Forest ecosystems depend on species of tree variety. Remote sensing for obtaining large-scale spatial distribution information of tree species diversity is a geoscience research hotspot to overcome the limitations of conventional tree species diversity survey approaches. Airborne LiDAR or synergy with airborne optical imagery has been used to model and estimate tree species diversity for specific forest communities, with many revealing results. However, the data collection for such research is costly, the breadth of monitoring findings is limited, and obtaining information on the geographical pattern is challenging. To this end, we propose a method for mapping forest tree species diversity by synergy satellite optical remote sensing and satellite-based LiDAR based on the spectral heterogeneity hypothesis and structural variation hypothesis to improve the accuracy of the remote sensing monitoring of forest tree species diversity while considering data cost. The method integrates horizontal spectral variation from GF-1/PMS image data with vertical structural variation from ICESat-2 spot data to estimate the species diversity of trees. The findings reveal that synergistic horizontal spectral variation and vertical structural variation overall increase tree species diversity prediction accuracy compared to a single remote sensing variation model. The synergistic approach improved Shannon and Simpson indices prediction accuracy by 0.06 and 0.04, respectively, compared to the single horizontal spectral variation model. The synergistic model, single vertical structural variation model, and single horizontal spectral variation model were the best prediction models for Shannon, Simpson, and richness indices, with R² of 0.58, 0.62, and 0.64, respectively. This research indicates the potential of synergistic satellite-based LiDAR and optical remote sensing in large-scale forest tree species diversity mapping.

Soil moisture content has a direct effect on the growth rate and survival rate of trees. However, previous studies on soil moisture have often focused on the topsoil, lacking effective monitoring of long-term dynamic changes in deep soil layers. In this study, 16 time-domain reflectometer (TDR) probes were installed in the Haikou plantation in Kunming to conduct long-term continuous monitoring of soil moisture within a depth range of 0 to 300 cm. The results indicate that the vertical distribution of soil moisture can be classified into three levels: the active layer from 0 to 70 cm ($\theta =0.23\pm 0.08\mathrm{c}{\mathrm{m}}^3{\mathrm{c}\mathrm{m}}^{-3})$, where the moisture content fluctuates significantly due to precipitation events; the transitional accumulation layer from 70 to 170 cm $(\theta =0.26\pm 0.06\mathrm{c}{\mathrm{m}}^3{\mathrm{c}\mathrm{m}}^{-3})$, where moisture content increases with depth and peaks at 170 cm; and the deep dissipative layer from 170 to 300 cm $(\theta =0.24\pm 0.08\mathrm{c}{\mathrm{m}}^3{\mathrm{c}\mathrm{m}}^{-3})$, where moisture content decreases with depth, forming a noticeable steep drop zone at 290 cm. The Hydrus-1D (Version 4.xx) model demonstrated high simulation capabilities (${\mathrm{R}}^2=0.58$) in shallow (10 to 50 cm) and deep (280 to 300 cm) layers, while its performance decreased (${\mathrm{R}}^2=0.39$) in the middle layer (110 to 200 cm). This study systematically reveals the dynamics of soil moisture from the surface active zone to the deep transition zone and evaluates the simulation ability of the Hydrus-1D model in this specific environment, which is also significant for assessing the groundwater resource conservation function of plantation ecosystems.