Forest aboveground biomass (AGB) estimation is crucial for carbon cycle studies and climate change mitigation actions. However, because of limitations in timely and reliable forestry surveys and high-resolution remote sensing data, producing a fine resolution and spatial continuous forest AGB map of China is challenging. Here, we combined 4789 ground-truth AGB measurements and multisource remote sensing data such as a recently released forest canopy-height product, optical spectral indexes, topographic data, climatological data, and soil properties to train a random forest regression model for forest AGB estimation of China at 30-m resolution. The accuracy of the estimated AGB can yield R2 =0.67 and RMSE =70.71 Mg/ha. The nationwide estimates show that the average forest AGB and total forest carbon storage were 97.57±23.85 Mg/ha and 11.06 Pg C for the year 2019, respectively. The value of AGB uncertainty ranges from 0.68 Mg/ha to 37.80 Mg/ha, and the average AGB uncertainty was 4.32±1.75 Mg/ha. The forest AGB estimates of China in this study correspond reasonably well with the AGB estimates derived from the forestry and grassland statistical yearbook at the provincial level (R2=0.61, RMSE=30.15 Mg/ha). In addition, we found that previous AGB products generally underestimate forest AGB compared with our estimated AGB at the pixel-level and ground-truth AGB measurements. The high-resolution forest AGB map provides an important alternative data source for forest carbon cycle studies and can be used as a baseline map for forest management and conservation practices.
Note: To reduce the disk usage, the value in the forest AGB and AGB uncertainty (the standard deviation of the 30 AGB estimates) are is stored in centimeter using uint16.
Yang, Q., Niu, C., Liu, X., Feng, Y., Ma, Q., Wang, X., & Guo, Q. (2023). Mapping high-resolution forest aboveground biomass of China using multisource remote sensing data. GIScience & Remote Sensing, 60(1), 2203303.