# A Review of Regional and Global Gridded Forest Biomass Datasets
## 2 Principles for Estimating AGB from Remotely Sensed Data  
Generally, remote sensing techniques do not derive forest biomass directly, but use parameters that are related to forest biomass such as forest height, leaf area index, or net primary production. Because of established relationships with AGB, these parameters or variables are widely incorporated into the mapping of forest AGB. Forest height is often used as a surrogate for biomass. At the plot scale, AGB estimates are usually made using allometric equations that are based on hand-measured tree diameters and/or tree height, sometimes with wood density to improve accuracy [27]. The relationship between AGB and tree height generally follows a power law function. Published studies have indicated that the close relationship between forest biomass and height developed at the tree and plot scale still hold at a broad scale [28]. Since height can be directly obtained from Interferometry SAR (InSAR) and LiDAR data over large areas [29; 30], forest AGB can be estimated from these remotely sensed data. It should be noted that height sometimes refers to canopy height (e.g., InSAR height), which depends not only on tree height, but also on the shape of each tree crown and on stand density. Some studies indicate that tree crown or basal area might be a better indicator of biomass than tree height, and thus basal area weighted height (or Lorey's height) was proposed as the best proxy for biomass and widely used in LiDAR remote sensing [12; 19; 31]. Previous studies have found LAI is also a valuable AGB predictor, and have developed methodologies to estimate AGB from LAI [32]. LAI is mainly related to leaf biomass, and therefore foliage or leaf biomass can be quantified through a function of LAI and specific leaf area (SLA) which is the ratio of leaf surface area to carbon mass [33]. At the plot scale, total AGB is generally computed as the sum of wood and foliage biomass [34], but at larger scales, there have been two general approaches to establish a relationship between LAI and forest biomass. One estimates the components of AGB separately, which follow similar equations and can be derived in the same way as foliage biomass, and then calculates the sum of these biomass components. The other approach is to develop relationships between foliage biomass (or LAI) and AGB directly, in terms of the allocation of forest biomass carbon into different parts including leaves. Using either method, the proportion of foliage biomass in AGB varies with forest type, stand age, and environmental factors [35]. Similar to that of LAI, the relationship between tree productivity and biomass has been explored in previous studies that often use the demonstrated relationship to estimate AGB directly, either linear or nonlinear, assuming that productivity is the source of biomass [36]. Another approach to estimate AGB comes from the metabolic theory of ecology which describes the rates of production and biomass by a power function [37]. Also, the relationship between AGB and productivity varies with certain biotic and abiotic factors [38]. As a measure of chlorophyll abundance and energy absorption, the normalized difference vegetation index (NDVI) has been widely used as a proxy for vegetation productivity and foliage biomass [39; 40]. It is sensitive to the green components of biomass but insensitive to woody components where the majority of forest carbon is stored. One limitation of applying NDVI to estimate biomass is the saturation effect due to strong absorption in the red wavelength. With an increase in green vegetation, the sensitivity of NDVI and furthermore, the accuracy of estimated biomass is reduced. To improve sensitivity across the regions with high biomass, several vegetation indices were proposed such as the renormalized difference vegetation index and the modified simple ratio [41; 42].