Accurate maping of the forest aboveground carbon (AGC) stocks at national or regional scale is important for estimating carbon emissions and removals and contributing to global stocktake and balancing the carbon budget. Recently, several forest AGC maps have been produced for China by integrating remote sensing data and field measurements, yet significant discrepancies remain among these products with the estimated AGC stocks for China varying from 5.04 to 9.81 Pg C.

To reduce the uncertainty of the estimated AGC across China, here we first compiled an independent and high quality field measurements of AGC across China from 2011 to 2015. We applied two different approaches, including an optimal weighting technique (WT) and a random forest regression (RF), to develop two new forest AGC maps of China by integrating existing five maps (i.e., Saatchi, Baccini, Santoro, Su, and Huang). Finally, we used four microwave-derived vegetation optical depth (VOD) products (i.e., L-VOD, IB-VOD, LPDR-VOD, and Liu-VOD) as independent data to evaluate the performances of the estimated AGC maps in China.

The forest AGC stock in China was 7.73 Pg C for the WT estimates and 8.13 Pg C for the RF estimates. Evaluation with the field measurements showed that the two new AGC products had lower RMSE (14.8 and 12.2 Mg C/ha) and bias (-2.3 and -1.9 Mg C/ha) than all five participating AGC products. Evaluation with the independent VOD products showed WT and RF maps have the highest spatial consistency with VODs (median correlation value of 0.83 and 0.80), indicating that these two products well capture the spatial patterns of AGC across China. Our study demonstrated both the WT and RF methods can be used to harmonize existing AGC maps with field measurements to improve the spatial variability and reduce the uncertainty of carbon stocks. The new spatial AGC maps of China can be used to improve estimates of carbon emissions and removals at the national and sub-national scales.