## 2 Materials and Methods
### Satellite Data and Calculation of Vegetation Indices  
Satellite data were retrieved through Google Earth Engine (GEE) [16], which were used to access Sentinel-2 Surface Reflectance satellite imagery at 10 m spatial resolution and 5 day temporal resolution for the trial paddock from 24 April 2018 to 9 April 2020. Sentinel-2 satellite data were chosen primarily because of the high temporal resolution, and the number and type of spectral bands available. A cloud-masking filter was applied to remove all pixels that were affected by cloud cover as per [PERSON] et al. 2020 [17]. Images with more than 40% cloud cover of the study paddocks were removed from the analysis to ensure a representative portion of the paddock. The average value for each VI for each image was calculated for the study area.  
For the remaining pixels within each image, the number of VIs was calculated, including: Enhanced Vegetation Index (EVI) [18; 19], Normalised Difference Vegetation Index (NDVI) [20], Normalised Difference Red Edge (NDRE) [21], Colour Infrared (CIr) [22; 23], Vogelmann Red Edge Index (VREI) [24], and Bare Soil Index (BI) [25]. All indices were fitted with a penalised B-spline [26] to smooth the data and interpolated using the cubic method to fill in the gaps with no satellite data across the 2-year period. The VI was estimated for each day, matching the daily cattle weight data.  
These VIs were used to assess the relationship with LWC of animals in the present study. The value of VIs typically represented the density or quantity of green forage available in the trial paddock. Different indices often represented different aspects of vegetation or complemented each other in terms of their sensitivity to prevailing vegetation and soil reflectance. Therefore, a suite of VIs was assessed in the present study to determine their association with LWC and LW (Table 2). As an example, NDVI and VREI were chlorophyll sensitive [21], but NDVI often saturated at high levels of green vegetation and was impacted more by soil and shadow. In contrast, EVI addressed some of these issues and often had reduced interference from soil and atmospheric noise [27]. Similarly, NDRE is known to be more accurate for use in mid to late season biomass and tree crops, and was less likely to oversaturate compared to NDVI [28]. The BI was useful for improving the identification of bare soil areas [25], which were especially common in areas of high cattle traffic activity, such as water and supplement points. The CIr is commonly used to assess vegetation, as its combination of bands shows vegetation to appear red, giving different hues of the colour to show healthy or sparse areas of vegetation [22; 23].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ingredient \% as Fed** & **Dry Season Urea—2018** & **Dry Season Urea—2019** \\ \hline Urea equivalent & 36.21 & 32.86 \\ Phosphorus & 3.33 & 3.09 \\ Crude protein & 2.88 & 4.25 \\ Equivalent CP & 103.92 & 94.31 \\ Total CP & 106.80 & 98.56 \\ Calcium & 4.37 & 4.85 \\ Sulphur & 3.32 & 3.92 \\ Fibre & 0.55 & 0.95 \\ Magnesium & 0.31 & 0.18 \\ Potassium & 0.06 & 0.17 \\ Nitrogen & 0.45 & 15.27 \\ Chloride & 16.77 & 14.86 \\ Sodium & 10.57 & 9.37 \\ Dry matter & 76.52 & 95.07 \\ Metabolizable Energy, MJ /kg & 0.80 & 1.57 \\ DM & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nutritional information of dry season supplements for breeding animals under extensive conditions in northern Australia.