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Multiangular Polarimetry Enables Improved Marine Aerosol Remote Sensing Accuracy Using

Sep 02, 2025

Aerosols are one of the most uncertain components in quantifying climate radiative forcing. However, polarimetry improves the characterization of the microphysical properties of atmospheric aerosols.

A research group led by Prof. SUN Xiaobing from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences investigated the impact of multiangular polarimetry on the quantification of marine aerosol remote sensing applications.

They examined the importance of the spectral range, spectral band, number of viewing angles, and polarimetric accuracy for retrieving aerosols from polarization measurements over the ocean. Using Bayesian optimization theory and a vector radiative transfer model, the researchers employed degrees of freedom for signal (DFS) as an indicator to compare and analyze different observation scenarios.

The experimental results showed that the DFS can increase by at least 1.02 when shortwave infrared (SWIR) intensity and polarization measurements are added to the single-angle observation mode. This improvement corresponds to the retrieval of one to two additional aerosol parameters. In the multi-viewing experiment, the columnar volume concentration, effective radius, and complex refractive index for both the fine and coarse size modes increased with additional viewing angles. 

Furthermore, incorporating additional multi-angle SWIR measurements can enhance the total aerosol DFS by approximately 1.1-3.3. The analysis also indicated that the polarimetric accuracy has a significant impact on the uncertainty of aerosol retrieval.

Their findings, published in the Optics Expressprovide an important reference for the design of future polarimetric instruments and the development of retrieval algorithms.

Contact

ZHAO Weiwei

Hefei Institutes of Physical Science

E-mail:

Remote sensing of aerosol properties over the ocean using near-infrared and shortwave infrared multiangular polarimetry: information content analysis

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