# Types of spectral satellite sensors When defining a sensors spectral characteristics, there are two broad categories it would fall in to; multispectral and hyperspectral. ### Multispectral sensors * 3-10 bands – usually include visible and Near Infra-red (VNIR) * Bands are discreet and seperate from each other to pick out a specific part of the spectrum * Examples: Landsat7 (8 bands), Sentinel2 (13 bands, but from three sensors), Quickbird (4 bands), WorldView (16 bands from two sensors). ![Sentinel-2: multispectral satellite, credit: Earth Imaging Journal](/files/FaC5jPDx76FFvtTQRtqf) ### Hyperspectral sensors * 100+ bands * Rather than measuring discrete parts of the EM spectrum, hyperspectral sensors have the potential to measure an entire spectral response curve of the objects they are imaging on the ground. * There are relatively few in space due to the challenge that operating these sensors in space has presented. They have been more widely flown on aircraft platforms. * Spectral analysis of hyperspectral sensor data is generally approached differently to multispectral data, with a range of methods designed specifically for hyperspectral data. ![PRISMA hyperspectral sensor, credit: Italian Space Agency](/files/2TLnioOka8R0kqDuGwNN) Increased spectral resolution comes at the expense of spatial resolution. In order to collect a clean signal with good signal to noise ratio, a sensor will be limited by the amount of energy it receives. It will receive more energy if the spatial resolution is coarser, and if the band width is wider. Spatial and spectral resolution are therefore related to one another. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://guides.geospatial.bas.ac.uk/spectral-analysis-of-imagery/types-of-spectral-satellite-sensors.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.