# Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors
## 3 Main Reflectance Indices
### Vegetation Indices
Many works show that the NDVI is effective to estimate various vegetation characteristics, including the total biomass (e.g., [99]), plant productivity (e.g., [100]), leaf area index (LAI, [101; 102; 103]), parameters of vegetation cover (e.g., [104]), content of chlorophylls in leaves (e.g., [105]), and stress changes in plants (e.g., [106; 107]). The NDVI can be used for the remote sensing of the abundance and spatial arrangement of green vegetation and estimation of the canopy photosynthetic capacity and net primary productivity [100; 107]. The sensitivity of the NDVI to soil background and atmospheric effects stimulates the development of new Vis, such as the soil-adjusted vegetation index (SAVI) [63], optimized soil-adjusted vegetation index (OSAVI) [64], and atmospherically resistant vegetation index (ARVI) [71]. All these reflectance indices include specific correction coefficients, minimizing errors caused by the soil and atmosphere. For example, the OSAVI is not dependent on the soil background [64; 98] through using the canopy background adjustment factor, which can be assumed as 0.16 for all types of soils. This index is often used for the monitoring of sparse vegetation where soil is visible [64; 98]. There are other Vis on the basis of the NDVI; e.g., the green normalized difference vegetation index (GNDVI) is more sensitive to chlorophyll concentration than the NDVI [23]. Another interesting VI is the triangular vegetation index (TVI), which is also related to the light absorption by photosynthetic pigments in plant leaves [73]. The TVI is based on the idea of increasing the total area of the triangle between reflectance at green (550 nm), red (670 nm), and infrared (750 nm) wavelengths with increasing the chlorophyll content (decrease of red reflectance) and leaf tissue abundance (increase of near-infrared reflectance) [59; 73; 74; 108]. There are modifications of the TVI, including modified triangular vegetation index 1 (MTVI1) and modified triangular vegetation index 2 (MTVI2), which permit a more accurate assessment of the LAI [59; 74]. Moreover, [PERSON] et al. [74] developed transformed TVI (TTVI), which is more sensitive to the LAI in comparison with other VIs. Additionally, this index has a minimal saturation effect under the high plant density or pigment concentrations [74]. Thus, triangular vegetation indices seem to be promising for the remote sensing of plants; they can be used for the development of new methods in agricultural monitoring. Thus, the vegetation indices (particularly, NDVI, OSAVI, EVI, and others) are widely used for plant remote sensing on the basis of manual measurements and different mobile platforms, including UAS and satellites [23; 49; 53]. Vis can be used for the remote sensing of spatial [59] and temporal [109] changes in the green vegetation, including the estimation of the morphological characteristics of plants (e.g., LAI or the total biomass [74; 99; 101]) or activity of important physiological processes (e.g., the light absorption by leaves or gross primary productivity (GPP) [110]). These changes can be observed for weeks, season(s), year(s), or tens of years [49; 106; 109]. Flying platforms, including UAS and satellites, are widely used for measurements of the vegetation indices; particularly, satellites can be used for measuring the GPP and other parameters on an ecosystem and landscape scale. However, the efficiency of these indices, which can be quantitatively estimated on the basis of the coefficients of determination (for regressions connecting VIs and investigated parameters) and correlation can be varied from high to low values [111; 112]. This variability in relations is caused by the influence of environmental conditions and geometry of measurements (some influencing factors are shown in Section 4).