## 1 Introduction  
Plant water content (PWC) is one of the most critical parameters of land surface-atmosphere interactions, considerably affecting the seasonal terrestrial water cycle [1]. This is because the quantity of water contained in the vegetation canopy structure has profound effects on physiological processes such as evapotranspiration, and photosynthesis, which directly affect an ecosystem's water balance [2; 3]. Thus, vegetation-water-related properties are critical for understanding key components of the hydrological cycle to derive insights into catchment water variation. Several interrelated physiological indicators have been used to assess vegetation water conditions at different structural levels. The commonly used indicators include relative water content [4], leaf water potential [5], equivalent water thickness [6], live fuel moisture content [7], canopy water content [8], canopy storage capacity, and stomatal conductance [9]. Amongst these, canopy storage capacity (CSC) is the volume of water that can be retained in plant canopies [10]. The CSC directly influences hydrology through rainfall interception and altering infiltration, thus affecting water redistribution [11]. Canopy water content (CWC) is the total water content per unit area of a ground surface, which depends on the balance between water losses from transpiration and water uptake from the soil [8]. Equivalent water thickness (EWT) is the quantity of water content per unit leaf area. It plays a critical role in eco-hydrological processes such as evaporation and transpiration [12]. The reflectance of vegetation water content is affected by many biophysical parameters, and it is challenging to separate the contribution of the leaf area index (LAI) and water content from remotely sensed data at the leaf level [13]. In this regard, the LAI and the above-explained water content indicators are of interest in this study. In the past few decades, water content indicators have been utilized as a proxy for crop water stress or drought assessment and monitoring [14; 15; 16], prediction of ecosystems' susceptibility to wildfire [17; 18] and in forestry studies [11; 19]. However, more effort needs to be made to study the seasonal variability in vegetation water content indicators specifically in the context of grassland ecosystems. By virtue of their location, being mostly in catchment areas, the grass water content (GWC) of these ecosystems significantly impacts on various hydrological processes since vegetation water content can be assumed to reflect the hydrological conditions of the antecedent hydrological year [20]. GWC relates to the quantity of water contained in grass. Since GWC indicators are closely related to hydrological variables, their estimation can be helpful for the hydrological modelling of surface run-off, recharge, and groundwater processes and soil moisture budget. Remote sensing has been widely recognized to be invaluable in providing effective, non-invasive and reliable techniques for assessing water content indicators [21]. Remote sensing sensors, including multispectral [22; 23; 24] and hyperspectral [10; 25; 26] sensors, have been applied for vegetation water content monitoring. For instance, MODIS and Landsat satellites provide Earth observation data free of charge, which may be used for global environmental monitoring [27; 28]. However, the multispectral sensors onboard these satellites are associated with a low spectral resolution, thus posing a challenge to optimally exploit the comprehensive water absorption features in the short-wave infrared region (SWIR) [29]. Although hyperspectral sensors are renowned for their high accuracy in monitoring vegetation water conditions, they are coupled with high prices, yet they cover a small spatial extent [30]. Recent advancement in Earth observation sensors has seen the advent of free-of-charge data from Sentinel-2, which is equipped with a multispectral imager (MSI), providing an opportunity for robust estimation of vegetation-water-related properties [11].