Spaceborne LiDAR has been widely used to obtain forest canopy heights over large areas, but it is still a challenge to obtain spatio-continuous forest canopy heights with this technology. In order to make up for this deficiency and take advantage of the complementary for multi-source remote sensing data in forest canopy height mapping, a new method to estimate forest canopy height was proposed by synergizing the spaceborne LiDAR (ICESat-2) data, Synthetic Aperture Radar (SAR) data, multi-spectral images, and topographic data considering forest types. In this study, National Geographical Condition Monitoring (NGCM) data was used to extract the distributions of coniferous forest (CF), broadleaf forest (BF), and mixed forest (MF) in Hua’ nan forest area in Heilongjiang Province, China. Accordingly, the forest canopy height estimation models for whole forest (all forests together without distinguishing types, WF), CF, BF, and MF were established, respectively, by Radom Forest (RF) and Gradient Boosting Decision Tree (GBDT). The accuracy for established models and the forest canopy height obtained based on estimation models were validated consequently. The results showed that the forest canopy height estimation models considering forest types had better performance than the model grouping all types of forest together. Compared with GBDT, RF with optimal variables had better performance in forest canopy height estimation with Pearson’s correlation coefficient (R) and the root-mean-squared error (RMSE) values for CF, BF, and MF of 0.72, 0.59, 0.62, and 3.15, 3.37, 3.26 m, respectively. It has been validated that a synergy of ICESat-2 with other remote sensing data can make a crucial contribution to spatio-continuous forest canopy height mapping, especially for areas covered by different types of forest.

Accurate prediction of soil organic carbon (SOC) content at fine spatial resolution is crucial for supporting precision soil management and ensuring food security in complex agricultural landscapes. Focusing on croplands in Hungary, this study developed a region‑specific framework that produces a 30 m resolution SOC content map from multi-temporal bare-soil composite images and DEM derivatives, with SHAP‑based interpretability and pixel‑wise uncertainty quantification. The results showed that the Gradient Boosted Decision Trees (GBDT) model achieved the best generalization performance on the test set (R² = 0.518, RMSE = 4.498 g·kg⁻¹, MAE = 3.499 g·kg⁻¹, RPIQ = 2.229, LCCC = 0.621). SHAP analysis revealed that both spectral and topographic variables exhibited markedly nonlinear effects, and that spectral variables contributed more to SOC prediction than topographic variables. In addition, prediction credibility varied considerably across the study area, with low-confidence zones concentrated mainly in transitional hilly and low‑mountain areas characterized by complex terrain. This study produced a 30 m resolution SOC content prediction map for Hungary cropland, offering a transferable methodology for accurate SOC mapping and uncertainty assessment in complex agricultural landscapes, and providing new insights for agricultural management decisions in Hungary and across Europe.

Accurate prediction of soil organic carbon (SOC) at fine resolution is crucial for precision soil management; however, existing national products for Hungary remain too coarse for farm-scale applications. Focusing on Hungarian croplands, we developed a 30 m resolution SOC map using multi-temporal bare-soil composites, DEM derivatives, SHAP interpretability and bootstrap uncertainty. Among five evaluated algorithms, the GBDT model achieved the best performance (test R² = 0.518, RMSE = 4.498 g·kg⁻¹, MAE = 3.499 g·kg⁻¹, RPIQ = 2.229, LCCC = 0.621). SHAP analysis revealed pronounced nonlinear effects of spectral and topographic variables within this modeling framework, with spectral predictors playing a dominant role in SOC prediction. Furthermore, the bootstrap uncertainty framework yielded a Prediction Interval Coverage Probability of 94.59% at the 95% confidence level, indicating reliable interval estimation for the test set. Spatial patterns of uncertainty varied considerably, with higher values in the western hills and southern sands, and moderate levels in the northern low-mountain areas. Benchmark comparisons showed that our 30 m map captures fine-scale heterogeneity often smoothed over by coarser products, while the uncertainty layer supports risk-aware interpretation. Overall, this study provides a regionally calibrated framework for mapping in similar heterogeneous agricultural landscapes, providing practical insights for local management.