Spectroscopy methods have been used for decades to obtain information about various materials, ranging from galaxies billions of light years from earth, to Petri dishes containing rich bacteria cultures. From spectrometers in chemical labs to spectral cameras on satellites, the high dimensionality of spectral data is proven to be an excellent source of information for detection and classification of materials. In this study, state-of-the-art solutions for aerial hyperspectral surveys are reviewed and improved upon. In the frame of Leman-Baikal project, stringent requirements stemming from large scale hyperspectral mapping, have led to the development of a novel pushbroom data acquisition system and automated processing pipeline. The resulting end-to-end solution was successfully deployed on ultralight aircraft and subsequently used to acquire and process more than 15 terabytes of spectral data over the course of three years. The spectral data collected has proved its usefulness in environmental monitoring, generating water turbidity maps and vegetation classifications. The insight gained from the pushbroom system experiments led to the design and prototyping of a compact snapshot hyperspectral system, well suited for unmanned aerial vehicles. Weighing only 250 g and being a frame camera, the snapshot system presents many advantages over the pushbroom, including compatibility with automated image stitching and georeferencing solutions. Similarly to the pushbroom platform, a camera has been prototyped and the corresponding processing pipeline has been implemented. However, being based on interferometric filters, the snapshot hyperspectral sensors require extremely accurate calibration due to their complex transmissions. Novel methods and devices are presented in this study to overcome the interferometric spectral transmission issue, using either machine learning or compressive sensing approaches. The snapshot hyperspectral solution has been used in multiple studies and is shown to be particularly useful in the case of precision agriculture, where important plant traits are shown to correlate strongly with computed spectral maps.