to its short atmospheric lifetime, high spatial heterogeneity, and limited coverage of existing monitoring resources. This study integrates airborne longwave-infrared (LWIR) hyperspectral imaging at similar to 2m spatial resolution with ground-based stationary and mobile in situ measurements to map and characterize NH3 emissions in two regions near the Salton Sea in southern California: Mecca in the northwest and Imperial in the southeast. Airborne surveys conducted in March and September 2023 with a wide-swath LWIR spectral imager revealed pronounced spatial and seasonal variability. Average NH3 levels in Imperial were 2.5-8 times higher than those in Mecca, linked primarily to large, concentrated animal feeding operations (CAFOs), geothermal power plants, fumaroles, and intensive agricultural activities. Ground-based mobile monitoring corroborated these findings, showing elevated NH3 levels near these sources and especially high NH3 concentrations downwind of CAFOs with large cattle populations. The results underscore the utility of airborne LWIR hyperspectral imaging in detecting and mapping NH3 at hyperlocal scales, including sources absent from existing inventories. They further highlight the need for routine airborne campaigns and the development of next-generation satellite missions with higher spatial resolution to achieve comprehensive, large-area monitoring. These findings inform air quality management strategies and emphasize the importance of improving emission inventories for effective mitigation of NH3-driven air pollution.

Airborne longwave-infrared (LWIR) hyperspectral imagery acquisitions were coordinated with stationary and mobile ground-based in situ measurements of atmospheric ammonia in regions surrounding California’s Salton Sea, an area of commingled intensive animal husbandry and agriculture operations that is encumbered by exceptionally high levels of persistent ammonia and PM2.5 pollution. The goal of this study was to validate remotely sensed ammonia retrievals against ground truth measurements as part of a broader effort to elucidate the behavior of the atmospheric ammonia burden in this area of abundant diffuse and point sources. The nominal 2 m pixel size of the airborne data revealed variability in ammonia concentrations at a diversity of scales within the study area. At this pixel resolution, ammonia plumes emitted by individual facilities could be clearly discriminated and their dispersion characteristics inferred. Several factors, including thermal contrast and atmospheric boundary layer depth, contributed to the overall uncertainty of the intercomparison between airborne ammonia quantitative retrievals and the corresponding in situ measurements, for which agreement was in the 16–37% range under the most favorable conditions. Hence, while the findings attest to the viability of airborne LWIR spectral imaging for quantifying atmospheric ammonia concentrations, the accuracy of ground-level estimations depends significantly on precise knowledge of these atmospheric factors.

Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) was a field campaign aimed at better understanding the impact of wildfires and agricultural fires on air quality and climate. The FIREX-AQ campaign took place in August 2019 and involved two aircraft and multiple coordinated satellite observations. This study applied and evaluated a self-supervised machine learning (ML) method for the active fire and smoke plume identification and tracking in the satellite and sub-orbital remote sensing datasets collected during the campaign. Our unique methodology combines remote sensing observations with different spatial and spectral resolutions. The demonstrated approach successfully differentiates fire pixels and smoke plumes from background imagery, enabling the generation of a per-instrument smoke and fire mask product, as well as smoke and fire masks created from the fusion of selected data from independent instruments. This ML approach has a potential to enhance operational wildfire monitoring systems and improve decision-making in air quality management through fast smoke plume identification12 and tracking and could improve climate impact studies through fusion data from independent instruments.

Traditionally, aerosol retrieval algorithms are customized to specific instruments because of the diverse nature of remote sensing hardware architectures and data formats. This diversity can hinder the utilization of lower-level data products. Furthermore, a generalized aerosol retrieval approach has the potential to allow simultaneous use of observations from multiple platforms within a single retrieval framework. In this work, a comprehensive set of solutions for integrating open-source aerosol retrieval software and publicly available multiangle spectropolarimetric data products is presented as a complementary software program (CSP). This CSP is adaptable to observations obtained from various instruments, including the Airborne Multiangle Spectropolarimetric Imager (AirMSPI) and the upcoming Multiangle Imager for Aerosols (MAIA) (Diner et al., 2013); (Maia 2022). Established methods for reconciling coordinate systems, curating data, and structuring data for input to the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) open-source software are addressed by the CSP (Grasp-open, 0000). The CSP provides the functionality to compute multiple GRASP aerosol retrievals from the same polarimetric observations by varying the user-defined coordinate system selection. Each coordinate system is defined by a unique reference plane, which rotates the reported polarization orientation but does not alter the information content. Therefore retrieving aerosol values from different coordinate systems is suggested as a tool for: (1) verifying the coordinate systems of the instrument and algorithm have been properly reconciled and (2) quantifying numerical variations in the retrieval’s optimization algorithm. The CSP is demonstrated using AirMSPI data from the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign (Diner et al., 2013); (Warneke, 2019). Aerosol retrievals performed with the meridian versus the scattering reference plane differed up to 0.068 in aerosol optical depth and 0.015 in single scattering albedo. These variations in smoke properties from multiangle spectropolarimetric observations quantify uncertainty which is applicable for cross-validation instrument comparisons and studies of different retrieval algorithms.

