Joint retrieval of the aerosol fine mode fraction and optical depth using MODIS spectral reflectance over northern and eastern China: Artificial neural network method
by X. Chen (AirCAS), G. de Leeuw (KNMI), A. Arola (FMI), S. Liu (Jiangxi University of Science and Technology, Nanchang), Y. Liu (Baidu Technology, Beijing)K. Zhang (Chang'an University, Xi'an)
The Fine Mode Fraction (FMF) of atmospheric aerosol is very important for environment and climate studies.
Attempts have been made to retrieve the FMF from satellite data with varying success. In this work, the development
of an artificial Neural Network for AEROsol retrieval (NNAero) is presented. NNAero uses data from
the NASA MODerate resolution Imaging Spectroradiometer (MODIS) flying on the NASA Terra and Aqua satellites.
The MODIS-derived spectral reflectances of solar radiation at the top of the atmosphere (TOA) and at the
surface were used together with ground-based Aerosol Robotic Network (AERONET) measurements of Aerosol
Optical Depth (AOD) and FMF to train a Convolutional Neural Network (CNN) for the joint retrieval of FMF and
AOD. The NNAero results over northern and eastern China were validated against an independent reference
AERONET dataset (i.e. not used in training the CNN). The results show that 68% of the NNAero AOD values are
within the MODIS expected error (EE) envelope over land of±(0.05 + 15%), which is similar to the results
from the MODIS Deep Blue (DB) algorithm (63% within EE), and both are better than the Dark Target (DT)
algorithm (31% within EE). The validation of the NNAero FMF vs AERONET data shows a significant improvement
with respect to the DT FMF, with Root Mean Squared Prediction Errors (RMSE) of 0.1567 (NNAero)
and 0.34 (DT). The NNAero method shows the potential of improved retrieval of the FMF.
Chen, X., G. de Leeuw, A. Arola, S. Liu, Y. Liu and K. Zhang, Joint retrieval of the aerosol fine mode fraction and optical depth using MODIS spectral reflectance over northern and eastern China: Artificial neural network method