Zachary Fasnacht

Retrievals of ocean color (OC) properties from space are important for understanding the ocean ecosystem, the carbon cycle, and monitoring events such as harmful algal blooms (HABs). The recently launched U.S. National Aeronautics and Space Administration (NASA) Earth Venture Instrument, the geostationary Tropospheric Emissions: Monitoring of Pollution (TEMPO), provides a unique opportunity to examine diurnal variability in ocean ecology across coastal waters of North America and prepare for future hyperspectral geostationary OC missions. Although TEMPO does not match the spatial resolution or spectral coverage of planned coastal ocean sensors, such as NASA's Geosynchronous Littoral Imaging and Monitoring Radiometer or the U.S. National Oceanic and Atmospheric Administration Geostationary Extended Observations Ocean Color Instrument, it provides hourly observations at approximately 5 km over U.S. coastal regions and the Great Lakes. Here, we apply a newly developed atmospheric correction approach based on principal component analysis combined with machine learning (ML) to retrieve OC properties using TEMPO's hyperspectral measurements. Principal component coefficients derived from measured reflectances are used to train a neural network to estimate OC properties, including chlorophyll concentration, informed by collocated physically‐based retrievals from MODIS, VIIRS, and Ocean and Land Color Instrument. This ML‐based approach complements traditional radiative transfer retrievals, particularly under challenging conditions such as glint and moderate cloud coverage. This approach demonstrates the value of near‐real‐time OC products, with significant potential for monitoring HABs and transient oceanic phenomena.

Satellite-based observations of nitrogen dioxide (NO₂) are important for monitoring atmospheric composition, estimating nitrogen oxide emissions, and informing chemistry transport models. While advancements have been made in space-based NO₂ observations and retrievals, most measurements are still unable to resolve the detailed structure of NO₂ plumes. Designed primarily for aerosol and ocean applications, the plankton, aerosol, cloud, ocean ecosystem ocean color instrument (OCI) provides a unique opportunity to retrieve NO₂ from high spatial resolution (∼1 km)² hyper-spectral measurements. We exploit a machine learning technique to show that OCI, with a spectral resolution of 5 nm, can provide high spatial resolution information about NO₂ when trained with high quality retrievals from the Tropospheric Monitoring Instrument (TROPOMI). This work demonstrates the potential to rapidly produce high spatial resolution NO₂ columns by making use of well validated retrievals derived from instruments with higher spectral resolution. These data can potentially enable emissions estimates with reduced uncertainties and higher spatial resolution. Additionally, the data could provide higher resolution information for exposure estimates used in epidemiological studies.

Retrievals of ocean color (OC) properties from space are important for understanding the ocean ecosystem, the carbon cycle, and monitoring events such as harmful algal blooms (HABs). NASA’s recently launched Earth Venture Instrument (EVI), the geostationary Tropospheric Emissions: Monitoring of Pollution (TEMPO), provides a unique opportunity to examine diurnal variability in ocean ecology across coastal waters of North America and prepare for future hyperspectral geostationary OC missions. Although TEMPO does not match the spatial resolution or spectral coverage of planned coastal ocean sensors, such as NASA’s Geosynchronous Littoral Imaging and Monitoring Radiometer (GLIMR) or NOAA’s Geostationary Extended Observations Ocean Color Instrument (GeoXO OCX), it provides hourly observations at approximately 5\,km over U.S. coastal regions and the Great Lakes. Here, we apply a newly developed atmospheric correction approach based on principal component analysis combined with machine learning (ML) to retrieve OC properties using TEMPO’s hyperspectral measurements. Principal component coefficients derived from measured reflectances are used to train a neural network to estimate OC properties, including chlorophyll concentration, informed by collocated physically-based retrievals from MODIS, VIIRS, and OLCI. This ML-based approach complements traditional radiative transfer retrievals, particularly under challenging conditions such as glint and moderate cloud coverage. This approach demonstrates the value of near-real-time OC products, with significant potential for monitoring HABs and transient oceanic phenomena.

