Hyperspectral Satellite Remote Sensing of Aerosol Parameters: Sensitivity Analysis and Application to TROPOMI/S5P

Abstract

Precise knowledge about aerosols in the lower atmosphere (optical properties and vertical distribution) is particularly important for studying the Earth’s climatic and weather conditions. Measurements from satellite sensors in sun-synchronous and geostationary orbits can be used to map distributions of aerosol parameters in global or regional scales. The new-generation sensor Tropospheric Monitoring Instrument (TROPOMI) onboard the Copernicus Sentinel-5 Precursor (S5P) measures a wide variety of atmospheric trace gases and aerosols that are associated with climate change and air quality using a number of spectral bands between the ultraviolet and the shortwave infrared. In this study, we perform a sensitivity analysis of the forward model parameters and instrument information that are associated with the retrieval accuracy of aerosol layer height (ALH) and optical depth (AOD) using the oxygen (O2) A-band. Retrieval of aerosol parameters from hyperspectral satellite measurements requires accurate surface representation and parameterization of aerosol microphysical properties and precise radiative transfer calculations. Most potential error sources arising from satellite retrievals of aerosol parameters, including uncertainties in aerosol models, surface properties, solar/satellite viewing geometry, and wavelength shift, are analyzed. The impact of surface albedo accuracy on retrieval results can be dramatic when surface albedo values are close to the critical surface albedo. An application to the real measurements of two scenes indicates that the retrieval works reasonably in terms of retrieved quantities and fit residuals.

Aerosol Parameters Retrieval From TROPOMI/S5P Using Physics-Based Neural Networks

“…1) The mean values of the aerosol optical depth delivered by the three neural network algorithms are in general underestimated. The reason for this discrepancy is that the official retrieval algorithm uses different aerosol microphysical properties (a fact also seen in the previous study [8]).…”

“…A number of passive satellite sensors enable to monitor aerosol properties on both regional and global scale using spectral information at various wavelengths. For instance, measurements in the O 2 A-band from the Global Ozone Mapping Experiment (GOME) [1] and GOME-2 [2], the Scanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY) [3][4][5], the Greenhouse Gases Observing Satellite (GOSAT) [6], and the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor (S5P) [7,8] are used to retrieve aerosol optical depth and height information.…”

Aerosol layer height (ALH) retrievals from oxygen absorption bands: Intercomparison and validation among different satellite platforms, GEMS, EPIC, and TROPOMI

“…With the retirement of CALIPSO in August 2023, passive remote sensing will become the only routine technique from space for filling the data gap of measuring aerosol vertical distribution before next lidar dedicated to measure aerosols are launched into space. However, only limited information for aerosol extinction vertical profile can be obtained in passive remote sensing due to the need for multiple assumptions regarding surface and aerosol properties in the retrieval process (Geddes and Bösch, 2015;Rao et al, 2022). Several parameters, including spectral coverage, radiance, polarization, spectral resolution, signal-to-noise ratio (SNR), and the number of viewing angles, can influence the information content and retrieval uncertainties of aerosol profiles.…”

Reply on RC1

“…With the retirement of CALIPSO in August 2023, passive remote sensing will become the only routine technique from space for filling the data gap of measuring aerosol vertical distribution before next lidar dedicated to measure aerosols are launched into space. However, only limited information for aerosol extinction vertical profile can be obtained in passive remote sensing due to the need for multiple assumptions regarding surface and aerosol properties in the retrieval process (Geddes and Bösch, 2015;Rao et al, 2022). Several parameters, including spectral coverage, radiance, polarization, spectral resolution, signal-to-noise ratio (SNR), and the number of viewing angles, can influence the information content and retrieval uncertainties of aerosol profiles.…”