Space-Time Machine Learning Models to Analyze COVID-19 Pandemic Lockdown Effects on Aerosol Optical Depth over Europe
Abstract
:1. Introduction
2. Material and Data
2.1. Study Area and Period
2.2. Data
2.2.1. MODIS Data
2.2.2. Copernicus Atmosphere Monitoring Service (CAMS) Data
2.2.3. Digital Elevation Model
2.2.4. Ground-Based AOD Data
2.2.5. European Centre for Medium-Range Weather Forecasts reanalysis (ECMWF)
3. Methodology
4. Space-Time Models
4.1. Extra Trees Algorithm
4.2. Improved Spatiotemporal Information
5. Results
5.1. Models
5.2. AOD Maps
5.3. Validation with AERONET
5.4. AOD Relative Percentage Difference
6. Discussion
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Product | Spatial Resolution | Temporal Resolution | Layer |
---|---|---|---|
MODIS MCD19A2 | 1 km | Daily | AOD-055 Quality Assurance (QA) |
CAMS | 80 km | 3 h | Total aerosol optical depth at 550 nm |
ALOS DSM | 30 m | - | Elevations |
AERONET | - | ~15 min | Level 2.0 |
ECMWF ERA-5 | 0.1° | Hourly | Wind U and V components Total precipitation 2 m surface temperature |
ECMWF ERA-5 | 0.25° | Monthly | Relative humidity |
Year | R-Squared (%) | RMSE | MAE |
---|---|---|---|
2015 | 95 | 0.017 | 0.011 |
2016 | 94.3 | 0.018 | 0.011 |
2017 | 93.8 | 0.018 | 0.011 |
2018 | 92.5 | 0.02 | 0.012 |
2019 | 92.9 | 0.019 | 0.012 |
2020 | 94.1 | 0.016 | 0.010 |
D (Km) | N | R | MAE | RMSE | Bias | EE(%) |
---|---|---|---|---|---|---|
20 | 10916 | 0.762 | 0.043 | 0.067 | −0.014 | 83.7 |
50 | 12212 | 0.767 | 0.043 | 0.066 | −0.014 | 83.7 |
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Ibrahim, S.; Landa, M.; Pešek, O.; Pavelka, K.; Halounova, L. Space-Time Machine Learning Models to Analyze COVID-19 Pandemic Lockdown Effects on Aerosol Optical Depth over Europe. Remote Sens. 2021, 13, 3027. https://doi.org/10.3390/rs13153027
Ibrahim S, Landa M, Pešek O, Pavelka K, Halounova L. Space-Time Machine Learning Models to Analyze COVID-19 Pandemic Lockdown Effects on Aerosol Optical Depth over Europe. Remote Sensing. 2021; 13(15):3027. https://doi.org/10.3390/rs13153027
Chicago/Turabian StyleIbrahim, Saleem, Martin Landa, Ondřej Pešek, Karel Pavelka, and Lena Halounova. 2021. "Space-Time Machine Learning Models to Analyze COVID-19 Pandemic Lockdown Effects on Aerosol Optical Depth over Europe" Remote Sensing 13, no. 15: 3027. https://doi.org/10.3390/rs13153027