In complex urban environments, the information of high-resolution Aerosol Optical Depth (AOD) is of great importance for effective air pollution control, air navigation, public health assessment, and meteorological forecasting. High resolution AOD may be produced by merging and/or fusing existing AOD products or reproduced by merging and/or fusing the reflectance data at the top of atmosphere (TOA) and ground levels through the deep blue method. However, the former can only lead to the production of AOD with 500 m~1 km spatial resolution at best. To overcome this barrier, it is necessary to fuse the reflectance values of Landsat and MODIS imageries at the TOA level to be in concert with the fused land surface reflectance values for advanced synthesis. Such a collective endeavor can lead to the production of AOD with daily 30m spatial resolution via the deep blue method. This paper thus presents such a synthetic effort that synergizes the spatial and temporal advantages of two satellite sensors (MODIS Terra and Landsat 8) to reach the goal with the aid of machine learning and high-performance computing. Based on the deep blue method, the practical implementation of the synthetic image processing was assessed by a case study of the downtown Atlanta area in the United States. 10-fold cross validation was applied stepwise to control the uncertainty via machine learning. The predictions of AOD at the ground level were calibrated using the AErosol RObotic NETwork (AERONET) AOD data and finally validated by the AERONET) AOD data too.
Data fusion algorithms help extract information from “asynchronous” time series satellite data whereas data merging data help extract information from “synchronous” time series satellite data into a series of synthetic images by using the temporal, spatial, or even spectral properties. Such data fusion algorithms including Bayesian maximum entropy (BME) and spatial and temporal adaptive reflectance fusion model (STARFM) have greatly improved the coverage, enhancing data application potential with higher spatiotemporal resolution via multi-sensor earth observations. The goal of this study is to assess the utility of BME and modified BME algorithm with the aid of a data merging algorithm called Modified Quantile-Quantile Adjustment (MQQA), in comparison with STARFM for the retrieval of Aerosol Optical Depth in an urban environment. MQQA heavily counts on big data to support the systematic bias correction from “synchronous” time series satellite data. Such assessment of algorithmic efficiency needs to be carried out for both top of atmosphere reflectance and ground reflectance levels in support of the deep blue method for the retrieval of atmospheric optical depth at the ground level.
Fine particulates less than 2.5 microns in aerodynamic diameter (PM2.5) has been widely considered to
be one of the main pollutant threating human health. Ground-level PM2.5 monitoring can provide
accurate point data, but its value is hard to scale up to large scale. In this respects, satellite data with
large coverage areas and long term range, could enhance our ability to estimate PM2.5 concentration. In
this study, a Multilinear correlation model (MLC) based on MODIS AOD level 2 data was developed
to estimate PM2.5 concentration in Northeastern China from 2013-2016, then ground-level PM2.5
monitoring data from 15 stations covering study area were used for validation. Results showed that 1)
the annual PM2.5 is 63.98μg/m2, AOD values agreed well with estimated PM2.5 concentration, 2) the
spatial variations of PM2.5 were not clear, while the temporal dynamic of PM2.5 were observed, the
highest values were observed in winter, opposite to what were observed in fall. 3) the MLC model
coupled with meteorological data could improve the precision of PM2.5 estimations. Therefore, we
suggest that the developed MLC model is useful for the PM2.5 estimations in northeastern China.
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