Research Interest
Satellite-based PM2.5 retrieval and environmental applications
Fine suspended particulate matter with aerodynamic diameters less than 2.5μm (PM2.5) poses a serious threat to public health through increased risks to mortality, cardiovascular, and respiratory illness, among others. However, the traditional station-based monitoring has limitations with respect to wide-range coverage and spatial continuity. Accordingly, satellite remote sensing provides a possibility to continuously monitor air pollutants around the world with high spatial resolution and coverage, which especially benefits regions with less monitoring stations.
My current research mainly focuses on atmospheric remote sensing, air quality modelling, air pollution impacts to promote the development of atmospheric environment. I am looking forward to applying the advanced satellite remote sensing technique to a wide range of environmental applications, including health impact and environmental inequality.
Education
- MSc, Surveying and Mapping, China University of Geosciences (Beijing), 2017
- BSc, Geographic Information Science, China University of Geosciences (Beijing), 2014
Publications
- Liu M, Zhou G, Saari RK, Li S, Liu X, *Li J, 2019. Quantifying PM2.5 mass concentration with particle radius based on optic-mass conversion algorithm using satellite data, ISPRS Journal of Photogrammetry and Remote Sensing, 158, 90-98.
- Zhou G, Liu M, *Liu X, Li J, 2019. An autoencoder-based model for forest disturbance detection using Landsat time series data, , Remote Sensing of Environment, under review.
- Zhou G, *Liu X, Liu M, 2019. Assimilating remote sensing phenological information into the WOFOST model for rice growth simulation, Remote Sensing, 11(3): 268.
- Liu M, Zhou G, Saari RK, Li J*, 2019, Long-term trend of ground-level PM2.5 concentrations over 2012-2017 in China, IGARSS 2019, IEEE
- Zhou G, Liu M, *Liu X, Li J, 2018. Combination of crop growth model and radiation transfer model with remote sensing data assimilation for FAPAR estimation, IGARSS 2018, pp. 1882-1885.
- Chen M, Gu Y, Liu M, *Li J, 2018. Estimating PM 2.5 in British Columbia before and after wildfires using 3 km MODIS AOD products from February to August 2017, IGARSS 2018, pp. 7585-7588.
- Zhou G, *Liu X, Zhao S, Liu M, Wu L, 2017. Estimating FAPAR of rice growth period using radiation transfer model coupled with the WOFOST model for analyzing heavy metal stress. Remote Sensing, 9(5): 424.
- Tian L, *Liu X, Zhang B, Liu M, Wu L, 2017. Extraction of rice heavy metal stress signal features based on long time series leaf area index data using ensemble empirical mode decomposition. International Journal of Environmental Research and Public Health, 14(9): 1018.
- Liu M, *Liu X, Liu M, Liu F, Jin M, Wu L, 2016. Root mass ratio: index derived by assimilation of synthetic aperture radar and the improved World Food Study model for heavy metal stress monitoring in rice. Journal of Applied Remote Sensing, 10(2): 026038.