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Estimating Atmospheric Visibility Using Synergy of Modis Data and Ground-based Observations : Volume 368, Issue 368 (06/05/2015)

By Komeilian, H.

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Book Id: WPLBN0003992592
Format Type: PDF Article :
File Size: Pages 5
Reproduction Date: 2015

Title: Estimating Atmospheric Visibility Using Synergy of Modis Data and Ground-based Observations : Volume 368, Issue 368 (06/05/2015)  
Author: Komeilian, H.
Volume: Vol. 368, Issue 368
Language: English
Subject: Science, Proceedings, International
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Historic
Publication Date:
2015
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

Citation

APA MLA Chicago

Bateni, S. M., Nielson, J., Xu, T., & Komeilian, H. (2015). Estimating Atmospheric Visibility Using Synergy of Modis Data and Ground-based Observations : Volume 368, Issue 368 (06/05/2015). Retrieved from http://www.ebooklibrary.org/


Description
Description: State Key Laboratory of Remote Sensing Science, Research Center for Remote Sensing and GIS, and School of Geography, Beijing Normal University, Beijing, 100875, China. Dust events are intricate climatic processes, which can have adverse effects on human health, safety, and the environment. In this study, two data mining approaches, namely, back-propagation artificial neural network (BP ANN) and supporting vector regression (SVR), were used to estimate atmospheric visibility through the synergistic use of Moderate Resolution Imaging Spectroradiometer (MODIS) Level 1B (L1B) data and ground-based observations at fourteen stations in the province of Khuzestan (southwestern Iran), during 2009–2010. Reflectance and brightness temperature in different bands (from MODIS) along with in situ meteorological data were input to the models to estimate atmospheric visibility. The results show that both models can accurately estimate atmospheric visibility. The visibility estimates from the BP ANN network had a root-mean-square error (RMSE) and Pearson’s correlation coefficient (R) of 0.67 and 0.69, respectively. The corresponding RMSE and R from the SVR model were 0.59 and 0.71, implying that the SVR approach outperforms the BP ANN.

Summary
Estimating atmospheric visibility using synergy of MODIS data and ground-based observations

 

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