Accurate forest type classification in subtropical regions is essential for ecological monitoring and sustainable management. Multimodal remote sensing data provide rich information support, yet the synergy between network architectures and fusion strategies in deep learning models remains insufficiently explored. This study established a multimodal deep learning framework with integrated interpretability analysis by combining high-resolution Beijing-2 RGB imagery, Sentinel-1 data, and time-series Sentinel-2 NDVI data. Two representative architectures (U-Net and Swin-UNet) were systematically combined with three fusion strategies, including feature concatenation (Concat), gated multimodal fusion (GMU), and Squeeze-and-Excitation (SE). To quantify feature contributions and decision patterns, three complementary interpretability methods were also employed: Shapley Additive Explanations (SHAP), Grad-CAM++, and occlusion sensitivity. Results show that Swin-UNet consistently outperformed U-Net. The SwinUNet-SE model achieved the highest overall accuracy (OA) of 82.76%, exceeding the best U-Net model by 3.34%, with the largest improvement of 5.8% for mixed forest classification. The effectiveness of fusion strategies depended strongly on architecture. In U-Net, SE and Concat improved OA by 0.91% and 0.23% compared with the RGB baseline, while GMU slightly declined. In Swin-UNet, all strategies achieved higher gains between 1.03% and 2.17%, and SE effectively reduced NDVI sensitivity. SHAP analysis showed that RGB features contributed most (values > 0.0015), NDVI features from winter and spring ranked among the top 50%, and Sentinel-1 features contributed less. These findings reveal how architecture and fusion design interact to enhance multimodal forest classification.

Gross primary productivity (GPP) is a crucial indicator for understanding the global carbon cycle and climate change. Sun-Induced Chlorophyll Fluorescence (SIF) provides a direct link to plant photosynthesis, offering a novel approach for GPP estimation in terrestrial ecosystems. Given the complex factors influencing the canopy SIF–GPP relationship and the limited generality of empirical linear models, we developed a Light Use Efficiency (LUE) model incorporating the photochemical reflectance index (PRI) and structural vegetation indices, with a nonlinear function fitted using support vector machine regression. The model was evaluated across multiple vegetation types, SIF products, and vegetation indices to identify optimal SIF–VI combinations. Results indicate that training by vegetation type improves accuracy by 8.5%–14.4%, with shrublands and evergreen broadleaf forests showing the best performance. The combination of GOSIF with NDVI achieved the highest overall estimation accuracy across all vegetation types. Additionally, the combination of GOME-2 SIF with NDVI × NIRv performed best in inland arid low-GPP regions, while TCSIF with NDVI × NIRv was most accurate in cold high-latitude low-GPP regions. All three combinations effectively captured interannual GPP variations at individual sites. Using these combinations, monthly GPP from 2007 to 2014 was estimated and compared with GPP-MODIS and GPP-GLASS datasets, showing consistent temporal and spatial patterns that reflect seasonal dynamics in the Northern Hemisphere. These findings demonstrate that integrating SIF with targeted vegetation indices enhances GPP estimation and provides a robust framework for large-scale photosynthesis monitoring.

Cephalotaxus hainanensis, a valuable medicinal and endangered conifer, requires scientific conservation and precision management to ensure the sustainable utilization of its genetic and ecological resources. Nitrogen (N) is a key nutrient that regulates plant growth and metabolism; rapid and accurate nitrogen diagnosis is vital for optimizing fertilization, reducing nutrient losses, and promoting healthy plant development. This study employed a combined approach integrating stepwise regression, correlation analysis, and Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify leaf color features strongly correlated with leaf nitrogen content (LNC). A support vector regression (SVR) model, suitable for small-sample datasets, was then employed to accurately estimate LNC across canopy layers. Nine color variables were found to be highly associated with LNC, among which the Green Minus Blue index (GMB) consistently appeared across all correlation methods, demonstrating strong robustness and generality. Color features effectively reflected LNC variations among nitrogen treatments—especially between N1 and N4—and across canopy layers, with the most pronounced contrasts observed between upper and lower leaves. The Spearman-based SVR model revealed that the middle canopy maintained the highest and most stable LNC. However, the lower leaves were most sensitive to nitrogen deficiency, while the upper leaves were more sensitive to nitrogen excess. Comprehensive analysis identified N2 as the optimal nitrogen treatment, representing a balanced nutrient state. Overall, this study confirms the reliability of color features for LNC estimation and highlights the importance of vertical canopy LNC distribution in nitrogen diagnostics, providing a theoretical and methodological foundation for color-based nitrogen diagnosis and precision nutrient management in evergreen conifers.