## 5 Conclusions  
This study sought to test the utility of multisource data in estimating GWC variables across wet and dry seasons. Based on the findings of this study, it can be concluded that:  
* The use of multisource data in conjunction with RF in the GEE can be used to model the LAI, CSC, CWC, and EWT with acceptable accuracies. The LAI was best estimated in the wet season with an accuracy of RMSE = 0.03 m\({}^{-2}\) and R\({}^{2}\) = 0.83 as compared to the dry season (RMSE = 0.04 m\({}^{-2}\) and R\({}^{2}\) = 0.90). Similarly, CSC was estimated with a high accuracy in the wet seasons, yielding an RMSE of 0.01 mm and R\({}^{2}\) of 0.86, compared to the dry season (RMSE = 0.03 mm and R\({}^{2}\) = 0.93). For CWC, the wet season results show an RMSE of 19.42 g/m\({}^{-2}\) and R\({}^{2}\) of 0.76, which were lower than those obtained for the dry season (RMSE = 1.35 g/m\({}^{-2}\) and R\({}^{2}\) = 0.87). Finally, EWT was best estimated in the dry season (RMSE = 2.01 g/m\({}^{-2}\) and R\({}^{2}\) = 0.91) as compared to the wet season (RMSE = 10.75 g/m\({}^{-2}\) and R\({}^{2}\) = 0.65).
* The optimal model for estimating the LAI (RMSE of 0.03 m\({}^{-2}\) and R\({}^{2}\) of 0.83) had MNDWI, B7, B6, B11, B8A, B8, NDWI, Minimum curvature, Rainfall, Positive openness, Temperature, and Direct insolation as the optimal predictor variables.
* Overall, CSC performed optimally as an indicator of grass water content across both seasons based on MNDWI, B6, B11, B8A, B7, NDVI705, Rainfall, Elevation, Aspect, Temperature, and Positive openness in the wet season and B12, B2, B4, B3, B11, NDII, RR1, B5, MSI, Rainfall, Wind effect, Positive openness, Temperature, Direct insolation, and Negative openness in the dry season.
* CWC was best estimated in the dry season based on B8, B6, B7, NDWI, B8A, MSI, NDII, NDVI705, RRII, NDRE, Aspect, Wind effect, Slope, Rainfall, Skyview factor, Temperature, and Positive openness as the optimal predictor variables, also in order of importance.
* EWT was estimated with high accuracies in the dry season using B3, B5, B6, B12, B4, B2, B11, B8A, B7, Aspect, Rainfall, Longitudinal curvature, MSI, Temperature, NDII, and Skyview factor as optimal predictor variables.  
**Author Contributions:** Conceptualization, A.M., O.M., T.D. and M.S.; methodology, A.M., O.M., T.D. and M.S.; software, A.M. and O.O.; validation, A.M. and O.O.; formal analysis, A.M. and O.O.; investigation, A.M., O.M., T.D. and M.S.; resources, O.M. and T.M.; data curation, A.M. and O.O.; writing--original draft preparation, A.M., O.M., T.D. and M.S.; writing--review and editing, O.M., T.D. and M.S.; visualization, A.M., O.M., T.D. and M.S.; supervision, O.M., T.D. and M.S.; project administration, O.M. and T.M.; funding acquisition, O.M. and T.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by The Water Research Commission of South Africa under the WRC Project No. CON2020/2021-00490, \"Geospatial modelling of rangelands productivity in water-limited environments of South Africa\" and funded in part by the National Research Foundation of South Africa (NRF) SARChI Chair in Land Use Planning and Management--UKZN under grant number 84157.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.