Journal
REMOTE SENSING
Volume 14, Issue 5, Pages -Publisher
MDPI
DOI: 10.3390/rs14051053
Keywords
aerosol optical depth; machine learning; Landsat; high resolution
Categories
Funding
- National Key Research and Development Program of China [2020YFA0608704]
- National Natural Science Foundation of China [42001299, 42090012]
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This study investigates the possibility of using Landsat imagery and machine learning algorithms to develop high-resolution aerosol optical depth (AOD) estimations. Six machine learning algorithms were assessed, with the extremely randomized trees algorithm demonstrating the best performance. Comparison with AERONET observations confirms the accuracy of the method.
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 R-2 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 R-2 = 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.
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