Ultraviolet (UV) radiation effects on Earth’s ecosystems on a global scale can be assessed on a basis of satellite estimates of hyperspectral irradiance on the surface and in ocean waters and the spectral biological weighting functions. The satellite UV surface irradiance algorithms combine satellite retrievals of extraterrestrial solar irradiance, cloud/surface reflectivity, aerosol optical depth, and total column ozone with radiative transfer computations. The assessment of in-water irradiance requires additional information on inherent optical properties (IOPs) of ocean water. Our Ozone Monitoring Instrument (OMI) surface hyperspectral irradiance algorithm is updated by implementing a new absorbing aerosol correction based on OMI daily retrievals of UV aerosol absorption optical depth (AAOD). To provide insight into the temporal and spatial variability of absorbing aerosols, we consider a monthly global AAOD climatology derived from the OMI UV aerosol algorithm. Hyperspectral underwater irradiance is computed using Hydrolight radiative transfer calculations along with a Case I water model of IOPs extended into UV. Both planar and scalar irradiances are computed on the Earth’s surface and propagated underwater. The output surface products include the UV index. The output underwater products include the hyperspectral diffuse attenuation coefficients of the planar and scalar irradiances. Effects of the seasonal variability of AAOD on the UV index and the deoxyribonucleic acid (DNA) damage dose rates are considered. The reduction in the UV index and DNA damage dose rate due to the presence of absorbing aerosols can be as large as 30–40%.

This Algorithm Theoretical Basis Document (ATBD) describes the retrieval algorithm and sensitivities of the Version 3 cloud product derived from the spectra collected by the Tropospheric Emissions: Monitoring of POllution (TEMPO) instrument. The cloud product is primarily produced for supporting the retrievals of TEMPO trace gases that are important for understanding atmospheric chemistry and monitoring air pollution. The TEMPO cloud algorithm is adapted from NASA's Ozone Monitoring Instrument (OMI) oxygen collision complex (O₂‐O₂) cloud algorithm. The retrieval generates effective cloud fraction (ECF) from the normalized radiance at 466 nm and generates cloud optical centroid pressure (OCP) using the O₂‐O₂ column amount derived from the spectral absorption feature near 477 nm. The slant column of O₂‐O₂ is retrieved using Smithsonian Astrophysical Observatory's spectral fitting code with optimized retrieval parameters. ECF and OCP are used by TEMPO trace gas retrievals to calculate Air Mass Factors which convert slant columns to vertical columns. The sensitivities of the cloud retrieval to various input parameters are investigated.

Retrievals of ocean color (OC) properties from space are important for better understanding the ocean ecosystem and carbon cycle. The launch of atmospheric hyperspectral instruments such as the geostationary Tropospheric Emissions: Monitoring of Pollution (TEMPO) and GEMS, provide a unique opportunity to examine the diurnal variability in ocean ecology across various waters in North America and prepare for the future suite of hyperspectral OC sensors. While TEMPO does not have as high spatial resolution or full spectral coverage as planned coastal ocean sensors such as the Geosynchronous Littoral Imaging and Monitoring Radiometer (GLIMR) or GeoXO OC instrument (OCX), it provides hourly coverage of US coastal regions and great lakes, such as Lake Erie and the Gulf of Mexico at spatial scales of approximately 5 km. We will apply our newly developed machine learning (ML) based atmospheric correction approach for OC retrievals to TEMPO data. Our approach begins by decomposing measured hyperspectral radiances into spectral features that explain the variability in atmospheric scattering and absorption as well as the underlying surface reflectance. The coefficients of the principal components are then used to train a neural network to predict OC properties such as chlorophyll concentration derived from collocated MODIS/VIIRS physically-based retrievals. This ML approach compliments the standard radiative transfer-based OC retrievals by providing gap-filling over cloudy regions where the standard algorithms are limited. Previously, we applied our approach using blue and UV wavelengths with the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) to show that it can estimate OC properties in less-than-ideal conditions such as lightly to moderately cloudy conditions as well as sun glint and thus improve the spatial coverage of ocean color measurements. TEMPO provides an opportunity to improve on this approach since it provides extended spectral measurements at green and red wavelengths which are important particularly for coastal waters. Additionally, our ML technique can be applied to provisional data early in the mission and has potential to demonstrate the value of near real time OC products that are important for monitoring of harmful algae blooms and transient oceanic phenomena.

