Estimating ground-level PM2.5 concentration using Landsat 8 in Chengdu, China

Yunping Chen; Weihong Han; Shuzhong Chen; Ling Tong

doi:10.1117/12.2068886

20 November 2014 Estimating ground-level PM2.5 concentration using Landsat 8 in Chengdu, China

Yunping Chen, Weihong Han, Shuzhong Chen, Ling Tong

Proceedings Volume 9259, Remote Sensing of the Atmosphere, Clouds, and Precipitation V; 925917 (2014) https://doi.org/10.1117/12.2068886
Event: SPIE Asia-Pacific Remote Sensing, 2014, Beijing, China

Abstract

An empirical multilinear model was developed for estimating ground-level PM2.5 concentration at city scale (Chengdu, China) using Landsat 8 data. In this model, the improved DDV (dense dark vegetation) algorithm (V5.2) was used to retrieve aerosol optical thickness (AOT), Image-based Method (IBM) was used to compute the land surface temperature (LST), and TVDI was calculated to reflect the air humidity. The three parameters (AOT, LST, TVDI) and in-situ measured PM2.5 (particulate matter) data were then utilized to establish the empirical model by partial least square (PLS) regression. In the computation, the band 9, cirrus band, was used to reduce the influence of atmospheric vapor to LST retrieval. The results show that when considering moisture and temperature, the correlation between AOT and PM2.5 would be efficiently improved; furthermore, moisture shows more impact on the relationship than temperature. Station record hourly average PM2.5 also shows higher correlation coefficients than 24-hr average. As a result, PM2.5 concentration distribution across Chengdu was retrieved using this model developed in this paper. The method could be a beneficial complement to ground-based measurement and implicate that remote sensing data has enormous potential to monitor air quality at city scale.

Citation Download Citation

Yunping Chen, Weihong Han, Shuzhong Chen, and Ling Tong "Estimating ground-level PM2.5 concentration using Landsat 8 in Chengdu, China", Proc. SPIE 9259, Remote Sensing of the Atmosphere, Clouds, and Precipitation V, 925917 (20 November 2014); https://doi.org/10.1117/12.2068886

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