NASA is now operating Landsat 8 and more than 300 other Earth observation satellites are in orbit with almost half providing optical imaging.
In order to best evaluate the different offerings, it is important to understand the variability in remote sensing processes. In the end, different insights can be produced with access to the same data source.
So, how does remote sensing work?
Optical satellites acquire images from solar radiation reflected by the Earth. Because satellites utilize different sensors and technology, the information available from each satellite varies.
While using satellite remote sensing data doesn’t require an in-depth understanding of how the technology works, having working knowledge of a few key concepts helps to better evaluate what is feasible with the technology and how it can meet your agribusiness needs.
Pixels and Resolution
Understanding the multiple forms of resolution is important when evaluating satellite imagery. It is common to oversimplify imagery by referring to it as high, medium or low resolution. This creates confusion and misconceptions based on general photography knowledge in which resolutions is simply measured by the number of pixels in an image, whereas in satellite imagery what is often being referred to is the spatial resolution.
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Distinguishing between pixel size and spatial resolution is important, as they are often confused, and not interchangeable.
As with any digital image, satellite imagery is also made up of pixels. The image is acquired by sampling the light reflected by the surface and recording the measurements in a matrix of pixels which creates a grid. The ground sampling distance (GSD) refers to the distance between each pixel of this predefined grid.
When talking about resolution, satellite data providers are often referring to the spatial resolution, which refers to the smallest object that can be identified on the ground. This varies based on the position of the sensor relative to its target, atmospheric diffractions and other factors. Meaning spatial resolution – and therefore the quality of the information provided – varies from the image center to the swath edge.
For example, MODIS images have a GSD of 250 meters. Therefore, each pixel represents an area of 250 meters x 250 meters, or 6.25 hectare. The spatial resolution of MODIS at NADIR (point right below the satellite) equals 250 meters, but off-NADIR it can be more than 500 meters within the 250 meters x 250 meters pixel.
The same is true with higher resolution satellites – and even more so when a satellite points its sensor at a wide angle, off-NADIR. For example, RapidEye satellites (Planet) can point up to 20° off-NADIR to observe target areas further away from its direct path, which impacts the normal spatial resolution of 6m.
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