## 3 Mapping of Land Cover and Physiological or Structural Variables
### Land Cover Products  
Land Cover (LC) map is one of the primary inputs for implementing carbon stock assessment models across a wide range of spatial coverage [53; 54]. Figure 3 shows the distribution of forests across the tropics derived from the recently released global LC product by the European Space Agency (ESA) under the Copernicus Global Land Service (CGLS) [55]. Changes in LC dynamically interact with the regional and global carbon cycle and influence AGB [56]. The changes in physiology, functional attributes and allometry of different vegetation types varies across space and have a different environmental response. The LC maps help to extrapolate plot-level measurements to landscape-level estimates of a generalized carbon budget. The primary limitation of the LC map-based estimation of the biomass is the generalizability issue associated with the spatial resolution, i.e., mixing of different vegetation forms, as well as at major classes of the vegetation type, e.g., forest, which may vary in structural and productivity characteristics across the landscape and grain unit. Figure 2: Remote sensing approaches for mapping biomass by extracting various parameters indicating productivity, composition and structure of forest from passive or optical (multispectral and hyperspectral) and active (Light Detection and Ranging (LiDAR) and Synthetic Aperture Radar (SAR)) sensors. Global-scale LC classification started with the advent of daily Advanced Very High Resolution Radiometer (AVHRR) datasets (4-km spatial resolution) in the early 1980s [57], which were later enhanced by using phenological and seasonal attributes derived from bi-monthly vegetation indices from the reprocessed AVHRR data at an 8-km spatial resolution in the framework of the Global Inventory Monitoring and Modeling System (GIMMS) project [58]. A Moderate-Resolution Imaging Spectroradiometer (MODIS)-based global forest condition map is produced by the Atlas of Forest Landscape Restoration Opportunities by Global Restoration Initiative jointly produced by collaboration among the World Resource Institute (WRI), University of Maryland (UMD) and International Union for Conservation of Nature (IUCN) [59]. The atlas aimed at providing the spatial extent of current forest coverage and forest degradation restoration opportunities at a 1-km spatial resolution. These maps are derived from 250 m global mosaics of MODIS for 2000 to 2009 and MODIS vegetation continuous field (VCF) [60]. However, the maps may not be good for local scenarios due to averaging at a 1-km spatial unit. Detailed mapping of forest AGB estimation has been accelerated since 2008 after the implementation of open access policy of the Landsat images [61]. Several studies have been conducted to map land cover attributes on the local, regional and global scales. An online portal, Global Forest Watch (GFW), provides [62] quantitative estimates of the global forest and land cover coverage and change statistics by using various mapping products, such as tropical forest cover change alerts [63] and global tree cover changes, with different spatial resolutions ranging from 30 to 500 m, temporal coverages between 2001 and 2019 and update frequencies (daily, weekly, monthly, annual, 12 and 18 years) [63; 64; 65; 66; 67; 68]. Figure 3 represents the spatial distribution of tropical forests extracted from the Copernicus Global Land Service-Land Cover 100 (CGLS-LC100) produced at a 10 m spatial resolution by the ESA under the Climate Change Initiative (CCI) [69; 70]. Recently, a global habitat map of terrestrial ecosystems, based on the IUCN habitat classification scheme, has been developed by integrating satellite-derived LCLU maps (CGLS-LC100) with climate, topography and other ancillary datasets [71]. Details of other studies and datasets of forest cover extent, losses, and gains at the pan-tropical or global level are given in Tables 1 and 2. Global Land Analysis and Discovery (GLAD) is a robust humid tropical rainforest alert system developed by using Landsat data [63].