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Optimal estimation algorithm research for aerosol fine-mode fraction retrieval from polarimetric measurements

  • role： First author第一作者
  • Affiliation：

    College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China

    State Environmental Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • Email：zhengfengxun@126.com
  • Introduction：郑逢勋，研究方向为偏振遥感最优化反演。E-mail：zhengfengxun@126.com
ZHENG Fengxun12,  
  • Affiliation：

    State Environmental Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

LI Zhengqiang2,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Environmental Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • Email：houwz@radi.ac.cn
  • Introduction：侯伟真，研究方向为气溶胶卫星遥感。E-mail：houwz@radi.ac.cn
HOU Weizhen2*,  
  • Affiliation：

    Science and Technology on Optical Radiation Laboratory, Beijing Institute of Environmental Characteristics, Beijing 100854, China

DONG Xiaogang3,  
  • Affiliation：

    Science and Technology on Optical Radiation Laboratory, Beijing Institute of Environmental Characteristics, Beijing 100854, China

ZHOU Zhiyuan3

Resümee

Monitoring the atmospheric particle pollution is one of priorities for environmental protection. To infer the near-surface fine particulate matter (PM2.5) mass concentration, aerosol Fine-Mode Fraction (FMF) is an important parameter, which can separate contributions from smaller and bigger particles in Aerosol Optical Depth (AOD). However, there still have great challenges for the conversion between FMF and remotely sensed optical measurements. As one of the most promising methods of remote sensing, polarimetry is widely employed for atmospheric aerosol monitoring, and has a good potential for improving FMF inversion. In order to investigate the contribution of polarization to the for improved characterization of FMF, an algorithm for FMF retrieval from multispectral intensity and Degree Of Linear Polarization (DOLP) measurements is proposed in this paper.The proposed algorithm is based on the Optimal Estimation (OE) inversion theory. The UNified Linearized Vector Radiative Transfer Model (UNL-VRTM) is adopted as the forward model, and the quasi-Newton approach implemented by the L-BFGS-B code is used to find the minimum of the cost function. In order to test the performance of the algorithm, synthetic data for ground-based measurements of sky light, in the conditions of different aerosol optical depth (AOD, from 0.1 to 3.0) and FMF (from 0.05 to 0.95), are simulated. In addition, near-infrared (NIR) measurements at a wavelength of 1610 nm were introduced to improve the retrieval of coarse mode aerosol. Under the OE inversion framework, the AOD and FMF can be retrieved simultaneously after several iterations.Based on the synthetic data, analysis shows that the DOLP is more sensitive to FMF in the NIR band (centered at 1610 nm) than in the visible band (centered at 490, 550, 670 and 870 nm). Numerical inversion test furtherly show that the algorithm has well self-consistency, the error of retrieved FMF caused by the algorithm itself is 0.014%. In the case of 5% observation error is considered, the average fitting residual, differences between the simulations with best inversion results and the measurements, is 5.2%, which is slightly higher than the intensity observation error (5%). By introducing DOLP measurements into the retrieval, the inversion accuracy improved significantly than only using the intensity measurements. The retrieval error of AOD has decreased from 1% to 0.3%, and the retrieval error of FMF has decreased from 1.4% to 0.18%.These results strongly validate the feasibility and potentiality of the proposed OE inversion method in atmospheric aerosol polarimetric remote sensing. This mechanism is expected to be a new approach to improving the remote sensing capabilities of PM2.5 monitoring.

Schlüsselwort

remote sensing;degree of linear polarization;aerosol;fine mode fraction;optimal estimation retrieval

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