Capturing woody aboveground biomass historical change and potential under 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 is 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 trajectory of woody AGB estimates cannot accurately reflect woody vegetation growth regularity in dryland. In this study, surface reflectance (SR) sensitive to woody AGB was firstly selected and interannual time-series of composited SR was smoothed using S-G filter for each pixel, and then 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 spatial-temporal extended using random forest model combining environmental variables under current climate condition and CMIP6 climate models. Results show that: 1) minimum value composite based on NIRv during Jul.-Sep. is more capable of explaining woody AGB variation in dryland (R = 0.87, p < 0.01), and Random Forest (RF) model has the best performance in estimating woody AGB (R = 0.75, RMSE = 4.74 t·ha) among sis commonly used machine learning models. 2) Annual woody AGB estimates can be perfectly fitted with a logistic growth curve (R = 0.97, p < 0.001) indicating explicit growth regularity of woody vegetation, which provides physiological foundation for determining woody AGB potential. 3) Woody AGB potential can be accurately simulated by RF combining environmental variables (R = 0.95, RMSE = 2.89 t·ha), and current woody AGB still has a potential of small increase, whereas the overall losses of woody AGB potential were observed in 2030, 2040 and 2050 under CMIP6 SSP-RCP scenarios.