## 1 Introduction
The Sentinel-2 MSI offers the best combination of high spatial (i.e., up to 10 m pixel resolution), spectral (i.e., 13 bands), and optimal temporal sampling resolution of 5 days, making it suitable for characterizing the spatiotemporal variations in vegetation attributes [31]. Moreover, Sentinel-2 MSI incorporates critical red-edge (RE) bands, which are centred at 705, 740, and 783 nm of the electromagnetic spectrum [11]. Specifically, the RE is the section of thespectrum where there is rapid change in vegetation spectral reflectance; hence, it can be used to monitor the functioning of vegetation [16; 32]. Meanwhile, the literature underscores the performance of spectral derivatives such as vegetation indices in estimating vegetation attributes such as water content, amongst others [15; 33]. The most widely used vegetation indices in the literature are derived from the visible and near-infrared region (NIR) wavelengths [15; 33]. Meanwhile, the SWIR, which is heavily influenced by water in plant tissues, also plays a significant role in mapping and monitoring moisture content [34]. Thus, the use of moisture-sensitive indices derived from longer wavelengths such as NIR and SWIR reflectance could be more viable for monitoring vegetation water content [35]. Other than optical spatial data, site-specific spatial data such as topographic and climate-related data are critical and required to improve the accuracy of optical models. Topographic variables derived from digital elevation models such as the Shuttle Radar Topographic Mission (SRTM) can effectively increase the accuracy of vegetation monitoring [36; 37]. Specifically, variations in topographic metrics such as the slope, topographic position index, state of curvature, and aspect are directly linked to vegetation aspects, such as sunlight and temperature, while indirectly linked to other aspects, such as soil moisture and soil quality [38; 39]. Furthermore, climatic variables such as rainfall and temperature are critical factors controlling photosynthetic activities in vegetation as well as foliar water content variation [40]. Specifically, seasonal precipitation and temperature result in soil moisture content variations, which affect vegetation water content despite the impact of topographic variations [41]. However, the interacting influence of seasonal precipitation and temperature with topographic variations on GWC indicators has not been extensively researched, especially in communal grazing lands. Since topographic and climatic factors have been proven to be important variables in facilitating the spatial distribution of vegetation water content, there is a need to assess and understand the interacting influence of seasonality and topo-climatic variables on the spatial variability of grassland water content indicators. Cloud-processing systems, such as the Google Earth Engine (GEE), provide a safe, efficient, and advanced platform designed to effectively deal with the challenges of storing, computing, processing, and analysing huge datasets [42; 43]. The GEE provides free access to global time-series satellite and other ancillary data collected over 40 years, thus allowing a vast amount of computation analyses to be produced for unprecedented time and extent scales [44; 45]. Importantly, the GEE platform provides users with a convenient environment for interactive data and algorithm development via its internet-based Python Application Programming Interfaces (APIs) and JavaScript web-based Interactive Development Environment (IDE) [46; 42]. The high-performance computing capacity of the GEE for environmental monitoring has been demonstrated in several applications, such as soil erosion assessment [47; 48], burned area mapping [49; 50], hydrology [51; 52], agriculture [53; 54], and landcover mapping [55; 56], as well as in vegetation mapping and monitoring [57; 58]. [PERSON], [PERSON] [12] reported that grass-water-related parameters can be optimally estimated using the combined use of spectral data and environmental variables.