## 3 Advanced Techniques in Remote Sensing of EBC
### Vegetation Index  
Vegetation indices (VIs) are 'dimensionless, radiometric relative abundance and activity of green vegetation, including LAI, percentage green cover, chlorophyll content, green biomass, and absorbed photosynthetically active radiation (APAR)' [1]. [PERSON] summarized VIs benefit in maximizing sensitivity to biophysical parameters, normalizing or modeling external effects, normalizing internal effects, and assisting validation effort and quality control [1]. Additionally, VIs are simple to understand and implement, easy to quickly calculate, and useful to track temporal characteristics. To date, hundreds of VIs have been used in all kinds of applications of remote sensing. VIs can be roughly categorized into two groups, _i.e._, biophysical indices and biochemical indices [74]. Biophysical indices represent those designed to link with vegetation biophysical characteristics including structure and condition. They can be grouped into simple ratio-based indices (e.g., Simple Ratio [SR]; [75]), soil-line-related indices (e.g., Soil Adjusted Vegetation Index [SAVI]; [76]), and chlorophyll-corrected indices (Ratio TCARI/OSAVI [Transformed Absorption in Reflectance Index/Optimized Soil Adjusted Vegetation Index]; [77]). Biochemical indices are those mainly employed to estimate vegetation biochemical properties such as Cellulose Absorption Index (CAI) and Lignin-Cellulose Absorption Index (LCAI) [78].  
No doubt that NDVI is the most well-known vegetation index. Its use in EBC has been considerably reviewed by [2, 13, 14], _etc._ Nonetheless, other indices that are commonly used in the relevant applications are not taken seriously enough in the aforementioned review papers. For example, SR was validated to perform best in early and intermediate forest stages for the assessment of LAI based on ASTER data in East African rainforest ecosystems [79]. The modified soil adjusted vegetation index (MSAVI) was selected as the optimal vegetation index in a linear mixture model to map canopy fractional cover in tropical forests in the Amazonian state of Mato Grosso (Brazil) [80]. [PERSON] _et al._ demonstrated that the existing VIs (e.g., NDVI, SAVI, Triangular Vegetation Index [TVI], and Modified Chlorophyll Absorption Ratio Index [MCARI], _etc._) were either sensitive to chlorophyll concentration changes or affected by saturation at high LAI levels, whereas a modified triangular vegetation index (MTVI2) and a modified chlorophyll absorption ratio index (MCARI2) are proved to be the best predictors of green LAI [77]. Additionally, other recently proposed VIs such as WDRVI (Wide Dynamic Range Vegetation Index; [81]), L-ATSAVI (Litter-corrected Adjusted Transformed Soil Adjusted Vegetation Index; [74]), and VIUPD (Vegetation Index based on a Universal Pattern Decomposition; [82]). However, traditional measures such as the coefficient of determination and root mean square based on regression statistics, are not capable of evaluating the performance of VIs on the estimation of biophysical parameters because the sensitivity of a VI may change substantially with vegetation density [83]. Therefore, a statistical sensitivity function was developed to summarize the overall relationship between VIs and biophysical parameters instead of a constant [83].