A spatio-temporal unmixing with heterogeneity model for the identification of remotely sensed MODIS aerosols: Exemplified by the case of Africa

Longshan Yang; Peng Luo; Zehua Zhang; Yongze Song; Kai Ren; Ce Zhang; Joseph Awange; Peter M. Atkinson; Liqiu Meng

doi:10.1016/j.jag.2024.104068

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A spatio-temporal unmixing with heterogeneity model for the identification of remotely sensed MODIS aerosols: Exemplified by the case of Africa

Journal article

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A spatio-temporal unmixing with heterogeneity model for the identification of remotely sensed MODIS aerosols: Exemplified by the case of Africa

Longshan Yang, Peng Luo, Zehua Zhang, Yongze Song, Kai Ren, Ce Zhang, Joseph Awange, Peter M. Atkinson and Liqiu Meng

International journal of applied earth observation and geoinformation, Vol.132, p.104068

08/01/2024

DOI: 10.1016/j.jag.2024.104068

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Abstract

Aerosols are crucial constituents of the atmosphere, with significant impacts on air quality. Aerosol optical depth (AOD) is critical in assessing solar resources and modeling sky radiance. However, comprehensive aerosol studies at a continental scale are limited, and existing methodologies need to consider spatial characteristics. This study develops a spatio-temporal unmixing with heterogeneity (STUH) model to evaluate spatial patterns and temporal trends of atmospheric aerosols across the African continent. The spatio-temporal AOD data cube, comprising monthly averaged MODIS-derived AOD data from 2001 to 2015, was decomposed using spatially non-negative matrix variabilization to explore the spatial determinants and the impacts of their interactions to AOD using a geographically optimal zones-based heterogeneity (GOZH) model. Our findings reveal an increasing trend of aerosol levels across Africa in the past 15 years, combined with the spatio-temporal AOD pattern explained by five abundance variables. We find that in different regions across Africa, the impact of natural variables on AOD was 1.56 to 3.01 times the impact of human variables, with significant spatial variations. These results are essential for understanding the climatic implications of atmospheric aerosols in Africa. •A spatio-temporal unmixing with heterogeneity model (STUH) is proposed.•The STUH combines unmixing model with the spatially stratified heterogeneity model.•STUH is used to assess aerosol patterns in Africa over 15 years.•The key factors influencing aerosol changes in Africa are identified.•Natural variables impact AOD more than human variables.

Aerosol optical depth

Africa

Remote sensing

Spatial heterogeneity

Spatio-temporal unmixing

Details

Title: Subtitle: A spatio-temporal unmixing with heterogeneity model for the identification of remotely sensed MODIS aerosols: Exemplified by the case of Africa
Creators: Longshan Yang - Guizhou University
Peng Luo - Massachusetts Institute of Technology
Zehua Zhang - Curtin University
Yongze Song - Curtin University
Kai Ren - Curtin University
Ce Zhang - University of Bristol
Joseph Awange - Curtin University
Peter M. Atkinson - University of Southampton
Liqiu Meng - Technical University of Munich
Resource Type: Journal article
Publication Details: International journal of applied earth observation and geoinformation, Vol.132, p.104068
DOI: 10.1016/j.jag.2024.104068
ISSN: 1569-8432
eISSN: 1872-826X
Publisher: Elsevier B.V
Language: English
Date published: 08/01/2024
Academic Unit: School of Earth, Environment, and Sustainability
Record Identifier: 9985219864702771

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