Global aerosol retrieval over land from Landsat imagery integrating Transformer and Google Earth Engine

Abstract

Landsat imagery offers remarkable potential for various applications, including land monitoring and environmental assessment, thanks to its high spatial resolution and over 50 years of data records. However, the presence of atmospheric aerosols greatly hinders the precision of land classification and the quantitative retrieval of surface parameters. There is a pressing need for reliable and accurate global aerosol optical depth (AOD) data derived from Landsat imagery, particularly for atmospheric correction purposes and various other applications. To address this issue, we introduce an innovative framework for retrieving AOD from Landsat imagery over land, which leverages the deep-learning Transformer model (named AeroTrans-Landsat) and operates on the Google Earth Engine (GEE) cloud platform. We gather Landsat 8 and 9 images starting from their launch dates (February 2013 and September 2021, respectively) until the end of 2022, which are used to construct a robust aerosol retrieval model. The global AOD retrievals are then rigorously validated across ∼560 monitoring stations on land using diverse spatiotemporally independent methods. Leveraging information from multiple spectral channels, which contributes to 80 % according to the SHapley Additive exPlanation (SHAP) method, our retrieved AODs from 2013 to 2022 generally agree well with surface observations, with a sample-based cross-validation correlation coefficient of 0.905 and a root-mean-square error of 0.083. Around 86 % and 55 % of our AOD retrievals meet the criteria of Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue expected errors [±(0.05 + 20 %)] and the Global Climate Observation System {[max(0.03, 10 %)]}, respectively. Additionally, our model is not as sensitive to fluctuations in both surface and atmospheric conditions, enabling the generation of spatially continuous AOD distributions with exceptionally fine-scale information over dark to bright surfaces. This capability extends to areas characterized by high pollution levels originating from both anthropogenic and natural sources.