An improved deep learning network for AOD retrieving from remote sensing imagery focusing on sub-pixel cloud

作者：He Cai, Bo Zhong, Huilin Liu, Bailin Du, Qinhuo Liu, Shanlong Wu, Li Li, Aixia Yang, Junjun Wu, Xingfa Gu, Jinxiong Jiang · 发表于：GIScience & Remote Sensing · 年份：2023 · DOI：10.1080/15481603.2023.2262836 · 被引用次数：10 · 研究领域：Atmospheric aerosols and clouds、Atmospheric Ozone and Climate、Atmospheric and Environmental Gas Dynamics

Following the success of MODIS, several widely used algorithms have been developed for different satellite sensors to provide global aerosol optical depth (AOD) products. Despite the progress made in improving the accuracy of satellite-derived AOD products, the presence of sub-pixel clouds and the corresponding cloud shadows still significantly degrade AOD products. This is due to the difficulty in identifying sub-pixel clouds, as they are hardly identified, which inevitably leads to the overestimation of AOD. To overcome these conundrums, we proposed an improved deep learning network for retrieving AOD from remote sensing imagery focusing on sub-pixel clouds especially and we call it the Sub-Pixel AOD network (SPAODnet). Two specific improvements considering sub-pixel clouds have been made; a spatial adaptive bilateral filter is applied to top-of-atmosphere (TOA) reflectance images for removing the noise induced by sub-pixel clouds and the corresponding shadows at the first place and channel attention mechanism is added into the convolutional neural network to further emphasize the relationship between the uncontaminated pixels and the ground measured AOD from AERONET sites. In addition, a compositive loss function, Huber loss, is used to further improve the accuracy of retrieved AOD. The SPAODnet model is trained by using ten AERONET sites within Beijing-Tianjin-Hebei (BTH) region in China, along with their corresponding MODIS images from 2011 to 2020; Subsequently, the tra...