## 1 Introduction  
Forests cover approximately 30% of the total land area and account for 80% of Earth's total plant biomass [1, 2]. Forests' ecosystems contribute 75% of the terrestrial gross primary production (GPP) and contains more carbon in plants and soils than those contained in the atmosphere [2, 3]. Therefore, knowledge of the distribution and biomass density of forests is critical for understanding the global carbon balance and, further, for research on climate change. Several satellite missions aiming at the monitoring of forest aboveground biomass (AGB) have been launched or scheduled, such as the L-band synthetic aperture radar (SAR) onboard the advanced land observing satellite (ALOS) launched by the Japanese Space Agency (JAXA) in 2006 [4], the advanced land observing satellite 2 (ALOS-2) equipped with an enhanced L-band SAR (PALSAR-2) [5], the BIOMASS mission of European Space Agency specifically designed for the measurements of forest AGB and its changes [6], and so on. In addition to these satellite missions, several regional maps of forest AGB or related forest structure parameters have been released [7; 8; 9]. For example, [PERSON] et al. [10] mapped the forest AGB of the United States of America using the ground inventory, the Moderate Resolution Imaging Spectroradiometer (MODIS) data, and other ancillary data; [PERSON] et al [11] mapped the total carbon stock in the live biomass of all tropical forests by extrapolating the in situ inventory and the Geoscience Laser Altimeter System (GLAS) footprints using optical and microwave imagery. However, [PERSON] et al. [12] reported obvious discrepancies in existing forest biomass stock maps with in-situ observations in Mexico. As pointed out by [PERSON], [PERSON], et al. [13], these existing global or regional datasets were only approximations based on combining land cover types and representative values instead of measurements of actual forest aboveground biomass. The importance of uncertainty analysis for remote sensing-derived forest AGB estimates has been recognized in last decade [14; 15]. The high-quality ground reference data is the prerequisite of the mapping of forest AGB and the uncertainty analysis [16; 17]. As pointed out by [PERSON] et al. [16], the identification of sensitive variables should be based on correlation analysis between ground reference data and potential variables; the estimation model was developed by relating ground reference data with selected variables; the model evaluation and uncertainty analysis also relies on the ground reference data. The collection of ground reference data is always carried out through field measurements over forest plots. The structure parameters of each tree within the plots are recorded, including diameters at breast height, tree species, and the heights of selected representative trees. This collection of ground reference data is time-consuming, labor-intensive, and the number of sampling plots is always limited due to the cost. Moreover,uncertainties associated with this kind of data collection also exist. For example, the uncertainties caused by the inaccurate positioning of sample plot centers or corners due to the low signal quality of global positioning systems (GPS) [18; 19; 20]; the measurement of tree heights is always interrupted by the complex forests vertical structures preventing surveyors targeting tree canopy tops correctly from the ground [21]; another uncertainty that appears when relating with the remote sensing dataset is that the boundary of the sampling plot cannot exactly coincide with the boundary of remote sensing pixels. Although field measurements are associated with these uncertainties, they are considered to be the most accurate source of data we can obtain. Therefore, we cannot completely eliminate field measurements, but we can reduce the number of sample plots and make the data collection and biomass estimation more efficient and effective with the help of other tools.