This review article by Jinru Xue and Baofeng Su discusses the development and applications of Vegetation Indices (VIs) derived from remote sensing. VIs are simple and effective algorithms used to evaluate vegetation cover, vigor, and growth dynamics. The authors highlight the lack of a unified mathematical expression for all VIs due to the complexity of light spectra combinations, instrumentation, platforms, and resolutions. Customized algorithms have been developed and tested for specific applications, often tailored to meet specific requirements and validated with ground-based methods. The article reviews over 100 VIs, discussing their specific applicability and representativeness based on the type of vegetation, environment, and implementation precision. It covers various VIs, including the Normalized Difference Vegetation Index (NDVI), Atmopherically Resistant Vegetation Index (ARVI), Soil-Adjusted Vegetation Index (SAVI), and Tasseled Cap Transformation of Greenness Vegetation Index (GVI, YVI, and SBI). The authors also discuss VIs based on Unmanned Aerial Vehicles (UAV) remote sensing, which offer high spatial and temporal resolutions and are less affected by atmospheric factors. The review emphasizes the importance of considering the spectral characteristics of vegetation and the limitations of different VIs in various environments. It concludes that the choice of a specific VI should be made carefully, considering its advantages and limitations, and that new VIs developed with hyperspectral and multispectral remote sensing technology will broaden research areas and enhance practical applications, particularly with the increasing use of UAV platforms.This review article by Jinru Xue and Baofeng Su discusses the development and applications of Vegetation Indices (VIs) derived from remote sensing. VIs are simple and effective algorithms used to evaluate vegetation cover, vigor, and growth dynamics. The authors highlight the lack of a unified mathematical expression for all VIs due to the complexity of light spectra combinations, instrumentation, platforms, and resolutions. Customized algorithms have been developed and tested for specific applications, often tailored to meet specific requirements and validated with ground-based methods. The article reviews over 100 VIs, discussing their specific applicability and representativeness based on the type of vegetation, environment, and implementation precision. It covers various VIs, including the Normalized Difference Vegetation Index (NDVI), Atmopherically Resistant Vegetation Index (ARVI), Soil-Adjusted Vegetation Index (SAVI), and Tasseled Cap Transformation of Greenness Vegetation Index (GVI, YVI, and SBI). The authors also discuss VIs based on Unmanned Aerial Vehicles (UAV) remote sensing, which offer high spatial and temporal resolutions and are less affected by atmospheric factors. The review emphasizes the importance of considering the spectral characteristics of vegetation and the limitations of different VIs in various environments. It concludes that the choice of a specific VI should be made carefully, considering its advantages and limitations, and that new VIs developed with hyperspectral and multispectral remote sensing technology will broaden research areas and enhance practical applications, particularly with the increasing use of UAV platforms.