## 2 Field Inventories and Remote Sensing for Biomass Estimation  
Estimates of the AGB of forests are obtained from different methodologies and a variety of sources, such as field measurements, national forest inventories, administrative-level statistics, model outputs and regional satellite products [21]. The conventional methods in obtaining biomass are carried by developing allometric equations, biomass expansion factors (BEF) or conversion factors which are based on regression between tree biomass and tree attributes such as height, diameter, wood density and leaf biomass at a sampled location. These models are extrapolated using statistical or machine learning approaches to estimate total biomass stock across the landscapes of interest [36]. AGB is referred to total living biomass, including trees, shrubs and vines above the ground. It is measured in units of tons of carbon per unit area by oven drying the total mass of organic living plant matter [37]. The reference or direct biomass is measured by the cutting down of selected trees (including stump, stem, twig, branches, bark and foliage) belonging to different species, dried in an oven and then their weight is measured in a laboratory. This is a destructive and unsustainable approach, but is the most accurate method of biomass estimation. Then, the reference biomass is used to develop species-specific allometric models by regressing it as a function of physical attributes of the trees such as diameter at breast height (DBH), tree height, canopy spread and wood density [38]. Once the allometric equations are derived, nondestructive methods can be followed to determine AGB. In situ nondestructive estimation of AGB is carried by measuring height and DBH in randomly dispersed field sample plots. Establishing 50 forest inventory plots of 1 ha (100 m by 100 m) is a recommended practice for assessing biomass and stem density in tropical forests [39]. FAO has established protocols in assessing AGB using field-based inventories in the 'Manual for Building Tree Volume and Biomass Allometric Equations' [40]. These estimations are important to understand forest structure, productivity, carbon fluxes, carbon sequestration, and condition of tree site [41]. However, measuring height and DBH across the landscape could be time-consuming, costly and difficult to repeat. Errors in height and DBH measurements can also induce uncertainties in biomass estimation from allometric models [42]. Besides, caution must be taken while extrapolating biomass from allometric models across a landscape as the growing conditions can vary with land-use history, successional stage of forests, environmental and climatic constraints [43; 44]. Figure 1: The number of documents on above-ground biomass (AGB) of tropical forests published in the last 40 years, and expected increase in the relevant publication in the coming decade, the dotted line indicates polynomial fit. Remote-sensing-based products play a pivotal role in upscaling and parameterization of field/plot level inventories of forest structure, ecosystem processes and models. The basic idea of remote-sensing-based upscaling revolves around association of field/plot based attributes with land cover characteristics or forest structure or spectral response and then extrapolation of the relationship across the extent of the landscape [45]. [PERSON] et al. [46] documented the advances and milestones achieved in biomass estimations from remote sensing since the late 1970s. A brief description of relevant AGB mapping studies in tropical forests is provided in the Table 12 (please see Appendix A). Precise synopsis of several procedures, algorithms and methods of modelling and estimating biomass from a variety of remote sensing data is presented by [PERSON] et al. [44]. They highlighted the importance of multi-sensor data integration in biomass modelling studies.