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Open AccessArticle

Retrieval of Horizontal Visibility Using MODIS Data: A Deep Learning Approach

by Bo Hu 1,2,3, Xingying Zhang 4,*, Rui Sun 1,2,* and Xianchun Zhu 3
1
State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
2
Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
3
Ningbo Meteorological Administration, Ningbo 315012, China
4
National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China
*
Authors to whom correspondence should be addressed.
Atmosphere 2019, 10(12), 740; https://doi.org/10.3390/atmos10120740
Received: 10 October 2019 / Revised: 19 November 2019 / Accepted: 22 November 2019 / Published: 25 November 2019
(This article belongs to the Section Aerosols)
Horizontal visibility (HVIS) is a primary index used for assessing air quality. Although satellite images provide information regarding atmospheric aerosols, atmospheric visibility is not directly measured. In this paper, a deep learning approach is proposed to retrieve HVIS using moderate-resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) data, the European Centre for Medium-Range Weather Forecasts reanalysis dataset, and ground-based visibility observations. The deep neural network model comprises a multi-layer unsupervised restricted Boltzmann machine (RBM) and a layer for supervised learning. The dropout mechanism was used in the training process to overcome the errors caused by over-fitting. The results demonstrate that the correlation coefficient values between HVIS observations and retrievals during training, pre-validating, and evaluation were 0.74, 0.723, and 0.697, respectively. The retrieved HVIS in Eastern China exhibited a north-to-south increasing trend, increasing and decreasing in summer and winter, respectively. In conclusion, the proposed model presents an effective and more reliable method for HVIS retrieval. However, the small samples, low AOD, low albedo, high total column water, high longitude, and the low vertical wind component at 10 m likely cause HVIS bias. View Full-Text
Keywords: HVIS; deep learning; RBM; MODIS AOD; Eastern China HVIS; deep learning; RBM; MODIS AOD; Eastern China
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Hu, B.; Zhang, X.; Sun, R.; Zhu, X. Retrieval of Horizontal Visibility Using MODIS Data: A Deep Learning Approach. Atmosphere 2019, 10, 740.

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