## 2 Materials and Methods
### Spatial Analysis  
All statistical analysis in this study was performed using the GEE platform. A random forest (RF) ensemble was used to predict grass water content elements. RF is an ensemble learning technique that was first proposed by [PERSON] [93]. It is considered a robust regression technique that uses multiple decision trees to make predictions [94]. In RF, a large number of decision trees are randomly created (with replacement) from the original training data and variables [95]. Each node per regression tree is grown with a randomized subset of input variables considered for binary splitting [96]. The final output of RF regression is obtained via averaging the outputs of all decision trees to produce accurate predictions that do not overfit the data [97]. Generally, two user-defined parameters need to be optimized in RF regression. The first is the number of regression trees. The default value for the number of trees is usually 500. However, in this study, hyperparameter tuning was performed, and 400 trees were selected as the optimal value. The second parameter is the number of input predictor variables per node. In the case of RF regression, the default value of input predictor variables per node requires that all variables be divisible by 3 [98].  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Topographic Variable** & **Description** \\ \hline Slope & Degree of inclination of land surface \\ Elevation & Height above sea level \\ Aspect & Compass direction of a slope \\ Minimum curvature & Lowest deviation from slope curve \\ Maximum curvature & Highest deviation from slope curve \\ Longitudinal curvature & Explains the flowing speed of a substance downslope \\ Cross-section curvature & Explains the divergence or convergence of a flowing substance \\ Profile curvature & Represents morphology of the topography \\ General curvature & Total curvature of the surface \\ Plan curvature & Horizontal curvature of contour lines \\ Catchment area & Run-off water flow, forming a waterway \\ Positive openness & Dominance of a landscape location \\ Negative openness & Enclosure of a landscape location \\ Standardized height & Slope position and height \\ Normalized height & Slope position and height \\ Valley depth & Relative height of a valley \\ Convergence index & Calculates valleys and ridges \\ Wind effect & Effects of the direction and speed of wind on the surface \\ Direct insolation & Incoming solar radiation \\ Terrain roughness index & Surface heterogeneity \\ Topographic wetness index & Quantifies topographic control on hydrological processes \\ Skyview factor & Visible sky \\ Mass balance index & Terrain morphometry \\ \hline \hline \end{tabular}
\end{table}
Table 2: Topographic variables used in this study as explained in Odebiri, Mutanga [39].  
RF was chosen and used in this study because it is fast, insensitive to overfitting, and effective in handling data multicollinearity and dimensionality [99]. RF is renowned for being more accurate and outperforming other regression algorithms [100; 101; 102; 103; 104]. Above all, it offers various importance matrices in its computation, which provide valuable insights into the effects of each predictor variable on the response variable [105]. RF variable importance scores are derived from assessing the lowest Gini Index (a variable selection measure that measures the error of a variable with respect to the output model) [96; 106]. In this regard, the variable importance from RF was used to select the most influential predictor variables with the high predictive power. In predicting the grass water content, RF regression models were built in 4 stages of analysis:  
1. Stand-alone Sentinel 2 MSI bands (analysis stage 1);
2. Vegetation indices only (analysis stage 2);
3. Environmental variables only (analysis stage 3);
4. Combined variables (analysis stage 4).  
Prior to each analysis, the sampled data were randomly split into 70% for training and 30% for assessing the accuracy of predictive models.