## 5 Discussion
### Assessing the Current Remote Sensing of Pasture Monitoring  
Despite the widescale coverage of studies and the current utility of satellite sensors (Figure 5a), a high proportion of this effort focuses on regional, continental, and global scales (Figure 8), with less emphasis on field-based monitoring. The number of studies that have focused on fields within 50 ha is less than 20. Higher-resolution multispectral satellites are not free but are also only constrained to a few bands (mainly visible and NIR), except Worldwide and Venus, with 29 and 12 bands, respectively (Table 2). Therefore, the publicly available optical satellites MODIS, Landsat, and the recently launched Sentinel-2 have played a central role following their specifications in monitoring vegetation dynamics. Sentinel-2 arrivals in 2015 were thought to address cloud constraints for optical applications, especially with a 5-day revisit and 10 m resolution fleet. This review shows that apart from the over-emphasis on medium to coarse resolutions (Landsat and MODIS), which limited field-based monitoring, the arrival of Sentinel-2 has not resolved missing data due to cloud contamination, especially in places known for persistent cloud cover (i.e., tropical rainforest, mountain regions, and polar and monsoon areas). Researchers have used different approaches to resolve cloud contamination, such as cloud removing algorithms (e.g., CFmask) to mask cloudy pixels [222; 14; 45; 67; 134; 136; 21; 45], multi-temporal satellite data [140; 7; 34; 45; 132; 141], and conducting field campaigns in cloud-free days [164], and the stacking of satellite scenes [21; 133]. More specifically, researchers and practitioners have used photogrammetry UAS cameral (visible) equipped with a 3-dimensional point cloud [116; 184; 193] and LIDAR sensors [181] on demand to capture near-real-time imagery as an alternative to satellite applications. Additionally, the number of studies that have utilised daily and weekly satellite imagery for analysis is less than 20 (Section 3.4.3), meaning that the current revisit would not support/sustain operational pasture management. Intensity grown pastures are dynamic and require more frequent imagery between 5-7 days to capture sward regrowth depending on environmental conditions. The current satellite fleets with daily revisit are not available for public utility, thus limiting this application for R&D. Leveraging radar capability, the all-weather satellite data (i.e., dual-polarisation, backscatter, with C, L, and X bands), especially the free and open-source Sentinel-1 data (R&D) usage in this review, were relatively low (Figures 6 and 7). In most studies involving pasture production (estimation of biomass and height) and species classification [17; 195; 7; 45; 139] except a few [132], the integration of SAR imagery has improved model accuracy more than the performance of either the optical or radar data alone. For example, the fusion of ERS andWorldview imagery using the WPCA method to extract relevant image features in a suitable rangeland environment (i.e., rangeland rehabilitated from mining activities) significantly improved the model performance (R\({}^{2}\) = 79%) [195]. However, with cloud containment, spectral information due to surface reflectance (especially the red-edge and NIR bands) from optical data is more accurate for assessing pasture biomass and discriminating species than dual-polarised features and the backscatter coefficient of Sentinel-1 [132; 139]. Hence, optical hyper or multispectral sensors in fair weather conditions offer more accuracy in distinguishing vegetation species than SAR data because of their spectral responses along multiple bands. At the same time, the microwave is not sensitive to chlorophyll content but to the structure and volume of vegetation. Therefore, we conclude that the accuracy of SAR modelling depends on the knowledge domain applied to suit the biophysical variables and target environment.