Publications

Liu, JJ; Weng, FZ; Li, ZQ (2022). Ultrahigh-Resolution (250 m) Regional Surface PM2.5 Concentrations Derived First From MODIS Measurements. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 60, 4101312.

Abstract
Aerosol optical depth from different satellite sensors are widely used to estimate surface PM2.5 concentrations. However, these products generally have coarse resolutions, limiting the ability to evaluate PM2.5 concentrations in urban regions where the human activities are relatively high. This study first develops an ensemble machine learning approach to produce PM2.5 concentrations with an extremely high spatial resolution of 250 m, based on Moderate Resolution Imaging Spectroradiometer (MODIS) measurements of top-of-atmosphere reflectance and related meteorological variables. The Yangtze River Delta region, with one of the highest levels of PM2.5 pollution in China, is the study region chosen. The model shows a very high and stable performance with a coefficient of determination (R-2) of 0.90, a root-mean-square error (RMSE) of 12.0 mu g/m(3), a mean prediction error (MPE) of 7.8 mu g/m(3), and a mean relative prediction error (RPE) 16.9% for sample-based cross validation. The model can accurately capture the distribution patterns and magnitudes of PM2.5 concentrations over the study region for seasonal mean, daily variations, and different levels of air pollution. The very high resolution of the model has the advantage of capturing the uneven spatial distribution of PM2.5 concentrations at small spatial scales and identifying small areas with very high PM2.5 concentrations, offering a possible approach for locating the sources of PM2.5 emissions. In general, the model developed here estimates very well PM2.5 concentrations at a very high spatial resolution, providing detailed information, useful for air-pollution-related studies, as well as pollution monitoring and evaluation by governments, especially in urban and urban-center areas.

DOI:
10.1109/TGRS.2021.3064191

ISSN:
1558-0644