Virtually all aspects of our societal functioning from food security to energy supply to healthcare depend on the dynamics of environmental factors. Nevertheless, the social dimensions of weather and climate are noticeably less explored by the artificial intelligence community. By harnessing the strength of geometric deep learning (GDL), we aim to investigate the pressing societal question the potential disproportional impacts of air quality on COVID-19 clinical severity. To quantify air pollution levels, here we use aerosol optical depth (AOD) records that measure the reduction of the sunlight due to atmospheric haze, dust, and smoke. We also introduce unique and not yet broadly available NASA satellite records (NASAdat) on AOD, temperature, and relative humidity and discuss the utility of these new data for biosurveillance and climate justice applications, with a specific focus on COVID-19 in the states of Texas and Pennsylvania. The results indicate, in general, that the poorer air quality tends to be associated with higher rates for clinical severity and, in the case of Texas, that this phenomenon particularly stands out in Texan counties characterized by higher socioeconomic vulnerability. This, in turn, raises a concern of environmental injustice in these socioeconomically disadvantaged communities. Furthermore, given that one of NASA's recent long-term commitments is to address such inequitable burden of environmental harm by expanding the use of Earth science data such as NASAdat, this project is one of the first steps toward developing a new platform integrating NASA's satellite observations with deep learning (DL) tools for social good. SIGNIFICANCE STATEMENT: By leveraging the strengths of modern deep learning models, particularly, graph neural networks to describe complex spatiotemporal dependencies and by introducing new NASA satellite records, this study aims to investigate the problem of potential environmental injustice associated with COVID-19 clinical severity and caused by disproportional impacts of poor air quality on disadvantaged socioeconomic populations.

Cloud condensation nuclei (CCN) are mediators of aerosol–cloud interactions (ACIs), contributing to the largest uncertainties in the understandings of global climate change. We present a novel remote-sensing-based algorithm that quantifies the vertically resolved CCN number concentrations (NCCN) using aerosol optical properties measured by a multiwavelength lidar. The algorithm considers five distinct aerosol subtypes with bimodal size distributions. The inversion used the lookup tables developed in this study, based on the observations from the Aerosol Robotic Network, to efficiently retrieve optimal particle size distributions from lidar measurements. The method derives dry aerosol optical properties by implementing hygroscopic enhancement factors in lidar measurements. The retrieved optically equivalent particle size distributions and aerosol-type-dependent particle composition are utilized to calculate critical diameters using κ-Köhler theory and NCCN at six supersaturations ranging from 0.07 % to 1.0 %. Sensitivity analyses indicate that uncertainties in extinction coefficients and relative humidity greatly influence the retrieval error in NCCN. The potential of this algorithm is further evaluated by retrieving NCCN using airborne lidar from the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign and is validated against simultaneous measurements from the CCN counter. The independent validation with robust correlation demonstrates promising results. Furthermore, the NCCN has been retrieved for the first time using a proposed algorithm from spaceborne lidar – Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) – measurements. The application of this new capability demonstrates the potential for constructing a 3D CCN climatology at a global scale, which helps to better quantify ACI effects and thus reduce the uncertainty in aerosol climate forcing.

