Remote sensing and characterization of high temperature targets on the Earth’s surface is required for many cross-disciplinary science investigations and applications including wildland 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 (1-5 μm) where the spectral radiance from high temperature (>800 K) surfaces is most significant.

Given that persons with a prior history of respiratory diseases tend to demonstrate more severe illness from COVID-19 and, hence, are at higher risk of serious symptoms, ambient air quality data from NASA's satellite observations might provide a critical insight into which geographical areas may exhibit higher numbers of hospitalizations due to COVID-19, how the expected severity of COVID-19 and associated survival rates may vary across space in the future, and most importantly how given this information, health professionals can distribute vaccines in a more efficient, timely, and fair manner.

Primary productivity (PP) has been recently investigated using remote sensing-based models over quite limited geographical areas of the Red Sea. This work sheds light on how phytoplankton and primary production would react to the effects of global warming in the extreme environment of the Red Sea and, hence, illuminates how similar regions may behave in the context of climate variability. study focuses on using satellite observations to conduct an intercomparison of three net primary production (NPP) models-the vertically generalized production model (VGPM), the Eppley-VGPM, and the carbon-based production model (CbPM)-produced over the Red Sea domain for the 1998-2018 time period. A detailed investigation is conducted using multilinear regression analysis, multivariate visualization, and moving averages correlative analysis to uncover the models' responses to various climate factors. Here, we use the models' eight-day composite and monthly averages compared with satellite-based variables, including chlorophyll-a (Chla), mixed layer depth (MLD), and sea-surface temperature (SST). Seasonal anomalies of NPP are analyzed against different climate indices, namely, the North Pacific Gyre Oscillation (NPGO), the multivariate ENSO Index (MEI), the Pacific Decadal Oscillation (PDO), the North Atlantic Oscillation (NAO), and the Dipole Mode Index (DMI). In our study, only the CbPM showed significant correlations with NPGO, MEI, and PDO, with disagreements relative to the other two NPP models. This can be attributed to the models' connection to oceanographic and atmospheric parameters, as well as the trends in the southern Red Sea, thus calling for further validation efforts.

Airborne particulate matter (PM) is a well-known cause of cardiovascular disease and mortality and has also been associated with respiratory disease, low birth weight, lung cancer, and other adverse health outcomes. However, our understanding of the relative toxicity of specific PM types-mixtures with different proportions of particles of various sizes, shapes, and compositions-is less well understood. Observations from space, in conjunction with data from surface monitors and chemical transport models, offer a practical means of generating frequent, high spatial resolution maps of PM concentrations in major population centers around the world. The Multi-Angle Imager for Aerosols (MAIA) investigation, currently in development, is motivated by the scientific and societal benefits of linking PM types with adverse health outcomes and the value of satellite observations for addressing this objective.

Operational retrievals of aerosol optical depth (AOD) from Multi-angle Imaging SpectroRadiometer (MISR) data have been shown to have a high bias in pristine oceanic areas. One line of evidence involves comparison with Maritime Aerosol Network (MAN) observations, including the areas of low aerosol loading close to Antarctica. In this paper, a principal reason for the AOD overestimation is identified, which is stray light measured by the MISR cameras in dark regions of high-contrast scenes. A small fraction of the light from surrounding bright areas, such as clouds or sea ice, is redistributed to dark areas, artificially increasing their brightness. Internal reflections and light scattering from optical elements in MISR's pushbroom cameras contribute to this veiling light effect. A simple correction model is developed that relies on the average scene brightness and an empirically determined set of veiling light coefficients for each MISR camera and wavelength. Several independent methods are employed to determine these coefficients. Three sets of coefficients are further implemented and tested in prototype MISR 4.4-km AOD retrievals. The results show dramatic improvements in retrieved AODs compared against MAN observations and the currently operational V22 MISR retrievals. For the best performing set of coefficients, the bias is reduced by 51%, from 0.039 to 0.019, the RMSE is lowered by 19%, from 0.062 to 0.050, and 84% of retrievals fall within the uncertainty envelope compared with 66% of retrievals in V22. The best performing set will be implemented operationally in the next V23 MISR AOD product release.

