Comparar métodos

	InSAR	Radar de Penetração no Solo	NDVI
Área	Geofísica	Geofísica	Geofísica
Família	Process / pipeline	Process / pipeline	Process / pipeline
Ano de origem	1989	1989	1973
Autor original	Gabriel, Goldstein, and Zebker	James Davis and Anthony Annan	Rouse, Haas, Schell, and Deering
Tipo	Radar interferometry for millimeter-precision surface deformation	Shallow subsurface electromagnetic pulse detection	Spectral index for vegetation assessment
Fonte seminal	Gabriel, A. K., Goldstein, R. M., & Zebker, H. A. (1989). Mapping small elevation changes over large areas: Differential radar interferometry. Journal of Geophysical Research, 94(B7), 9183-9191.	Davis, J. L., & Annan, A. P. (1989). Ground-penetrating radar for high-resolution mapping of soil and rock stratigraphy. Geophysical Prospecting, 37(5), 531-551.	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.
Outros nomes	InSAR	GPR	NDVI
Relacionados	3	3	3
Resumo	Interferometric Synthetic Aperture Radar (InSAR) is a radar remote sensing technique that measures millimeter-scale ground surface deformation by analyzing the phase difference between radar images acquired from slightly different orbital positions. Pioneered by Gabriel, Goldstein, and Zebker in 1989, InSAR has become essential for earthquake rupture characterization, volcanic monitoring, landslide detection, and subsidence quantification.	Ground-Penetrating Radar (GPR) is a near-surface geophysical method that uses high-frequency electromagnetic pulses (typically 10 MHz to 2.5 GHz) to image shallow subsurface structures with exceptional spatial resolution. Pioneered by Davis and Annan in 1989, GPR is widely used in archaeology, civil engineering, environmental assessment, and shallow mineral exploration due to its ability to resolve features at decimeter to centimeter scales.	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.