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.
This paper proposes a new spatial scale conversion method, which validates moderate resolution imaging spectroradiometer (MODIS) leaf area index (LAI) product when geometry information from the MODIS 1B product and classification result is combined. The in situ LAI data, Landsat Thematic Mapper (TM), and MODIS 1B product were utilized in this research. An object-oriented method was used to classify TM imaging, where each class was computed using an empirical model to achieve LAI respectively. The 30-m TM LAI image was aggregated into the MODIS 1B product based on the geometry information of MODIS 1B. The simulated MODIS 1B image was then converted into a MODIS LAI product and compared with the simulated LAI map pixel by pixel. The results showed a lower root mean square error and higher normalization of the absolute error with the new method. In addition, the field LAI was not significantly correlated with MODIS LAI, but it did show a strong correlation with TM LAI. The new method achieved a higher correlate coefficient with the MODIS product than the conventional methods. Using this validation method based on classification and image simulation can improve the accuracy of product certification.