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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Description
Summary: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 estimation model is proposed to obtain the continuous spatial distribution of O 3 concentrations, where the Landsat 8 images, meteorological parameters, and air quality data have been integrated together as the input for the model training and validation. In order to enhance the estimation accuracy, the proposed model has been optimized with respect to the influencing factors and spatial extent. In the test areas of Beijing, Tianjin, and Shijiazhuang of China, the proposed O 3 estimation model has demonstrated quite satisfactory performance-with the average coefficient of determination ( R 2 ) larger than 0.90 and root-mean-square error smaller than 19.0  μ g/m 3 . It is worth mentioning that all the data employed in this research are freely available and can be applied nationwide in the mainland of China. Taking advantage of the generic nature and the positive O 3 estimation results with high accuracy and spatial resolution, the proposed model can be expected to be a new way for studying air-pollution characteristics and, thus, support the decision making for environmental governance.
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content type line 14
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2021.3101206