The Multi-angle Imaging SpectroRadiometer (MISR) instrument has been collecting global Earth data from NASA's Terra satellite since February 2000. With its nine along-track view angles, four visible/near-infrared spectral bands, intrinsic spatial resolution of 275 m, and stable radiometric and geometric calibration, no instrument that combines MISR's attributes has previously flown in space. The more than 10-year (and counting) MISR data record provides unprecedented opportunities for characterizing long-term trends in aerosol, cloud, and surface properties, and includes 3-D textural information conventionally thought to be accessible only to active sensors.

Plume heights from wildfires are used in climate modeling to predict and understand trends in aerosol transport. This paper examines whether smoke from grassland fires in the desert regions of western and central Australia ever rises above the atmospheric boundary layer. Three methods for deriving plume heights from the Multi-angle Imaging SpectroRadiometer (MISR) instrument were utilized: (1) the MISR standard stereo-height algorithm; (2) the MISR enhanced stereo product; and (3) the MISR INteractive eXplorer (MINX) v.1 tool. To provide context and to search for correlative factors, stereo heights were combined with fire radiant energy flux from the Moderate Resolution Imaging Spectroradiometer instrument, atmospheric structure information from the National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis project model, surface cover from the Australia National Vegetation Information System, and forward and backward trajectories from the National Oceanic and Atmospheric Administration Hybrid Single-Particle Lagrangian Integrated Trajectory model. Although most smoke plumes concentrate in the near-surface boundary layer as expected, some appear to rise higher. Smoke that gets above the boundary layer will travel farther, remain in the atmosphere longer, and therefore have a larger environmental impact. It was previously thought unlikely for grassland fires to inject smoke above the boundary layer. Our findings suggest that climate modelers should reevaluate common assumptions about the heights of smoke plumes when producing aerosol transport models involving grassland fires. A closer examination of grassland fire energetics may also be warranted.

The Aerosol Measurement and Processing System (AMAPS) simplifies access to large satellite and ground-based aerosol data sets by allowing computationally expensive and I/O intensive operations to be performed by the systems that host the data sources and then returning the condensed results in a simple common format. This significantly reduces much of the overhead associated with data retrieval and storage, particularly for large and intrinsically complex data sets. AMAPS further facilitates data analysis by providing generalized algorithms that are consistently applied to different data sources in a manner transparent to the user. For example, common geolocation and collocation algorithms are available for multiple instruments, along with interpolation of geophysical retrievals, such as aerosol optical thickness (AOT), to common wavelengths. AMAPS also includes a common set of tools for data reduction, analysis, and plotting. In this paper, we describe the AMAPS system and demonstrate the application of the tool for aerosol studies tailored to the African continent and surrounding areas.

In this paper, Multi-angle Imaging SpectroRadiometer (MISR) aerosol product attributes are described, including geometry and algorithm performance flags. Actual retrieval coverage is mapped and explained in detail using representative global monthly data. Statistical comparisons are made with coincident aerosol optical depth (AOD) and Angstrom exponent (ANG) retrieval results from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. The relationship between these results and the ones previously obtained for MISR and MODIS individually, based on comparisons with coincident ground-truth observations, is established. For the data examined, MISR and MODIS each obtain successful aerosol retrievals about 15% of the time, and coincident MISR-MODIS aerosol retrievals are obtained for about 6%-7% of the total overlap region. Cloud avoidance, glint and oblique-Sun exclusions, and other algorithm physical limitations account for these results. For both MISR and MODIS, successful retrievals are obtained for over 75% of locations where attempts are made. Where coincident AOD retrievals are obtained over ocean, the MISR-MODIS correlation coefficient is about 0.9; over land, the correlation coefficient is about 0.7. Differences are traced to specific known algorithm issues or conditions. Over-ocean ANG comparisons yield a correlation of 0.67, showing consistency in distinguishing aerosol air masses dominated by coarse-mode versus fine-mode particles. Sampling considerations imply that care must be taken when assessing monthly global aerosol direct radiative forcing and AOD trends with these products, but they can be used directly for many other applications, such as regional AOD gradient and aerosol air mass type mapping and aerosol transport model validation. Users are urged to take seriously the published product data-quality statements.