Hyperspectral imaging holds immense potential for detailed land cover analysis due to its rich spectral information across the electromagnetic spectrum. However, the inherent trade-off between spatial and spectral resolution limits its applicability. The study explores the deployment and enhancement of Single Hyperspectral Image Super-Resolution (SSPSR) model, employing the Spatial-Spectral Prior Network (SSPN), to notably improve the spatial and spectral quality of hyperspectral images. This model stands out for its ability to elevate image resolution without relying on supplementary hardware enhancements. Our research focused on training this model using the comprehensive Chikusei dataset, this dataset features 128 spectral bands from 363 nm to 1018 nm, captured over Chikusei, Japan, enhancing deep learning research in agricultural and urban land cover analysis, followed by fine-tuning with the CAVE dataset, renowned for its hyperspectral indoor images across numerous spectral bands, to ensure adaptability to diverse real-world scenarios. A refined loss function was introduced to enhance image fidelity, spatial smoothness, spectral fidelity, and perceptual quality. Through meticulous hyperparameter tuning and leveraging of public datasets, we significantly improved upon existing methodologies, as evidenced by quantitative performance measures. The fine-tuned SSPSR model achieved noteworthy gains in peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM); thus, contributing to the precision and efficiency of hyperspectral image analysis. Our results demonstrate the model's enhanced performance in generating high-resolution images from low-resolution data, promising substantial advancements in remote sensing and land mapping applications.