Multispectral and hyperspectral image sensors provide a highly granular detail of spatial and spectral resolution. For remote sensing satellites, spatial resolution is typically quantified as the size of the Earth’s surface in meters that one pixel of an image represents. High spectral resolution suggests the ability to define a great number of bands of electromagnetic energy that span a very narrow bandwidth. Multispectral image sensors typically capture around 5-30 bands of the electromagnetic spectrum, each with a width of ~100nm. Hyperspectral sensors capture hundreds of contiguous spectral bands more narrow than multispectral bands. This ability to capture such wide arrays of spectral data allows researchers to manipulate the resultant images to emphasize certain bandwidths based on what they are studying. Different bandwidths can be used to observe tiny molecules of aerosols and clouds, identify the temperature and turbidity of a body of water, and even determine the mineral makeup of observed materials. While multispectral imaging can classify and categorize material on the Earth’s surface or in its atmosphere, hyperspectral imaging can determine the characterization and even abundance of said material. Many high-profile Earth observation satellite missions such as Landsat have shifted toward offering open-source data free of charge to the public. This has enabled researchers from a growing number of fields to access data that can be tailored to their interests, thereby increasing the rate of interdisciplinary and collaborative processing and use. The impressive power of spectral remote sensing technology is key to monitoring our ever-changing planet and climate.