Estimation of Aerosol Optical Depth at 30 m Resolution Using Landsat Imagery and Machine Learning

Tianchen Liang,Bing Li,Tao He,Lin Sun,Feng Tian,Shunlin Liang,Hao Lin,Linqing Zou

doi:10.3390/rs14051053

Abstract

Current remote sensing-based aerosol optical depth (AOD) products have coarse spatial resolutions, which are useful for studies at continental and global scales, but unsatisfactory for local scale applications, such as urban air pollution monitoring. In this study, we investigated the possibility of using Landsat imagery to develop high-resolution AOD estimations at 30 m based on machine learning algorithms. We assessed the performance of six machine learning algorithms, including Extreme Gradient Boosting, Random Forest, Cascade Random Forest, Gradient Boosted Decision Trees, Extremely Randomized Trees, and Multiple Linear Regression. To obtain accurate AOD estimations, we used prior knowledge from multiple sources as inputs to the machine learning models, including the Global Land Surface Satellite (GLASS) albedo, the 1-km AOD product from MODIS data using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, and meteorological and surface elevation data. A total of 13,624 AOD measurements from Aerosol Robotic Network (AERONET) sites were used for model training and validation. We found that all six algorithms exhibited good performance, with R2 values ranging from 0.73 to 0.78 and AOD root-mean-square errors (RMSE) ranging from 0.089 to 0.098. The extremely randomized trees algorithm, however, demonstrated marginally superior performance as compared to the other algorithms; hence, it was used to produce AOD estimates at a 30 m resolution for one Landsat scene coving Beijing in 2013–2019. Through a comparison with overlapping AERONET observations, a high level of accuracy was achieved, with an R2 = 0.889 and an RMSE = 0.156. Our method can be potentially used to generate a global high-resolution AOD dataset based on Landsat imagery.

Highlights

Atmospheric aerosols directly affect the radiation energy budget of the earth by scattering and absorbing solar radiation [1,2,3]
The results show that high aerosol optical depth (AOD) values often occur in the southern and eastern parts of the city, which are within the urban area of Beijing and are characterized by high traffic flows, a dense population, and intense anthropogenic activity
The machine learning model estimated the AOD by learning the relationship between AODs measured by the Aerosol Robotic Network (AERONET) sites and satellite apparent reflectance information, the Global Land Surface Satellite (GLASS) broadband albedo dataset, Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD, and other auxiliary data

Summary

Introduction

Atmospheric aerosols directly affect the radiation energy budget of the earth by scattering and absorbing solar radiation [1,2,3]. Aerosols indirectly affect the climate through the processes of cloud generation and dissipation, precipitation [4], photosynthesis, and ecosystem evapotranspiration [5,6,7], and contribute to the terrestrial carbon cycle through the diffuse radiation fertilization effect and hydrometeorological feedback [8]. It is important to accurately estimate the spatial and temporal variations of aerosols across the globe. Parameter, which is the combined measurement of various aerosols distributed within an air column. Ground-based sun photometers can continuously measure AOD with a high level of accuracy and are installed at Aerosol Robotic Network (AERONET) field sites 4.0/).

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Results

Conclusion