A Generic Model to Estimate Ozone Concentration From Landsat 8 Satellite Data Based on Machine Learning Technique

As ground-level O 3 has become one of the major pollutants affecting air quality in recent years, monitoring and revealing the spatial distribution pattern of O 3 are of great significance to study air-pollution characteristics. Based on the multilayer backpropagation neural network, one O 3 estimat...

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Bibliographic Details
Published in:	IEEE journal of selected topics in applied earth observations and remote sensing Vol. 14; pp. 7938 - 7947
Main Authors:	Yang, Xiaoting, Zhang, Meng, Zhang, Bo
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Accuracy Air Air pollution Air quality Artificial satellites Atmospheric modeling Back propagation Back propagation networks Backpropagation neural network Decision making Distribution Distribution patterns Earth Environmental governance Estimation Governance high spatial resolution Image quality Landsat Landsat satellites Learning algorithms Machine learning Meteorological parameters Model accuracy Monitoring Multilayers multisourced data Neural networks O<named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX"> 3</tex-math> </inline-formula> </named-content> estimation model O<named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <tex-math notation="LaTeX">$3$</tex-math> </named-content> estimation model Ozone Pollutants Pollution monitoring Remote sensing Satellite imagery Spatial discrimination Spatial distribution Spatial resolution Training China
ISSN:	1939-1404, 2151-1535
Online Access:	Get full text
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