This review article discusses the development and applications of significant vegetation indices (VIs) in remote sensing. VIs are simple algorithms derived from remote sensing data that provide quantitative and qualitative assessments of vegetation cover, vigor, and growth dynamics. These indices have been widely used in various applications, including environmental monitoring, agriculture, forestry, and urban green infrastructure. The article highlights the complexity of different light spectra combinations, instrumentation, platforms, and resolutions used in VI development, leading to the need for customized algorithms tailored to specific applications. The review covers over 100 VIs, discussing their specific applicability and representativeness based on the vegetation of interest, environment, and implementation precision. It emphasizes the importance of validation processes, which involve direct or indirect correlations between VIs and in situ measurements of vegetation characteristics. The article also discusses the limitations of traditional VIs, such as the Normalized Difference Vegetation Index (NDVI), under atmospheric effects and soil background influences. To address these limitations, several improved VIs have been developed, including the Atmospherically Resistant Vegetation Index (ARVI), the Soil-Adjusted Vegetation Index (SAVI), and the Enhanced Vegetation Index (EVI). The review also explores the use of Unmanned Aerial Vehicles (UAVs) in remote sensing for vegetation monitoring, highlighting their advantages in terms of affordability, high spatial and temporal resolution, and ability to capture data in difficult-to-reach areas. The article discusses various VIs based on visible and near-infrared spectra, including the Visible-Band Difference Vegetation Index (VDVI), which has been shown to have high accuracy in vegetation extraction. Additionally, the review covers VIs related to vegetation status, such as the Wide Dynamic Range Vegetation Index (WDRVI) and the Chlorophyll Absorption Ratio Index (CARI), which are sensitive to chlorophyll content and leaf chlorophyll concentrations. The article concludes that the development of new VIs, particularly those based on hyperspectral and UAV platforms, will have wide applicability in various fields. These new developments are expected to be readily adopted by UAV platforms and become one of the most important research areas in aerospace remote sensing. The review emphasizes the importance of selecting the appropriate VI for specific applications, considering the advantages and limitations of existing VIs and combining them for optimal results.This review article discusses the development and applications of significant vegetation indices (VIs) in remote sensing. VIs are simple algorithms derived from remote sensing data that provide quantitative and qualitative assessments of vegetation cover, vigor, and growth dynamics. These indices have been widely used in various applications, including environmental monitoring, agriculture, forestry, and urban green infrastructure. The article highlights the complexity of different light spectra combinations, instrumentation, platforms, and resolutions used in VI development, leading to the need for customized algorithms tailored to specific applications. The review covers over 100 VIs, discussing their specific applicability and representativeness based on the vegetation of interest, environment, and implementation precision. It emphasizes the importance of validation processes, which involve direct or indirect correlations between VIs and in situ measurements of vegetation characteristics. The article also discusses the limitations of traditional VIs, such as the Normalized Difference Vegetation Index (NDVI), under atmospheric effects and soil background influences. To address these limitations, several improved VIs have been developed, including the Atmospherically Resistant Vegetation Index (ARVI), the Soil-Adjusted Vegetation Index (SAVI), and the Enhanced Vegetation Index (EVI). The review also explores the use of Unmanned Aerial Vehicles (UAVs) in remote sensing for vegetation monitoring, highlighting their advantages in terms of affordability, high spatial and temporal resolution, and ability to capture data in difficult-to-reach areas. The article discusses various VIs based on visible and near-infrared spectra, including the Visible-Band Difference Vegetation Index (VDVI), which has been shown to have high accuracy in vegetation extraction. Additionally, the review covers VIs related to vegetation status, such as the Wide Dynamic Range Vegetation Index (WDRVI) and the Chlorophyll Absorption Ratio Index (CARI), which are sensitive to chlorophyll content and leaf chlorophyll concentrations. The article concludes that the development of new VIs, particularly those based on hyperspectral and UAV platforms, will have wide applicability in various fields. These new developments are expected to be readily adopted by UAV platforms and become one of the most important research areas in aerospace remote sensing. The review emphasizes the importance of selecting the appropriate VI for specific applications, considering the advantages and limitations of existing VIs and combining them for optimal results.