Evaluation of MODIS Aerosol Optical Depth and Surface Data Using an Ensemble Modeling Approach to Assess PM2.5 Temporal and Spatial Distributions

The use of statistical models and machine-learning techniques along satellite-derived aerosol optical depth (AOD) is a promising method to estimate ground-level particulate matter with an aerodynamic diameter of

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Bibliographische Detailangaben
Veröffentlicht in:	Remote sensing (Basel, Switzerland) Switzerland), 2021-08, Vol.13 (16), p.3102, Article 3102
Hauptverfasser:	Carmona, Johana M., Gupta, Pawan, Lozano-Garcia, Diego F., Vanoye, Ana Y., Hernandez-Paniagua, Ivan Y., Mendoza, Alberto
Format:	Artikel
Sprache:	eng
Schlagworte:	Aerosols Air pollution Air quality Algorithms Correlation coefficient Correlation coefficients Datasets Density Domains Emissions ensemble models Environmental Sciences Environmental Sciences & Ecology fine particulate matter Geology Geosciences, Multidisciplinary Imaging Science & Photographic Technology Learning algorithms Life Sciences & Biomedicine Machine learning Mathematical models Meteorological parameters Meteorology Metropolitan areas MODIS Neural networks Optical analysis Optical thickness Outdoor air quality Particulate matter Physical Sciences Pollutants Quality standards Regions Regression models Remote Sensing Root-mean-square errors satellite data Science & Technology Seasonal variations Spatial discrimination Spatial distribution Spatial resolution Statistical analysis Statistical models Technology Urban areas
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Beschreibung
Zusammenfassung:	The use of statistical models and machine-learning techniques along satellite-derived aerosol optical depth (AOD) is a promising method to estimate ground-level particulate matter with an aerodynamic diameter of
ISSN:	2072-4292 2072-4292
DOI:	10.3390/rs13163102