Journal of Geophysical Research: Atmospheres
Journal of Geophysical Research: Atmospheres
5 years ago

Improving remote sensing of aerosol microphysical properties by near-infrared polarimetric measurements over vegetated land: Information content analysis

Xiaoguang Xu, Jun Wang, Zhengqiang Li, Weizhen Hou, Lili Qie, Philippe Goloub
While polarimetric measurements contain valuable information regarding aerosol microphysical properties, polarization data in the near-infrared (NIR) bands have not been widely utilized. This paper evaluates whether the aerosol property information contents from single-viewing satellite polarimetric measurements at 1610 and 2250 nm can be used to improve the retrieval of aerosol parameters over vegetated land, in combination with shorter-wavelength bands (490, 670, 870 nm). The priori information and errors for the analysis are derived by assuming that the surface reflectance at visible wavelengths can be derived from the top-of-atmosphere at 2250 nm. The information content in the synthetic dataset is investigated for ten aerosol parameters characterizing the columnar aerosol volumes ( V0f, V0c), particle size distributions ( refff, vefff, reffc, veffc) and refractive indices ( mrf, mif, mrc, mic) for the fine- and coarse-mode aerosol models respectively, and one parameter C characterizing the surface polarization. The results indicate that the degree-of-freedom-for-signal (DFS) can be increased by at least 2 with the addition of NIR measurements and that 1−3 additional parameters could be further retrieved with significantly decreased uncertainties. In addition, the 1610 nm band is necessary for the simultaneous retrieval of V0f, mrf and refff for the fine-mode dominated aerosols, while the 1610 and 2250nm bands are both indispensable for retrieving V0f, V0c, mrc, refff and reffc in tandem for the coarse-mode dominated aerosols. The analysis also reveals that C could be further retrieved by including scalar radiance, and that measurement errors have significantly larger influences on the retrieval uncertainties than model errors.

Publisher URL: http://onlinelibrary.wiley.com/resolve/doi

DOI: 10.1002/2017JD027388

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