Annual global satellite-based estimates of fine particulate matter (PM2.5) are widely relied upon for air-quality assessment. Here, we develop and apply a methodology for monthly estimates and uncertainties during the period 1998-2019, which combines satellite retrievals of aerosol optical depth, chemical transport modeling, and ground-based measurements to allow for the characterization of seasonal and episodic exposure, as well as aid air-quality management. Many densely populated regions have their highest PM2.5 concentrations in winter, exceeding summertime concentrations by factors of 1.5-3.0 over Eastern Europe, Western Europe, South Asia, and East Asia. In South Asia, in January, regional population-weighted monthly mean PM2.5 concentrations exceed 90 μg/m3, with local concentrations of approximately 200 μg/m3 for parts of the Indo-Gangetic Plain. In East Asia, monthly mean PM2.5 concentrations have decreased over the period 2010-2019 by 1.6-2.6 μg/m3/year, with decreases beginning 2-3 years earlier in summer than in winter. We find evidence that global-monitored locations tend to be in cleaner regions than global mean PM2.5 exposure, with large measurement gaps in the Global South. Uncertainty estimates exhibit regional consistency with observed differences between ground-based and satellite-derived PM2.5. The evaluation of uncertainty for agglomerated values indicates that hybrid PM2.5 estimates provide precise regional-scale representation, with residual uncertainty inversely proportional to the sample size.

In this presentation, we will report our recent efforts in achieving high performance in Antimonides type-II superlattice (T2SL) based infrared photodetectors using the barrier infrared detector (BIRD) architecture. The high operating temperature (HOT) BIRD focal plane arrays (FPAs) offer the same high performance, uniformity, operability, manufacturability, and affordability advantages as InSb. However, mid- wavelength infrared (MWIR) HOT-BIRD FPAs can operate at significantly higher temperatures (>150K) than InSb FPAs (typically 80K). Moreover, while InSb has a fixed cutoff wavelength (similar to 5.4 mu m), the HOT-BIRD offers a continuous adjustable cutoff wavelength, ranging from similar to 4 mu m to >15 mu m, and is therefore also suitable for long wavelength infrared (LWIR) as well. The LWIR detectors based on the BIRD architecture has also demonstrated significant operating temperature advantages over those based on traditional p-n junction designs. HyTI (Hyperspectral Thermal Imager) and c-FIRST (compact Fire Infrared Radiance Spectral Tracker) based on JPL's T2SL BIRD FPAs. Based on III-V compound semiconductors, the BIRD FPAs offer a breakthrough solution for the realization of low cost (high yield), high-performance FPAs with excellent uniformity and pixel-to-pixel operability.

In this presentation we will describe the technologies developed at the Jet Propulsion Laboratory for space remote sensing applications in mid-wavelength infrared (MWIR) and long-wavelength infrared (LWIR) spectral regions. Remote sensing and characterization of high temperature targets on the Earth’s surface is required for many cross-disciplinary science investigations and applications including fire and volcano impacts on ecology, the carbon cycle, and atmospheric composition. For decades this research has been hindered by insufficient spatial resolution and/or detector saturation of satellite sensors operating at short and mid-infrared wavelengths (1-5μm) where the spectral radiance from high temperature (<800 K) surfaces is most significant. To address this critical need, the Jet Propulsion Laboratory and partnering institutions are developing a compact modular high dynamic range (HDR) multispectral imager concept, with the flexibility to operate in the short, mid- or long-wavelength infrared spectral bands.

Identification of global methane (CH4) sources is critical to the quantification and mitigation of this greenhouse gas. Future imaging spectrometer missions, such as Carbon Mapper, will provide global, spatially resolved observations that will make it possible to accurately map methane sources. However, the sheer data volume of these missions make manual source identification infeasible, and expected artifacts in matched filter methane plume identification challenge simple thresholding. Recent works have demonstrated the feasibility of Convolutional Neural Networks (CNNs) for plume detection; however, in the past, these models have suffered from high false positive rates and were limited in their training and evaluation to individual flight campaigns. We have assembled quality-controlled tiled datasets from three Airborne Visible/Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) campaigns: a 2020 California campaign (“COVID”), a 2019 Texas Permian Basin campaign (“Permian”), and another 2018 California campaign (“CACH4”). These datasets are notable for their diversity of surface conditions, spatial resolutions, and source types (oil & gas, energy, waste, livestock). Labeled methane sources in these datasets have been manually verified, and flightlines with systematic artifacts have been filtered out. We trained a GoogLeNet CNN classifier model on each of these campaigns to evaluate intra- and inter- campaign performance. We also trained a model on all three campaigns and evaluated its performance on each dataset. We observed an F1 performance of 0.7 or greater for each model trained and evaluated on its own dataset. We also observed that the model trained on all three datasets often outperforms individual models on multiple metrics. Finally, we converted the model into a fully convolutional network (FCN) for methane plume saliency map generation. We plan to extend this work to datasets acquired by the Global Airborne Observatory (GAO) and prepare a model for deployment for the Carbon Mapper orbital data product pipeline.

