# Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors
## 4 Problems of Measurement and Analysis of Reflectance Indices
### Variability of Plants
It is known that a decrease in the luminal pH inhibits light photosynthetic reactions and activates the xanthophyll cycle [119]; these effects influence reflectance indices (e.g., PRI [11; 97; 137]). The increase in the Ca\({}^{2+}\) concentration in the stroma of chloroplasts modifies the activity of the Calvin-Benson cycle [176]; this modification contributes to a decrease in the luminal pH and, thereby, can also induce changes in RIs. The ROS generation both damages photosynthetic machinery [170]and induces adaptive photosynthetic changes through the activation of transcription of defense genes [177]. As a result, the individual reflectance spectra of a plant (or even parts of plants) can be caused by a unique combination of factors influencing the pigment absorption (profile expression of genes for individual plant or specie, stage of development, and local environmental conditions); these factors contribute to variability in the plant reflectance spectra. Differences in the leaf anatomy and orientation are another important reason for the variability in the reflectance spectra. It is known that the reflectance of the adaxial leaf surface is lower at the visible light range than this reflectance of the abaxial surface; in the NIR range, this effect is the opposite [20; 178; 179]. This effect is caused by the different optical properties of palisade and spongy mesophylls: the palisade mesophyll has high light absorption of the visible light, low light absorption of the NIR light, and low light scattering; in contrast, the spongy mesophyll has great light scattering (through numerous air spaces) and relatively low light absorption [20; 180]. Other important factors influencing the leaf surface reflectance are the leaf thickness [150; 180; 181] and presence of hairs [182]. Additionally, the orientation of leaves [20; 150] and their fluttering under the wind [183] can also modify measurements of reflectance. The leaf parameters can also be dependent on the plant nutrition; e.g., the nitrogen content can be an important factor influencing reflectance through plant morphology and canopy structure [20]. The minimization of the influence of these processes on the estimation of the content of photosynthetic pigments on the basis of the reflectance measurements can be based on the development of models of RIs that are insensitive to differences in the reflectance of the leaf surface [179; 183]. An analysis of the current mathematical models shows an important role of the leaves' distribution, frequency, and orientation of leaves for canopy reflectance [150; 151; 152; 153]. Potentially, differences in the water content in plants should also be reasons for the variability in water RIs. These differences can be related to spatial heterogeneity in the soil water content (e.g., as a result of microrelief [1; 184]) and to plant responses on environmental factors (e.g., heating-induced electrical signals can modify transpiration and water index in pea seedlings [80]). Hereby, the plant heterogeneity is the important factor that influences plant spectra and RIs [11; 54]. A solution to the problem can be based on the combined use statistical methods, including spatial and temporal averaging, and simple empirical models of relations between RIs and plant characteristics [20; 88; 149]. The analysis of whole reflectance spectra using heat maps of correlation between physiological parameters and RIs [19; 185] or heat maps of the significance of differences between RIs under action of environmental factors [186] can also be used for revealing RIs that are insensitive to the plant variability. The other prospective instrument of the minimization of the influence of RIs' variability is a meta-analysis of the literature data [11; 54; 137], which shows the influence of environmental factors, physiological processes and biochemical composition in plants, type of vegetation, and other factors on the efficiency of using RIs for plant remote sensing.