PolInSAR is an active remote sensing technique that is widely used for forest canopy height estimation, with the random volume over ground (RVoG) model being the most classic and effective forest canopy height inversion approach. However, penetration of microwave energy into the forest often leads to a downward shift of the canopy phase center, which leads to model underestimation of the forest canopy height. In addition, in the case of sparse and low forests, the canopy height is overestimated, owing to the large ground-to-volume amplitude ratio in the RVoG model and severe temporal decorrelation effects. To solve this problem, in this study, we conducted an experiment on forest canopy height estimation with the RVoG model using L-band multi-baseline fully polarized PolInSAR data obtained from the Lope and Pongara test areas of the AfriSAR project. We also propose various RVoG model error correction methods based on penetration depth by analyzing the model’s causes of underestimation and overestimation. The results show that: (1) In tall forest areas, there is a general underestimation of canopy height, and the value of this underestimation correlates strongly with the penetration depth, whereas in low forest areas, there is an overestimation of canopy height owing to severe temporal decorrelation; in this instance, overestimation can also be corrected by the penetration depth. (2) Based on the reference height RH100, we used training sample iterations to determine the correction thresholds to correct low canopy overestimation and tall canopy underestimation; by applying these thresholds, the inversion error of the RVoG model can be improved to some extent. The corrected R2 increased from 0.775 to 0.856, and the RMSE decreased from 7.748 m to 6.240 m in the Lope test area. (3) The results obtained using the infinite-depth volume condition p-value as the correction threshold were significantly better than the correction results for the reference height, with the corrected R2 value increasing from 0.775 to 0.914 and the RMSE decreasing from 7.748 m to 4.796 m. (4) Because p-values require a true height input, we extended the application scale of the method by predicting p-values as correction thresholds via machine learning methods and polarized interference features; accordingly, the corrected R2 increased from 0.775 to 0.845, and the RMSE decreased from 7.748 m to 6.422 m. The same pattern was obtained for the Pongara test area. Overall, the findings of this study strongly suggest that it is effective and feasible to use penetration depth to correct for RVoG model errors.

To explore the feasibility of reusing solid waste to stabilize high-content arsenic (As) and antimony (Sb) tailings, red mud, fly ash, dried sludge, ferrous sulfate (FeSO 4 ), and rice husk ash (RHA) were used as the stabilizer to stabilize the Sb tailings in Qing Long and Du Shan. The combined treatment with 5% red mud, 10% fly ash, 5% dried sludge, 1% FeSO 4 , and 1% rice husk ash had the best stabilization effects on As and Sb, the leaching concentrations of As and Sb decreased considerably under neutral conditions, this indicates that the leaching behavior of As and Sb is controlled by the alkaline and acid-retarding capacity of the materials. In addition, the leaching of heavy metals decreases with the formation of (C-S-H) and calcite (CaCO 3 ), indicating that heavy metals exist in the form of metal hydration or hydroxide and precipitate on the surface of calcium silicate hydrate (C-S-H) and calcite particles. Leaching of heavy metals in stabilized materials can be considered as a pH-dependent and control process of stabilization products.