Synthesizing bare soil pictures in regions with complex vegetation is challenging, which hinders the accuracy of predicting soil organic carbon (SOC) in specific areas. An SOC prediction model was developed in this study by integrating the convolutional neural network and long and short-term memory network (CNN-LSTM) algorithms, taking into consideration soil-forming factors such as climate, vegetation, and topography in Hainan. Compared with common algorithmic models (random forest, CNN, LSTM), the SOC prediction model based on the CNN-LSTM algorithm achieved high accuracy (R² = 0.69, RMSE = 6.06 g kg⁻¹, RPIQ = 1.96). The model predicted that the SOC content ranged from 5.49 to 36.68 g kg⁻¹, with Hainan in the central and southern parts of the region with high SOC values and the surrounding areas with low SOC values, and that the SOC was roughly distributed as follows: high in the mountainous areas and low in the flat areas. Among the four models, CNN-LSTM outperformed LSTM, CNN, and random forest models in terms of R² accuracy by 11.3%, 23.2%, and 53.3%, respectively. The CNN-LSTM model demonstrates its applicability in predicting SOC content and shows great potential in complex areas where obtaining sample data is challenging and where SOC is influenced by multiple interacting factors. Furthermore, it shows significant potential for advancing the broader field of digital soil mapping.

Accurately mapping the forest canopy height is vital for conserving forest ecosystems. Employing the forest height measured by satellite light detection and ranging (LiDAR) systems as ground samples to establish forest canopy height extrapolation (FCHE) models presents promising opportunities for mapping large-scale wall-to-wall forest canopy height. However, despite the potential to provide more samples and alleviate the stripe effect by synergistically using the data from two existing LiDAR datasets, Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), the fundamental differences in their operating principles create measurement biases, and thus, there are few studies combining them for research. Furthermore, previous studies have typically employed existing regression algorithms as FCHE models to predict forest canopy height, without customizing a model that achieves optimal performance based on the current samples. These shortcomings constrain the accuracy of predicting forest canopy height using satellite LiDAR data. To surmount these difficulties, we proposed a genetic programming (GP) guided method for mapping forest canopy height by combining the GEDI and ICESat-2 LiDAR data with Sentinel-1/2, terrain, and climate data. In this method, GP autonomously constructs the fusion model of the GEDI and ICESat-2 datasets (hereafter GIF model) and the optimal FCHE model based on the explanatory variables for the specific study area. The outcomes demonstrate that the fusion of the GEDI and ICESat-2 data shows high consistency (${\mathrm{R}}^2$ = 0.85, RMSE = $2.2$$\mathrm{m}$, pRMSE = 11.24%). The synergistic use of the GEDI and ICESat-2 data, coupled with the optimization of the FCHE model, substantially improves the precision of forest canopy height predictions, and finally achieves ${\mathrm{R}}^2$, RMSE, and pRMSE of 0.64, $3.38$$\mathrm{m}$, and 16.08%, respectively. In summary, our research presents a reliable approach to accurately estimate forest canopy height using remote sensing data by addressing measurement biases between the GEDI and ICESat-2 data and overcoming the limitations of traditional FCHE models.

Accurate estimation of stratified forest canopy height is crucial for understanding forest dynamics and estimating carbon stocks. While remote sensing is widely used for regional canopy mapping, its accuracy is often limited in mountainous and dense forests, particularly for understory canopy layers, because of signal saturation and restricted sensor penetration through the overstory canopy. To address these issues, we design a novel deep learning model, which combines the self-attention mechanism and the Vision Transformer encoder–decoder architecture (ViT-ed) with conditional modeling to achieve precise prediction of stratified canopy height using multi-temporal Sentinel-1/2 and Global Ecosystem Dynamics Investigation (GEDI) data. This model structure is tailored to the ecological correlations between vertical forest layers and optimized with a hybrid loss function and achieves significant improvements over the encoder-only convolutional neural network baseline (CNN-e). Validated against field inventory data, our model achieves a correlation coefficient of 0.843 (root mean square error (RMSE) = 1.532 m) for overstory canopy height and 0.825 (RMSE = 0.377 m) for understory canopy height, markedly outperforming the CNN-e benchmarks (RMSE = 1.846 m and 0.451 m, respectively). These results demonstrate improved understory canopy height estimation relative to CNN-e in the study area. Based on freely available data, this method generates continuous canopy height maps for the study area and has potential to enhance carbon stock estimation across broader regions.