Aerosol Optical Property Retrieval Techniques in Remote Sensing

Aerosol optical properties such as aerosol optical depth, single scattering albedo and particle size distribution are fundamental parameters for assessing the impact of airborne particles on climate forcing, air quality and human health. Retrieval techniques in remote sensing exploit both passive and active platforms, ranging from polar-orbiting spectroradiometers to geostationary imagers and ground-based sun-photometer networks. Passive methods typically invert measured top-of-atmosphere radiances using radiative transfer models constrained by surface reflectance characterisations and cloud-screening algorithms. Multi-angle and polarimetric instruments enhance sensitivity to particle morphology and absorption, while machine-learning and data-assimilation approaches now augment traditional lookup-table inversions with data-driven gap-filling and uncertainty estimation. Active lidar systems provide vertical profiling of aerosol backscatter and extinction coefficients, enabling synergistic fusion with passive columnar retrievals. Recent advances have focused on coupling high-resolution bidirectional reflectance distribution function models with optimal estimation schemes, integrating multi-sensor observations and enhancing real-time quality control for global monitoring. These developments offer improved accuracy in diverse environments, from urban to dust-dominated regions, and support applications in climate attribution, air-quality forecasting and policy evaluation.

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Aerosol Optical Property Retrieval Techniques in Remote Sensing publication trend

The graph below shows the total number of articles in aerosol optical property retrieval techniques in remote sensing across all publications each year (not limited to Nature Index journals).

Technical terms

Aerosol Optical Depth (AOD): A columnar measure of light extinction by aerosols between satellite and surface.

Single Scattering Albedo (SSA): The ratio of scattering to total (scattering plus absorption) aerosol optical extinction.

Bidirectional Reflectance Distribution Function (BRDF): A model describing how surface reflectance varies with illumination and viewing geometry.

Optimal Estimation Algorithm: A retrieval scheme combining measurements and prior knowledge to derive best-estimate aerosol properties and uncertainties.

Lookup Table (LUT): A precomputed set of radiative transfer simulations used to invert satellite radiances for aerosol retrievals.

Machine Learning: Data-driven methods, such as random forests or deep neural networks, applied to gap-filling and retrieval enhancement.

Polarimetry: Remote sensing of the state of polarisation of scattered light, providing sensitivity to aerosol size and shape.

References

1. LGHAP v2: a global gap-free aerosol optical depth and PM2.5 concentration dataset since 2000 derived via big Earth data analytics. Earth System Science Data (2024).
2. Retrieval of hourly aerosol single scattering albedo over land using geostationary satellite data. npj Climate and Atmospheric Science (2024).
3. Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements. Atmospheric Measurement Techniques (2019).
4. Polarimetric remote sensing of atmospheric aerosols: Instruments, methodologies, results, and perspectives. Journal of Quantitative Spectroscopy and Radiative Transfer (2019).