## 3 Indices Related to Plant Productivity  
Plant productivity indices are directly related to the concept of remote sensing, which is defined as \"the study of obtaining information about an object by analyzing data from a device that is not in contact with the object\" [125]. Such data can be obtained from devices such as sensors, digital cameras, and video recorders, which are placed on various platforms (airplanes, satellites, unmanned aerial vehicles, or handheld radiometers) and obtain data in various forms, including the distribution of acoustic waves or the type of electromagnetic energy [126]. In remote sensing, land cover monitoring has become one of the most active areas of research [127; 128]. The development of forecasting models using remote sensing data is a solution with high use potential. These data provide quantitative and up-to-date information on the development of crops over a large area in a cost-effective manner. Moreover, the advantage of such measurements is their non-invasive nature, which means that they can be obtained without the need to destroy plant tissue [129; 130; 9; 131; 132]. Plant productivity indices may be divided into the following [133; 134; 135]:1. Growth analysis indices, i.e., leaf area index (LAI), leaf area ratio (LAR), relative growth rate (RGR), leaf area duration (LAD), unit leaf rate (ULR), and weighted difference vegetation index (WDVI);
2. Indices that show the relationship between photosynthetic production and biomass growth, i.e., photosynthetically active radiation (PAR), fraction of photosynthetically active radiation (FPAR), radiation use efficiency (RUE), and gross primary production (GPP);
3. Vegetation indicators, i.e., normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), enhanced vegetation index (EVI 2), and ratio vegetation index (RVI);
4. Indices determining the content of chlorophyll, i.e., MERIS terrestrial chlorophyll index (MTCI), triangular vegetation index (TVI), modified triangular vegetation index 1 (MTVI 1), modified triangular vegetation index 2 (MTVI 2), chlorophyll absorption reflectance (CAR), canopy chlorophyll content index (CCCI), etc. The NDVI, EVI, and LAI indices are some of the most commonly used plant productivity indices. Their calculation methods are presented in the following formulas:  
\[\text{NDVI}=\frac{NIR-RED}{NIR+RED} \tag{3}\]  
where **NIR** and **RED** are the reflectance in the near infrared (**NIR**) and red bands, respectively [136];  
\[\text{EVI}=\text{G}\cdot\frac{\rho_{NIR}-\rho_{red}}{\rho_{NIR}+C_{1}\cdot \rho_{rad}-\rho_{blue}+L} \tag{4}\]  
where **G** is a scaling factor; \(\rho\)**x** is the atmospherically corrected surface reflectance, differential **NIR**, and red radiative transfer through a canopy; C1 and C2 are the coefficients of the aerosol resistance term; and L is the canopy background adjustment for correcting nonlinearity [137];  
\[\text{LAI}=\frac{A_{c}}{P} \tag{5}\]  
where \(\mathbf{A_{c}}\) is the total area of the leaves of the whole canopy and \(\mathbf{P}\) is the ground area occupied by the plants [81]. The NDVI determines plant vitality and photosynthetic activity and is calculated from the reflection of light in the near infrared and red-light bands. The EVI, however, is calculated by turning on the reflection of the blue band to solve the saturation problem of the NDVI. The LAI is defined as the total leaf area per unit ground surface area and is used as an approximation of the leaf biomass. LAI is also used to model the evapotranspiration of crops. Relatively commonly in estimating the yield of crops, signals are used to determine the content of chlorophyll in plants, such as CCCI [126; 5; 138], which is calculated as  
\[\text{CCCI}=\frac{NDRE-NDRE_{min}}{NDRE_{max}-NDRE_{min}} \tag{6}\]  
where **NDRE** is the normalized difference red edge; and **NDRE\({}_{min}\)** and **NDRE\({}_{max}\)** are the minimum and maximum values of this index, respectively [139].