This study introduces the Hierarchical Pixel-Wavele ngth Fusion Network (HPWF-Net), a novel hyperspectral reconst ruction framework designed to address the challenges of tree spe cies classification and spectral reconstruction in complex forest ecosystems. The proposed method employs a hierarchical pixel-wise and target-wavelength-wise reconstruction strategy, progressivel y recovering hyperspectral images by dynamically selecting and r efining target wavelengths at each layer. HPWF-Net utilizes an e nhanced U-Net architecture with multi-level loss supervision and convolutional consistency constraints, enabling accurate reconstr uction by supervising intermediate encoder outputs, convolutiona l regressions, and final decoder predictions. This design preserves consistency with the original multispectral input while enhancin g spectral fidelity and robustness. The model is trained on hypers pectral data from the NEON dataset and applied to Sentinel-2 m ultispectral imagery of the Daiyun Mountain National Nature Re serve. Experimental results demonstrate that the reconstructed h yperspectral data improve tree species classification accuracy by 5.9% compared to the original Sentinel-2 multispectral data. This work contributes to hyperspectral reconstruction and fine-grain ed ecological monitoring, offering a practical solution for remote sensing applications in forest ecosystems. Furthermore, we offer a Python-based package that enable to replicate our results at https://github.com/DarkFlameMaster02/Hierarchical-Pixel-Wave length-Fusion-Network-for-Progressive-Hyperspectral-Image-Re construction.

Cloud contamination in optical remote sensing imagery can obscure land-surface information and induce structural blurring and spectral shifts, thereby seriously affecting subsequent land-cover interpretation and quantitative analysis. To address the instability of optical image restoration under complex cloud occlusion and the difficulty of effectively exploiting SAR auxiliary information, this paper proposes LTGC-Diff, a latent diffusion model for SAR-assisted remote sensing image cloud removal. The proposed method formulates cloud-free RGB image restoration as a latent denoising process constrained by multi-source observations. To better incorporate SAR auxiliary information into diffusion reconstruction, a diffusion-oriented conditional representation learning module is designed to transform SAR-derived features into effective conditional representations. In addition, a token-level structural condition adaptation module is designed to transform SAR patch representations into local structural guidance suitable for diffusion-based reconstruction. By jointly exploiting global scene-level guidance and local structure-aware constraints, the proposed model enables more stable and reliable recovery of cloud-obscured regions. Experimental results demonstrate that LTGC-Diff improves the structural consistency and restoration reliability of SAR-assisted optical remote sensing image cloud removal under complex cloud-contaminated scenarios. Specifically, LTGC-Diff achieves a PSNR of 32.87 dB while maintaining improved structural and spectral consistency, demonstrating its effectiveness for SAR-assisted cloud removal.