Handling Missing Data in Large-Scale MODIS AOD Products Using a Two-Step Model

Abstract

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.

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“…AOD is a key parameter reflecting the characteristics of aerosols, and plays a significant role in predicting the concentration of pollutants in the atmosphere. However, as highlighted in the work of Chi et al [3], the AOD data obtained by satellites are often found to be missing, and thereby, impose serious research challenges. The existing methods of AOD recovery primarily focus on to the accuracy of AOD restoration while neglecting the AOD recovery ratio.…”

“…The existing methods of AOD recovery primarily focus on to the accuracy of AOD restoration while neglecting the AOD recovery ratio. In order to solve the issue, Chi et al [3] have proposed a light gradient boosting-based two-step model, termed as TWS, that fills the missing AOD data by combining data from multiple sources and at the same time learning spatio-temporal relationships of AOD. Experimental evaluation of TWS with respect to recovering AOD from Terra Satellite's 2018 AOD product has demonstrated the reliability of TWS method in producing competitive and consistent performance in AOD restoration.…”