Fine-mode aerosols (FMA), generally generated from anthropogenic sources, play an essential role in global radiation balance, atmospheric environment, climate change, and human health. However, the FMA retrieval remains a challenge.
Recently, a research team led by Prof. LI Zhengqiang from the Aerospace Information Research Institute (AIR) of the Chinese Academy of Sciences has proposed a new aerosol retrieval algorithm for better FMA retrieval, called Spectral Neutrality of Surface Polarized Reflectance, or SNOSPR for short. The study was published in Remote Sensing of Environment.
Multi-angle polarized observation has great advantages in FMA retrieval, and the accurate estimation of surface polarization reflectance (SPR) is crucial during the retrieval process. Traditionally, SPR is estimated by a surface Bidirectional Polarization Distribution Function (BPDF) model.
Although many BPDF models have been developed, there is still no single model that can be applied to all kinds of surface types with high precision. Large estimation error still exists on certain types of surface.
Compared to traditional methods, SNOSPR can retrieve FMA optical depth (AODf) with high precision based on the characteristics that SPR hardly changes at wavelengths from the visual to near-infrared. The new method does not depend on the BPDF, and can get Angstrom exponent (AE) and SPR simultaneously.
The researchers also discussed the uncertainties of the new method from various aspects, such as the uncertainty caused by the hypothesis that SPR does not change with wavelength and the use of multiangle data.
SNOSPR was applied for POLDER-3 data and all the retrievals were tested against AERONET observations. The validation results showed that SNOSPR could fit for different surface types, and had better retrieval qualities and higher spatial resolution than POLDER official products.
The method was applied on Directional Polarimetric Camera (DPC) data onboard the GF-5 satellite and good validation results were obtained against AERONET observations.
The results in this study demonstrated the effectiveness of the SNOSPR algorithm and its potential for more reliable AODf/SPR retrievals for monitoring local atmospheric pollution.
This study was supported by the National Outstanding Youth Foundation of China, and the National Natural Science Foundation of China.
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