Early wildfire detection is a key point for saving lives, protecting ecosystems, and reducing the impact on vegetation and infrastructure. In this scenario, hyperspectral cameras emerge as useful tools due to their high spectral resolution, providing detailed spectral signatures for machine learning algorithms. Despite advancements in deep learning and high-resolution RGB image-based detection systems, these methods can still be subject to errors. Satellite-based hyperspectral systems can improve detection accuracy and reduce errors. However, some challenges remain, including limitations in spatial resolution and the low sampling frequency of satellites. This can lead to delays in fire identification, allowing the fire to spread to a large area before detection. Ground-based hyperspectral cameras offer better spectral resolution but are expensive, especially in large forest areas requiring continuous monitoring. This chapter aims to assess spectral reconstruction techniques to convert RGB images into hyperspectral data. Three methods were evaluated: ridge linear regression, clustering, and multi-layer perceptron (MLP) neural networks. The simplest method, ridge linear regression, provides quick results but lacks accuracy. MLP offered the best performance with increased complexity. Combining MLP with clustering presented a cost-effective solution without significant loss of performance. Preliminary data analysis using factor analysis revealed data structures and dimensions, aiding in mapping RGB space into hyperspectral data. This research addresses the challenges in early wildfire detection, exploring innovative techniques to improve accuracy and efficiency in monitoring and prevention.