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Application of a Partial Convolutional Neural Network for Estimating Geostationary Aerosol Optical Depth Data
Author(s) -
Lops Yannic,
Pouyaei Arman,
Choi Yunsoo,
Jung Jia,
Salman Ahmed Khan,
Sayeed Alqamah
Publication year - 2021
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2021gl093096
Subject(s) - geostationary orbit , missing data , pixel , convolutional neural network , computer science , remote sensing , artificial neural network , data set , imputation (statistics) , satellite , artificial intelligence , environmental science , data mining , machine learning , geology , engineering , aerospace engineering
Satellite‐derived aerosol optical depth (AOD) is negatively impacted by cloud cover and surface reflectivity. As these issues lead to biases, they need to be discarded, which significantly increases the amount of missing data within an image. This paper presents a unique application of the partial convolutional neural network (PCNN) for imputing missing data from the Geostationary Ocean Color Imager (GOCI) by training the PCNN model with the Community Multiscale Air Quality model simulated AOD. The PCNN model outperforms various models and algorithms for imputing GOCI images with a significant amount of missing data (45% of the data set has at least 80% missing pixels) and distance to the nearest known pixel within the GOCI image. Once trained, the model requires significantly less processing time and fewer resources than the other models and methods. The model allows the accurate imputation of remote sensing images within significant amounts of missing data.