Shallow coastal waters are often biologically productive (eutrophic), turbidity from runoff is common, and the atmosphere above is typically more aerosol-laden than over open ocean waters. Although the NASA Earth Observing System’s Multi-angle Imaging SpectroRadiometer (MISR) onboard the Terra satellite has been observing aerosols for 23 years, the current operational retrieval algorithm (V23) does not report results over shallow waters (< 50 m in depth or < 5 km from land), due to the complexity of accounting for water-leaving radiance under these conditions. However, MISR measures radiances at four wavelengths and nine view angles, which makes it possible to distinguish atmospheric from ocean surface contributions to the top-of-atmosphere signal. Here, we implement and validate a "Shallow Water" aerosol retrieval algorithm, following Limbacher and Kahn (2019), which constrains a Lambertian surface reflectivity term to account for water-leaving radiance from shallow, turbid, and eutrophic waters. We develop custom quality filtering and find that this modified retrieval algorithm compares well to independent surface-based observations and is better suited for use over shallow waters than the operational Dark Water retrieval algorithm. Globally, retrieving aerosols over shallow waters increases the number of over-water measurements by more than 7%, preferentially in some of the most biologically active coastal regions of the ocean.

Abstract. Shallow and coastal waters are often rich in nutrients (eutrophic) and biologically productive, turbid from runoff, and located where the atmosphere above can be more aerosol-laden than over open ocean waters due to proximity to aerosol sources on land. Although the NASA Earth Observing System’s Multi-angle Imaging SpectroRadiometer (MISR) on board the Terra satellite has been monitoring global aerosols for over 23 years, the current operational retrieval algorithm (V23) is not applied over waters less than 50 m in depth or within 5 km of land, designated as "shallow water." This is due to the simplicity of the Dark Water algorithm, applied operationally over deep waters, which assumes the surface is essentially black in the primarily-used red and near-infrared spectral bands. In this work, we describe the implementation and validation of a "Shallow Water" aerosol retrieval algorithm for MISR, which takes advantage of all four available spectral bands and includes a Lambertian surface reflectivity term to account for water-leaving radiance. This algorithm compares well to independent, surface-based observations and demonstrates better performance over shallow waters than the operational Dark Water retrieval algorithm. Globally, aerosol retrievals over shallow waters increase the total number of MISR over-water measurements by more than 7 %, including new retrievals made over some of the most biologically productive parts of the ocean.

A SmallSat mission concept is formulated here to carry out Time-varying Optical Measurements of Clouds and Aerosol Transport (TOMCAT) from space while embracing low-cost opportunities enabled by the revolution in Earth science observation technologies. TOMCAT's "around-the-clock" measurements will provide needed insights and strong synergy with existing Earth observation satellites to 1) statistically resolve diurnal and vertical variation of cirrus cloud properties (key to Earth's radiation budget), 2) determine the impacts of regional and seasonal planetary boundary layer (PBL) diurnal variation on surface air quality and low-level cloud distribu-tions, and 3) characterize smoke and dust emission processes impacting their long-range transport on the subseasonal to seasonal time scales. Clouds, aerosol particles, and the PBL play critical roles in Earth's climate system at multiple spatiotemporal scales. Yet their vertical variations as a function of local time are poorly measured from space. Active sensors for profiling the atmosphere typically utilize sun-synchronous low-Earth orbits (LEO) with rather limited temporal and spatial coverage, inhibiting the characterization of spatiotemporal variability. Pairing compact active lidar and passive multiangle remote sensing technologies from an inclined LEO platform enables measurements of the diurnal and vertical variability of aerosols, clouds, and aerosol-mixing-layer (or PBL) height in tropical-to-midlatitude regions where most of the world's population resides. TOMCAT is conceived to bring potential societal benefits by delivering its data products in near-real time and offering on-demand hazard-monitoring capabilities to profile fire injection of smoke particles, the frontal lofting of dust particles, and the eruptive rise of volcanic plumes.