Satellite-based observations of nitrogen dioxide (NO2) are important for monitoring air quality, estimating nitrogen oxide emissions, and informing chemistry transport models. While advancements have been made in space-based NO2 retrievals, most measurements are still unable to resolve the detailed structure of NO2 plumes. Designed primarily for aerosol and ocean applications, the plankton, aerosol, cloud, ocean ecosystem (PACE) ocean color instrument (OCI) provides a unique opportunity to retrieve NO2 from high spatial resolution (∼1km^2) hyper-spectral measurements. We exploit a machine learning technique to show that OCI with a spectral resolution of 5nm can provide high spatial resolution information about NO2 when trained with tropospheric monitoring instrument (TropOMI) retrievals. This work demonstrates the potential to rapidly spatially downscale NO2 observations from heritage instruments and retrievals. This can potentially enable emissions estimates with reduced uncertainties and higher temporal resolution. Additionally, it could provide higher resolution information for exposure estimates used in health studies.

Satellite-derived spatiotemporal patterns of nitrogen oxide (NO_x) emissions can improve accuracy of emission inventories to better support air quality and climate research and policy studies. In this study, we develop a new method by coupling the chemical transport Model-Independent SATellite-derived Emission estimation Algorithm for Mixed-sources (MISATEAM) with a divergence method to map high-resolution NO_x emissions across US cities using TROPOspheric Monitoring Instrument (TROPOMI) tropospheric nitrogen dioxide (NO₂) retrievals. The accuracy of the coupled method is validated through application to synthetic NO₂ observations from the NASA-Unified Weather Research and Forecasting (NU-WRF) model, with a horizontal spatial resolution of 4 km × 4 km for 33 large and mid-size US cities. Validation reveals excellent agreement between inferred and NU-WRF-provided emission magnitudes (R = 0.99, Normalized Mean Bias, NMB = −0.01) and a consistent spatial pattern when comparing emissions for individual grid cells (R = 0.88 ± 0.06). We then develop a TROPOMI-based database reporting annual emissions for 39 US cities at a horizontal spatial resolution of 0.05° × 0.05° from 2018 to 2021. This database demonstrates a strong correlation (R = 0.90) with the national emission inventory (NEI) but reveals some bias (NMB = −0.24). There are noticeable differences in the spatial patterns of emissions in some cities, which suggests potential misallocation of emissions and/or missing sources in bottom-up emission inventories.

Retrieving ozone from limb measurements necessitates the modeling of scattered light through the atmosphere. However, accurately modeling multiple scattering (MS) during retrieval requires excessive computational resources; consequently, operational retrieval models employ approximations in lieu of the full MS calculation. Here we consider an alternative MS approximation method, where we use radiative transfer (RT) simulations to train neural network models to predict the MS radiances. We present our findings regarding the best-performing network hyperparameters, normalization schemes, and input/output data structures. Using RT calculations based on measurements by the Ozone Mapping and Profiling Suite's Limb Profiler (OMPS/LP), we compare the accuracy of these neural-network models with both the full MS calculation as well as the current MS approximation methods utilized during OMPS/LP retrievals.

Satellite-based visible and near-infrared imaging of the Earth's surface is generally not performed in moderate to highly cloudy conditions; images that look visibly cloud covered to the human eye are typically discarded. Here, we expand upon previous work that employed machine learning (ML) to estimate underlying land surface reflectances at red, green, and blue (RGB) wavelengths in cloud contaminated spectra using a low spatial resolution satellite spectrometer. Specifically, we apply the ML methodology to a case study at much higher spatial resolution with the Hyperspectral Imager for the Coastal Ocean (HICO) that flew on the International Space Station (ISS). HICO spatial sampling is of the order of 90 m. The purpose of our case study is to test whether high spatial resolution features can be captured using multi-spectral imaging in lightly cloudy and overcast conditions. We selected one clear and one cloudy image over a portion ofthe panhandle coastline of Florida to demonstrate that land features are partially recoverable in overcast conditions. Many high contrast features are well recovered in the presence of optically thin clouds. However, some of the low contrast features, such as narrow roads, are smeared out in the heavily clouded part of the reconstructed image. This case study demonstrates that our approach may be useful for many science and applications that are being developed for current and upcoming satellite missions including precision agriculture and natural vegetation analysis, water quality assessment as well as disturbance, change, hazard, and disaster detection.