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.