Himawari-8 Aerosol Optical Depth (AOD) Retrieval Using a Deep Neural Network Trained Using AERONET Observations

Lu She,Hankui K Zhang,Gerrit De Leeuw,Zhengqiang Li,Bo Huang

doi:10.3390/rs12244125

Abstract

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.

Highlights

Aerosols play an important role in the Earth’s radiation balance, hydrological cycle, and biogeochemical cycles [1]
Spectral aerosol optical depth (AOD), defined as the extinction of solar radiation due to aerosols, as function of wavelength and integrated over the whole atmospheric column, is one of the principal parameters retrieved from satellite observations
We explore the use of data-driven machine learning methods to derive AOD from Advanced Himawari Imager (AHI) observations to investigate whether the data-driven method could achieve at least a similar level of accuracy as the radiative transfer models (RTMs)-based AOD retrieval

Summary

Introduction

Aerosols play an important role in the Earth’s radiation balance, hydrological cycle, and biogeochemical cycles [1]. Spectral aerosol optical depth (AOD), defined as the extinction of solar radiation due to aerosols, as function of wavelength and integrated over the whole atmospheric column, is one of the principal parameters retrieved from satellite observations. Many different AOD retrieval algorithms have been developed and most of them use radiative transfer models (RTMs) to develop relations between the observed top-of-atmosphere (TOA) reflectance and AOD and assume prior knowledge of the surface reflectance and the aerosol type [2,3]. The RTMs calculate the scattering and absorption of solar radiation by atmospheric molecules and aerosols, which is time consuming and, to save computer time, usually look up tables (LUTs) are created providing RTM results for discrete values of the deciding input parameters such as the angles describing the observation geometry, aerosol models, and discrete values of the AOD. Apart from relying on RTMs, each sensor algorithm may be different and designed for sensor characteristics [4], e.g., spectral band configuration [5,6], multiple view capability [7,8], and polarization [9,10]

Methods

Results

Discussion

Conclusion