## 3 Results  
### Estimating Grass Water Content Variables Using Spectral and Topo-Climatic Variables  
Table 3 shows the predictive accuracies exhibited in estimating the LAI, CSC, CWC, and EWT based on the four levels of analysis across wet and dry seasons. For the wet season, the spectral bands and vegetation index models for estimating the LAI showed high variation between the observed and predicted values, but both models exhibited the same RMSE. For instance, when estimating the LAI, the spectral band model yielded an RMSE of 0.03 m\({}^{-2}\)/ m\({}^{-2}\) and R\({}^{2}\) of 0.78. The vegetation index model had an increased R\({}^{2}\) of 0.86 but exhibited a similar RMSE of 0.03 m\({}^{-2}\)/m\({}^{-2}\). On the other hand, the results show that there were insignificant differences between the spectral band and vegetation index predictive models for estimating CSC. When estimating CWC and EWT, the spectral band models yielded considerable high accuracies of an RMSE of 20.42 g/m\({}^{-2}\) and R\({}^{2}\) of 0.68 and an RMSE of 10.98 g/m\({}^{-2}\) and R\({}^{2}\) of 0.69, respectively. The vegetation index models for these water content variables showed a decrease in the accuracy to an RMSE = 21.5 g/m\({}^{-2}\) and R\({}^{2}\) = 0.55 and an RMSE of 11.4 g/m\({}^{-2}\) and R\({}^{2}\) = 0.55, respectively. The topo-climatic variables produced the lowest model accuracies across all GWC indicators. Figure 2: Research methodology work chart used on each GWC variable across wet and dry season. Meanwhile, the RF regression results obtained in the dry season indicate that the spectral band model for estimating the LAI yielded an RMSE accuracy of 0.05 m\({}^{-2}\) and R\({}^{2}\) = 0.91. The results of the dry season also show that the vegetation indices and top-climatic models for estimating the LAI retained the same RMSE of 0.09 m\({}^{-2}\). The results obtained using bands and combined data models for estimating CSC show the same accuracies (RMSE = 0.03 mm and R\({}^{2}\) = 0.93). Although the vegetation index and the top-climatic models for estimating CSC had significant variations of about 14% in terms of the R\({}^{2}\), the two models still produced the same RMSE of 0.05 mm. For CWC, the dry season results indicate that Sentinel-2 MSI bands and vegetation index models yielded the same RMSE of 1.63 g/m\({}^{-2}\), although the model's R\({}^{2}\) values had a slight difference of 2%. The top-climatic variable model performed poorly in estimating CWC. In terms of estimating EVT, the dry season results show significant variations amongst the spectral band, vegetation index, and top-climatic variable models. Overall, the models that integrated bands, vegetation indices, and topo-climatic variables exhibited the highest accuracies for estimating GWC indicators across both seasons except for the LAI in the wet season, where the vegetation index model had a higher coefficient of determination across all stages of analysis and CSC in the dry season, whereas bands and combined data exhibited the same accuracies. Nevertheless, these differences were insignificant since the models yielded the same RMSE accuracies. Comparing the Optimal Seasonal Models of Grass Water Content Elements between the Dry and Wet Seasons  
The LAI was optimally estimated during the wet season at an RMSE of 0.03 m\({}^{-2}\) and R\({}^{2}\) of 0.83 based on MNDWI, B7, B6, B11, B8A, B8, NDWI, Minimum curvature, Rainfall, Positive openness, Temperature, and Direct insolation in order of importance (Figures 3a and 4a). During the dry season, the LAI was estimated at an RMSE = 0.04 m\({}^{-2}\) and R\({}^{2}\) = 0.90 based on B2, B4, B12, B3, B11, B8 NDII, RRI, NDRE, NDVI05, MSI, Wind