基于最优化理论的大气气溶胶偏振遥感反演方法研究

项目名称： 基于最优化理论的大气气溶胶偏振遥感反演方法研究

项目编号： No.41505022

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 天文学、地球科学

项目作者： 侯伟真

作者单位： 中国科学院遥感与数字地球研究所

项目金额： 21万元

中文摘要： 大气气溶胶偏振遥感反演是当前的一个研究热点，本项目充分考虑卫星和机载传感器多角度偏振观测的信息，结合先验知识的约束以及地表BRDF和BPDF模型，基于线性化的矢量辐射传输模型UNL-VRTM前向模拟的Stokes和灵敏度（Jacobian）信息，研究进行最优化反演建模，将气溶胶遥感偏振反演问题转化为一个最优化问题，进而利用L-BFGS-B优化算法进行迭代求解，针对各反演要素提出了可操作的实施方案，可实现气溶胶和地表多参数的联合反演，有效解决病态反演问题。同时，综合考虑实际观测和具体误差来源，对传感器的观测信息量及模型的误差传递进行定量分析和评价，为反演建模和算法设计提供可参考的依据。本研究不需要建立查找表，可灵活地进行实时和动态多参数的联合反演，预期可有效提高遥感反演精度，反演得到如体积柱浓度、粒子谱分布、复折射指数和Angstrom指数等更多的气溶胶信息，为环境遥感监测提供支撑。

中文关键词： 最优化反演方法；先验知识约束；多角度偏振遥感；大气气溶胶；地表偏振

英文摘要： Nowadays, the atmospheric aerosol polarized remote sensing inversion algorithm is a hot international research topic. In this project, fully considering the multi-angle and polarized observations information from the satellite and airborne sensors, combined with the constraints of prior knowledge as well as the bidirectional polarization distribution function (BRDF) and bidirectional reflectance distribution function (BPDF) model of surface, we try to use the Stokes and Jacobian information to set the optimization inversion modeling. Those Stokes and Jacobian information could be simulated by the unified linearized - vector radiative transfer model (UNL-VRTM), and then the inversion problem of aerosol remote sensing polarization could be transformed into an optimization problem, which can be iteratively solved by the L-BFGS-B optimization algorithm. Thus, for each retrieval parameters, the operational plan could be proposed, the joint inversion with aerosol and surface parameters could be completed, and the ill-posed inversion problem also could be effectively solved. Meanwhile, considering the sensors’ actual observation and specific source of errors, the observation information of observation and error transfer of the inversion model could be quantitative evaluated, which provides a useful reference basis for the inversion modeling and algorithm design. This study does not need to relay on a lookup table, and the real-time and dynamic joint inversion could be achieved flexibly. With the study in this project, the inversion accuracy could be effectively improved, and more aerosol information such as column volume concentration, particle size distribution, complex refractive index and the Angstrom index could be retrieved, further providing the technical support for the environmental remote sensing monitoring.

英文关键词： optimized inversion algorithm;prior knowledge constraints;multi-angle polarized remote sensing;Atmospheric aerosol;surface polarized