Aerosols play a critical role in atmospheric chemistry, and affect clouds, climate, and human health. However, the spatial coverage of satellite-derived aerosol optical depth (AOD) products is limited by cloud cover, orbit patterns, polar night, snow, and bright surfaces, which negatively impacts the coverage and accuracy of particulate matter modeling and health studies relying on air pollution characterization. We present a random forest model trained to capture spatial dependence of AOD and produce higher coverage through imputation. By combining the models with and without the mean filters, we are able to create full-coverage high-resolution daily AOD in the conterminous U.S., which can be used for aerosol estimation and other studies leveraging air pollutant concentration levels.

翻译：气溶胶在大气化学中扮演关键角色，并对云层、气候及人类健康产生影响。然而，卫星反演的气溶胶光学厚度（AOD）产品的空间覆盖率受到云覆盖、卫星轨道模式、极夜、积雪及亮地表因素的限制，这严重影响了依赖空气污染表征的颗粒物建模与健康研究的覆盖范围与准确性。我们提出一种基于随机森林的模型训练方法，该模型能够捕捉AOD的空间依赖性，并通过插补技术实现更高空间覆盖率。通过将含均值滤波器与不含均值滤波器的模型相结合，我们能够生成美国本土全覆盖、高分辨率的每日AOD数据，这些数据可用于气溶胶估算及其他基于空气污染物浓度水平的研究。