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，并影响云、气候及人类健康。然而，卫星反演的气溶胶光学厚度（AOD）产品因云覆盖、轨道模式、极夜、雪地及亮地表等因素导致空间覆盖受限，这严重影响了依赖空气污染表征的颗粒物模型建模及健康研究的覆盖范围与准确性。我们提出一种随机森林模型，该模型经过训练以捕捉AOD的空间依赖性，并通过插补实现更高覆盖度。通过结合使用与不使用均值滤波器的模型，我们能够生成覆盖美国本土的全覆盖高分辨率日AOD数据，该数据可用于气溶胶估算及其他依赖空气污染物浓度水平的研究。