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Co-authored Journal Article

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

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

JournalInternational Journal of Applied Earth Observation and Geoinformation,

https://doi.org/10.1016/j.jag.2024.104068

Overview

This paper proposes the STUH model to decompose long-term aerosol patterns across Africa, identify spatially varying aerosol determinants, and compare the relative contributions of natural and anthropogenic variables.

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 on 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.

Highlights

  • A spatio-temporal unmixing with heterogeneity model (STUH) is proposed.
  • The STUH combines an 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.

Figures and Tables

Note: only figures drawn or visually refined by Kai Ren are displayed here. 注：此处仅展示由 Kai Ren 绘制或美化整理的图片。

Figure 2. Spatial and temporal statistics on African AOD. (a) Spatial distribution of annual average AOD; (b) temporal trends of AOD by year; (c) AOD statistics by region; and (d) spatial distribution of AOD slope.
Figure 2. Spatial and temporal statistics on African AOD. (a) Spatial distribution of annual average AOD; (b) temporal trends of AOD by year; (c) AOD statistics by region; and (d) spatial distribution of AOD slope. 图 2. 非洲 AOD 的时空统计。(a) 年平均 AOD 的空间分布；(b) 按年份统计的 AOD 时间变化趋势；(c) 分区域 AOD 统计；(d) AOD 斜率的空间分布。
Figure 3. Spatial patterns of explanatory variables presented by the annual average values over 15 years: NOx, SO2, PM2.5, OC, VOC, BC, NDVI, EVI, LST, and precipitation.
Figure 3. Spatial patterns of explanatory variables presented by the annual average values over 15 years: NOx, SO2, PM2.5, OC, VOC, BC, NDVI, EVI, LST, and precipitation. 图 3. 以 15 年年平均值表示的解释变量空间格局：NOx、SO2、PM2.5、OC、VOC、BC、NDVI、EVI、LST 和降水。
Figure 4. Temporal patterns of explanatory variables presented by the slope of annual values among 15 years: NOx, SO2, PM2.5, OC, VOC, BC, NDVI, EVI, LST, and precipitation.
Figure 4. Temporal patterns of explanatory variables presented by the slope of annual values among 15 years: NOx, SO2, PM2.5, OC, VOC, BC, NDVI, EVI, LST, and precipitation. 图 4. 以 15 年年值斜率表示的解释变量时间变化格局：NOx、SO2、PM2.5、OC、VOC、BC、NDVI、EVI、LST 和降水。
Figure 6. Spatial distributions of impacts of different human variables on AOD quantified using the abundance-based Omega, including impacts of (a) NOx, (b) SO2, (c) PM2.5, (d) OC, (e) VOC, and (f) BC.
Figure 6. Spatial distributions of impacts of different human variables on AOD quantified using the abundance-based Ω, including impacts of (a) NOx, (b) SO2, (c) PM2.5, (d) OC, (e) VOC, and (f) BC. 图 6. 基于丰度的 Ω 量化不同人为变量对 AOD 影响的空间分布，包括 (a) NOx、(b) SO2、(c) PM2.5、(d) OC、(e) VOC 和 (f) BC 的影响。
Figure 7. Spatial distributions of impacts of different natural variables on AOD quantified using the abundance-based Omega, including impacts of (a) NDVI, (b) EVI, (c) LST, and (d) precipitation.
Figure 7. Spatial distributions of impacts of different natural variables on AOD quantified using the abundance-based Ω, including impacts of (a) NDVI, (b) EVI, (c) LST, and (d) precipitation. 图 7. 基于丰度的 Ω 量化不同自然变量对 AOD 影响的空间分布，包括 (a) NDVI、(b) EVI、(c) LST 和 (d) 降水的影响。