## 3 Results
### Approaches for Pasture Quantification  
#### 3.4.1 Pasture Production  
Studies have used pasture heights [7; 164; 135; 162; 184; 185; 186; 187; 188; 189; 190; 162; 184; 186] LAI [32; 45; 49; 66; 135; 150; 164; 187], fractional cover (fCOVER) [188], above-ground net primary production (ANPP) (unit mass per unit area per unit time) [8; 34; 96; 111; 189; 190], fraction of photosynthetic active radiation (fPAR) [191] as quantitative parameters to express pasture production. ANPP and fPAR are mainly derived from Landsat and MODIS time-series products to quantify the managed ecosystem productivity, making them less applicable compared to LAI and pasture heights. Studies have compared pasture biomass with LAI [32; 45; 49; 119; 188; 191] and height [132; 150; 162]; hence, the goal is to use LAI and pasture heights as proxies in estimating pasture biomass. Vegetation indices (VI) are the most adopted retrieval scheme with empirical approaches (Table 4) to estimate the pasture height or biophysical parameters (LAI, fCOVER, ANPP, and fPAR) (Table 4) and relate them with pasture biomass, where the normalized difference vegetation index (NDVI) [45; 69; 133; 166; 192] accounts for 83% of this method. [PERSON] et al. [193] correlated the canopy height (R\({}^{2}\) = 78%) with the ground biomass to infer pasture biomass utilisation at the field scale. Next to NDVI are the enhanced vegetation index (EVI) and soil-adjusted vegetation index (EVI), used with other indices to provide complementary information about their sensitivity to sparse and dense vegetation [181; 74; 183; 144; 150; 183]. Index-based retrievals significantly rely on the visible and NIR bands and SWIR for those that require soil water content and dry biomass estimation [149]. A mathematical transformation function (e.g., power and logarithm) is used to normalise data (i.e., expand or compress the index value) to minimise the saturation effects of vegetation indices [64; 194]. Figure 9: The frequency with which pastures were monitored via satellite imagery passes. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Satellite** & **Version** & **Altitude (km)** & **Launch Year** & **Revivivi (Day)** & **Spatial** & **Spectral Bands** & **Red Edge** & **Main Forces** & **References** \\
**Instrument** & & & & & & & & & & & \\ \hline MODIS & & 705 & 1999/2002 & 1 & 250/50  
A physical-based approach using the radiative transfer model (RTM) PROSAIL has been used to retrieve LAI as a vegetation canopy when combined with multispectral satellites [32; 135; 164; 187] and often parametrized and optimised with ML algorithms [45; 135; 164]. Some studies [32; 135] have used proximal hyperspectral data resampled to a satellite multi-spectral dataset (i.e., Sentinel-2) to constrain the assumption of the homogeneous canopy of the RTM and enhance the accuracy of the model. For example, [32] established a correlation coefficient of 50% between modelled (PROSAIL + resampled data) and in situ biomass. In most cases, LAI correlated better with referenced biomass data than NDVI or other indices [32; 188; 191]. Furthermore, both the perpendicular vegetation index (PVI) and SAVI derived from Landsat have been found to correlate with the referenced LAI (R = 50%) more than NDVI and other indices [66], confirming the site specificity of LAI-based modelling. Measured LAI is converted to biomass through a linear relationship and compared with the satellite spectral index [32; 135]. Other physical-based approaches like the light use efficiency (LUE) (the amount of absorbed photosynthetically active radiation (APAR) that is converted into biomass and expressed as the net primary production (NPP)) model has been used to estimate available pasture biomass (Equation (1) shows the linear association between NPP and LUE). \[NPP=APAR\times LUE \tag{1}\]  
where NPP is the available biomass, expressed as the net primary production (NPP), and APAR is the absorbed photosynthetically active radiation that plants can utilise to produce biomass.