In passive imagery systems, sensors are designed to detect electromagnetic emissions from constituents of the Earth's surface and atmosphere. These emissions can be locally produced (e.g. thermal radiation from vegetation in the infrared spectrum) or be the result of reflected sunlight in the visible spectrum. Hence, passive imagery is usually dependant on the day-night cycle and can be degraded or blocked by perturbations coming from unwanted sources of emissions or cloud cover. ### **1 Panchromatic** Panchromatic images are the result of the measure of light intensity over a broad range of the electromagnetic spectrum. Collecting light from a wide range of wavelengths allows for more energy being collected and hence high resolution images (up to 30 cm in resolution for the best commercially available satellite instruments). A standard example of panchromatic measurement will measure the light intensity coming from the observed scene in the full visible spectrum. This measurement would typically cover wavelengths between 0.47 and 0.83 μm. The resulting product is generally an image displayed as shades of grey, such as presented in Figure 2.  **Figure 2** (Source: [http://www.cscrs.itu.edu.tr/assets/downloads/PleiadesUserGuide.pdf](http://www.cscrs.itu.edu.tr/assets/downloads/PleiadesUserGuide.pdf)) Another example of panchromatic measurement is done by thermal infrared sensors, at wavelengths between 10 and 12 μm. The intensity of the IR radiation reaching the satellite is directly correlated with the temperature of the object emitting that radiation. Regions where the ground or the ocean is warm will emit the most intense radiation. Because IR is constantly emitted by the Earth and by clouds, it is possible to obtain IR satellite imagery even when the scene is not illuminated by the sun. In contrast, visible satellite imagery which relies on sunlight reflected up to the satellite can only be obtained during the daylight hours. ### **2 Multi-spectral** Multi-spectral imagery denotes the remote sensing of an observed scene in several narrow bands of the electromagnetic spectrum. Since the range of wavelengths contributing to the radiation energy detected by the sensor is reduced, multi-spectral instruments will typically have to collect energy on larger spatial extents to “fill” the imaging detector, resulting in a lower resolution than for panchromatic images. A common example of multi-spectral images is the production of “natural colour” images by the combination of measurements in 3 bands of the visible spectrum (narrow bands centred around the blue, green and red wavelengths), in the same way as is done in classical consumer cameras. See Figure 3 (left-hand side) for an example of a "natural colour" image. Multi-spectral images are not restricted to the visible spectrum: measurements can be done in the infrared (IR) fields, ultraviolet (UV), microwave, etc. Figure 3 (right-hand side) presents an example of a "false colour" image, combining the green band (displayed in the blue component of the image), the red band (displayed in the green component of the image) and a near infrared band (displayed in the red component of the image). This visualisation combination allows highlighting the presence and health of the vegetation: healthy vegetation creates chlorophyll which reflects near-infrared energy, and therefore appears in darker red on the image.  **Figure 3:** 3-band multi-spectral imagery (Source: [http://www.cscrs.itu.edu.tr/assets/downloads/PleiadesUserGuide.pdf](http://www.cscrs.itu.edu.tr/assets/downloads/PleiadesUserGuide.pdf)) Many other combinations of wavelength bands are possible, depending on the information to be extracted. For example: - Shortwave infrared (red), near infrared (green), and green (blue): often used to show floods or newly burned land - Blue (red), two different shortwave infrared bands (green and blue): used to differentiate between snow, ice, and clouds - Blue (blue), near infrared (green), mid infrared (red): used to picture on one image water depth, vegetation coverage, soil moisture content, and the presence of fires ### **3 Pan-sharpened** Pan-sharpening is a numerical process that merges multi-spectral images with panchromatic images to provide high resolution coloured images. This technique is useful to perform image analysis combining the spectral resolution of multi-spectral images with the improved spatial resolution of panchromatic images. This is illustrated in Figure 4.  **Figure 4:** Example of Pan-Sharpening (Source: [http://www.cscrs.itu.edu.tr/assets/downloads/PleiadesUserGuide.pdf](http://www.cscrs.itu.edu.tr/assets/downloads/PleiadesUserGuide.pdf)) ### **4 Hyper-spectral** | | |---| || |**Figure 5**: Example of a Hyperspectral Data Product <br>(Source: [http://www.hyspex.no/hyperspectral_imaging/](http://www.hyspex.no/hyperspectral_imaging/))| Hyperspectral imagery aims at obtaining a nearly-continuous spectrum for each pixel in the image of a scene, extending the benefits of multi-spectral imagery, which measures light intensity on a limited number of separate bands of the electromagnetic spectrum. Figure 5 provides an example of representation of a hyperspectral data product, each layer of the cube picturing the same 2D scene observed in one specific wavelength λ. For each pixel, a hyperspectral sensor acquires the light intensity for a large number (typically a few tens to several hundred) of contiguous narrow spectral bands. To every pixel in the image is thus attached a nearly continuous spectrum. The high spectral resolution of a hyperspectral imager allows for detection, identification and quantification of surface materials, as well as inferring biological and chemical processes. Hyperspectral Earth Observation is for now mainly limited to aerial imagery and scientific demonstration missions. ### **5 Microwave Radiometry** The main objective of the Microwave Radiometer (MWR) is the measurement of the integrated atmospheric water vapour column and cloud liquid water content, as correction terms for the radar altimeter signal. In addition, MWR measurement data are useful for the determination of surface emissivity and soil moisture over land, for surface energy budget investigations to support atmospheric studies, and for ice characterisation.