An algorithm for retrieving nighttime aerosol optical depth (AOD) from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB) observations of reflected moonlight is presented for rural areas during the western U.S. fire seasons. The algorithm uses the UNified and Linearized Vector Radiative Transfer Model (UNL-VRTM) with newly developed capabilities for considering lunar illuminations. Cloud and fire pixels are screened out by utilizing the radiance from the VIIRS Moderate-resolution Bands (M-Band) and the DNB. Rural and city pixels are classified based on a pre-calculated city light database. The surface spectral reflectance for DNB ranging from 342 to 1107 nm is estimated by a random forest approach, which is trained using the surface spectral reflectance from the existing spectral libraries. For the fire seasons of 2017 and 2020, the nighttime AOD retrieval is shown to play an indispensable role in describing the nonlinear diurnal movement of smoke transport and discerning the source of smoke plumes heretofore observable only in the daytime. The retrieved AOD values show good agreement with spatiotemporally collocated Aerosol Robotic NETwork (AERONET) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) AOD values, with linear correlation coefficient values of ~0.96/0.95 and ~86%/69% of the AOD pairs falling in an uncertainty envelope of ±(0.085 + 0.10AOD), which is superior to AOD reanalysis from Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2). These results affirm the significant potential of nighttime AOD to improve the analysis and forecast of regional to global biomass-burning aerosol distributions, filling a critical gap in the diurnal description of a key element of Earth's climate system.

针对美国西部火灾季节期间的农村地区，提出了一种用于从可见红外成像辐射计套件 (VIIRS) 日夜波段 (DNB) 观测反射月光中检索夜间气溶胶光学深度 (AOD) 的算法。该算法使用统一和线性化矢量辐射传递模型 (UNL-VRTM)，该模型具有新开发的考虑月球照明的能力。利用 VIIRS 中等分辨率波段 (M-Band) 和 DNB 的辐射，筛选出云和火像素。农村和城市像素根据预先计算的城市灯光数据库进行分类。DNB 范围从 342 到 1107 nm 的表面光谱反射率是通过随机森林方法估计的，该方法使用来自现有光谱库的表面光谱反射率进行训练。对于 2017 年和 2020 年的火灾季节，夜间 AOD 反演被证明在描述烟雾传输的非线性昼夜运动和识别迄今为止只能在白天观察到的烟羽来源方面发挥着不可或缺的作用。检索到的 AOD 值与时空并列的气溶胶机器人网络 (AERONET) 和具有正交偏振的云气溶胶激光雷达 (CALIOP) AOD 值显示出良好的一致性，线性相关系数值为 ~0.96/0.95 和 ~86%/69% 的 AOD对落在 ±(0.085 + 0.10AOD) 的不确定性包络中，这优于现代研究与应用回顾性分析第 2 版 (MERRA-2) 中的 AOD 再分析。