## 3 Results
### Estimating Grass Water Content Variables Using Spectral and Topo-Climatic Variables
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Wet Season**} & \multicolumn{3}{c}{**Dry Season**} \\ \hline
**Water Content Variable** & **Explanatory Variable** & **R\({}^{2}\)** & **RMSE** & **RMSE\%** & **R\({}^{2}\)** & **RMSE** & **RMSE\%** \\ \hline LAI (m\({}^{-2}\)) & Bands & 0.78 & 0.03 & 1.8 & 0.91 & 0.05 & 3.2 \\ \hline  & Vegetation indices & 0.86 & 0.03 & 1.8 & 0.57 & 0.09 & 5.7 \\ \hline  & Topo-climatic & 0.77 & 0.04 & 2.4 & 0.59 & 0.09 & 5.7 \\ \hline  & Combined & 0.83 & 0.03 & 1.8 & 0.90 & 0.04 & 2.6 \\ \hline CSC (mm) & Bands & 0.80 & 0.01 & 0.6 & 0.93 & 0.03 & 1.8 \\ \hline  & Vegetation indices & 0.79 & 0.01 & 0.6 & 0.57 & 0.05 & 2.9 \\ \hline  & Topo-climatic & 0.36 & 0.02 & 1.1 & 0.71 & 0.05 & 2.9 \\ \hline  & Combined & 0.86 & 0.01 & 0.6 & 0.93 & 0.03 & 1.8 \\ \hline CWC (g/m\({}^{-2}\)) & Bands & 0.68 & 20.42 & 10.6 & 0.77 & 1.63 & 1.8 \\ \hline  & Vegetation indices & 0.55 & 21.5 & 11.2 & 0.75 & 1.63 & 1.8 \\ \hline  & Topo-climatic & 0.34 & 24.52 & 13.2 & 0.09 & 3.07 & 3.4 \\ \hline  & Combined & 0.76 & 19.42 & 10.1 & 0.87 & 1.35 & 1.5 \\ \hline EWT (g/m\({}^{-2}\)) & Bands & 0.69 & 10.98 & 9.6 & 0.89 & 2.21 & 3.6 \\ \hline  & Vegetation indices & 0.55 & 11.4 & 10 & 0.31 & 5.37 & 8.9 \\ \hline  & Topo-climatic & 0.22 & 14.29 & 12.5 & 0.56 & 4.65 & 7.7 \\ \hline  & Combined & 0.65 & 10.75 & 9.4 & 0.91 & 2.01 & 3.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Estimation accuracies of LAI, CSC, CWC, and EWT derived using spectral data and topo-climatic variables. effect, Temperature, Elevation, Negative openness, Rainfall, and Standardized height in order of importance (Figures 3b and 4b). Figure 3: Relationship between observed and predicted grass LAI (**a**,**b**), CSC (**c**,**d**), CWC (**e**,**f**), and EWT (**g**,**h**) derived using optimal selected predictor variables for the wet and dry seasons, respectively. Figure 4: \(Cont.\)  
Figure 4: Variable importance scores of selected variables in estimating (**a**,**b**) LAI, (**c**,**d**) CSC, (**e**,**f**) CWC, and (**g**,**h**) EWT for wet and dry seasons, respectively. Similarly, CSC was also more accurately estimated during the wet season than the dry season. Specifically, CSC was predicted with an RMSE of 0.01 mm and R\({}^{2}\) of 0.86 based on MNDWI, B6, B11, B8A, B7, NDVI05, Rainfall, Elevation, Aspect, Temperature, and Positive openness (Figures 3c and 4c) in the wet season. It was then estimated at an RMSE = 0.03 mm and R\({}^{2}\) = 0.93 with B12, B2, B4, B3, B11, NDII, RR1, B5, MSI, Rainfall, Wind effect, Positive openness, Temperature, Direct insolation, and Negative openness being the most influential variables in the dry season (Figures 3d and 4d). Contrary to the CSC and LAI, CWC was optimally estimated during the dry season at an RMSE = 1.35 g/m\({}^{-2}\) and R\({}^{2}\) = 0.87 using B8, B6, B7, NDWI, B8A, MSI, NDII, NDVI05, RRII, NDRE, Aspect, Wind effect, Slope, Rainfall, Skyview factor, Temperature, and Positive openness as the optimal predictor variables, also in order of importance (Figures 3f and 4f). The wet season yielded relatively lower estimation accuracies of CWC of an RMSE of 19.42 g/m\({}^{-2}\) and R\({}^{2}\) of 0.76 based on NDWI, NDRE, NDVI705, B4, RRII, B2, Cross-section, Maximum curvature, Elevation, Rededge2, B5, Negative openness, Rededge1, MSI, B8, B3, Rainfall, and Temperature (Figures 3e and 4e). Additionally, dry-season EVT exhibited a high accuracy of RMSE = 2.01 g/m\({}^{-2}\) and R\({}^{2}\) = 0.91 based on B3, B5, B6, B12, B4, B2, B11, B8A, B7, Aspect, Rainfall, Longitudinal curvature, MSI, Temperature, NDII, and Skyview factor as optimal predictor variables in order of importance (Figures 3h and 4h). During the wet season, EVT was estimated at RMSE = 10.75 g/m\({}^{-2}\) and R\({}^{2}\) = 0.65 based on NDRE, NDWI, B8, NDVI705, B4, Negative openness, RRI1, Rainfall, Rededge 1, B8A, Elevation, B7, and Temperature (Figures 3g and 4g).