Võrdle meetodeid

	Aerosooli optiline paksus	Üldine tsirkulatsioonimudel	NDVI
Valdkond	Geofüüsika	Geofüüsika	Geofüüsika
Perekond	Process / pipeline	Process / pipeline	Process / pipeline
Tekkeaasta	1929	1975	1973
Looja	Anders Ångström	Syukuro Manabe and Richard Wetherald	Rouse, Haas, Schell, and Deering
Tüüp	Optical parameter for aerosol loading quantification	Deterministic coupled atmosphere-ocean simulation	Spectral index for vegetation assessment
Algallikas	Ångström, A. (1929). On the atmospheric transmission of sun radiation and on dust in the air. Geografiska Annaler, 11(2), 156-166.	Manabe, S., & Wetherald, R. T. (1975). The effects of doubling the CO2 concentration on the climate of a general circulation model. Journal of the Atmospheric Sciences, 32(1), 3-15.	Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring vegetation systems in the Great Plains with ERTS. Third Earth Resources Technology Satellite Symposium Proceedings, 1, 309-317.
Rööpnimetused	AOD, Aerosol Optical Thickness	GCM, Global Climate Model	NDVI
Seotud	3	3	3
Kokkuvõte	Aerosol Optical Depth (AOD) is a dimensionless measure of aerosol light extinction in the atmosphere, quantifying how much sunlight is scattered and absorbed by particles suspended in air. Formalized by Ångström in 1929 and now routinely measured via satellite (MODIS, Sentinel-5P) and ground networks (AERONET), AOD is essential for air quality monitoring, climate forcing assessment, and visibility prediction.	A General Circulation Model (GCM), also called a Global Climate Model, is a three-dimensional numerical representation of the Earth's atmosphere, oceans, ice, and land surface that simulates physical processes governing weather and climate. Pioneered by Manabe and Wetherald in 1975, GCMs are the primary tools for understanding past climate, projecting future climate change, and investigating climate sensitivity to greenhouse gases and other forcings.	The Normalized Difference Vegetation Index (NDVI) is a spectral index computed from satellite or aerial multispectral imagery that quantifies vegetation greenness and vigor. Introduced by Rouse and colleagues in 1973 using Landsat data, NDVI has become the most widely used remote sensing metric for vegetation monitoring, drought assessment, crop productivity forecasting, and land cover change detection.