## 1 Introduction
### Earth Observation and the Use of Sentinels  
EO has played a vital role in biophysical parameter retrieval, especially in recent years, during which the abundance of satellite data and the tools accessible to ecologists has surged [18], with the former becoming increasingly available freely and in near-real time. In the context of biomass retrieval, several sensor types have been used such as optical (e.g., [19; 20; 21]), Synthetic Aperture Radar (SAR) (e.g., [22; 23; 24; 25; 26]), Light Detection And Ranging (LiDAR) (e.g., [27; 28; 29]), and a fusion of the above (e.g., [30; 31; 32; 33; 34; 35; 36; 37]). Other experimental datasets have also been used such as Google Earth images. On this basis, [PERSON] et al. [38] claimed to have undertaken reliable mapping of tropical forest AGB in the Western Ghats of India. In tropical areas, studies have concentrated on the valorization of SAR imagery as a means to circumvent the frequent cloud presence encountered in this climate (e.g., [39; 40; 41]).  
Optical satellites have long been used to retrieve biophysical parameters such as chlorophyll content [42] and AGB, with a wide range of reporting accuracies. For instance, in a study estimating grass AGB across different fertilizer treatments, [PERSON] et al. [43] reported correlations of R\({}^{2}\) = 0.81 and R\({}^{2}\) = 0.76 for Sentinel-2 and Landsat 8 imagery, respectively. [PERSON] et al. [19] reported a correlation of R\({}^{2}\) = 0.81 in an estimation of the AGB of a subtropical Nepalese forest based on Sentinel-2 imagery. In contrast, [PERSON] and [PERSON] [44] reported a low R\({}^{2}\) = 0.24 for AGB in a study on three boreal forests in Finland, while for other biophysical variables, namely Fraction of Absorbed Photosynthetically Active Radiation (fPAR) and Leaf Area Index (LAI), the correlation was considerably higher. More sophisticated methods have examined the use of Artificial Neural Networks (ANN) and multiple input layers such as day of year, canopy height, and NDVI [45].  
Nevertheless, the usability of optical images is hindered by two main constraints. First, cloud coverage can impede regular image acquisition and, hence, reduce the temporal availability of satellite observations. Secondly, certain biophysical parameters, including biomass, saturate at low values of remote sensing data [46]. As an example, LAI can be retrieved based on a linear regression of an optimized vegetation index (e.g., [47]), the accuracy of which relates to the vegetation type. For instance, [PERSON] et al. [48], in a study conducted in a boreal environment, showcase that, for conifer stands, there does not seem to be a saturation point (up to LAI = 10, which is the highest reported in this case study); however, in a deciduous forest, LAI saturates at values of 2-3, which is similar to the case of crops. This saturation effect becomes even more prominent in tropical environments [49] and restricts the usability of optical satellite images. The retrieval of biophysical parameters also depends on factors such as the soil type and plant architecture; hence, information about the landscape is crucial before the selection of the spectral information for the estimation of biophysical variables [50]. SAR sensors, on the other hand, are not considerably hampered by clouds and are independent from solar angle and radiation intensity. However, saturation of the SAR signal often exists in both the broadleaf and conifer tree-types [51]. Lastly, a few studies have attempted to use synergistically optical and SAR data for biomass estimation (e.g., [35; 52]), and they report improvement of accuracy when fusing datasets of differing natures. A review of remote sensing for AGB can be found in Tsitsi [53].