Detecting early-stage vegetation stress at the individual tree scale is a pivotal remote sensing application. The “green shoulder” band at 530 nm serves as a key signal for early stress detection due to its sensitivity to carotenoid changes. However, existing remote sensing systems often struggle to simultaneously capture fine-scale canopy structures and stress-sensitive spectral data, making heterogeneous fusion a promising topic. Unlike mainstream supervised methods that rely on prescribed degradation models and high-quality samples, an unsupervised blind fusion framework based on Implicit Neural Representation and low-rank decomposition is proposed in this paper. Guided by orthorectified aerial images, the framework performs per-band super-resolution on PlanetScope SuperDove data to achieve a 0.16-meter resolution. It employs Sinusoidal Representation Networks to learn a continuous joint implicit representation of spatio-spectral information, effectively modeling the non-linear relationship between canopy structure and spectral response.To mitigate high-dimensional feature redundancy during heterogeneous data fusion, low-rank decomposition is integrated to reduce computation overhead. Experimental results show that the proposed method can fuse heterogeneous images effectively, providing a solid solution with practical guidance for subsequent early stress monitoring at the individual tree level.