## 2 Materials and Methods
### Remote Sensing Calculations and Field Measurements
#### 2.3.1 Multi-Temporal Image Analysis  
Soil-Adjusted Vegetation Index (SAVI)  
The SAVI is a commonly recognized and robust method utilized for vegetation delineation by exploiting the unique absorption property in the red spectrum and the high reflectance in the near-infrared (NIR) spectrum [30]. These specific spectral regions correspond to bands 3 and 4 in the Enhanced Thematic Mapper Plus (ETM+) and bands 4 and 5 in the Operational Land Imager (OLI) sensor image datasets. An advantageous aspect of employing SAVI is its normalization to a standardized reference point, with values ranging from \(-1\) to 1, ensuring comparability among SAVIs derived from different images. SAVI was applied to ETM+ and OLI images in the White Nile study region to assess vegetation cover density as per Qi, Chehbouni [31]. \[\text{SAVI}=\frac{\text{NIR}-\text{RED}}{\left(\text{NIR}+\text{RED}+\text{L} \right)}\times\left(1+\text{L}\right) \tag{1}\]  
where L is the soil-brightness correction factor ranging from 0 to 1. In this study, L was 0.5 by default. Normalized Difference Vegetation Index (NDVI)  
The NDVI is the most widely used vegetation index, originally introduced by [PERSON] [32]. It can be expressed mathematically as follows:  
\[\text{NDVI}=\frac{\text{NIR}-\text{IR}}{\text{NIR}+\text{IR}} \tag{2}\]  
Due to the normalization process in its computation, NDVI values range from \(-1\) to 1, displaying a heightened sensitivity to green vegetation, even in regions with limited vegetation cover. The Normalized Difference Water Index (NDWI)  
The NDWI quantifies variations in leaf water content by utilizing the near-infrared (NIR) and shortwave infrared (SWIR) spectral bands. Being sensitive to both plant water \begin{table}
\begin{tabular}{l l c c c c} \hline \hline \multicolumn{1}{c}{**Index**} & \multicolumn{1}{c}{**Platform**} & \multicolumn{1}{c}{**Spatial Resolution**} & \multicolumn{1}{c}{**Temporal Resolution (d)**} & \multicolumn{1}{c}{**Data Level**} & \multicolumn{1}{c}{**Years**} \\ \hline \multirow{2}{*}{EVI} & Landsat7 ETM + Sensor & \multirow{2}{*}{30} & \multirow{2}{*}{2} & \multirow{2}{*}{L2} & 2001–2005, 2006–2010, \\  & Landsat8 OLI Sensor & & & & 2011–2015, 2016–2020 \\ \hline \multirow{2}{*}{NDVI} & Landsat7 ETM + Sensor & \multirow{2}{*}{30} & \multirow{2}{*}{2} & \multirow{2}{*}{L2} & 2001–2005, 2006–2010, \\  & Landsat8 OLI Sensor & & & & 2011–2015, 2016–2020 \\ \hline \multirow{2}{*}{SAVI} & Landsat7 ETM + Sensor & \multirow{2}{*}{30} & \multirow{2}{*}{2} & \multirow{2}{*}{L2} & 2001–2005, 2006–2010, \\  & Landsat8 OLI Sensor & & & & 2011–2015, 2016–2020 \\ \hline \multirow{2}{*}{NDWI} & Landsat7 ETM + Sensor & \multirow{2}{*}{30} & \multirow{2}{*}{2} & \multirow{2}{*}{L2} & 2001–2005, 2006–2010, \\  & Landsat8 OLI Sensor & & & & 2011–2015, 2016–2020 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Vegetation indices and remote sensing data description. content and water bodies, NDWI is frequently utilized for monitoring droughts, tracking yield reductions, assessing reservoir discharge, groundwater level decreases, etc. [33]. \[\text{NDWI}=\frac{(\text{NIR}-\text{SWIR})}{(\text{NIR}+\text{SWIR})} \tag{3}\]  
Values above 0.5 are indicative of water bodies, while vegetation typically exhibits lower values, facilitating the differentiation between vegetation and water bodies. EVI is a valuable method in remote sensing for evaluating vegetation health and monitoring changes over time. Derived from satellite imagery, EVI offers a quantitative assessment of vegetation density and vigour. Unlike conventional vegetation indices like NDVI, EVI factors in aerosol scattering and canopy background reflectance, making it more suitable for heavily vegetated regions or areas affected by atmospheric disturbances. EVI values range from \(-1\) to 1, with higher values denoting healthier and denser vegetation.