Retrieving Aboveground Biomass in the United States’ Largest Dry Woodland Ecosystem Using Simulated ICESat-2 Data

The precision of remote sensing methodologies in estimating live aboveground biomass (AGB) exhibits significant variability across ecosystems. Dryland systems, owing to their critical contributions to global carbon cycling, necessitate highly resolved spatiotemporal AGB assessments to elucidate their role in climate feedback mechanisms. However, the structural characteristics of arid and semiarid vegetation introduce substantial methodological constraints in biomass quantification via remote sensing platforms. This letter puts forward a new method to retrieve AGB using simulated ICESat-2 light detection and ranging (LiDAR) data. By using simulated photon-counting LiDAR data of spaceborne ICESat-2 and using multiple machine learning algorithms [random forest (RF), extreme gradient boosting (XGBoost) tree, and support vector machine (SVM)] to train simulated photon-counting LiDAR data, we created a novel model to retrieve AGB based on simulated ICESat-2 data. The results showed that the RF algorithm was the best (R ${}^{2}= 0.61$ , root mean square error (RMSE) = 17.45, and relative error (RE) = 49.24%) in AGB inversion. The result maps could provide timely guidance and suggestions for ecological protection and economic construction in the study area, and also provide a reliable basis for large-scale vegetation AGB remote sensing monitoring and carbon cycle research.