## 4 Discussion  
The objective of this study was to test the utility of multisource data in estimating GWC indicators across wet and dry seasons. In this study, RF was used to model GWC indicators based on remotely sensed data and topo-climatic variables.  
Predictive Performance of Spectral and Environmental Variables in Determining Grass Water Content Indicators  
The results of this study show that Sentinel-2 MSI bands performed better than all the other variables in estimating different grass foliar water elements across all seasons based on visible, NIR, and SWIR bands 5, 6, 7 8, 8a 11 and 12. This could be explained by the fact that Sentinel-2 MSI data are characterized by medium-spatial-resolution bands (i.e., 10 m), which can capture distinct and discreet interaction between grass canopy and the reflected radiation [114]. Moreover, the MSI sensor operates on a 12-bit radiometric resolution with a radiometric accuracy of less than 5%, which enables the distinction of differences in the reflected electromagnetic energy within the same band, making each band more sensitive, especially in mapping and monitoring vegetation attributes such as foliar moisture content [115; 116]. Furthermore, the sensor is equipped with high spectral variability spanning from the VIS to the SWIR, which are suitable for monitoring the moisture content of grass. Specifically, the results show that the red-edge, NIR, and SWIR bands were more influential in estimating LAI CSC, CWC, and EWT.  
Although comparing bands and vegetation indices was not the key component of this study, the results show that spectral band models yielded high accuracies as compared to those derived based on vegetation index models for all GWC indicators across both seasons. This is an interesting finding that is contrary to what is documented in the literature. For instance, related studies [11; 12; 34; 117; 118] that utilized spectral indices in estimating vegetation-water-related properties illustrated that vegetation indices outperform conventional bands. Spectral indices tend to effectively deal with the scattering effects of single bands as well as noise such as that from the soil background, angle of the sun, angle of the sensor, and topographic effects [21]. The optimal performance of bands, which was comparable to vegetation in this study, could be explained by the fact that the grass covered much of the ground surface; hence, there was a limited background effect influencing the spectra. Additionally, the terrain was predominantly mountainous such that vegetation indices were influenced by topographic shadow effects.  
The results illustrate that topo-climatic variables exhibited low accuracies across all models. The results of this study concur with those of [PERSON], [PERSON] [12], who reported that environmental variables yielded poor model accuracies when estimating grass foliar moisture content as standalone variables. This could be explained by the fact that topographic indices are derived from digital elevation models, which only represent the height above sea level. In this regard, they are not directly related to physical foliar moisture content such as the spectral reflection measured by Sentinel-2 MSI spectral bands. Furthermore, the SRTM DEM used to derive topographic variables in this study is characterized by a relatively coarse spatial resolution of about 30 m. This could further explain the low performance exhibited by the topographic variables.