A synergistic UAV-Landsat novel strategy for enhanced estimation of above-ground biomass and shrub dominance in Sandy land

Accurate estimation of above-ground biomass (AGB) and shrub dominance in sandy lands is crucial for monitoring desertification risk and guiding effective management policies. This study proposed a novel strategy for large-scale AGB estimation in sandy landscapes, focusing on Horqin Sandy Land, by integrating ground reference data with unmanned aerial vehicle (UAV) observations and Landsat 8 OLI imagery. This integration enhanced both the accuracy and efficiency of mapping AGB and shrub dominance. Initially, UAV data were employed to classify shrub and herbaceous vegetation using an object-oriented method, followed by estimating shrub and herbaceous AGB using an allometric growth model (AGM) and partial least squares regression (PLSR). UAV-derived biomass estimates were then aggregated into landscape-scale samples and combined with Landsat imagery to develop Shapley Additive explanation-extreme gradient boosting (SHAP-XGBoost) models for shrub and total AGB. Finally, shrub dominance was mapped as the shrub AGB /total AGB across the region. At the plot scale, AGM coupled with shrub volume provided the highest accuracy for shrub AGB estimation (R 2 = 0.97, MAE = 176.24 g). Visible-light features from UAV data significantly contributed to herbaceous AGB estimation, achieving a PLSR model accuracy of R 2 of 0.91 and an MAE of 14.76 g/m 2 . At the landscape scale, the SHAP-XGBoost models demonstrated excellent accuracy, yielding R 2 values of 0.78 (MAE = 14.96 g/m 2 ) for shrub AGB and 0.83 (MAE = 30.47 g/m 2 ) for total AGB. These high-precision estimation results facilitated the mapping of the shrub dominance. • A novel multi-scale remote sensing strategy estimates AGB and shrub dominance. • UAV-based visible-light and 3D features enhance herbaceous, shrub AGB accuracy. • High-precision shrub and total AGB mapping with limited ground sampling points. • Assessing shrub dominance is important for evaluating the health sandy ecosystems. • The framework offers high spatiotemporal scalability and precision.