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Estimating Hourly Full-Coverage Himawari-8 AHI AOD with Spatiotemporal Random Forest Model

Aerosol optical depth (AOD) is closely related to atmospheric pollutants. However, a large number of missing values in satellite AOD severely limits its application. We proposed a spatiotemporal random forest (RF) model to estimate the missing AHI AOD in this study. In addition to the commonly used meteorological and topographic parameters, the spatiotemporal data and MERRA-2 AOD were introduced as the model inputs. Specifically, the training data was divided into multiple subsets based on the land cover types and local times to explicitly characterize the spatiotemporal variation of AOD. The validation results indicated that the RF model achieved promising results with RMSE of 0.03 to 0.17, MBE of −0.01 to 0.02, and R of 0.87 to 0.97 at different land cover types and local times.