## 3 Results
### Remotely Sensed Environmental Parameters Applied to Pasture Monitoring  
Two primary drivers, namely, anthropogenic and climate/weather, influence pasture mapping (production, composition, and degradation) globally (Figure 4a). Anthropogenic factors (referenced by 75 studies) are further categorised to include stocking rate, stocking density, grazing intensity, grazing system, livestock weights, moving, soil and fire management, land use, pasture treatment (i.e., fertilizer, herbicide, nitrogen, etc.) and irrigation [2; 7; 49; 65; 66; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90]. These variables are quantified through field measurements (and/or ancillary data) and correlated against remote sensing data [50; 70]. Assessing climate or weather's influence on pastures (i.e., mapping pasture phenology) involves (see Figure 4a) correlating climate and weather data with vegetation parameters (i.e., aboveground biomass, ground cover, and canopy cover) [6; 35; 49; 66; 80; 91; 92; 93; 94; 95]. Historical time series satellites (Landsat, AVHRR, MODIS) (medium to low sensors) [2; 17; 19; 70; 73; 84; 91; 92; 94; 96; 97; 98; 99; 92; 98; 99; 100; 96; 101; 92; 97; 102; 98; 99; 11; 103] are used mainly. Other methods include simulation and modelling approaches to distinguish between human activities and climate [19; 84; 104; 105; 106; 107]. Examples of such modelling approaches include annual unharnessed net primary productivity (NPP) from livestock grazing intensity using a defoliation formulation model (DFM) [107], terrestrial ecosystem model (TEM) (potential) and Carnegie Ames Stanford Approach (CASA) [19]. A total of 54 studies (Figure 4a) used climate and weather data to predict climate's effects on pastures; authors have used climatic data (over at least ten years) and historical time series satellites. Studies focusing on less than ten years were those using a monthly  
Figure 4: (**a**) The two main drivers of pasture variability, climate, and anthropogenic (**b**) studies using remote sensing to understand how adaptive pasture management could be used to mitigate climate change. Rainfall and temperature variables are regarded as weather and climate data. seasonal approach (154 studies) to monitor pastures; we categorised these studies here as \"medium to low resolution\" (Figure 4b) [49; 64; 66; 80; 108; 109; 110; 111; 112]. In general, the studies reviewed were conducted using low-spatial-resolution satellite instruments. A few studies used high-spatial-resolution datasets (i.e., Sentinel-2 (four studies), PlanetScope (1 study), QuickBird (1 study)) to understand climate and weather effects on pasture composition [108; 113], pasture biomass [3; 74; 114] and pasture quality [50]. One study invoked a very high temporal time-lapse camera to study the phenology of pasture species at the paddock scale compared with that from the landscape scale using MODIS and Landsat instruments [80]. Only 18 studies considered the adaptive management of pastures with remote sensing strategies [114; 115; 116; 117; 21; 28; 23; 21] (Figure 4b). Regarding climate data, authors have primarily focused on temperature and rainfall; few have used only temperature [8; 90] or rainfall data [91; 96; 100; 106; 124; 125; 126; 127] to correlate with remote sensing data. Many studies have attempted to establish a correlation between rainfall and the growing season (mostly early and mid-growing season) [128; 128; 98; 103; 56; 64; 66; 70; 98]. Some studies have showed that temperature correlated positively to pasture growth according to the climatic zone (e.g., temperature contributed to growth rates in the desert steppe of Inner Mongolia [64], suggesting the steppe possesses more resilience in this region). Other studies have aimed to elucidate the effects of the climate on soil carbon stocks [113; 129; 130] and/or the soil water content [63; 81].