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First 265 results for ” Fons de Leeuw”

1. Improved inversion of aerosol components in the atmospheric column from remote sensing data
   Y Zhang, Z Li, Y Chen, G de Leeuw, C Zhang, Y Xie, K Li | Status: published | Journal: Atm. Chem. Phys. | Volume: 20 | Year: 2020 | First page: 12795 | Last page: 12811 | doi: https://doi.org/10.5194/acp-20-12795-2020

   Publication

2. An AeroCom–AeroSat study: intercomparison of satellite AOD datasets for aerosol model evaluation

   To better understand and characterize current uncertainties in the important observational constr...

   N Schutgens, AM Sayer, A Heckel, C Hsu, H Jethva, G de Leeuw, Et al. | Status: published | Journal: Atm. Chem. Phys. | Volume: 20 | Year: 2020 | First page: 12431 | Last page: 12457 | doi: https://doi.org/10.5194/acp-20-12431-2020

   Publication

3. The impacts of the COVID-19 lockdown on air quality in the Guanzhong Basin, China
   K Zhang, G de Leeuw, Z Yang, X Chen, J Jiao | Status: published | Journal: Remote Sensing | Volume: 12 | Year: 2020 | First page: 1-23 | doi: doi:10.3390/rs12183042

   Publication

4. Satellite Observations of PM2.5 Changes and Driving Factors based Forecasting over China 2000-2025

   In China, atmospheric fine particulate matter (PM2.5) pollution is a challenging environmental pr...

   Y Zhang, Z Li, W Chang, Y Zhang, G de Leeuw, JJ Schauer | Status: published | Journal: Remote Sensing | Volume: 12 | Year: 2020 | First page: 1 | Last page: 20 | doi: doi:10.3390/rs12162518

   Publication

5. Joint retrieval of the aerosol fine mode fraction and optical depth using MODIS spectral reflectance over northern and eastern China: Artificial neural network method

   The Fine Mode Fraction (FMF) of atmospheric aerosol is very important for environment and climate...

   X Chen, G de Leeuw, A Arola, S Liu, Y Liu, K Zhang | Status: published | Journal: Remote Sens. Environ. | Volume: 247 | Year: 2020 | First page: 1 | Last page: 16 | doi: https://doi.org/10.1016/j.rse.2020.112006

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