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

ZHENG Fengxun; LI Zhengqiang; HOU Weizhen; DONG Xiaogang; ZHOU Zhiyuan

doi:10.11834/jrs.20210276

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Optimal estimation algorithm research for aerosol fine-mode fraction retrieval from polarimetric measurements
Vol. 26, Issue 12, Pages: 2497-2506(2022)
Received：15 July 2020，

Published：07 December 2022
DOI： 10.11834/jrs.20210276
稿件说明：

移动端阅览

郑逢勋，李正强，侯伟真，董晓刚，周志远.2022.气溶胶细粒子比偏振遥感最优化反演方法研究.遥感学报，26（12）： 2497-2506 DOI： 10.11834/jrs.20210276.

Zheng F X，Li Z Q，Hou W Z，Dong X G and Zhou Z Y. 2022. Optimal estimation algorithm research for aerosol fine-mode fraction retrieval from polarimetric measurements. National Remote Sensing Bulletin， 26（12）：2497-2506 DOI： 10.11834/jrs.20210276.

摘要

针对PM

2.5

遥感模型对气溶胶细粒子比FMF（Fine mode fraction）参数的需求，结合多光谱偏振传感器对大气探测的优势，基于最优估计OE（Optimal Estimation）反演框架，提出了一种基于线偏振度（Degree of Linear Polarization）测量的FMF最优化反演方法。采用矢量化的辐射传输模式UNL-VRTM进行地基天空光的线偏振度观测模拟，分析了线偏振度对FMF参数的波段敏感性，并基于仿真数据开展了算法的反演测试。研究结果表明：偏振测量在长波近红外波段对FMF的敏感性高于可见光波段；基于OE框架的FMF反演算法具有良好的闭合性；在地基天顶观测模式下，引入线偏振度测量参与反演能够有效提高FMF的反演精度，FMF反演误差从1.4%下降到了0.18%。最优化反演方法对于气溶胶遥感具有一定的潜力和可行性，有望成为提高PM

2.5

遥感监测能力的新途径。

Abstract

Monitoring the atmospheric particle pollution is one of priorities for environmental protection. To infer the near-surface fine particulate matter (PM

2.5

) 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 PM

2.5

monitoring.

关键词

Keywords

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