## 3 Mapping of Land Cover and Physiological or Structural Variables
### Land Cover Products
The data are produced with high accuracy, but limitation arises due to continuous cloud cover over the tropical regions and lack of ground validation data. Inaccuracies induced by the presence of cloud cover over the region can be reduced by incorporating SAR data [72]. Moreover, the accuracy of the alert system can be increased by using spectral unmixing methods [73]. Similar limitation of cloud cover presence was found in a Terra-i real-time monitoring system by the International Center for Tropical Agriculture (CIAT) [66] and CGLS-LC100 by the ESA [69; 70], which reduces the accuracy of the products in tropical regions. The CGLS-LC100 is a recently released product and has not been used in estimating tropical AGB, though it has strong potential for pan-tropical forest cover change and biomass models due to its higher temporal frequency. The MODIS based Atlas of Forest Landscape and Restoration Opportunities [59] product estimated that more than two billion hectares of land globally have a potential for monitoring restoration of forests mostly in temperate and tropical landscapes. Use of this product requires cautions as the product is produced at a coarse spatial resolution (1 km), lower in accuracy, lacks data for field validation and land-use dynamics. This global map provides no information for policym regarding restoration intervention efforts that are needed for regional- or local-scale restoration projects [74]. Forest Monitoring for Action (FORMA) [64], a monthly product, has the limitation of coarse resolution 500 m but the main advantage of this product lies in its geographic focus over the humid tropics. FORMA250 [65], an updated version of the FORMA, improves the previous model by providing daily updates at a 250 m spatial resolution and extended geographic coverage across the tropics. The FORMA products are ingested in the Google Earth Engine (GEE), making them more resourceful for cloud bases biomass mapping studies. The Global Mangrove Watch (GMW) [75] integrates the Phased Array type L-band Synthetic Aperture Radar (PALSAR) and Landsat data to understand mangroves cover dynamics with a minimum mapping unit (MMU) of 1 ha. The 1 ha MMU induces uncertaintieswhile assessing smaller vegetated areas as well as a disturbance occurring on a finer scale. The 34\({}^{\circ}\) off-nadir angle of PALSAR is too high for the detection of sparsely forested areas, which results in an underestimation of the total areas covered with forests [76]. Mapping accuracies in most of the studies involving Landsat data are affected by cloud cover and Scan Line Corrector (SLC) error of the Enhanced Thematic Mapper Plus (ETM+) sensor onboard Landsat-7. Statistical errors in different methodologies adopted by different forest mapping techniques can increase the discrepancies in land cover global products hence caution should be taken while using these datasets for local or regional policies. Apart from land cover mapping products, the focus of global ecological assessment from remote sensing has shifted towards estimating functional traits such as Fraction of Vegetation Cover (FVC) and Tree Density [67; 77], LAI, and Fractionally Absorbed Prosthetically Active Radiation (FAPAR) [78]. On a finer scale of plant functional traits such as chlorophyll concentration, carbon-nitrogen ratio, and SIF, estimation is commonly carried through high spectra resolution and hyperspectral remote sensing techniques. However, it remains challenging to transform these leaf-level processes to canopy and landscape-level processes which could be strongly influenced by canopy structure, branches, trunks, leaf area and canopy shadows [79; 80; 81].