The inversion of forest aboveground biomass

Aboveground biomass (AGB) is a vital indicator for studying the carbon sink in forest ecosystems. Semi-arid forests harbor substantial carbon storage but received little attention as high spatial-temporal heterogeneity. This study assessed the performance of different data sources (annual monthly time-series radar: Sentinel-1 (S1), annual monthly time-series optical: Sentinel-2 (S2), and single-temporal airborne LiDAR) and seven prediction approaches to map AGB in the semi-arid forests at the border between Gansu and Qinghai provinces in China. Five experiments were conducted using different data configurations from SAR backscatter, multispectral reflectance, LiDAR point cloud, and their derivatives (polarimetric combination indices, texture information, vegetation indices, biophysical features, and tree height- and canopy-related indices). Results showed that compared to S1 (R2:0.24 - 0.45 and RMSE:47.36 - 56.51 Mg/ha), S2 acquired better prediction (R2:0.62 - 0.75 and RMSE:30.08 - 38.83 Mg/ha), although their integration further improved the results (R2:0.65 - 0.78 and RMSE:28.68 – 35.92 Mg/ha). The addition of single-temporal LiDAR highlighted the structural importance in semi-arid forests. The best mapping accuracy was achieved by XGBoost combining metrics from S2 and S1 time series and LiDAR-based canopy height information (R2:0.87, RMSE:21.63 Mg/ha, and RMSEr:14.45%). Images obtained during the dry season were effective for AGB prediction. Sequential forward selection was used to determine the most contributing predictors. Our methodology advocates for an economical, extensive, and precise AGB retrieval tailored for semi-arid forests.