# A Review of Regional and Global Gridded Forest Biomass Datasets
## 1 Introduction  
Forests cover about 30 percent of the Earth's land surface, providing renewable materials and energy for humans, maintaining biodiversity, preventing soil erosion, and playing a major role in the global carbon cycle and climate system [1; 2]. As forests grow, they absorb carbon dioxide from the atmosphere via photosynthesis, storing carbon within living biomass and soil, and to a lesser extent, in dead wood and litter. When forests are disturbed (e.g., by fire or deforestation), their stored carbon is released into the atmosphere, therefore an accurate estimation of forest carbon stocks is essential to addressing carbon exchange between terrestrial ecosystems and the atmosphere. To attain detailed and accurate forest carbon stocks, numerous studies have mapped the spatial distribution of forest biomass using various algorithms assuming that carbon content in plant biomass is constant (approximately 50%). According to the Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas Inventories, terrestrial ecosystem carbon pools included above-ground biomass (AGB), below-ground biomass (BGB), necromass and litter. Among these carbon pools, AGB is the most dynamic, visible and important, comprising 15%-30% of the totalterrestrial ecosystem carbon pool [3]. Therefore, previous studies on biomass estimation mainly focus on AGB, while BGB is generally determined from AGB using root-to-shoot ratios [4]. In this paper, forest biomass refers to AGB, which is expressed as dry weight per unit area and defined as all living aboveground biomass, including stems, branches, bark, seeds and foliage. According to dataset sources used to derive forest AGB, estimation methods can be categorized into field measurements, remote sensing-based approaches, and ecological model simulations. Field measurements are valuable resources for biomass estimation as they can provide the most accurate biomass estimates through the use of allometric equations, but are limited in spatial coverage [5; 6]. Countries with existing national forest inventory (NFI) data typically use field measurements together with biomass factors and biomass equations to estimate regional means of forest biomass and biomass change for the national forest resources report [7; 8]. However, these cannot provide spatially explicit forest biomass information either. Due to the above limitations of traditional field measurements to estimate biomass on a regional scale, remote sensing has been widely used for estimation during the past decades due to its wide-area coverage capability. In remote sensing-based biomass estimation, field measurements remain important, especially because they are indispensable to both the calibration of remotely sensed data and the validation of estimated biomass results. Importantly, adequate field data is critical to forest biomass mapping from remotely sensed data, no matter which methods are chosen [9]. To estimate forest AGB accurately on a regional scale, much effort is currently being made to integrate field data with remotely sensed data including optical, synthetic aperture radar (SAR) and light detection and ranging (LiDAR) data using advanced methods [10; 11; 12]. In addition to field measurements and remote-sensing-based methods, ecological process models are promising tools for regional assessment of carbon fluxes and biomass dynamics [13]. However, these models require site specific calibration as well as a large number of input parameters for which appropriate values may be difficult to obtain [14; 15]. Therefore, remotely sensed data remain the dominant data sources for AGB mapping. All forest AGB maps selected for inclusion in this paper were retrieved with remotely sensed data in combination with other datasets. Among the three types of remotely sensed datasets, LiDAR is recognized as the most accurate and promising approach for AGB estimation, but cannot provide wall-to-wall biomass results [16].