## 2 Materials and Methods
### Methods  
#### 2.3.1 Vegetation Spectral Indices  
The remote sensing of natural grasslands and the measuring of their productivity primarily relies on vegetation indices. Vegetation spectral indices are a basic method for remote sensing data analysis. In this study, the following vegetation indices: NDVI, NDWI, GNDVI, GLI, EVI, DVI, SAVI, MSAVI, and GSAVI were used, gained based on the Near infrared (NIR), Red, Green, and Blue spectral bands (Table 3). The most commonly used index to assess grassland vegetation and the state of biomass and grass growth rates is the Normalized Difference Vegetation Index (NDVI) presented by [PERSON] et al. (1974) [58; 66]. NDVI and the Enhanced Vegetation Index (EVI) have been widely used due to their strong correlation with plant growth status and spatial distribution density [62; 67; 68]. [PERSON] et al. [69] demonstrated the high sensitivity of NDVI and EVI to the spatial-temporal variation of vegetation, land cover, and biophysical parameters. However, the applicability of NDVI can be limited [1] in states of sparse vegetation due to significant soil background effects on green aboveground biomass estimation [70; 71]. To address this limitation, [PERSON] [64] proposed the Soil Adjusted Vegetation Index (SAVI), which incorporates a correction factor to minimize soil background effects [72]. Modified and improved versions of SAVI, such as the Modified Soil Adjusted Vegetation Index (MSAVI) and the Green Soil Adjusted Vegetation Index (GSAVI), have been developed to further reduce noise and soil background effects that affect NDVI [65; 72; 73]. In this context, the Difference Vegetation Index (DVI) is also noteworthy, as it separates vegetation from soil but does not account for atmospheric conditions and shadow presence [63]. The Green Leaf Index (GLI) is more accurate for distinguishing vegetation from the background and is also used for identifying single plant vegetation against different backgrounds [61]. [PERSON] \begin{table}
\begin{tabular}{c c} \hline
**Specification** & **Description** \\ \hline Sensor Type & Complementary Metal Oxide Semiconductor (COMS) \\ \hline Number of Cameras & 6 \\ \hline Spatial resolution (Multispectral) & Ground Sampling Distance (DGS)—4 cm (nadir) \\ \hline Bands & RGB (combined from natural color), Multispectral (5 Bands) \\ \hline Wavelength (by Bands) & 1. Blue (approx. 450 nm) 2. Green (approx. 560 nm) 3. Red (approx. 650 nm) 4. Near Infrared (NIR) (approx. 700 nm) 5. Red Edge (approx. 740 nm) \\ \hline \end{tabular}
\end{table} 
Table 2: The technical characteristics of UAV (P4 Multispectral). \begin{table}
\begin{tabular}{c c c c} \hline
**Index** & **Index Name** & **Formula** & **Citation** \\ \hline NDVI & Normalized Difference Vegetation Index & \(\frac{NIR-Red}{NIR+Red}\) & [58] \\ \hline NDWI & Normalized Difference Water Index & \(\frac{Green-NIR}{Green+NIR}\) & [59] \\ \hline GNDVI & Green Normalized Difference Vegetation Index & \(\frac{NIR-Green}{NIR+Green}\) & [60] \\ \hline GLI & Green Leaf Index & \(\frac{2*Green-Red-Blue}{2*Green+Red+Blue}\) & [61] \\ \hline EVI & Enhanced Vegetation Index & \(-2.5*(NIR-Red)\) & [62] \\ \hline DVI & Difference Vegetation Index & \(NIR+Red\) & [63] \\ \hline SAVI & Soil Adjusted Vegetation Index & \(\frac{1.5*(NIR-Red)}{(NIR+Red+0.5)}\) & [64] \\ \hline GSAVI & Green Soil Adjusted Vegetation Index & \(\frac{1.5*(NIR-Green)}{NIR+Green+0.5}\) & [65] \\ \hline MSAVI & Modified Soil Adjusted Vegetation Index & \(2*\text{NIR+1-}\sqrt{(2*NIR+1)^{2}-8*(NIR-Red)}\) & [65] \\ \hline \end{tabular}
\end{table} 
Table 3: Applied spectral vegetation indices. et al. [60] analyzed the sensitivity of visible reflectance to chlorophyll, noting that the green wavelength is more sensitive to chlorophyll than the red wavelength (used in NDVI), leading to the formation of the Green Normalized Difference Vegetation Index (GNDVI). This index achieved sufficient accuracy using the green channel to estimate vegetation cover.