## 3 Results
### Remote Sensing Technologies Used for Pasture Monitoring  
#### 3.3.1 Description of Remote Sensing Technologies Used  
A total of 18 sensors from satellites and UAS were reviewed (Figure 5a). Our results show that contemporary scientific capabilities in monitoring pasture dynamics in the past decade are gaining momentum, from satellite and UAS sensors to aerial stereoscopic imagery. Figure 5b categorised sensors using combinations of two optical instruments (OO) or optical and radar instruments (OR) [45; 58; 87; 131]. A greater number of studies used satellites than UAS sensors (Figure 6), and fewer studies used SAR (Synthetic Aperture Radar). The main objective for combining sensors [132; 45; 118] is to address cloud contamination, especially in places where cloud poses significant challenges (i.e., tropical rainforest, mountain regions, polar and monsoon areas) [133; 134; 135; 99; 136; 137], with multi-temporal sensors approach [136; 99; 137] or by using SAR imagery [138; 139; 7; 132; 133]. Other objectives include comparing model performances between sensors [140; 141; 7; 34; 45; 132; 7; 34] and when greater detail is needed for field measurements and species discrimination [142; 143; 114]. Fused data of 30 cm resolution from UAS and PlanetScope imagery achieved a higher correlation of 87% compared with ground measurement for estimating pastures at the field level (10 ha) than those obtained from Planet (65%) data [114].  
Moderate Resolution Imaging Spectroradiometer (MODIS) (Terra and Aqua) and Landsat instruments were the most used for studies (Figure 6; Tables 2 and 3). The use of MODIS is enabled by daily revisit, 16-day composite, three spatial and global resolutions (250 m, 500 m, and 1 km), and 36 multispectral bands for wider applications. Long-term data continuity, moderate-resolution imaging, multispectral capabilities, and open data policy (Table 2) are among the factors that have aided Landsat's utility. In general, the use of sensors by practitioners tends to follow their release, accessibility, and applications (Tables 2 and 3).  
Figure 5: (**a**) Remote sensing instruments to study pasture conditions. (**b**) Number of studies according to how instruments were combined for investigation. OO = combination of optical instruments, OR= combination of optical and radar instruments, and UAS = unmanned aerial systems.  
Figure 6: Number of studies ranked by the topics covered: OO = combinational of optical instruments, OR= optical and radar instruments; and UAS = unmanned aerial systems.  
Figure 7 shows the areas of coverage and scale of focus for the current monitoring of pasture with satellite sensors. MODIS and Landsat sensors are used primarily to support the regional and global monitoring of pastures at scales \(\geq\)100 ha [144, 145, 126, 146, 147, 115, 67]. Figure 8 is an example of hyper-spatial paddock monitoring. Time-series analysis showed that eight studies utilised daily remote sensing data (Figure 9) [144, 146, 58, 147], five focused on weekly [148, 126, 149, 150, 3, 126], while others considered monthly [151, 8] and yearly data [92].  
Figure 8: (**a**) A high PlanetScope imagery quantifying pasture biomass variation at paddock level (image acquired from Planet Lab Inc.; and accessed on 6 April 2021); (**b**) was georeferenced from (**a**). Landholders can make management decisions based on pasture availability. (**b**) was georeferenced using the map features provided (i.e., Ngahinapouri, Waipa District, Waikato, 3882, New Zealand).  
Figure 7: Summary of the scale of focus enabled by satellite sensors. Note: NS = “Not specified” for studies that do not provide a definite statement about the scale of coverage in the reviewed studies. Studies (i.e., 63) indicated study locations without providing details about the scale of focus [152, 153, 76, 154].