## 1 Introduction
Estimates of above-ground biomass (AGB) of forests are obtained from different methodologies and variety of sources such as field measurements, national forest inventories, administrative-level statistics, model outputs and regional satellite products [21]. FAO's FRA [22] is the sole global dataset which provides carbon stock aggregated at the country-level from 1990 to 2015, covering all 229 countries and territories of the world [3]. Complementary to this is an additional repository of a database on forest carbon dynamics by the United Nations Framework Convention on Climate Change (UNFCCC), which contains data submitted by its parties. Remote sensing cannot provide direct measurements of biomass [23], although the role of remote sensing is pivotal for accurate and up-to-date measurement of forest structure to estimate carbon stocks and fluxes of the forests. Field-inventory-based methods of biomass estimation are time-consuming, suffer from the low sample size, are inconsistent across space and do not indicate actual estimation of the AGB in a landscape [24; 25; 26]. However, these methods are important and have played a significant role in developing allometric equations for biomass estimation. Remote sensing can help in resolving the restraints of the conventional AGB measurement methods, especially in inaccessible tropical forests, by providing cost-effective, repeatable, spatially contiguous and explicit observations [27; 28]. Nonetheless, remote-sensing-based methods are in transition to become independent and solicit methods of AGB modelling. Therefore, current conventional measurements (biophysical characteristics of vegetation) are being integrated with various remote sensing datasets to quantify and monitor biomass stocks across a wide range of spatial and temporal scales [9; 20; 29; 30; 31]. For example, vital structural attributes of forests or trees to develop allometric models of biomass estimations can be retrieved from laser scanners [32; 33; 34; 35]. Thus, pan-tropical or sampling-based remote sensing data can be acquired to complement the destructive field-based inventories and reduced efforts from conventional field surveys, for estimating tree attributes to develop allometric models in an inaccessible area or expand the existing tree inventories to all tropical forest types. Thus, remote-sensing-based biomass measurements are derived by statistical and/or machine learning-based integration of tree/stand/plot level allometric models with spatially contiguous forest extent and structural information derived from remote sensing data [18]. In this paper, we reviewed the remote sensing methods applied to determine the AGB in the tropical forests and future biomass measurement missions. This review focuses on the large-scale studies at the pan-tropical, regional or national level. Local scales studies are not incorporated in the analysis and discussion, but a brief overview is included whenever it was necessary to explain a relevant method or advancement. The scope of this paper covers a wide range of relevant sources--journal papers, books and internet resources between the year 1980 and 2020 with the keywords of Tropical Forest, Remote Sensing, Biomass, Above-Ground Biomass, Carbon Stock, Forest Change, Tropical Degradation and Tropical Deforestation in Web of Science, Scopus and Google Scholar (accessed in April 2020). The keywords were used with the search operator of 'AND', with the following combinations, 'tropical AND forest','remote sensing AND biomass', 'above-ground biomass AND carbon stock', 'forest change', 'degradation AND deforestation'. Another filter 'tropical AND remote sensing AND biomass' was applied to narrow down the search results. Besides, considering the importance of land cover products in the AGB estimation, we provided a brief overview of the global or pan-tropical land cover products used in the AGB estimation studies or have potential to be used in near future.