Royal Dutch Meteorological Institute; Ministery Of Infrastructure And The Environment

Publications, presentations and other activities
Himawari-8 aerosol optical depth (AOD) retrieval using a deep neural network trained using AERONET observations
2020
by L. She (Ningxia University, Yinchuan, China), H. Zhang (South Dakota State University, Brookings, USA), Z. Li (AirCAS, Beijing), G. de Leeuw (KNMI), B. Huang (The Chinese University of Hong Kong, Shatin, N.T., Hong Kong)

Spectral aerosol optical depth (AOD) estimation from satellite-measured top of atmosphere
(TOA) reflectances is challenging because of the complicated TOA-AOD relationship and a nexus
of land surface and atmospheric state variations. This task is usually undertaken using a physical
model to provide a first estimate of the TOA reflectances which are then optimized by comparison
with the satellite data. Recently developed deep neural network (DNN) models provide a powerful
tool to represent the complicated relationship statistically. This study presents a methodology based
on DNN to estimate AOD using Himawari-8 Advanced Himawari Imager (AHI) TOA observations.
A year (2017) of AHI TOA observations over the Himawari-8 full disk collocated in space and time
with Aerosol Robotic Network (AERONET) AOD data were used to derive a total of 14,154 training
and validation samples. The TOA reflectance in all six AHI solar bands, three TOA reflectance
ratios derived based on the dark-target assumptions, sun-sensor geometry, and auxiliary data are
used as predictors to estimate AOD at 500 nm. The DNN AOD is validated by separating training
and validation samples using random k-fold cross-validation and using AERONET site-specific
leave-one-station-out validation, and is compared with a random forest regression estimator and
Japan Meteorological Agency (JMA) AOD. The DNN AOD shows high accuracy: (1) RMSE = 0.094,
R2 = 0.915 for k-fold cross-validation, and (2) RMSE = 0.172, R2 = 0.730 for leave-one-station-out
validation. The k-fold cross-validation overestimates the DNN accuracy as the training and validation
samples may come from the same AHI pixel location. The leave-one-station-out validation reflects
the accuracy for large-area applications where there are no training samples for the pixel location
to be estimated. The DNN AOD has better accuracy than the random forest AOD and JMA AOD.
In addition, the contribution of the dark-target derived TOA ratio predictors is examined and
confirmed, and the sensitivity to the DNN structure is discussed.

Bibliographic data
She, L., H. Zhang, Z. Li, G. de Leeuw and B. Huang, Himawari-8 aerosol optical depth (AOD) retrieval using a deep neural network trained using AERONET observations
Remote Sensing, 2020, 12, 1-19, doi:10.3390/rs12244125.
Abstract (html)  Complete text (pdf: 4 MB)