Shallow and coastal waters are often rich in nutrients (eutrophic), biologically productive, turbid from runoff, and located where the atmosphere above can be more aerosol-laden than over open-ocean waters due to proximity to aerosol sources on land. Although the NASA Earth Observing System's Multi-angle Imaging SpectroRadiometer (MISR) on board the Terra satellite has been monitoring global aerosols for over 23 years, the current operational retrieval algorithm (V23) is not applied over waters less than 50 m in depth or within 5 km of land, designated as “shallow water.” This is due to the simplicity of the Dark Water algorithm, applied operationally over deep waters, which assumes the surface is essentially black in the primarily used red and near-infrared spectral bands. In this work, we describe the implementation and validation of a “Shallow Water” aerosol retrieval algorithm for MISR, which takes advantage of all four available spectral bands and includes a Lambertian surface reflectivity term to account for water-leaving radiance. This algorithm compares well to independent, surface-based observations and demonstrates better performance over shallow waters than the operational Dark Water retrieval algorithm. Globally, aerosol retrievals over shallow waters increase the total number of MISR over-water measurements by more than 7 %, including new retrievals made over some of the most biologically productive parts of the ocean.

Exposure to fine particulate matter (PM2.5) is the leading environmental risk factor for mortality globally. Satellite-derived estimates of surface PM2.5 developed from a combination of satellites, simulations, and ground monitor data are relied upon for health impact studies. The ability to develop satellite-derived PM2.5 estimates requires the continued availability of aerosol optical depth (AOD) sources. This work examines the impact of the addition or loss of satellite AOD data sources on global PM2.5 estimation and the impact of continuing the long-term record with AOD from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi-National Polar orbiting Partnership (S-NPP) satellite after the loss of the MODIS (MODerate resolution Imaging Spectroradiometer) and MISR (Multi-angle Imaging Spectroradiometer) instruments on board the Terra and Aqua satellites. We find that the addition of VIIRS S-NPP AOD products to geophysical PM2.5 estimates from satellites and simulations causes regional differences that correspond to differences in the VIIRS and MODIS Deep Blue AOD algorithms and sampling. Changes in long-term trends and timeseries due to the addition or loss of AOD data sources are generally within their uncertainties. Statistical fusion with ground monitor data partially corrects for changes due to sampling differences when introducing the VIIRS AOD products, but uncertainty remains over desert regions where ground monitor coverage is sparse. This work provides promise for the sustained development of global satellite-derived PM2.5 estimates, despite discontinuities in instruments and retrieval methods.

Wildland fire smoke is one of the major sources of biomass-burning aerosols in the Earth’s atmosphere. As the smoke plume evolves, the biomass-burning aerosols undergo aging processes that change their physical and chemical composition. Capturing rapid temporal changes is not suitable for satellites due to the time lapse between observations. Airborne remote sensing offers the ability to increase the spatial and temporal resolution of smoke plume observations compared to satellite observations. However, this increase in spatial and temporal resolution from airborne observations amplifies the need for better spatial coverage from ground-based instruments for validation. In the summer of 2019, the Airborne Multiangle Spectropolarimetric Imager (AirMSPI) was deployed during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign on the NASA ER-2 high-altitude research aircraft. This aircraft performed multiple overpasses of the Williams Flats fire near the town of Spokane, Washington, USA, in August 2019, sampling smoke plumes at a georectifed spatial resolution of 10 m2. This work performs aerosol retrievals along the smoke plume observed during one flight on 7 August 2019. The retrieval methods used here follow those established by DeLeon et al. (in review) using the Generalized Retrieval of Atmosphere and Surface Properties (GRASP). Two points along the plume were selected: one at 1.35km and the other at 3.78km from the fire source. The fraction of fine mode aerosols and single scattering albedo increased at the greater distance from this wildfire source. These retrieved aerosol properties were used to simulate ground-based polarimetry in ultraviolet, visible, and infrared wavebands. For all wavebands, the maximum degree of linear polarization (DoLP) decreased farther from the source. Notably, the ultraviolet wavebands retained a higher polarimetric signal farther from the source, compared to the visible and infrared. At 865 nm the DoLP decreased from 48.5% to 14.6%. At 355 nm the DoLP decreased from 33.6% to 22.5%. These polarimetric simulations are intended to inform instrument development for ground-based detection of wildfire smoke.