## 5 Discussion
### Assessing the Approaches Used in the Remote Sensing of Pasture Monitoring  
The retrieval of biophysical variables has been significantly restricted to empirical methods using vegetation indices, with NDVI being the most used index to understand pasture production, species classification, management indicators, and the degradation of pastures. Likewise, the physically based retrieval schemes to assess the following biophysical variables--LAI, fPAR, and fCOVER, ANPP--using LUE and RTM are driven by sites and parameters that restrict their generalisation and repeatability. Consequently, their comparison and relationship with the field (destructive and non-destructive field samplings) data has reached a milestone in addressing the problems associated with VIs and physically based modelling approaches, while at the same time revealing the potential areas where more research efforts are needed. Using ML approaches with careful integration of appropriate satellite sensors in addition to environmental data, the modelling of pasture biomass from the selection of VIs has achieved better accuracy and a higher level of prediction (i.e., an increase from 1500 kg DM/ha to above 3000 kg DM/ha) before reaching saturation [45; 223], with red-edge, NIR, and SWIR bands as the main contributors. In contrast, red and NIR bands' usages (NDVI) have continued to trigger a debate on the effect of the saturation, soil background influence, sensitivity to vegetation types, and heterogeneity of canopy-to-model calibration, which have caused researchers to develop more indices and parametrize with ML [45; 58; 74; 132; 192] algorithms. However, NDVI has performed poorly compared to spectrally driven ML retrieval (R\({}^{2}\) = 60% and R\({}^{2}\) = 78%), especially when dealing with total standing dry matter [3; 74; 132]. Therefore, owing to the emphasis on ML-driven index-based retrieval, which is restricted to a few bands and constrained to sites, more research is needed to retrieve a detailed characterization of vegetation properties based on reflectance values, using spectral features to understand the relationship between pixels. The size of field data used for validations in this review is relatively small (test data reveal \(\leq\) 120) compared to what is needed (see \(\sim\)1000 [3]) for training to capture image patterns and improve model calibration. Apart from the problem of generalisation associated with LAI, fPAR, fCOVER, and ANPP, the biophysical variables are mostly computed from medium to coarse sensors (Landsat and MODIS), which makes them widely applicable (i.e., daily, weekly, and monthly composite global data) but more challenging to use for field monitoring and management. Retrieving these variables from higher-spatial-resolution (1-5 m) sensors will significantly facilitate the monitoring of \(\leq\)50 ha fields. Indeed, the current hyperspectral UASs (i.e., HyMap, HySpiri, and AisaFENIX with 3.5-11 nm resolution and \(\sim\)450 bands) have shown great potential in discriminating species and characterizing macro and micronutrients in mixed heterogeneous pastures, which indicates that the availability of these tools (i.e., upscaling to satellites) will significantly enable pasture management at the field scale. Issues relating to costs and logistics would possibly be resolved through disruptive technology and public partnership. The near-future hyperspectral satellites launched by the German (EnMap) and European Space Agency-(ESA) (FLEX) would help determine the cost and logistics, since pilot studies have shown promising results [133; 224; 225; 226]. Adaptive Pasture Management and Factors That Influence the Monitoring of Pastures with Remote Sensing  
Anthropogenic variables and prevailing environmental factors significantly condition pastures. Therefore, adaptive management that focuses on goal-oriented outcomes using suitable remote sensing tools is highly recommended to improve the sustainability and resilience of pastures and grazing systems over time.