Preliminary Investigation of a New AHI Aerosol Optical Depth (AOD) Retrieval Algorithm and Evaluation with Multiple Source AOD Measurements in China

The Himawari-8 geostationary weather satellite, which is an Earth observing satellite launched in October 2014, has been applied in climate, environment, and air quality studies. Using hourly observation data from the Advanced Himawari Imager (AHI) on board Himawari-8, a new dark target algorithm was proposed to retrieve the aerosol optical depth (AOD) at 1 km and 5 km resolutions over mainland China. Because of the short satellite operation time and lack of AErosol RObotic NETwork (AERONET) sites across China, we cannot derive robust and representative surface reflectance relationships for the visible to near-infrared channels by atmospheric correction. Therefore, we inherited the empirical reflectance relationship from the Moderate Resolution Imaging Spectroradiometer (MODIS) and we used the AHI and MODIS spectral response functions to make the relationship more suitable for AHI. Ultimately, our AOD products can better reflect the regional characteristics with the AHI sensor. Seasonal averages showed that our product is more similar to MODIS Collection 6 (C6) Dark Target (DT) AOD than the Japan Aerospace Exploration Agency (JAXA) AHI AOD, but the difference is largest in winter. In addition, we evaluated several satellite retrieval products (our AHI AOD, JAXA AHI AOD, the National Oceanic and Atmospheric Administration (NOAA) VIIRS AOD, MODIS DT AOD, and MODIS DB AOD) against AERONET AOD from July 2016 to June 2017. The results showed that our AHI measurements demonstrate good agreement with, but exhibit a little overestimation, as compared to ground-based AERONET measurements with a correlation coefficient of 0.83 and an root-mean-square error (RMSE) of 0.112. The hourly validation also showed stable statistical results. A time series comparison with ground-based observations from two AERONET sites (Beijing-CAMS and XiangHe) showed that our AHI AOD products have trends as those in MODIS DB AOD, but that the bias in Beijing-CAMS is positive and higher than that in XiangHe. This error and the slight overestimation may be caused by the single continental aerosol model assumption and not considering the Normalized Difference Vegetation Index (NDVI)

Estimating PM2.5 Concentrations Using 3 KM MODIS AOD Products: A Case Study in British Columbia, Canada

PM2.5 refers to fine particles with diameters smaller than 2.5 μm. The rising level of PM2.5 reveals adverse effects on climate change, economic losses, international conflicts, and public health. Exposure to the high level of PM2.5 would increase the risk of premature death, especially for people with weak immune systems, such as children and elder people. The main sources of PM2.5 include combustion of biomass, vehicle and industrial emissions, and wildfire smoke. British Columbia (BC), Canada, with a land area of 944,735 km2 and 27 regional districts, experienced its record-breaking wildfire season in 2017. However, due to the uneven distribution of PM2.5 ground monitoring stations in BC, PM2.5 concentrations in the rural area are difficult to retrieve. Remote sensing techniques and geographical information systems (GIS) could be used as supplementary tools to estimate PM2.5 concentrations. Aerosol Optical Depth (AOD) has been proven to have a strong correlation with PM2.5. Moderate Resolution Imaging Spectroradiometer (MODIS) provides AOD products in both 3 km and 10 km resolutions. The 3 km MODIS AOD products were released in 2013, and have been widely used to estimate PM2.5 concentrations in several studies. This study adopted the 3 km Aqua MODIS AOD products to estimate PM2.5 concentrations in BC in the year of 2017 by combining ground station measurements, meteorological and supplementary data. MODIS AOD products were validated with ground-level AErosol RObotic NETwork (AERONET) AOD data. The Multiple Linear Regression (MLR) model, Geographically Weighted Regression (GWR) model, and a novel theoretical model were then conducted to estimate PM2.5 concentrations by integrating MODIS AOD products, ground-level PM2.5 concentrations, meteorological and supplementary data. After comparing the performance of the three models, the GWR model was used to generate annual, seasonal, and monthly spatial distribution maps of PM2.5. The application feasibility of MODIS AOD products in predicting PM2.5 was also examined. The validation results showed that there was a strong correlation between the MODIS AOD and the AERONET AOD. The GWR model had the best prediction performance, while the MLR generated the worst prediction results. After analyzing the spatial distribution maps of PM2.5 with ground-level PM2.5 distribution maps, it could be concluded that the PM2.5 concentrations estimated by the GWR model almost follow the same trend as ground station measured PM2.5. In addition, PM2.5 concentrations were the highest in summer and August based on the estimation results of seasonal and monthly GWR models. It indicated that the application feasibility of MODIS AOD products in predicting PM2.5 concentrations during BC’s wildfire season was high