Estimation of the Hourly Aerosol Optical Depth From GOCI Geostationary Satellite Data: Deep Neural Network, Machine Learning, and Physical Models

In this study, a new deep learning method was developed to estimate the spatiotemporal properties of the hourly aerosol optical depth (AOD) because existing physical models are limited in their abilities to separate the reflectance between aerosols and the underlying surface over land, accurately an...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing 2022, Vol.60, p.1-12
Hauptverfasser:	Yeom, Jong-Min, Jeong, Seungtaek, Ha, Jong-Sung, Lee, Kwon-Ho, Lee, Chang-Suk, Park, Seonyoung
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Aerosol optical depth (AOD) Aerosol Robotic Network Aerosols Artificial neural networks Atmospheric measurements Atmospheric modeling Atmospheric models Coefficients Color imagery Correlation coefficient Correlation coefficients Deep learning deep neural network (DNN) Diurnal variations geostationary ocean color imagery (GOCI) satellite Image processing Imagery Land surface Learning algorithms Machine learning Neural networks Northeast Asia Ocean color Ocean colour Ocean models Optical analysis Optical imaging Optical properties Optical sensors Optical thickness random forest (RF) Reflectance Regression analysis Root-mean-square errors Spatial analysis Statistical analysis Statistics Sun Support vector machines support vector regression (SVR) Synchronous satellites
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