QUANTIFYING THE RELATIONSHIP BETWEEN NATURAL AND SOCIOECONOMIC FACTORS AND WITH FINE PARTICULATE MATTER (PM2.5) POLLUTION BY INTEGRATING REMOTE SENSING AND GEOSPATIAL BIG DATA

5 citations as recorded by crossref.

1. Downscaling Land Surface Temperature Using Multiscale Geographically Weighted Regression Over Heterogeneous Landscapes in Wuhan, China C. Yang et al. https://doi.org/10.1109/JSTARS.2019.2955551
2. Characterizing the spatial and temporal variation of the land surface temperature hotspots in Wuhan from a local scale C. Yang et al. https://doi.org/10.1080/10095020.2020.1834882
3. How do greenspace landscapes affect PM2.5 exposure in Wuhan? Linking spatial-nonstationary, annual varying, and multiscale perspectives Q. Zhan et al. https://doi.org/10.1080/10095020.2022.2085187
4. Spatial Disparity of Visitors Changes during Particulate Matter Warning Using Big Data Focused on Seoul, Korea S. Lee & J. Kang https://doi.org/10.3390/ijerph19116478
5. Satellite Aerosol Optical Depth Retrieval Based on Fully Connected Neural Network (FCNN) and a Combine Algorithm of Simplified Aerosol Retrieval Algorithm and Simplified and Robust Surface Reflectance Estimation (SREMARA) Y. Fan & L. Sun https://doi.org/10.1109/JSTARS.2023.3281777