Aerosol optical depth (AOD) is a key parameter that reflects the characteristics of aerosols, and is of great help in predicting the concentration of pollutants in the atmosphere. At present, remote sensing inversion has become an important method for obtaining the AOD on a large scale. However, AOD data acquired by satellites are often missing, and this has gradually become a popular topic. In recent years, a large number of AOD recovery algorithms have been proposed. Many AOD recovery methods are not application-oriented. These methods focus mainly on to the accuracy of AOD recovery and neglect the AOD recovery ratio. As a result, the AOD recovery accuracy and recovery ratio cannot be balanced. To solve these problems, a two-step model (TWS) that combines multisource AOD data and AOD spatiotemporal relationships is proposed. We used the light gradient boosting (LightGBM) model under the framework of the gradient boosting machine (GBM) to fit the multisource AOD data to fill in the missing AOD between data sources. Spatial interpolation and spatiotemporal interpolation methods are limited by buffer factors. We recovered the missing AOD in a moving window. We used TWS to recover AOD from Terra Satellite’s 2018 AOD product (MOD AOD). The results show that the MOD AOD, after a 3 × 3 moving window TWS recovery, was closely related to the AOD of the Aerosol Robotic Network (AERONET) (R = 0.87, RMSE = 0.23). In addition, the MOD AOD missing rate after a 3 × 3 window TWS recovery was greatly reduced (from 0.88 to 0.1). In addition, the spatial distribution characteristics of the monthly and annual averages of the recovered MOD AOD were consistent with the original MOD AOD. The results show that TWS is reliable. This study provides a new method for the restoration of MOD AOD, and is of great significance for studying the spatial distribution of atmospheric pollutants.
The spatiotemporal distribution pattern of the aerosol optical depth (AOD) is influenced by many environmental factors, such as meteorological condition changes, atmospheric pollution, and topographic changes. Understanding the relationship between the vegetation land cover and the AOD would favor the improvement of forest ecosystem services. This quantitative research integrated remote sensing and ground survey data and used spatial statistical methods to explore the drivers that influence the AOD of the exurban national forest park and analyze the differences between various forest types. The driver analysis was carried out in the hot (Z ≥ 1.64) and cold (Z ≤ −1.64) spots of AOD in 2010 and 2017. Our results showed that (1) the forest type was proved to be the main factor contributing to the AOD pattern and (2) from 2010 to 2017, the average growth rate of broad-leaved forest, coniferous forest, bamboo, and shrub in hot spots was significantly higher than that in cold spots, while there was no significant difference in the mixed forest. The average growth rate of biomass densities of bamboo, coniferous forest, and mixed forest were higher than that of the shrub and broad-leaved forest. These findings provided the guidance for the rational allocation of tree species to increase the biomass and improve the ecosystem service values of forest parks.
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