Capturing woody aboveground biomass historical change and potentialunder climate change using Landsat time-series for afforestation in dryland of China

Capturing long-term dynamics and the potential under climate change of woody aboveground biomass (AGB) is imperative for calculating and raising carbon sequestration of afforestation in dryland. It has always been a great challenge to accurately capture AGB dynamics of sparse woody vegetation mixed with grassland using only Landsat time-series, resulting in changing trajectories of woody AGB estimates that cannot accurately reflect woody vegetation growth regularity in dryland. In this study, surface reflectance (SR) sensitive to woody AGB was first selected, and interannual time-series of composited SR were smoothed using an S–G filter for each pixel; then, the optimal machine learning algorithm was selected to estimate woody AGB time-series. Pixels that have reached AGB potential were detected based on the AGB changing trajectory, and the potential was spatially and temporally extended using a random forest model combining environmental variables under current climate conditions and CMIP6 climate models. Results show that: (1) minimum value composites based on NIRv during July–September are more capable of explaining woody AGB variation in dryland (R = 0.87, p < 0.01), and the random forest (RF) model has the best performance in estimating woody AGB (R² = 0.75, RMSE = 4.74 t·ha⁻¹) among commonly used machine learning models; (2) annual woody AGB estimates can be perfectly fitted with a logistic growth curve (R² = 0.97, p < 0.001), indicating explicit growth regularity of woody vegetation, which provides a physiological foundation for determining woody AGB potential; and (3) woody AGB potential can be accurately simulated by RF combining environmental variables (R² = 0.95, RMSE = 2.89 t·ha⁻¹), and current woody AGB still has a small potential for increase, whereas overall losses of woody AGB potential are projected for 2030, 2040, and 2050 under CMIP6 SSP-RCP scenarios.