## 4 Discussion
### Comparing Predictor Variables for Estimating Grass Water Content Indicators  
The results of this study illustrate that different sets of predictor variables could be noted for estimating each GWC indicator across both seasons. This was expected as variations in GWC as a result of wet and dry season variations tend to affect the spectral properties of vegetation. For instance, when estimating GWC indicators in the wet season, optimal predictor variables were based on spectral variables derived from NIR and the red edge. Similar results were obtained by [PERSON], [PERSON] [119] and [PERSON], [PERSON] [120], who reported that spectral variables derived from the NIR were the best indicators for estimating vegetation with a high water content. During the wet season, grass has a lot of moisture available to facilitate and optimize photosynthetic activities as it drawsmore moisture and produces more chlorophyll content. Subsequently, the grass moisture elements become sensitive and can be optimally estimated using the NIR. It has widely been proven that healthy vegetation reflects highly in the NIR as a result of the cellulose structure of vegetation leaves [77]. Meanwhile, RE (680-780 nm) spectral information has been widely proven to be sensitive to chlorophyll content variations, which have been generalized to be directly proportional to the quantity of water present in vegetation [14; 121]. As the amount of vegetation chlorophyll increases, major chlorophyll absorption features around 680 nm are broadened, causing a shift in the slope and RE position towards longer wavelengths [122]. The chlorophyll concentration changes with the changes in moisture content required to optimize photosynthetic activities. Subsequently, the RE is one of the optimal estimation variables of foliar content associated with the wet season. Meanwhile, for the dry season, optimal spectral variables were derived from the visible, SWIR, and RE bands. The importance of the blue (B2) and red (B4) bands can be attributed to the lack of pigmentation and photosynthetic activities of vegetation in the dry season [123]. A study by [PERSON], [PERSON] [124] illustrates that the SWIR is effective in characterizing water stress in vegetation water attributes due to the higher reflectance of dry vegetation when its water content decreases along spectral wavelengths of 1400 nm and 2500 nm [34]. Moreover, the RE portion is also considered useful in studying dry vegetation because water deficit changes vegetation foliar chlorophyll composition, resulting in a shift of RE reflectance toward shorter wavelengths. In this regard, RE can detect plant pigment changes and has been correlated with water stress variations in leaf photosynthetic rates; therefore, it could be used in assessing foliar moisture variability [125]. The strength of RE in estimating vegetation under water stress was demonstrated by [PERSON], [PERSON] [125] and [PERSON], [PERSON] [126], who illustrated that it can effectively detect changes in plant stress as indicated by changes in chlorophyll content. This could possibly explain the importance of the visible-, SWIR- and RE-based spectral variables in determining GWC in the dry season. Overall, the results show that the combination of bands, vegetation indices, and topo-climatic variables improved prediction accuracies and yielded the best models for estimating GWC indicators in the two seasons. Spectral bands offer data that can accurately provide the near-real-time state of the amount of water in vegetation, vegetation indices tend to surpass the influence of atmospheric and soil background noise [127], and topographic indices reveal the eco-hydroological attributes that facilitate the distribution of vegetation water content [128]. Although topographic variables yielded poor model accuracies as standalone estimation variables, it is important to note that they played a critical role in combined data estimation models.