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Estimation of PMx Concentrations from Landsat 8 OLI Images Based on a Multilayer Perceptron Neural Network

Zhang, Bo and Zhang, Meng and Kang, Jian and Hong, Danfeng and Xu, Jian and Zhu, Xiaoxiang (2019) Estimation of PMx Concentrations from Landsat 8 OLI Images Based on a Multilayer Perceptron Neural Network. Remote Sensing, 11 (6), pp. 1-19. Multidisciplinary Digital Publishing Institute (MDPI). DOI: 10.3390/rs11060646 ISSN 2072-4292

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Official URL: https://www.mdpi.com/2072-4292/11/6/646

Abstract

The estimation of PMx concentrations using satellite observations is of great significance for detecting environmental issues in many urban areas of north China. Recently, aerosol optical depth (AOD) data is being used to estimate the PMx concentrations by implementing linear and/or nonlinear regression analysis methods. However, many relevant researches based on AOD published so far have demonstrated some limitations in estimating the spatial distribution of PMx concentrations with respect to estimation accuracy and spatial resolution. In this research, the Google Earth Engine (GEE) platform is employed to obtain the band reflectance (BR) data of a large number of Landsat 8 Operational Land Imager (OLI) remote sensing images. Combined with the meteorological, time parameter and the latitude and longitude zone (LLZ) method proposed in this article, a new BR (band reflectance)-PMx (including PM10 and PM2.5) model based on a multilayer perceptron neural network is constructed for the estimation of PMx concentrations directly from Landsat 8 OLI remote sensing images. This research used Beijing, China as the test area and the conducted experiments demonstrated that the BR-PMx model achieved satisfactory performances for the PMx-concentration estimations. The coefficient of determination (R2) of the BR-PM2.5 and BR-PM10 models reached 0.795 and 0.773, respectively, and the RMSE reached 20.09 μg/m3 and 31.27 μg/m3. Meanwhile, the estimation results have been compared with the results calculated by Kriging interpolation at the same time point, and the spatial distribution is consistent. Therefore, it can be concluded that the proposed BR-PMx model provides a new promising method for acquiring accurate PMx concentrations for various cities of China.

Item URL in elib:https://elib.dlr.de/126786/
Document Type:Article
Title:Estimation of PMx Concentrations from Landsat 8 OLI Images Based on a Multilayer Perceptron Neural Network
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Zhang, BoXi’An Jiaotong UniversityUNSPECIFIED
Zhang, MengXi’An Jiaotong UniversityUNSPECIFIED
Kang, JianTUMUNSPECIFIED
Hong, Danfengdanfeng.hong (at) dlr.deUNSPECIFIED
Xu, Jianjian.xu (at) dlr.deUNSPECIFIED
Zhu, Xiaoxiangxiao.zhu (at) dlr.deUNSPECIFIED
Date:March 2019
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:11
DOI :10.3390/rs11060646
Page Range:pp. 1-19
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2072-4292
Status:Published
Keywords:multilayer perceptron; neural network; Landsat 8 OLI; remote sensing image; estimation; PMx concentrations
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Hong, Danfeng
Deposited On:12 Mar 2019 14:13
Last Modified:14 Dec 2019 04:26

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