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Open AccessArticle

Machine Learning Based Algorithms for Global Dust Aerosol Detection from Satellite Images: Inter-Comparisons and Evaluation

1
Department of Atmospheric Sciences, Texas A&M University, College Station, TX 77840, USA
2
Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
3
Climate and Radiation Laboratory (613), NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
4
Department of Occupational and Environmental Health, University of Oklahoma, Oklahoma City, OK 73019, USA
5
I M Systems Group Inc., Rockville, MD 20852, USA
6
Center for Satellite Applications and Research, National Oceanic and Atmospheric Administration, College Park, MD 20740, USA
7
Department of Information Systems, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
8
Department of Physics, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Wei Gong
Remote Sens. 2021, 13(3), 456; https://doi.org/10.3390/rs13030456
Received: 6 December 2020 / Revised: 15 January 2021 / Accepted: 21 January 2021 / Published: 28 January 2021
(This article belongs to the Special Issue Active and Passive Remote Sensing of Aerosols and Clouds)
Identifying dust aerosols from passive satellite images is of great interest for many applications. In this study, we developed five different machine-learning (ML) based algorithms, including Logistic Regression, K Nearest Neighbor, Random Forest (RF), Feed Forward Neural Network (FFNN), and Convolutional Neural Network (CNN), to identify dust aerosols in the daytime satellite images from the Visible Infrared Imaging Radiometer Suite (VIIRS) under cloud-free conditions on a global scale. In order to train the ML algorithms, we collocated the state-of-the-art dust detection product from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) with the VIIRS observations along the CALIOP track. The 16 VIIRS M-band observations with the center wavelength ranging from deep blue to thermal infrared, together with solar-viewing geometries and pixel time and locations, are used as the predictor variables. Four different sets of training input data are constructed based on different combinations of VIIRS pixel and predictor variables. The validation and comparison results based on the collocated CALIOP data indicate that the FFNN method based on all available predictor variables is the best performing one among all methods. It has an averaged dust detection accuracy of about 81%, 89%, and 85% over land, ocean and whole globe, respectively, compared with collocated CALIOP. When applied to off-track VIIRS pixels, the FFNN method retrieves geographical distributions of dust that are in good agreement with on-track results as well as CALIOP statistics. For further evaluation, we compared our results based on the ML algorithms to NOAA’s Aerosol Detection Product (ADP), which is a product that classifies dust, smoke, and ash using physical-based methods. The comparison reveals both similarity and differences. Overall, this study demonstrates the great potential of ML methods for dust detection and proves that these methods can be trained on the CALIOP track and then applied to the whole granule of VIIRS granule. View Full-Text
Keywords: CALIOP; VIIRS; machine learning; deep learning; dust detection CALIOP; VIIRS; machine learning; deep learning; dust detection
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MDPI and ACS Style

Lee, J.; Shi, Y.R.; Cai, C.; Ciren, P.; Wang, J.; Gangopadhyay, A.; Zhang, Z. Machine Learning Based Algorithms for Global Dust Aerosol Detection from Satellite Images: Inter-Comparisons and Evaluation. Remote Sens. 2021, 13, 456. https://doi.org/10.3390/rs13030456

AMA Style

Lee J, Shi YR, Cai C, Ciren P, Wang J, Gangopadhyay A, Zhang Z. Machine Learning Based Algorithms for Global Dust Aerosol Detection from Satellite Images: Inter-Comparisons and Evaluation. Remote Sensing. 2021; 13(3):456. https://doi.org/10.3390/rs13030456

Chicago/Turabian Style

Lee, Jangho; Shi, Yingxi R.; Cai, Changjie; Ciren, Pubu; Wang, Jianwu; Gangopadhyay, Aryya; Zhang, Zhibo. 2021. "Machine Learning Based Algorithms for Global Dust Aerosol Detection from Satellite Images: Inter-Comparisons and Evaluation" Remote Sens. 13, no. 3: 456. https://doi.org/10.3390/rs13030456

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