## 3 Results
### Approaches for Pasture Quantification
#### 3.4.1 Pasture Production
MODIS-ANPP products are converted to biomass using biomass-to-carbon conversion [8; 90; 167]. [PERSON] et al. [35] fused UAS and PlanetScope, while [190] used MODIS to model ANPP as a function of APAR derived from NDVI and light use efficiency (LUE). Similarly, the LUE model has been used to estimate other variables like the green canopy cover, vegetation density, fractional vegetation cover, and fAPAR [8]. Table 5 shows how ML models were integrated with remote sensing for retrievals. Random forest (RF) is the most widely applied ML algorithm due to its capability for regression and classification problems. Generally, ML is used to parametrize index-based  
\begin{table}
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retrievals [45; 58; 74; 132; 192] more than to retrieve spectral features [3; 7]. The ML-based modelling of biomass is more accurate than NDVI [3; 74]. [PERSON] et al. [3] established a non-linear relationship between NDVI and in situ biomass. [PERSON] et al. [132] used RF/Support vector machine (SVM)/Multi linear regression (MLR) to parametrize 77 vegetation indices derived from Senitnel-2 to estimate total standing dry matter (TSDM) at an accuracy of R\({}^{2}\) = 45%. In cloudy situations, authors have used three-dimensional photogrammetric point cloud modelling to assess grassland heights (i.e., between 1-20 cm) with the visible camera from UAS [116; 184; 185; 193]. [PERSON] et al. [193] found a correlation of R\({}^{2}\) = 78% between aerial imagery and in situ estimates and an average utilisation of 20% from imagery against the 18% of ground-based imagery at a scale of 150 ha. Furthermore, LIDAR has also been used to quantify biomass from different plant species using a 3-D approach at a field level (6.7 ha) with 77% accuracy [181]. Studies have used SAR imagery to complement optical applications using the backscatter signal of microwaves to estimate pasture biomass [7; 45; 132; 153; 195] and height [7]. The accuracy of models increases when SAR data are combined with optical imagery compared to a single application. For example, the combination of Sentinel-2, Sentinel-1, and Landsat improved the estimation of biomass by 30% compared to each of the sensors alone [45], and RMSE was significantly lowered when [195] fused Worldview-3 and Sentinel-1. Similarly, the L-band of ALOS (PAR-SAR-2), which is capable of penetrating the canopy structure, was combined with the C-band of Sentinel-1 and 13 spectral bands of Sentinel-2 to map and discriminate pasture heights (short, medium, and tall) from soil inundated with the vegetation canopy using RF [7]. The model's overall accuracy improved (R\({}^{2}\) = 86%) by integrating the three sensors rather than individual contributions. Furthermore, the wavelet principal component analysis (WPCA) used for SAR and optical image data extraction (based on relevant features) was used to improve the fusion between ERS and Worldview, leading to the higher accuracy of the model [195]. They [195] reported a strong correlation (R\({}^{2}\) = 79%) between the backscatter coefficient of Sentinel-1 and ground biomass from rangeland rehabilitated from mining activities. Similarly, [175] established a strong correlation (59-84%) between ERS backscatter coefficients and targeted wet grassland biomass by applying a linear inversion algorithm to the image data. In contrast, [132] found no significant contribution of Sentinel-1 data when combined with Sentinel-2 in estimating biomass and pasture height in Germany.